The document describes localized, self-organizing approaches for constructing energy-efficient data aggregation trees in sensor networks. It proposes Localized Power-Efficient Data Aggregation Protocols (L-PEDAPs) that use localized structures like LMST and RNG to approximate a minimum spanning tree. L-PEDAP then constructs an actual routing tree over these structures using localized parent selection strategies. Simulation results show L-PEDAP can achieve close to 90% of a theoretical upper bound on network lifetime derived in the paper, outperforming centralized solutions while meeting requirements like distributed operation, scalability, and robustness to failures.
ENERGY EFFICIENT AGGREGATION WITH DIVERGENT SINK PLACEMENT FOR WIRELESS SENSO...ijasuc
In WSN the data aggregation is a means for condensing the energy requirement by reducing number of
transmission by combining the data and sending the final required result to the base station. The lifetime
of the WSN can be improved by employing the aggregation techniques. During the process of aggregation
the numbers of transmission are reduced by combining the similar data from the nearby areas. By using
the clustering technique and aggregating the correlated data greatly minimize the energy consumed in
collecting and disseminating the data. In this work, we evaluate the performance of a novel energy
efficient cluster based aggregation protocol (EECAP) for WSN. The main focus in this proposed work is
to study the performance of our proposed aggregation protocol with divergent sink placements such as
when sink is at the centre of the sensing field, corner of the sensing field or at a location selected
randomly in the sensor field. We present experimental results by calculating the lifetime of network in
terms of number of sensing rounds using various parameters such as – average remaining energy of
nodes, number of dead nodes after the specified number of sensing rounds. Finally the performance of
various aggregation algorithms such as LEACH, SEP and our proposed aggregation protocol (EECAP)
are compared with divergent sink placements. The simulation results demonstrates that EECAP exhibits
good performance in terms of lifetime and the energy consumption of the wireless sensor networks and
which can be as equally compared with existing clustering protocols.
Energy Efficient Multipath Data Fusion Technique for Wireless Sensor NetworksIDES Editor
In wireless sensor networks (WSN), data fusion
should be energy efficient. But, determining the optimal
number of aggregators in an energy efficient manner is a
challenging task. Moreover, the existing data fusion
techniques mostly use the same path for transmitting
aggregated data to the sink which reduces the nodes lifetime.
In this paper, we propose a technique which combines energy
efficiency and multiple path selection for data fusion in WSN.
The network is partitioned into various clusters and the node
with highest residual energy is selected as the cluster head.
The sink computes multiple paths to each cluster head for
data transmission. The distributed source coding and the
lifting scheme wavelet transform are used for compressing
the data at the CH. During each round of transmission, the
path is changed in a round robin manner, to conserve the
energy. This process is repeated for each cluster. From our
simulation results we show that this data fusion technique
has less energy consumption with increased packet delivery
ratio, when compared with the existing schemes.
K dag based lifetime aware data collection in wireless sensor networksijwmn
Wireless Sensor Networks need to be organized for efficient data collection and lifetime maximization. In
this paper, we propose a novel routing structure, namely k-DAG, to balance the load of the base station's
neighbours while providing the worst-case latency guarantee for data collection, and a distributed
algorithm for construction a k-DAG based on a SPD (Shortest Path DAG). In a k-DAG, the lengths of the
longest path and the shortest path of each sensor node to the base station differ by at most k. By adding
sibling edges to a SPD, our distributed algorithm allows critical nodes to have more routing choices. The
simulation results show that our approach significantly outperforms the SPD-based data collection
approach in both network lifetime and load balance.
Energy Efficient Stable Election Protocol for Clustered Heterogeneous Wireles...IOSR Journals
This document summarizes an energy efficient stable election protocol (EE-SEP) for clustered heterogeneous wireless sensor networks. EE-SEP modifies the existing SEP protocol to improve energy efficiency, stability period, and network lifetime. It does this by calculating the optimal threshold value for selecting cluster heads based on the initial energy of sensor nodes, rather than the weighted election probability used in SEP. Simulations show EE-SEP performs better than SEP by increasing the number of alive nodes over time, reducing energy consumption, and prolonging network lifetime.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
ENERGY EFFICIENT HIERARCHICAL CLUSTER HEAD ELECTION USING EXPONENTIAL DECAY F...ijwmn
This document summarizes an article that proposes an improved algorithm for selecting cluster heads in wireless sensor networks. The algorithm uses an exponential decay function to predict the average energy of sensor nodes and selects cluster heads based on both the probabilistic LEACH algorithm and predicted energy levels. The algorithm was tested in MATLAB simulations of a homogeneous sensor network and showed improvements in stability, average energy dissipation per round, and lifespan over the baseline LEACH protocol.
Clustering Based Lifetime Maximizing Aggregation Tree for Wireless Sensor Net...IJASCSE
This document proposes a Clustering Based Lifetime Maximizing Aggregation Tree (CLMAT) algorithm for wireless sensor networks. The algorithm aims to reduce energy consumption by creating an aggregation tree that minimizes distance traversed, energy consumed, and cost. It considers the three factors of energy, distance, and cost simultaneously when constructing the tree, unlike previous works. The tree is structured to maximize network lifetime by selecting nodes with higher residual energy as parents where possible. Pseudocode is provided to generate the aggregation tree using clustering by calculating branch and tree energy, distance, and cost at each step to ultimately select the tree with the highest lifetime, lowest energy consumption, distance, and cost.
Energy aware clustering protocol (eacp)IJCNCJournal
The document summarizes an Energy Aware Clustering Protocol (EACP) proposed for heterogeneous wireless sensor networks. EACP introduces heterogeneity by using two types of nodes: normal and advanced. Normal nodes elect cluster heads using a probability scheme based on residual and average energy. Advanced nodes use a separate probability scheme and act as gateways for normal cluster heads, transmitting their data to the base station. The performance of EACP is compared to SEP through simulations, showing better results for stability period, network life and energy savings.
ENERGY EFFICIENT AGGREGATION WITH DIVERGENT SINK PLACEMENT FOR WIRELESS SENSO...ijasuc
In WSN the data aggregation is a means for condensing the energy requirement by reducing number of
transmission by combining the data and sending the final required result to the base station. The lifetime
of the WSN can be improved by employing the aggregation techniques. During the process of aggregation
the numbers of transmission are reduced by combining the similar data from the nearby areas. By using
the clustering technique and aggregating the correlated data greatly minimize the energy consumed in
collecting and disseminating the data. In this work, we evaluate the performance of a novel energy
efficient cluster based aggregation protocol (EECAP) for WSN. The main focus in this proposed work is
to study the performance of our proposed aggregation protocol with divergent sink placements such as
when sink is at the centre of the sensing field, corner of the sensing field or at a location selected
randomly in the sensor field. We present experimental results by calculating the lifetime of network in
terms of number of sensing rounds using various parameters such as – average remaining energy of
nodes, number of dead nodes after the specified number of sensing rounds. Finally the performance of
various aggregation algorithms such as LEACH, SEP and our proposed aggregation protocol (EECAP)
are compared with divergent sink placements. The simulation results demonstrates that EECAP exhibits
good performance in terms of lifetime and the energy consumption of the wireless sensor networks and
which can be as equally compared with existing clustering protocols.
Energy Efficient Multipath Data Fusion Technique for Wireless Sensor NetworksIDES Editor
In wireless sensor networks (WSN), data fusion
should be energy efficient. But, determining the optimal
number of aggregators in an energy efficient manner is a
challenging task. Moreover, the existing data fusion
techniques mostly use the same path for transmitting
aggregated data to the sink which reduces the nodes lifetime.
In this paper, we propose a technique which combines energy
efficiency and multiple path selection for data fusion in WSN.
The network is partitioned into various clusters and the node
with highest residual energy is selected as the cluster head.
The sink computes multiple paths to each cluster head for
data transmission. The distributed source coding and the
lifting scheme wavelet transform are used for compressing
the data at the CH. During each round of transmission, the
path is changed in a round robin manner, to conserve the
energy. This process is repeated for each cluster. From our
simulation results we show that this data fusion technique
has less energy consumption with increased packet delivery
ratio, when compared with the existing schemes.
K dag based lifetime aware data collection in wireless sensor networksijwmn
Wireless Sensor Networks need to be organized for efficient data collection and lifetime maximization. In
this paper, we propose a novel routing structure, namely k-DAG, to balance the load of the base station's
neighbours while providing the worst-case latency guarantee for data collection, and a distributed
algorithm for construction a k-DAG based on a SPD (Shortest Path DAG). In a k-DAG, the lengths of the
longest path and the shortest path of each sensor node to the base station differ by at most k. By adding
sibling edges to a SPD, our distributed algorithm allows critical nodes to have more routing choices. The
simulation results show that our approach significantly outperforms the SPD-based data collection
approach in both network lifetime and load balance.
Energy Efficient Stable Election Protocol for Clustered Heterogeneous Wireles...IOSR Journals
This document summarizes an energy efficient stable election protocol (EE-SEP) for clustered heterogeneous wireless sensor networks. EE-SEP modifies the existing SEP protocol to improve energy efficiency, stability period, and network lifetime. It does this by calculating the optimal threshold value for selecting cluster heads based on the initial energy of sensor nodes, rather than the weighted election probability used in SEP. Simulations show EE-SEP performs better than SEP by increasing the number of alive nodes over time, reducing energy consumption, and prolonging network lifetime.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
ENERGY EFFICIENT HIERARCHICAL CLUSTER HEAD ELECTION USING EXPONENTIAL DECAY F...ijwmn
This document summarizes an article that proposes an improved algorithm for selecting cluster heads in wireless sensor networks. The algorithm uses an exponential decay function to predict the average energy of sensor nodes and selects cluster heads based on both the probabilistic LEACH algorithm and predicted energy levels. The algorithm was tested in MATLAB simulations of a homogeneous sensor network and showed improvements in stability, average energy dissipation per round, and lifespan over the baseline LEACH protocol.
Clustering Based Lifetime Maximizing Aggregation Tree for Wireless Sensor Net...IJASCSE
This document proposes a Clustering Based Lifetime Maximizing Aggregation Tree (CLMAT) algorithm for wireless sensor networks. The algorithm aims to reduce energy consumption by creating an aggregation tree that minimizes distance traversed, energy consumed, and cost. It considers the three factors of energy, distance, and cost simultaneously when constructing the tree, unlike previous works. The tree is structured to maximize network lifetime by selecting nodes with higher residual energy as parents where possible. Pseudocode is provided to generate the aggregation tree using clustering by calculating branch and tree energy, distance, and cost at each step to ultimately select the tree with the highest lifetime, lowest energy consumption, distance, and cost.
Energy aware clustering protocol (eacp)IJCNCJournal
The document summarizes an Energy Aware Clustering Protocol (EACP) proposed for heterogeneous wireless sensor networks. EACP introduces heterogeneity by using two types of nodes: normal and advanced. Normal nodes elect cluster heads using a probability scheme based on residual and average energy. Advanced nodes use a separate probability scheme and act as gateways for normal cluster heads, transmitting their data to the base station. The performance of EACP is compared to SEP through simulations, showing better results for stability period, network life and energy savings.
Application of Weighted Centroid Approach in Base Station Localization for Mi...IJMER
A Wireless Sensor Networks (WSNs) consisting of sensor with strategic locations, and a base-stations (BSs) whose locations are relatively flexible. A sensor cluster consists of many small sensor nodes (SNs) that capture, encode, and transmit relevant information from a designated area. This article is focused on the topology of positioning process for BSs in WSNs. Heterogeneous SNs are battery-powered and energy-constrained, their node lifetime directly affects the network lifetime of WSNs. We have proposed an algorithmic approach to locate BSs optimally such that we can maximize the topological network lifetime of WSNs deterministically, even when the initial energy provisioning for SNs is no longer always proportional to their average bit-stream rate. The obtained optimal BS locations are under different length of area field and number of nodes according to the mission criticality of WSNs. By studying energy consumption due to space loss and amplification losses in WSNs, we establish the upper and lower bounds of maximal topological parameters of area and number of nodes, which enable a quick assessment of energy provisioning feasibility and topology necessity. Numerical results and surface plot are given to demonstrate the efficiency and optimality of the proposed topology of BSs positioning approaches designed for maximizing network lifetime of WSNs.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Congestion Control Clustering a Review PaperEditor IJCATR
Wireless Sensor Networks consists of sensor nodes which are scattered in the environment, gather data and transmit it to a
base station for processing. Energy conservation in the Wireless Sensor Networks (WSN) is a very important task because of their
limited battery power. The related works so far have been done have tried to solve the problem keeping in the mind the constraints of
WSNs. In this paper, a priority based application specific congestion control clustering (PASCCC) protocol has been studied, which
often integrates the range of motion and heterogeneity of the nodes to detect congestion in a very network. Moreover a comparison of
the various clustering techniques has been done. From the survey it has been found that none of the protocol is efficient for energy
conservation. Hence the paper ends with future scope to overcome these issues.
International Journal of Advanced Smart Sensor Network Systems ( IJASSN )ijassn
With the availability of low cost, short range sensor technology along with advances in wireless networking, sensor networks has become a hot topic of discussion. The International Journal of Advanced Smart Sensor Network Systems is an open access peer-reviewed journal which focuses on applied research and applications of sensor networks. While sensor networks provide ample opportunities to provide various services, its effective deployment in large scale is still challenging due to various factors. This journal provides a forum that impacts the development of high performance computing solutions to problems arising due to the complexities of sensor network systems. It also acts as a path to exchange novel ideas about impacts of sensor networks research.
GREEDY CLUSTER BASED ROUTING FOR WIRELESS SENSOR NETWORKSijcsit
In recent years, applications of wireless sensor networks have evolved in many areas such as target tracking, environmental monitoring, military and medical applications. Wireless sensor network continuously collect and send data through sensor nodes from a specific region to a base station. But, data redundancy due to neighbouring sensors consumes energy, compromising the network lifetime. In order to improve the network lifetime, a novel cluster based local route search method, called, Greedy Clusterbased Routing (GCR) technique in wireless sensor network. The proposed GCR method uses arbitrary timer in order to participate cluster head selection process with maximum neighbour nodes and minimum distance between the source and base station. GCR constructs dynamic routing improving the rate of network lifetime through Mass Proportion value. Also, GCR uses a greedy route finding strategy for
balancing energy consumption. Experimental results show that GCR achieves significant energy savings and prolong network lifetime.
This document discusses improving the performance of mobile wireless sensor networks using a modified DBSCAN clustering algorithm. It first provides background on wireless sensor networks and discusses challenges related to mobility. It then reviews several existing works related to clustering, mobility, and extending network lifetime. The paper proposes using a modified DBSCAN algorithm that takes into account mobility, remaining energy, and distance to base station to select cluster heads. It evaluates the performance of this approach based on throughput, delay, and packet delivery ratio, finding improvements over other methods.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers reduces the energy consumption and increases the throughput of the wireless sensor networks.
The document compares two hierarchical routing schemes for wireless sensor networks: EEPSC and EEEPSC. EEPSC divides the network into static clusters and uses temporary cluster heads to distribute energy load. EEEPSC extends EEPSC by also considering distance between nodes and cluster heads when selecting cluster heads, in order to reduce inter-cluster communication costs. Simulation results show that EEEPSC increases network lifetime by consuming less energy and keeping more nodes alive over time compared to EEPSC.
Performance Evaluation of Ant Colony Optimization Based Rendezvous Leach Usin...IJERD Editor
- The document discusses the performance evaluation of an Ant Colony Optimization (ACO) based algorithm for wireless sensor networks with mobile sinks.
- It proposes using ACO along with rendezvous points and mobile sinks in a clustering protocol called Rendezvous LEACH (RZ LEACH) to optimize energy efficiency and network lifetime.
- Simulation results show that the ACO based RZ LEACH outperforms the original RZ LEACH protocol by prolonging the number of operational nodes and increasing average remaining energy in the network over time.
Clustering provides an effective method for
extending the lifetime of a wireless sensor network. Current
clustering methods selecting cluster heads with more residual
energy, and rotating cluster heads periodically to distribute the
energy consumption among nodes in each cluster. However,
they rarely consider the hot spot problem in multi hop sensor
networks. When cluster heads forward their data to the base
station, the cluster heads closer to the base station are heavily
burdened with traffic and tend to die much faster. To mitigate
the hot spot problem, we propose a Novel Energy Efficient
Unequal Clustering Routing (NEEUC) protocol. It uses residual
energy and groupsthe nodesinto clusters of unequal layers
Energy Consumption Reduction in Wireless Sensor Network Based on ClusteringIJCNCJournal
ABSTRACT
One of the important issues in the routing protocol design in Wireless Sensor Networks (WSNs) is minimizing energy consumption and maximizing network lift time. Nowadays networks and information systems are one of the main parts of modern life that without them, people cannot live. On the hand, the impairment of these networks leads to great and incalculable costs. In this paper, a new method based on clustering has presented that problem of energy consumption is solved. The proposed algorithm is that energy-based clustering can create clusters of the same energy level and distribute energy efficiency across the WNS nodes. This proposed clustering protocol classify network nodes based on energy and neighbourhood criteria and attempts to better balance energy in clusters and ultimately increase network lifetime and maintain network coverage. Results are shown that the proposed algorithm is on average 40% better than LEACH algorithm and 14% better than IBLEACH algorithm.
KEYWORDS
Wireless Sensor Network, Clustering, LEACH Algorithm, IBLEACH Algorithm
Abstract Link : http://aircconline.com/abstract/ijcnc/v11n2/11219cnc03.html
Full Details : http://aircconline.com/ijcnc/V11N2/11219cnc03.pdf
Neural network based energy efficient clustering and routingambitlick
This document summarizes a paper that proposes a neural network based approach for energy efficient clustering and routing in wireless sensor networks. The key points are:
1. It proposes a neural network based clustering algorithm to select cluster heads in a way that balances energy consumption.
2. It defines a routing metric based on transmission and reception energy and uses it to formulate the routing problem as a linear program to optimize energy efficiency.
3. It presents algorithms for cluster head selection using the neural network, and for multi-path routing and data transmission based on the routing metric and linear program formulation.
Energy Efficient Data Transmission through Relay Nodes in Wireless Sensor Net...IDES Editor
In a Wireless Sensor Network (WSN) having a single
sink, information is given to the distant nodes from beacons
by overhearing. Since it is out of the communication range,
information is not sent directly to the static sink (SS). If a
distant node is not able to communicate directly, then it should
send its own packet to another node which is closer to the
Base Station (BS) so that the received packets are relayed to
the BS by this node. In this paper, we propose a relay node
selection algorithm to reduce contention and improve energy
efficiency. In this algorithm, each data packet of direct
communication should include the received signal strength
(RSS) of the beacon packet. The distant node selects a node
with the maximum RSS value as a relay. The algorithm also
assigns transmitting intervals to each relay node. By our
simulation results, we show that our proposed algorithm
improves the packet delivery ratio and energy efficiency.
The document presents an improved clustering algorithm for load balancing in wireless sensor networks. The algorithm aims to address uneven cluster sizes that can negatively impact load balancing. It does this by selecting master and vice master nodes to partition regions and subregions, aiming to make cluster sizes more balanced. The algorithm has setup and steady state phases. In the setup phase, regions are partitioned recursively if needed to select balanced cluster heads. In the steady state phase, nodes send data to their cluster heads, which then send aggregated data to the base station. Simulation results show the improved algorithm increases network lifetime and energy efficiency compared to LEACH and E-LEACH algorithms.
A seminar report on data aggregation in wireless sensor networkspraveen369
This document summarizes a seminar report on data aggregation in wireless sensor networks. It discusses the goals of improving energy efficiency and network lifetime through data aggregation. It provides an overview of different data aggregation approaches, including in-network aggregation, tree-based approaches, and cluster-based approaches. It also discusses query processing and security challenges in wireless sensor networks.
Scheduling different types of packets, such as
real-time and non-real-time data packets, at sensor nodes with
resource constraints in Wireless Sensor Networks (WSN) is of
vital importance to reduce sensors’ energy consumptions and endto-end
data transmission delays. Most of the existing packetscheduling
mechanisms of WSN use First Come First Served
(FCFS), non pre-emptive priority and pre-emptive priority
scheduling algorithms. These algorithms incur a high processing
overhead and long end-to-end data transmission delay due to the
FCFS concept, starvation of high priority real-time data packets
due to the transmission of a large data packet in non pre-emptive
priority scheduling, starvation of non-real-time data packets due
to the probable continuous arrival of real-time data in preemptive
priority scheduling, and improper allocation of data
packets to queues in multilevel queue scheduling algorithms.
Moreover, these algorithms are not dynamic to the changing
requirements of WSN applications since their scheduling policies
are predetermined.
In the Advanced Multilevel Priority packet scheduling
scheme, each node except those at the last level has three levels of
priority queues. According to the priority of the packet and
availability of the queue, node will schedule the packet for
transmission. Due to separated queue availability, packet
transmission delay is reduced. Due to reduction in packet
transmission delay, node can goes into sleep mode as soon as
possible. And Expired packets are deleted at the particular node
at itself before reaching the base station, so that processing
burden on the node is reduced. Thus, energy of the node is saved.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Maximizing Lifetime of Homogeneous Wireless Sensor Network through Energy Eff...CSCJournals
The objective of this paper is to develop a mechanism to increase the lifetime of homogeneous wireless sensor networks (WSNs) through minimizing long range communication, efficient data delivery and energy balancing. Energy efficiency is a very important issue for sensor nodes which affects the lifetime of sensor networks. To achieve energy balancing and maximizing network lifetime we divided the whole network into different clusters. In cluster based architecture, the role of aggregator node is very crucial because of extra processing and long range communication. Once the aggregator node becomes non functional, it affects the whole cluster. We introduced a candidate cluster head node on the basis of node density. We proposed a modified cluster based WSN architecture by introducing a server node (SN) that is rich in terms of resources. This server node (SN) takes the responsibility of transmitting data to the base station over longer distances from the cluster head. We proposed cluster head selection algorithm based on residual energy, distance, reliability and degree of mobility. The proposed method can save overall energy consumption and extend the lifetime of the sensor network and also addresses robustness against even/uneven node deployment.
This document summarizes several energy-efficient routing protocols for wireless sensor networks. It begins by introducing the basic components and architecture of wireless sensor networks. It then categorizes routing protocols based on network structure (flat, hierarchical, location-based) and operation (multipath, query-based, etc.). The majority of the document focuses on reviewing hierarchical protocols, including LEACH, PEGASIS, Hierarchical PEGASIS, and HEED. It provides brief overviews of how these protocols work to reduce energy consumption and extend network lifetime through clustering and data aggregation approaches.
This document summarizes a research paper on developing an improved LEACH (Low-Energy Adaptive Clustering Hierarchy) communication protocol for energy efficient data mining in multi-feature sensor networks. It begins with background on wireless sensor networks and issues like energy efficiency. It then discusses the existing LEACH protocol and its drawbacks. The proposed improved LEACH protocol includes cluster heads, sub-cluster heads, and cluster nodes to address LEACH's limitations. This new version aims to minimize energy consumption during cluster formation and data aggregation in multi-feature sensor networks.
Optimized Projected Strategy for Enhancement of WSN Using Genetic AlgorithmsIJMER
This document summarizes an optimized projected strategy for enhancing wireless sensor networks using genetic algorithms. It describes a heterogeneous wireless sensor network model with normal, intermediate, and advanced sensor nodes having different initial energy levels. The proposed approach selects cluster heads based on the nodes' battery power and residual energy, giving intermediate and advanced nodes a higher probability of becoming cluster heads to balance energy consumption across the network. The strategy aims to increase the stability period when the first node dies and the overall network lifetime.
Application of Weighted Centroid Approach in Base Station Localization for Mi...IJMER
A Wireless Sensor Networks (WSNs) consisting of sensor with strategic locations, and a base-stations (BSs) whose locations are relatively flexible. A sensor cluster consists of many small sensor nodes (SNs) that capture, encode, and transmit relevant information from a designated area. This article is focused on the topology of positioning process for BSs in WSNs. Heterogeneous SNs are battery-powered and energy-constrained, their node lifetime directly affects the network lifetime of WSNs. We have proposed an algorithmic approach to locate BSs optimally such that we can maximize the topological network lifetime of WSNs deterministically, even when the initial energy provisioning for SNs is no longer always proportional to their average bit-stream rate. The obtained optimal BS locations are under different length of area field and number of nodes according to the mission criticality of WSNs. By studying energy consumption due to space loss and amplification losses in WSNs, we establish the upper and lower bounds of maximal topological parameters of area and number of nodes, which enable a quick assessment of energy provisioning feasibility and topology necessity. Numerical results and surface plot are given to demonstrate the efficiency and optimality of the proposed topology of BSs positioning approaches designed for maximizing network lifetime of WSNs.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Congestion Control Clustering a Review PaperEditor IJCATR
Wireless Sensor Networks consists of sensor nodes which are scattered in the environment, gather data and transmit it to a
base station for processing. Energy conservation in the Wireless Sensor Networks (WSN) is a very important task because of their
limited battery power. The related works so far have been done have tried to solve the problem keeping in the mind the constraints of
WSNs. In this paper, a priority based application specific congestion control clustering (PASCCC) protocol has been studied, which
often integrates the range of motion and heterogeneity of the nodes to detect congestion in a very network. Moreover a comparison of
the various clustering techniques has been done. From the survey it has been found that none of the protocol is efficient for energy
conservation. Hence the paper ends with future scope to overcome these issues.
International Journal of Advanced Smart Sensor Network Systems ( IJASSN )ijassn
With the availability of low cost, short range sensor technology along with advances in wireless networking, sensor networks has become a hot topic of discussion. The International Journal of Advanced Smart Sensor Network Systems is an open access peer-reviewed journal which focuses on applied research and applications of sensor networks. While sensor networks provide ample opportunities to provide various services, its effective deployment in large scale is still challenging due to various factors. This journal provides a forum that impacts the development of high performance computing solutions to problems arising due to the complexities of sensor network systems. It also acts as a path to exchange novel ideas about impacts of sensor networks research.
GREEDY CLUSTER BASED ROUTING FOR WIRELESS SENSOR NETWORKSijcsit
In recent years, applications of wireless sensor networks have evolved in many areas such as target tracking, environmental monitoring, military and medical applications. Wireless sensor network continuously collect and send data through sensor nodes from a specific region to a base station. But, data redundancy due to neighbouring sensors consumes energy, compromising the network lifetime. In order to improve the network lifetime, a novel cluster based local route search method, called, Greedy Clusterbased Routing (GCR) technique in wireless sensor network. The proposed GCR method uses arbitrary timer in order to participate cluster head selection process with maximum neighbour nodes and minimum distance between the source and base station. GCR constructs dynamic routing improving the rate of network lifetime through Mass Proportion value. Also, GCR uses a greedy route finding strategy for
balancing energy consumption. Experimental results show that GCR achieves significant energy savings and prolong network lifetime.
This document discusses improving the performance of mobile wireless sensor networks using a modified DBSCAN clustering algorithm. It first provides background on wireless sensor networks and discusses challenges related to mobility. It then reviews several existing works related to clustering, mobility, and extending network lifetime. The paper proposes using a modified DBSCAN algorithm that takes into account mobility, remaining energy, and distance to base station to select cluster heads. It evaluates the performance of this approach based on throughput, delay, and packet delivery ratio, finding improvements over other methods.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers reduces the energy consumption and increases the throughput of the wireless sensor networks.
The document compares two hierarchical routing schemes for wireless sensor networks: EEPSC and EEEPSC. EEPSC divides the network into static clusters and uses temporary cluster heads to distribute energy load. EEEPSC extends EEPSC by also considering distance between nodes and cluster heads when selecting cluster heads, in order to reduce inter-cluster communication costs. Simulation results show that EEEPSC increases network lifetime by consuming less energy and keeping more nodes alive over time compared to EEPSC.
Performance Evaluation of Ant Colony Optimization Based Rendezvous Leach Usin...IJERD Editor
- The document discusses the performance evaluation of an Ant Colony Optimization (ACO) based algorithm for wireless sensor networks with mobile sinks.
- It proposes using ACO along with rendezvous points and mobile sinks in a clustering protocol called Rendezvous LEACH (RZ LEACH) to optimize energy efficiency and network lifetime.
- Simulation results show that the ACO based RZ LEACH outperforms the original RZ LEACH protocol by prolonging the number of operational nodes and increasing average remaining energy in the network over time.
Clustering provides an effective method for
extending the lifetime of a wireless sensor network. Current
clustering methods selecting cluster heads with more residual
energy, and rotating cluster heads periodically to distribute the
energy consumption among nodes in each cluster. However,
they rarely consider the hot spot problem in multi hop sensor
networks. When cluster heads forward their data to the base
station, the cluster heads closer to the base station are heavily
burdened with traffic and tend to die much faster. To mitigate
the hot spot problem, we propose a Novel Energy Efficient
Unequal Clustering Routing (NEEUC) protocol. It uses residual
energy and groupsthe nodesinto clusters of unequal layers
Energy Consumption Reduction in Wireless Sensor Network Based on ClusteringIJCNCJournal
ABSTRACT
One of the important issues in the routing protocol design in Wireless Sensor Networks (WSNs) is minimizing energy consumption and maximizing network lift time. Nowadays networks and information systems are one of the main parts of modern life that without them, people cannot live. On the hand, the impairment of these networks leads to great and incalculable costs. In this paper, a new method based on clustering has presented that problem of energy consumption is solved. The proposed algorithm is that energy-based clustering can create clusters of the same energy level and distribute energy efficiency across the WNS nodes. This proposed clustering protocol classify network nodes based on energy and neighbourhood criteria and attempts to better balance energy in clusters and ultimately increase network lifetime and maintain network coverage. Results are shown that the proposed algorithm is on average 40% better than LEACH algorithm and 14% better than IBLEACH algorithm.
KEYWORDS
Wireless Sensor Network, Clustering, LEACH Algorithm, IBLEACH Algorithm
Abstract Link : http://aircconline.com/abstract/ijcnc/v11n2/11219cnc03.html
Full Details : http://aircconline.com/ijcnc/V11N2/11219cnc03.pdf
Neural network based energy efficient clustering and routingambitlick
This document summarizes a paper that proposes a neural network based approach for energy efficient clustering and routing in wireless sensor networks. The key points are:
1. It proposes a neural network based clustering algorithm to select cluster heads in a way that balances energy consumption.
2. It defines a routing metric based on transmission and reception energy and uses it to formulate the routing problem as a linear program to optimize energy efficiency.
3. It presents algorithms for cluster head selection using the neural network, and for multi-path routing and data transmission based on the routing metric and linear program formulation.
Energy Efficient Data Transmission through Relay Nodes in Wireless Sensor Net...IDES Editor
In a Wireless Sensor Network (WSN) having a single
sink, information is given to the distant nodes from beacons
by overhearing. Since it is out of the communication range,
information is not sent directly to the static sink (SS). If a
distant node is not able to communicate directly, then it should
send its own packet to another node which is closer to the
Base Station (BS) so that the received packets are relayed to
the BS by this node. In this paper, we propose a relay node
selection algorithm to reduce contention and improve energy
efficiency. In this algorithm, each data packet of direct
communication should include the received signal strength
(RSS) of the beacon packet. The distant node selects a node
with the maximum RSS value as a relay. The algorithm also
assigns transmitting intervals to each relay node. By our
simulation results, we show that our proposed algorithm
improves the packet delivery ratio and energy efficiency.
The document presents an improved clustering algorithm for load balancing in wireless sensor networks. The algorithm aims to address uneven cluster sizes that can negatively impact load balancing. It does this by selecting master and vice master nodes to partition regions and subregions, aiming to make cluster sizes more balanced. The algorithm has setup and steady state phases. In the setup phase, regions are partitioned recursively if needed to select balanced cluster heads. In the steady state phase, nodes send data to their cluster heads, which then send aggregated data to the base station. Simulation results show the improved algorithm increases network lifetime and energy efficiency compared to LEACH and E-LEACH algorithms.
A seminar report on data aggregation in wireless sensor networkspraveen369
This document summarizes a seminar report on data aggregation in wireless sensor networks. It discusses the goals of improving energy efficiency and network lifetime through data aggregation. It provides an overview of different data aggregation approaches, including in-network aggregation, tree-based approaches, and cluster-based approaches. It also discusses query processing and security challenges in wireless sensor networks.
Scheduling different types of packets, such as
real-time and non-real-time data packets, at sensor nodes with
resource constraints in Wireless Sensor Networks (WSN) is of
vital importance to reduce sensors’ energy consumptions and endto-end
data transmission delays. Most of the existing packetscheduling
mechanisms of WSN use First Come First Served
(FCFS), non pre-emptive priority and pre-emptive priority
scheduling algorithms. These algorithms incur a high processing
overhead and long end-to-end data transmission delay due to the
FCFS concept, starvation of high priority real-time data packets
due to the transmission of a large data packet in non pre-emptive
priority scheduling, starvation of non-real-time data packets due
to the probable continuous arrival of real-time data in preemptive
priority scheduling, and improper allocation of data
packets to queues in multilevel queue scheduling algorithms.
Moreover, these algorithms are not dynamic to the changing
requirements of WSN applications since their scheduling policies
are predetermined.
In the Advanced Multilevel Priority packet scheduling
scheme, each node except those at the last level has three levels of
priority queues. According to the priority of the packet and
availability of the queue, node will schedule the packet for
transmission. Due to separated queue availability, packet
transmission delay is reduced. Due to reduction in packet
transmission delay, node can goes into sleep mode as soon as
possible. And Expired packets are deleted at the particular node
at itself before reaching the base station, so that processing
burden on the node is reduced. Thus, energy of the node is saved.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Maximizing Lifetime of Homogeneous Wireless Sensor Network through Energy Eff...CSCJournals
The objective of this paper is to develop a mechanism to increase the lifetime of homogeneous wireless sensor networks (WSNs) through minimizing long range communication, efficient data delivery and energy balancing. Energy efficiency is a very important issue for sensor nodes which affects the lifetime of sensor networks. To achieve energy balancing and maximizing network lifetime we divided the whole network into different clusters. In cluster based architecture, the role of aggregator node is very crucial because of extra processing and long range communication. Once the aggregator node becomes non functional, it affects the whole cluster. We introduced a candidate cluster head node on the basis of node density. We proposed a modified cluster based WSN architecture by introducing a server node (SN) that is rich in terms of resources. This server node (SN) takes the responsibility of transmitting data to the base station over longer distances from the cluster head. We proposed cluster head selection algorithm based on residual energy, distance, reliability and degree of mobility. The proposed method can save overall energy consumption and extend the lifetime of the sensor network and also addresses robustness against even/uneven node deployment.
This document summarizes several energy-efficient routing protocols for wireless sensor networks. It begins by introducing the basic components and architecture of wireless sensor networks. It then categorizes routing protocols based on network structure (flat, hierarchical, location-based) and operation (multipath, query-based, etc.). The majority of the document focuses on reviewing hierarchical protocols, including LEACH, PEGASIS, Hierarchical PEGASIS, and HEED. It provides brief overviews of how these protocols work to reduce energy consumption and extend network lifetime through clustering and data aggregation approaches.
This document summarizes a research paper on developing an improved LEACH (Low-Energy Adaptive Clustering Hierarchy) communication protocol for energy efficient data mining in multi-feature sensor networks. It begins with background on wireless sensor networks and issues like energy efficiency. It then discusses the existing LEACH protocol and its drawbacks. The proposed improved LEACH protocol includes cluster heads, sub-cluster heads, and cluster nodes to address LEACH's limitations. This new version aims to minimize energy consumption during cluster formation and data aggregation in multi-feature sensor networks.
Optimized Projected Strategy for Enhancement of WSN Using Genetic AlgorithmsIJMER
This document summarizes an optimized projected strategy for enhancing wireless sensor networks using genetic algorithms. It describes a heterogeneous wireless sensor network model with normal, intermediate, and advanced sensor nodes having different initial energy levels. The proposed approach selects cluster heads based on the nodes' battery power and residual energy, giving intermediate and advanced nodes a higher probability of becoming cluster heads to balance energy consumption across the network. The strategy aims to increase the stability period when the first node dies and the overall network lifetime.
This document summarizes a survey of intelligent approaches for efficient energy consumption in wireless sensor networks. Artificial intelligence techniques have been applied to optimize routing protocols and aggregate sensor data more efficiently to conserve limited battery power. Some key approaches discussed are directed diffusion for data dissemination, low-energy adaptive clustering hierarchy (LEACH) for randomized clustering, and energy aware distributed aggregation trees for in-network data aggregation. The goal is to extend the lifetime of battery-powered sensor networks through intelligent energy management strategies.
INCREASING WIRELESS SENSOR NETWORKS LIFETIME WITH NEW METHODijwmn
The document proposes a new method to increase the lifetime of wireless sensor networks. It divides the sensor network environment into two virtual layers based on distance from the base station. It then uses residual energy, distance from base station, and position in the layers as factors in selecting cluster heads. Simulations show the proposed method outperforms LEACH and ELEACH algorithms in both homogeneous and heterogeneous sensor energy environments.
INCREASING WIRELESS SENSOR NETWORKS LIFETIME WITH NEW METHODijwmn
One of the most important issues in Wireless Sensor Networks (WSNs) is severe energy restrictions. As the
performance of Sensor Networks is strongly dependence to the network lifetime, researchers seek a way to
use node energy supply effectively and increasing network lifetime. As a consequence, it is crucial to use
routing algorithms result in decrease energy consumption and better bandwidth utilization. The purpose of
this paper is to increase Wireless Sensor Networks lifetime using LEACH-algorithm. So before clustering
Network environment, it is divided into two virtual layers (using distance between sensor nodes and base
station) and then regarding to sensors position in each of two layers, residual energy of sensor and
distance from base station is used in clustering. In this article, we compare proposed algorithm with wellknown LEACH and ELEACH algorithms in homogenous environment (with equal energy for all sensors)
and heterogeneous one (energy of half of sensors get doubled), also for static and dynamic situation of base
station. Results show that our proposed algorithm delivers improved performance.
E FFICIENT E NERGY U TILIZATION P ATH A LGORITHM I N W IRELESS S ENSOR...IJCI JOURNAL
With limited amount of energy, nodes are powered by
batteries in wireless networks. Increasing the lif
e
span of the network and reducing the usage of energ
y are two severe problems in Wireless Sensor
Networks. A small number of energy utilization path
algorithms like minimum spanning tree reduces tota
l
energy consumption of a Wireless Sensor Network, ho
wever very heavy load of sending data packets on
many key nodes is used with the intention that the
nodes quickly consumes battery energy, by raising t
he
life span of the network reduced. Our proposal work
aimed on presenting an Energy Conserved Fast and
Secure Data Aggregation Scheme for WSN in time and
security logic occurrence data collection
application. To begin with, initially the goal is m
ade on energy preservation of sensed data gathering
from
event identified sensor nodes to destination. Inven
tion is finished on Energy Efficient Utilization Pa
th
Algorithm (EEUPA), to extend the lifespan by proces
sing the collecting series with path mediators
depending on gene characteristics sequencing of nod
e energy drain rate, energy consumption rate, and
message overhead together with extended network lif
e span. Additionally, a mathematical programming
technique is designed to improve the lifespan of th
e network. Simulation experiments carried out among
different relating conditions of wireless sensor ne
twork by different path algorithms to analyze the
efficiency and effectiveness of planned Efficient E
nergy Utilization Path Algorithm in wireless sensor
network (EEUPA)
A LOW-ENERGY DATA AGGREGATION PROTOCOL USING AN EMERGENCY EFFICIENT HYBRID ME...IJCNCJournal
Recent wireless sensor network focused on developing communication networks with minimal power and cost. To achieve this, several techniques have been developed to monitor a completely wireless sensor network. Generally, in the WSN network, communication is established between the source nodes and the destination node with an abundant number of hops, an activity which consumes much energy. The node existing between source and destination nodes consumes energy for transmission of data and maximize network lifetime. To overcome this issue, a new Emergency Efficient Hybrid Medium Access Control (EEHMAC) protocol is presented to reduce consumption of energy among a specific group of WSNs which will increase the network lifetime. The proposed model makes a residual battery is utilized for effective transmission of data with minimal power consumption. Compared with other models, the experimental results strongly showed that our model is not only able to reduce network lifetime but also to increase the overall network performance.
This document summarizes research on topology control techniques in wireless sensor networks. It first discusses how topology control aims to reduce energy consumption while maintaining network connectivity by regulating nodes' transmission power. It then reviews several existing topology control algorithms proposed in other papers. These algorithms distribute transmission power control to maximize network lifetime. Finally, the document concludes that many topology control algorithms have been developed to achieve energy efficient routing, but implementing them on real-world testbeds poses challenges.
Designing an Energy Efficient Clustering in Heterogeneous Wireless Sensor Net...IJCNCJournal
Designing an energy-efficient scheme in a Heterogeneous Wireless Sensor Network (HWSN) is a critical issue that degrades the network performance. Recharging and providing security to the sensor devices is very difficult in an unattended environment once the energy is drained off. A Clustering scheme is an important and suitable approach to increase energy efficiency and transmitting secured data which in turn enhances the performance in the network. The proposed algorithm Energy Efficient Clustering (EEC) works for optimum energy utilization in sensor nodes. The algorithm is proposed by combining the rotation-based clustering and energy-saving mechanism for avoiding the node failure and prolonging the network lifetime. This shows MAC layer scheduling is based on optimum energy utilization depending on the residual energy. In the proposed work, a densely populated network is partitioned into clusters and all the cluster heads are formed at a time and selected on rotation based on considering the highest energy of the sensor nodes. Other cluster members are accommodated in a cluster based on Basic Cost Maximum flow (BCMF) to allow the cluster head for transmitting the secured data. Carrier Sense Multiple Access (CSMA), a contention window based protocol is used at the MAC layer for collision detection and to provide channel access prioritization to HWSN of different traffic classes with reduction in End to End delay, energy consumption, and improved throughput and Packet delivery ratio(PDR) and allowing the cluster head for transmission without depleting the energy. Simulation parameters of the proposed system such as Throughput, Energy, and Packet Delivery Ratio are obtained and compared with the existing system.
DESIGNING AN ENERGY EFFICIENT CLUSTERING IN HETEROGENEOUS WIRELESS SENSOR NET...IJCNCJournal
Designing an energy-efficient scheme in a Heterogeneous Wireless Sensor Network (HWSN) is a critical
issue that degrades the network performance. Recharging and providing security to the sensor devices is
very difficult in an unattended environment once the energy is drained off. A Clustering scheme is an
important and suitable approach to increase energy efficiency and transmitting secured data which in turn
enhances the performance in the network. The proposed algorithm Energy Efficient Clustering (EEC)
works for optimum energy utilization in sensor nodes. The algorithm is proposed by combining the
rotation-based clustering and energy-saving mechanism for avoiding the node failure and prolonging the
network lifetime. This shows MAC layer scheduling is based on optimum energy utilization depending on
the residual energy. In the proposed work, a densely populated network is partitioned into clusters and all
the cluster heads are formed at a time and selected on rotation based on considering the highest energy of
the sensor nodes. Other cluster members are accommodated in a cluster based on Basic Cost Maximum
flow (BCMF) to allow the cluster head for transmitting the secured data. Carrier Sense Multiple Access
(CSMA), a contention window based protocol is used at the MAC layer for collision detection and to
provide channel access prioritization to HWSN of different traffic classes with reduction in End to End
delay, energy consumption, and improved throughput and Packet delivery ratio(PDR) and allowing the
cluster head for transmission without depleting the energy. Simulation parameters of the proposed system
such as Throughput, Energy, and Packet Delivery Ratio are obtained and compared with the existing
system.
This paper considers a heterogeneous network of energy constrained sensors deployed over a region. Each
Normal sensor node in a network is systematically gathering and transmitting sensed data to the clusterhead,
and then cluster head sending data to a base station (via intermediate cluster- heads). This paper
focuses on reducing the energy consumption and hence improving lifetime of wireless sensor Networks.
Clustering sensor node is an effective topology for the energy constrained networks. So energy saving
algorithm has been developed in which clusters are formed considering a subset of high energy nodes as a
cluster-head and another subset of powerful nodes is ask to go to sleep. When Cluster heads deplete their
energy another subset of nodes becomes active and acts as a cluster head. Proposed approach is
implemented in MATLAB, Simulation results shows that it can prolong the network lifetime than LEACH
protocol, and achieves better performance than the existing clustering algorithms such as LEACH.
Energy Efficient Data Mining in Multi-Feature Sensor Networks Using Improved...IOSR Journals
This document proposes an improved LEACH (Low-Energy Adaptive Clustering Hierarchy) communication protocol for energy efficient data mining in multi-feature sensor networks. The original LEACH protocol has drawbacks like random cluster head selection and uneven energy consumption. The improved protocol designates both a cluster head and sub-cluster head to take over if the head dies. This addresses the issues with the cluster head dying and the cluster becoming useless. The improved LEACH protocol is proposed to cluster sensor nodes in multi-feature networks to enhance energy efficiency and reliability of data transfer compared to the original LEACH protocol.
A Review Study on Shortest Path in WSN to detect the Abnormal Packet for savi...Editor IJMTER
The main motive of this research is to study energy-efficient data-gathering mechanisms to
abnormal packet data for saving the energy. To detect the abnormal packet irregularities is useful for
saving energy, as well as for management of network, because the patterns found can be used for
both decision making in applications and system performance tuning. Node distribution in WSNs is
either deterministic or self-organizing and application dependant. The sensor nodes in WSNs have
minimum energy and they use their energy for communication and sensing.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and
packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which
ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer
from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of
sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads
in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption
and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer
and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers
reduces the energy consumption and increases the throughput of the wireless sensor networks.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and
packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which
ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer
from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of
sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads
in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption
and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer
and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers
reduces the energy consumption and increases the throughput of the wireless sensor networks.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers reduces the energy consumption and increases the throughput of the wireless sensor networks.
Similar to Computing localized power efficient data (20)
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Mutual Distance Bounding Protocols enable entities to determine an upper bound on their physical distance and authenticate each other. They have been actively researched due to distance-based attacks on wireless systems like RFID. While most protocols provide unilateral authentication of a tag to a reader, one was proposed to provide mutual authentication with a lower false acceptance rate. However, this analysis is shown to overestimate security, as a new attack achieves a higher false acceptance rate. A method is also introduced to modify existing unilateral authentication protocols into mutual authentication protocols.
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Efficient spread spectrum communication without pre shared secretsambitlick
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The document lists 23 networking and mobile computing projects implemented in NS2. It includes projects on topics like mobility in wireless networks, intrusion detection, neighbor discovery, energy renewal with wireless power transfer, load balancing, and spectrum access control. Contact information is provided for those interested in the project reports, presentations, source code, or implementing new projects.
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Mutual Distance Bounding Protocols enable entities to determine an upper bound on their physical distance and authenticate each other. They have been actively researched due to distance-based attacks on wireless systems like RFID. While most protocols provide unilateral authentication of a tag to a reader, one was proposed to provide mutual authentication with a lower false acceptance rate. However, this analysis is shown to overestimate security, as a new attack achieves a higher false acceptance rate. A method is also introduced to modify existing unilateral authentication protocols into mutual authentication protocols.
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This paper describes a distributed authoring system for campus workgroups that allows group members to modify any document type using their own devices. Each member maintains an updatable copy of shared content, and read-only copies are distributed based on wireless availability. Group members manually reconcile updates through moderation, merging changes from others into their copy. Over time, successive moderations converge the multiple versions into a single version. An evaluation found the asynchronous update propagation and moderation process intuitive for students.
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This document proposes a new mechanism called Time Reversed Message Extraction and Key Scheduling (TREKS) that allows for efficient spread spectrum communication without pre-shared secrets. TREKS is four orders of magnitude faster than previous solutions to this problem and enables long-term spread spectrum communication without establishing keys. It was evaluated through simulation and on a testbed and can sustain 1Mbps communication spread over a 100 Megachips bandwidth in real-time, with provably optimal energy cost and minimal storage overhead.
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This document summarizes and critiques the MABS-DSA protocol proposed by Zhou et al. for multicast authentication using batch verification. While MABS-DSA was intended to increase efficiency and security over other implementations, the author finds through reexamination of the arithmetic that the algorithm is actually incorrect and batch signature verification would fail almost always, even when individual packets were properly signed by an honest sender. The key issue is a flaw in protocol correctness rather than the intended security improvements.
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The document proposes a new probabilistic coverage protocol (PCP) for sensor networks that can employ different sensing models. PCP aims to address the costly task of designing and testing different coverage protocols for each sensing model. It works with common disk sensing models as well as probabilistic sensing models with minimal changes. Simulation results show that PCP outperforms other deterministic and probabilistic protocols in terms of number of activated sensors, total energy consumed, and network lifetime while being robust against failures and inaccuracies.
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How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
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How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
How to Build a Module in Odoo 17 Using the Scaffold Method
Computing localized power efficient data
1. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 22, NO. 3, MARCH 2011 489
Computing Localized Power-Efficient Data
Aggregation Trees for Sensor Networks
¨ ¨
Huseyin Ozgur Tan, Member, IEEE, Ibrahim Korpeoglu, Member, IEEE, and
Ivan Stojmenovic, Fellow, IEEE
Abstract—We propose localized, self organizing, robust, and energy-efficient data aggregation tree approaches for sensor networks,
which we call Localized Power-Efficient Data Aggregation Protocols (L-PEDAPs). They are based on topologies, such as LMST and
RNG, that can approximate minimum spanning tree and can be efficiently computed using only position or distance information of one-hop
neighbors. The actual routing tree is constructed over these topologies. We also consider different parent selection strategies while
constructing a routing tree. We compare each topology and parent selection strategy and conclude that the best among them is the
shortest path strategy over LMST structure. Our solution also involves route maintenance procedures that will be executed when a sensor
node fails or a new node is added to the network. The proposed solution is also adapted to consider the remaining power levels of nodes in
order to increase the network lifetime. Our simulation results show that by using our power-aware localized approach, we can almost have
the same performance of a centralized solution in terms of network lifetime, and close to 90 percent of an upper bound derived here.
Index Terms—Wireless networks, sensor networks, data gathering, minimum energy control, distributed algorithms.
Ç
1 INTRODUCTION
gathering data, meaning that all forwarded messages are of
T HE design of wireless sensor networks depends on the
application requirements. Environmental monitoring is
an application where a region is sensed by numerous sensor
the same size.
An important problem studied here is finding an energy-
nodes and the sensed data are gathered at a base station (a efficient routing scheme for gathering all data at the sink
sink) where further processing can be performed. The periodically so that the lifetime of the network is prolonged
sensor nodes for such applications are usually designed to as much a possible. The lifetime can be expressed in terms
work in conditions where it may not be possible to recharge of rounds where a round is the time period between two
or replace the batteries of the nodes. This means that energy sensing activities of sensor nodes.
is a very precious resource for sensor nodes, and commu- There are several requirements for a routing scheme to
nication overhead is to be minimized. These constraints be designed for this scenario. First, the algorithm should be
make the design of data communication protocols a distributed since it is extremely energy consuming to
challenging task [1]. calculate the optimum paths in a dynamic network and
A common scenario of sensor networks involves deploy- inform others about the computed paths in a centralized
ment of hundreds or thousands of low-cost, low-power manner. The algorithm must also be scalable. The message
sensor nodes to a region from where information will be and time complexity of computing the routing paths must
collected periodically. Hence, sensor nodes will periodically scale well with increasing number of nodes. Another
sense their nearby environment and send the information to desirable property is robustness, which means that the
a sink which is not energy limited. The collected informa- routing scheme should be resilient to node and link failures.
tion can be further processed at the sink for end-user The scheme should also support new node additions to the
queries. In order to reduce the communication overhead network, since not all nodes fail at the same time, and some
and energy consumption of sensors while gathering, the nodes may need to be replaced. In other words, the routing
received data can be combined to reduce message size. A scheme should be self-healing. The final and possibly the
simple way of doing that is aggregating the data. A most important requirement for a routing scheme for
wireless sensor networks is energy efficiency.
different way is data fusion (aggregation) which can be
A previous study [2] showed that the minimum
defined as producing a more accurate signal by combining
spanning tree (MST)-based routing provides good perfor-
several unreliable data measurements. In this paper, we
mance in terms of lifetime when the data are gathered using
focus on scenarios where perfect aggregation is used while aggregation. In that work, the authors proposed a new
centralized protocol called PEDAP. The idea in PEDAP is to
. H.O. Tan and I. Korpeoglu are with the Department of Computer use the minimum energy cost tree for data gathering. This
Engineering, Bilkent University, 06800 Ankara, Turkey. tree can be efficiently computed in centralized manner
E-mail: {hozgur, korpe}@cs.bilkent.edu.tr. using Prim’s minimum spanning tree algorithm [3]. In
. I. Stojmenovic is with SITE, University of Ottawa, 800 King Edward, PEDAP-PA, the authors changed the cost of the links so that
Ottawa, ON K1N 6N5, Canada. E-mail: ivan@site.uottawa.ca. the remaining energy of the sender is also taken into
Manuscript received 13 Nov. 2008; revised 4 Oct. 2009; accepted 13 Oct. consideration. Since the link costs vary over time, the
2009; published online 31 Mar. 2010. authors proposed recomputing the routing tree from time to
Recommended for acceptance by S. Olariu.
For information on obtaining reprints of this article, please send e-mail to: time using a power-aware cost function. By changing the
tpds@computer.org, and reference IEEECS Log Number TPDS-2008-11-0454. routing tree over time, the load on the nodes is balanced
Digital Object Identifier no. 10.1109/TPDS.2010.68. and a longer lifetime compared to the static version is
1045-9219/11/$26.00 ß 2011 IEEE Published by the IEEE Computer Society
2. 490 IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 22, NO. 3, MARCH 2011
achieved. In this way, the lifetime in terms of the first node this paper. The sensor nodes are homogeneous and energy
failure is almost doubled. constrained. Sensor nodes and sink are stationary and
The most important disadvantage of these two protocols, located randomly. Every node knows the geographic
however, is their centralized nature. In this paper, we location of itself by means of a GPS device or using some
propose a localized version of PEDAP, which tries to other localization techniques [7], [8]. Every node senses
combine the desired features of MST and shortest weighted
periodically its nearby environment and has data to send to
path-based gathering algorithms. We also expand the idea
the sink in each round. The nodes have a maximum
and propose a new family of localized protocols for the
power-efficient data aggregation problem. Our main con- transmission range denoted by R. Sensor nodes are thus
cern in this work is the lifetime of the network. We name normally not in direct communication range of each other.
our new approach localized power-efficient data aggregation Therefore, applying centralized approaches will have a high
protocol (L-PEDAP). communication cost for gathering network information at a
Our proposed routing approach consists of two phases node. Data aggregation is used to reduce the data volume.
and satisfies the requirements stated above. In the first We assume a perfect aggregation or correlation of data
phase, it computes a sparse topology over the visibility which means that combining n packets, each packet being
graph of the network in a localized manner. In the second of size k, results in only one packet of size k. We also
phase, it computes a data gathering tree over the edges of assume that the sensing period (the duration of a round) is
the computed sparse topology. The topology needs to be much larger than the time required for transmitting all the
efficiently computed by using only the one-hop neighbor- information from all nodes to the sink.
hood information.
For the first phase, we test two different sparse 2.1 Problem Statement
topologies in a distributed manner, namely, local minimum We model the reachability in a sensor network using a
spanning tree (LMST) [4] and relative neighborhood graph visibility graph G ¼ ðV ; EÞ, where V is the set of sensor
(RNG) [5]. These structures are supersets of MST and can be nodes and the base station, and E is the set of edges eij ,
efficiently computed in a localized manner. For the second where the distance between node i and j is smaller than the
phase, we propose three different methods and provide maximum transmission distance R. In the application
performance results of them. All of the methods are based scenario, we consider that for this network model (called
on flooding a special packet using only the edges of the the unit disk graph model), sensor nodes periodically sense
computed structure. According to the decisions made the environment and generate data in each round of
communication. Given a routing plan, each sensor node
during this flooding process, the tree is yielded. These
receives the data from its children, aggregates them into one
three methods that can be executed at a node for choosing
single packet, and sends the packet to the next node on its
the parent node toward the sink are to choose: 1) the first
way to the sink. Instances of such an application can be
node from which the special packet is received, 2) the node event (fire and intrusion) detection systems (starting from
that minimizes the number of hops to the sink, and 3) the all the sensors located near an event) or average data
node that minimizes the total energy consumed over the (temperature and humidity) extraction systems (where all
path to the sink. active sensors can participate).
Our solution can also handle new node arrivals and The problem is to find an energy-efficient routing plan
departures of existing nodes. Hence, it is adaptive. The which maximizes the network lifetime. The routing plan
routing path is maintained when those dynamic conditions determines for each node the incoming and outgoing
occur. We also propose power-aware versions of our neighbors for data forwarding and aggregation. In other
protocols that consider the dynamic changes in the words, a tree spanning all the nodes must be found as the
remaining energy levels of nodes while constructing the routing plan. The routing scheme should also include
sparse topologies and routing trees. mechanisms to handle node failures and support new
We also derive a new theoretical upper bound for the node arrivals.
lifetime in terms of the first node failure. We used this In the context of sensor networks, the network lifetime
upper bound to see how close our protocols are to the can be defined in various ways. One of the definitions can
theoretical limit. The simulation results showed that our be the time elapsed in terms of rounds until the first node
protocols can achieve up to 90 percent of the upper bound. depletes all of its energy, as investigated in Section 2.2. This
The rest of the paper is organized as follows: Section 2 metric is appropriate to measure the load balancing feature
defines our system model and describes the problem we of a routing algorithm. So, if the energy is balanced well
solve. Section 3 briefly discusses the related work. In among the nodes, the time until the first node drains out its
Section 4, we give a theoretical analysis about the lifetime. energy will be maximized. Another alternative definition
In Section 5, we describe our solution in detail. In Section 6, can be the time elapsed until the network is partitioned so
we present our comprehensive set of simulation results. that some of the alive sensor nodes cannot transmit their
Section 7 concludes the paper and discusses some future data to the base station. With this metric, one could measure
directions of research. Preliminary conference version of how the bottleneck nodes are handled. If the network
this paper appeared in [6]. becomes partitioned quickly, it means that the energy
expenditure of the bottleneck nodes is not managed well. It
is desirable that a routing scheme considers several lifetime
2 SYSTEM MODEL AND PROBLEM STATEMENT definitions and provide reasonably good results for them.
The following are our assumptions about the features of In our work, we provide results related to both of the
sensor networks and application scenarios we consider in definitions mentioned above.
3. TAN ET AL.: COMPUTING LOCALIZED POWER-EFFICIENT DATA AGGREGATION TREES FOR SENSOR NETWORKS 491
2.2 Energy Consumption Model There are also a number of protocols for data gathering
There are different models proposed for modeling energy with aggregation. Most of them are centralized approaches
consumption in sensor nodes. Here, we use the first order and assume that all the sensor nodes are in direct
radio model proposed in [9]. In this model, the energy communication range of each other and the sink. Kalpakis
consumed to transmit a k bit packet to a distance d et al. [17] propose a linear programming solution to
(denoted as Etx ) and the energy consumed to receive a k maximize the lifetime. The solution provides near-optimal
bit packet (denoted as Erx ) are given as follows: results. However, their approach has high computational
Etx ðk; dÞ ¼ ak þ bkdn ; Erx ðkÞ ¼ ck. In this model, a and c cost and must be applied in a central location. Heinzelman
are the energy consumption constants of the transmit and et al. [9] propose a distributed two-level hierarchy called
receiver electronics, respectively, and b is the energy LEACH. In this protocol, sensors randomly decide whether
consumption constant for the transmit amplifier. There or not to become clusterheads. If not, they join the nearest
are various studies in the literature based on this model. clusterhead and transmit sensed data to it. Clusterheads
Heinzelman et al. [9] propose and use this model, aggregate collected data and transmit directly to the sink.
assuming that a ¼ c ¼ 50, b ¼ 0:1, and n ¼ 2. On the other Since LEACH protocol relies on randomization, it is far
hand, in [10], ða þ cÞ ¼ 2 Â 108 , b ¼ 1, and n ¼ 4. from being optimal. Lindsey and Raghavendra [18] pro-
According to our model, if we express the total energy posed PEGASIS protocol in which the sensors are organized
into a chain by a centralized algorithm. They transmit to
cost of transmitting a k bit packet from a node i to a
each other along the chain, aggregate received data, and last
neighboring node j as Cij ðkÞ, then Cij ðkÞ is given as follows:
sensor in the chain transmits to the sink. This approach is
Cij ðkÞ ¼ ak þ bkdn ; if j is sink; ð1Þ also not very efficient, since the transmission distances can
ij
be quite long and finding a minimum distance chain is NP-
¼ ða þ cÞk þ bkdn ;
ij otherwise: ð2Þ
complete (traveling salesman problem). Also, the delay is
The costs of transmission of one packet to another node another problem for PEGASIS.
and to the sink are different, since the sink has no energy There are also algorithms in the literature that take the
constraints and its cost for receiving messages is ignored. data growth factor into consideration, where data may not
We are assuming that we are routing the data packets on be perfectly aggregated. The purpose of these papers is to
a tree rooted at the sink. Hence, a sensor node receives data provide an optimal routing solution which is adaptive to
from several children and transmits to a single parent after the data growth factor. Hua and Yum [19] described an
aggregation. Therefore, the total energy consumed at a algorithm for joint optimization of routing and data
node i (!i ðkÞ) for receiving a k bit packet from its children aggregation. Row data are sent to downstream neighbors.
and for sending the packet to its parent is given as: The receiving neighbor encodes the data using local
þ þ information, with certain compression rate. Transit data
!i ðkÞ ¼ i ck þ ak þ bkdn , where i denotes the in-degree of
ij
(already compressed by upstream neighbors) are directly
node i in the given routing tree. Thus, there are two
forwarded to the next-hop neighbors. Therefore, data
parameters that affect the energy consumption of a node:
aggregation is done only by neighbors of measuring
the in-degree of the node and the distance of the node to its
sensors, and the size of aggregated data varies. This
parent. Nodes with high in-degrees could quickly drain
problem statement and the model are different from the
their energies. Since distance has a power of n, the increase
ones used in this paper. Upadhyayula and Gupta [20]
in load is exponential when the distance is increased.
proposed a combination of single-source shortest path
Therefore, to obtain a routing tree that is maximizing the
spanning tree and minimal spanning tree algorithms to
lifetime, we have to balance the energy load among the
construct optimal data aggregation tree which controls
nodes, and we have to try to minimize the degree for a latency by limiting the number of children of each node
node while minimizing the distance the node will transmit. while optimizing energy consumption. Constant data
If we use a static routing tree, the lifetime of a node i growth factor spans aggregation level from no aggregation
(Li ) and the lifetime of the network (L) can be given in to full aggregation at each intermediate node. Although the
terms of rounds as follows: Li ¼ E0 =!i ; L ¼ minðLi Þ, where problem statement is more general than the one in this
E0 is the initial energy of the nodes (assuming that all paper, their algorithm is centralized. One important point is
nodes have the same initial energy). So, in order to that the authors consider MST as optimal solution in perfect
maximize L, we have to choose the routing tree which correlation case. Park and Sivakumar [21] optimized
maximizes the minimum Li value. number of messages sent while aggregating data originat-
ing from k of the n sensors, with various data growth
3 RELATED WORK factors. Their solution aggregates correlated data from
neighboring sources at nodes of minimum dominating set
3.1 Routing Protocols (MDS). It then creates shortest path of MDS nodes tree by
There exist several routing protocols for data gathering basic flooding. We consider perfect correlation with k ¼ n.
without aggregation. The majority of them use the shortest For this case, the work in [21] reduces to a constant number
weighted path approach using several combinations of of messages (one per each sensor) and does not consider
transmission power, reluctance, hop count, and energy energy optimization.
consumption metrics [11], [12], [13], [14]. The classical In [2], Tan and Korpeoglu showed that their protocol
routing algorithms such as AODV [15] or Directed Diffu- PEDAP, which routes the packets on the edges of an MST,
sion [16] can be considered also for this case. improves the system lifetime dramatically compared to its
4. 492 IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 22, NO. 3, MARCH 2011
Fig. 1. Comparison of different topologies. (a) MST. (b) LMST. (c) RNG.
alternatives. PEDAP protocol uses the link costs given in (1) shown to be NP-complete. They design a centralized
and (2) and computes the minimum energy cost tree by using algorithm which aims at finding a spanning tree whose
Prim’s MST algorithm. PEDAP protocol differs from the maximal degree is the minimum among all spanning trees,
euclidean MST with only the degree of the sink. Fig. 1a shows since energy consumption at each node only depends on the
this difference. Fortunately, for the nodes, the properties of number of messages received from children nodes, that is,
the euclidean MST are conserved. For example, the degree of on the number of children. Such tree then reduces the load
the nodes (except the sink) is at most 6. Also, as stated in [22], on bottleneck nodes.
the longest edge in the euclidean MST is the minimum
common transmission range for network to be connected. So, 3.2 Power-Efficient Topologies
the transmission distances are also optimal for the nodes There are many topologies proposed in the literature which
routing using PEDAP. As shown in Section 2, the energy load can be efficiently computed using the location information
of a node (!i ) is directly related with its degree and the of one-hop neighbors.
distance to its parent, and PEDAP balances these parameters Rodoplu and Meng [10] proposed a localized topology for
well. Also, PEDAP consumes the minimum amount of energy estimating the shortest weighted path tree which they called
in a single round. Moreover, the authors also propose a enclosure graph. An edge eij is in the enclosure graph if and
power-aware version of their protocol, which they call only if the direct transmission between node i and node j
PEDAP-PA. This protocol provides near-optimal lifetime consumes less energy than the total energy of all links of any
for the first node failure by sacrificing the lifetime for the last path between them. The enclosure graph includes the
alive node. The idea behind PEDAP-PA is to use a power- shortest weighted path tree, and thus, provides a good
aware cost function for a link that considers the remaining
performance when it is used in routing without aggregation.
energy of the sender. Specifically, the cost function is:
Ã
Cij ¼ Cij =ri , where ri is the normalized remaining energy of The topologies that we focus in this work are supersets of
node i. This cost metric is not symmetric. It is used by a node j euclidean MST. One of them is the RNG [5]. An edge eij is
when looking for candidate neighbor i on route toward sink. included in the euclidean RNG graph if there are no nodes
The PEDAP-PA algorithm simply finds the minimum closer to both nodes i and j than the distance between nodes
spanning tree with these link costs. In order to balance the i and j. That is, an edge eij remains in4RNG if it does not
load, it recomputes the routing tree after a predefined number have the largest cost in any triangle ikj , for all common
of rounds.
Hussain and Islam [23] proposed energy-efficient span- neighbors k. The euclidean MST of a graph is a subgraph of
ning tree approach (EESR) which is similar to PEDAP-PA its RNG.
but has some advantages over PEDAP-PA algorithm. For Li et al. [4] propose a neighborhood structure called local
instance, edge weight assignment used in EESR considers MST (LMST) as an alternative topology. LMST is computed
both transmitters and receivers remaining energy levels. as follows: First, each node determines its one-hop
With the edge weights they use, the algorithm prevents neighbors and computes an MST for that set of nodes,
transmitters and receivers from being overloaded. Another based on the distance between nodes as the weight of the
advantage of it is dynamic determination of the duration of edges. After computing the MST of the neighbors, each
recomputation period. The algorithm is, however, centra- node i selects the edges (eij ) where node j is a direct
lized. MST-based structure is suitable for environments neighbor of node i in its MST. The resulting structure is a
where all the nodes have data to send and data can be directed graph. The structure can be converted to an
aggregated (fused) in the relay nodes. The drawback of undirected one in two ways [4]. First way is to include
PEDAP and EESR protocols is the centralized nature of edge (eij ) only when both nodes i and j include that edge
MST and the lack of quick response to node failures. (LMSTÀ ). The second way is to include that edge when
Wu et al. [24] study the construction of a data gathering either node i or node j includes it (LMSTþ ). In this work,
tree to maximize the network lifetime, which is defined as we choose to use LMSTÀ in our simulations, but our
the time until the first node depletes its energy. Nodes do algorithm can support both.
not adjust their transmission radius to the distance to There are some desirable properties of the LMST structure
neighbors (different from our model). The problem is which make using the structure in the context of sensor
5. TAN ET AL.: COMPUTING LOCALIZED POWER-EFFICIENT DATA AGGREGATION TREES FOR SENSOR NETWORKS 493
networks advantageous. MST of a graph is a subgraph of its minimum spanning tree gives us jTmin j, and since we know
LMST and the LMST is a subgraph of its RNG [25]. The n and E0 , we can find the upper bound L.
maximum degree of a node is bounded by 6. This is a For static routing tree approach, it seems to be very
desirable property since the load of a node is directly related difficult to achieve this upper bound, since the load on the
to the degree of the node, as shown in Section 2. In [4], the nodes cannot be balanced in a static tree. As we will see in
authors compare their LMST structure with the enclosure our simulations, the lifetime of a static method will be far
graph and find out that the enclosure graph performs better from being optimal.
in terms of energy consumption. However, the comparison In this paper, instead of using a static routing tree, we
did not consider the effect of data aggregation.
propose dynamically changing the routing tree repeatedly
Although the RNG and LMST structures are defined
in order to balance the load among the nodes over the time.
based on euclidean distances, they can be used with other
Although optimal load balance (jTmin j=n) cannot be
link cost functions as long as the functions are symmetric
achieved for a single round, if we can use a good
[26], [27]. We can use, for instance, the cost function given in
randomization scheme, we expect maximum average value
(2), while computing the structures. Figs. 1b and 1c show
for !i to become closer to jTmin j=n, and consequently,
this case. For the rest of the paper, if we mention MST,
lifetime becomes closer to L.
LMST, and RNG, we mean the structures that are computed
As mentioned in Section 3, PEDAP-PA algorithm
using the link costs given in (1) and (2). They resemble the
recomputes the routing tree in every 100 rounds by using
original MST, LMST, and RNG structures, except replacing
an asymmetric cost function, by applying Prim’s minimum
some links by direct links to sink (the effect of adding (1)).
spanning tree algorithm [3]. However, our algorithms need
However, the structure may become considerably different
in the whole network, if a cost function that depends on a symmetric cost function to compute LMST and RNG.
nodes’ remaining energies is used to define them. Consequently, we changed the power-aware cost function
An important advantage of using structures like RNG to the one given below for our dynamic case:
and LMST is that they can be constructed very efficiently Ã
Cij ¼ Cij =ðri  rj Þ: ð3Þ
in a localized manner. Node deletions and additions do
not globally change the structure. Only local changes in the For the rest of the paper, whenever we refer to PEDAP-D
structure are required and they can be efficiently com- or LMST-D, we mean the structures that are computed
puted when a node fails or when a new node is introduced using the link costs given in (3). Our simulation results with
to the network. these cost functions showed that our dynamic approach is a
good randomization scheme.
4 LIFETIME ANALYSIS
In this section, we derive a theoretical upper bound for the 5 PROPOSED SOLUTION
lifetime of the first failing node in a sensor network using Our aim is to combine the energy-efficient features of the
tree-based routing. This upper bound will be used to test MST with the distributed nature of the shortest weighted
the performance of our protocols against a theoretical limit. path-based routing schemes, in order to efficiently and
Theorem 4.1. The lifetime of a sensor network (L) in terms of locally compute the routing paths that can also provide a
first failing node is bounded by L ¼ nE0 =jTmin j, where jTmin j superior network lifetime.
is the minimum possible total energy consumption for a round
that can be achieved. 5.1 Our Approach
Our approach for solving the aggregation and routing
Proof. Let Tmin be the tree that gives the minimum total
problem consists of two phases: topology construction and
energy consumption for any round. That is, it is a fixed
aggregation/routing tree computation.
tree which minimizes the total energy consumption for
the network. This tree can be derived from the minimum 5.1.1 Topology Construction
spanning tree algorithm [3] by using the cost functions
In this phase, we aim to construct a sparse and efficient
given in (1) and (2).
topology over the visibility graph of the network in a
Let jTmin j be the total energy expenditure in using
distributed manner. We have different alternatives for
Tmin as the routing tree. We can state that in any round,
sparse topologies that can be efficient for energy-aware
the total energy consumption is ! jTmin j.
Although the routing trees may change in each round, routing. In this work, we choose to investigate the use of
the total energy consumption in L rounds is always RNG and LMST and compare their relative performance.
! LjTmin j. This implies that there exists at least one node We expect that LMST performs better than RNG because it
whose energy consumption in L rounds is ! LjTmin j=n. is sparser. However, there are some aspects that make RNG
Since energy of each node is limited by E0 , and LMST comparable. First, the computation of RNG is
LjTmin j=n E0 . Consequently, L nE0 =jTmin j. u
t more efficient than LMST. RNG needs only the location
information of one-hop neighbors, whereas LMST needs a
Thus, we can easily compute the upper bound L for any second message for informing about the LMST neighbors.
sensor network, where we know the locations of the nodes, This second message contains the local MST neighbors of
by just computing Tmin which is the minimum spanning the nodes, and hence, it is larger in size compared to the
tree with edge weights as in (1) and (2). The total cost of the first message which contains only the location information.
6. 494 IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 22, NO. 3, MARCH 2011
Fig. 2. Comparison of different route computation techniques. (a) LMST. (b) Shortest path on LMST. (c) Minimum hop path on LMST.
On the other hand, one advantage of LMST is that it can children nodes toward the sink. However, the shortest path
approximate MST well especially when the density is high. algorithm yields the same tree as MST since having closer
In both topologies, we can also use the power-aware cost nodes reduces the total transmission cost especially when
functions, and consequently, we can efficiently approximate the power of the distance is high, and consequently, longer
PEDAP-PA. Figs. 1a, 1b, and 1c show MST, LMST, and paths in terms of hop count can be more efficient than the
RNG structures. As seen in the figure, LMST is sparser than shorter ones.
the RNG structure. Both can use the same cost functions ((1)
and (2)) used in PEDAP. 5.2 Algorithm Details
In our proposed routing scheme, at any time, each sensor
5.1.2 Routing Tree Computation node has to know its all one-hop neighbors and their
There are several methods for obtaining a tree structure locations, the neighbors on the computed topology, the
(spanning all the nodes) given a graph. In this work, we use parent node that it will send the data to in order to reach the
a flooding-based tree construction algorithm. A special sink, and the child nodes that it will receive the data from
route discovery packet is broadcasted by the sink and when before it sends the fused or aggregated packet to its parent
a node receives that packet, it decides its parent according node. Our solution consists of three parts: Route Computa-
to the information in the packet. After selecting the parent, tion, Data Gathering, and Route Maintenance.
it rebroadcasts the packet. The details will be given in
Section 5.2. Here, we investigate the efficiency of three 5.2.1 Topology and Route Computation
different methods: first parent path method (FP), nearest The main goal in this phase is to find a sparse topology and
minimum hop path method (MH), and shortest weighted set up the routes over it, which means determining the
path (i.e., least cost) method (SWP). children and parent nodes for each node. At the end of this
The FP method is the simplest among the three. In this phase, a data aggregation tree rooted at sink is constructed.
method, a node will set its parent as the first neighboring The pseudocode for this phase is given in Algorithm 1.
node (among neighbors in selected sparse structure) from
which the special route discovery packet was received. In Algorithm 1. Topology and Route Computation
the MH method, the node chooses its nearest neighbor 1: Send HELLO message
among those with minimum hops to reach to the sink. So, 2: Collect HELLO messages for thello
the node updates its parent only if the sender node has a 3: Reset Parent ( null)
smaller hop count or has the same hop count as the current 4: Compute neighbors on the sparse topology
parent, but it is closer than the current parent (among 5: while ROUTE-DISCOVERY packet RD received
neighbors in selected sparse structure). Otherwise, the in tdiscovery do
packet is ignored. The SWP method tries to yield a tree 6: if update required for RD then
that minimizes the cost of reaching the sink for each node. 7: Update parent ( sourceðRDÞ)
The details will be given in the next section. 8: Broadcast ROUTE-DISCOVERY
At first glance, we expect these three algorithms to give 9: end if
almost the same performance for approximating the 10: end while
minimum spanning tree, since the topologies are sparse
11: Inform to construct its child-list
enough. However, this is not always the case. Since we use
a cost function that uses a power of the distance, minimum Initially, the nodes and the sink are not aware about the
hop method cannot give always the most efficient tree. environment. In the setup phase, all nodes and the sink
Intermediate nodes at closer distances can make the packet broadcast HELLO messages, which include their location and
transmission more efficient [12]. Consider the LMST of a remaining energy, using their maximum allowed transmit
sample network given in Fig. 2a. Note that the sink is at the power. The remaining energy level is advertised only when
center. Only one edge removal yields a tree. As shown in dynamic (power-aware) protocols are used. We give a time
Fig. 2c, the longest edge is kept by the minimum hop threshold thello for waiting advertisements, which must be
method since choosing that edge reduces the hop count of long enough to hear all possible advertisements. After
7. TAN ET AL.: COMPUTING LOCALIZED POWER-EFFICIENT DATA AGGREGATION TREES FOR SENSOR NETWORKS 495
receiving HELLO messages, all nodes are informed about their tree is set up and stabilized. This means that each node
one-hop neighbors and their locations and energy levels. knows from which neighbors it will receive data and to
Each node can then locally compute its neighbors in the which node it will send the received data after aggregation.
desired sparse topology (static and dynamic versions of RNG
and LMST). After finding its neighbors in the sparse 5.2.2 Data Gathering
topology, a node can join the distributed route computation After the parent and children nodes for an individual
process in order to find its parent and children on the sensor node are determined, the node can join the data
aggregation tree. gathering process. In data gathering phase, each sensor
The route computation is done via a broadcasting node periodically senses its nearby environment and
process which starts at the sink node. The sink initiates a generates the data to be sent to the sink. However, before
ROUTE-DISCOVERY packet in order to find and set up the sending it directly to the parent node, it will wait all the
routes from all sensor nodes toward itself. When a sensor data from its child nodes and aggregate the data coming
node receives a ROUTE-DISCOVERY packet, it broadcasts from them together with its own data, and then, send the
the packet to all its neighbors on the computed topology if it aggregated data to the parent node. Thus, at the beginning
updates its routing table. By this way, the routing tree of data gathering step, only leaf nodes can transmit their
rooted at the sink is established over the sparse topology. data to their corresponding parent nodes. At each step, the
An important energy conserving feature of our algorithm is data are gathered upward in the tree and reaches the sink
that the packet is sent with a power just enough for reaching after h steps, where h is the height of the aggregation tree.
all the neighbors on the sparse topology instead of using the
The reason for waiting to receive data from child nodes is to
maximum power.
use the advantage of the aggregation. In this way, each
Each ROUTE-DISCOVERY packet has three fields: a
sensor only transmits once in a round, and as a result, saves
sequence ID which is increased when a new discovery is
initiated by the sink, an optional distance field which shows its energy.
the cost of reaching the sink, and an optional neighbor list 5.2.3 Route Maintenance
field which is the list of the neighbors of the sending node
in the chosen topology. The distance field is not required if After setting up the routes, three events can cause a change
FP algorithm is chosen. It holds the minimum number of in the routing plan: route recomputation, node failure, and
hops or minimum energy cost to reach the sink, respec- node addition. We will discuss them separately.
tively, if MH or SWP algorithm is chosen. The neighbor list Recomputation of the aggregation tree is required when
field must only be used if the LMST topology is chosen. So, power-aware (dynamic) cost functions are used. In power-
if we use the FP on RNG topology, we can decrease the aware methods, the tree must be recomputed at specified
message overhead. On the other hand, if we use SWP on intervals. Since the computation depends on the remaining
LMST, which gives the best performance in all cases energy of nodes, each time the computation takes place and
according to our experiments, we have to have some a different and more power-efficient plan is yielded. In our
overhead. But an important point to mention is that in our case, we handle this requirement by broadcasting a new
approach, since the LMST computation is combined with ROUTE-DISCOVERY packet with a new sequence ID.
the route computation, no extra messages are used for Apparently, in order to utilize the power-aware methods,
negotiation among LMST neighbors. Only overhead is the each node must know the remaining energy levels of its
size of the ROUTE-DISCOVERY packet. neighbors. In order to exchange the remaining energy
Upon receiving a new ROUTE-DISCOVERY packet, the levels, we use HELLO messages. So, at the beginning of each
sensor node ignores the packet if it is not coming from a recomputation phase, the nodes advertise their remaining
direct neighbor, in order to ensure using only the edges in energy levels. After that, ROUTE-DISCOVERY packet with a
new sequence ID can be broadcasted by the sink. It is worth
the computed topology.
to mention that in order to achieve recomputation, each
After that, according to the routing strategy chosen, the
node must know the predefined time (in terms of rounds) to
node decides whether or not to update its parent. If FP
send HELLO messages.
strategy is used, the node updates the parent information
Node failures can be due to various reasons. However,
only if it has not a parent yet. In MH strategy, the node the most critical reason is depletion of energy of a node.
compares its current parent with the sending node and Previous approaches (e.g., [9], [2], [18]) did not discuss the
chooses the sender as its new parent if it has a smaller hop node failure problem. In these approaches, however, a node
count to the sink or has the same number of hops but is failure in communication phase will cause a routing
closer to the node. And finally, if the SWP is chosen, the problem in which the descendants of the failed node cannot
node updates its parent only if the path using the sender send their data until next setup phase. In order to prevent
node is advantageous in terms of total energy consumption. this, failures must be handled as soon as possible. In our
Regardless of the chosen strategy if the packet has a higher solution, we handle the case where failures are due to
sequence ID, the node directly updates its parent. If the energy depletion. However, the idea behind the solution
node decides to update its parent, it rebroadcasts the can be applied to other failure causes as well.
ROUTE-DISCOVERY packet with updated fields. If in the Failure of a node due to energy depletion can be handled
time threshold tdiscovery , no other route discovery packets are gracefully, since the node can predict that it will die soon
received, we can conclude that the route setup converged. due to energy limitation. Algorithm 2 presents the route
At this step, each node can inform its parent, in order to recovery algorithm. In our solution, when a node’s energy
construct the children list which will be used in data reduces below a threshold value, the node broadcasts a BYE
gathering phase. After this final step, the data aggregation message using maximum allowed transmit power. All
8. 496 IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 22, NO. 3, MARCH 2011
nodes receiving the BYE message will immediately update TABLE 1
their local structure. This message is not required to be Comparison of Algorithms—Normalized Lifetime
retransmitted since the node failures do not affect the N:100, R:20, l:100, d:10
structure globally. However, in this case, the nodes that
cannot reach the sink because of the energy depletion of
their ancestor must find a new cost-efficient path to send
their packets. In our solution, this is handled in a localized
manner as follows: The child nodes of the failed node that
receive the BYE message reset their routing tables and enter
the parent-discovery phase by broadcasting a special newly deployed node can also determine its neighbors.
message PARENT-DISCOVERY to its neighbors on the Nodes that update their local structure send back a ROUTE-
structure. According to the receiver of that special message, DISCOVERY packet including their costs to the newly
if the sender is its own parent on the way to the sink, the deployed node. The new node selects the most efficient
receiver also resets its routing table and broadcasts the node as its parent and broadcasts this information by a new
packet to its neighbors. In this way, all the nodes that ROUTE-DISCOVERY packet. Since the sequence ID is again
should enter the parent-discovery phase will be reached. If not incremented, the new packet is broadcasted throughout
the PARENT-DISCOVERY packet is received by a neighbor- the network only when using the new node is advanta-
ing node of the sender and if it has a valid parent, the geous. This final step can be avoided if FP method is used.
receiver constructs a new ROUTE-DISCOVERY packet as So, the newly added node just chooses its closest neighbor
mentioned above and broadcasts it to the sender. This as its parent and starts sending data.
ROUTE-DISCOVERY packet is handled as mentioned in
Section 5.2.1. It is worth to mention that the sequence ID in 6 SIMULATION RESULTS
this new packet is not incremented; therefore, the update of
In this section, we will first try to choose the best parent
the routing table takes place only when the newly received
selection strategy, and then, continue the experiments with
cost is smaller. After the route discovery phase converges,
that strategy, since running the experiments with all
the new routes are set up and data gathering can continue.
topologies and strategies will become too complicated.
Algorithm 2. Route Recovery For our scenario, there are three parameters that we can
1: old change to see their effect: number of nodes N, maximum
2: if BYE message B received then transmission radius R, and side length of the square area l.
One other parameter that depends on these three para-
3: remove sourceðBÞ from neighbor list
meters and that gives direct intuition about the scenario is
4: compute the sparse topology
the density d, which is defined as the average number of
5: if sourceðBÞ ¼ then neighbors per node. For the sake of completeness, we will
6: Reset parent ( null) give the value of d for each scenario since it is immediately
7: Reset child list very informative.
8: Broadcast PARENT-DISCOVERY message We generated a network with parameters N ¼ 100;
9: Enter route discovery phase R ¼ 20m; l ¼ 100m ) d ¼ 10. On this network, we repeated
10: end if the experiments on LMST and RNG topologies with three
11: end if alternative parent selection strategies. We compared the
12: if PARENT-DISCOVERY message P D received then methods in terms of the lifetime they provide for the first
13: if sourceðP DÞ ¼ then node (normalized lifetime) and how well they approximate
14: Reset parent ( null) the PEDAP tree (approximation percentage). Normalized
15: Reset child list lifetime is the ratio of the lifetime to the lifetime provided by
16: Broadcast PARENT-DISCOVERY message PEDAP for the same network, whereas approximation
17: Enter route discovery phase percentage is the ratio of the number of common edges with
18: else PEDAP tree to the total number of edges.
19: if 6¼ null then In Tables 1 and 2, we provide results of experiments that
20: Send ROUTE-DISCOVERY compare the efficiency of three parent selection methods.
21: end if Here, n denotes the power of distance in the cost function.
22: end if We can conclude with these results that if the propagation is
free space (n ¼ 2), using FP algorithm on RNG can be
23: end if
advantageous because the setup cost is minimal in that case
24: if 6¼ old then
and the performance is almost the same as in the best
25: Inform old and to construct their child-list
solution SWP on LMST ( 9 percent lesser lifetime). If
26: end if
n ¼ 4, however, choosing the shortest weighted path on
Consider now the case of node additions. When a new LMST gives considerably better performance in terms of
node is deployed, it broadcasts a HELLO message. Its lifetime. We can also see that the difference among parent
neighbors update their local structure upon receiving this selection strategies is more striking when n ¼ 4. These
message and also inform the new node about their existence results also show that the SWP strategy outperforms its
and locations by replying a HELLO message so that the alternatives in each case. Therefore, for the rest of the
9. TAN ET AL.: COMPUTING LOCALIZED POWER-EFFICIENT DATA AGGREGATION TREES FOR SENSOR NETWORKS 497
TABLE 2 TABLE 3
Comparison of Algorithms—Approximation Percentage Upper Bound for FNF—R:20, l:100
N:100, R:20, l:100, d:10
upper bound L which is computed as given in Section 4. In
all cases, we provide the actual values of L.
Consider the impact of the number of nodes N on the
simulations, we always use SWP approach to compare the
lifetime. In Fig. 3a, we can see the normalized lifetimes for
performances of different topologies. various values of N in terms of FNF. Since the upper bound
The rest of the simulations evaluates the performance of
for each case is different, we give the exact values of the
our routing scheme. We conducted experiments with
upper bound in Table 3. We can see that the upper bound
different values of N, R, and l. For each parameter, we ran
slightly increases with increasing N. We observe that the
the experiments 100 times and obtained an average value for
effect of N is not much significant in static MST-based
the two evaluation metrics: First Node Failure Time (FNF)
approaches. For the static SPT approach, however, increas-
and Network Partition Time (NPT). The initial energies of
ing d decreases the lifetime of the system. This is mainly
the nodes were given as 0.5 J. For dynamic algorithms
because of the fact that the SPT approach cannot balance the
(PEDAP-D, LMST-D, etc.), we used the power-aware cost
degree of a node. So, if N is increased, the maximum node
functions given in (3) and recomputed the routing paths
degree will also increase. However, in MST-based ap-
every 100 rounds. For transmission costs, we used the
proaches, since the maximum node degree is bounded by 6,
parameters of the first order radio model given in [9]. In all of
the decrease is not much significant. On the other hand, in
the routing schemes, data aggregation is used at every step
all MST-based approaches, the maximum node degree is
for a fair comparison. Also, for all methods, the setup and
increasing (from 3-4 to 4-5) with increasing d, and thus, the
maintenance costs are not included in energy expenditures,
network lifetime is slightly decreased when N is increased.
which means that only the cost of data packets is considered.
Next, we study the impact of N on the dynamic versions of
We used a fixed value of 1,000 bits for data packet size k.
algorithms (PEDAP-D, LMST-D, RNG-D, and SPT-D). When
In order to compare our algorithms based on LMST and
N increases, the lifetimes increase until reaching a maximum
RNG, we also implemented the centralized PEDAP algo- and decrease afterward. Since the dynamic versions of the
rithm and the shortest weighted path tree (SPT) as other algorithms almost balance the degree among the nodes, this
alternatives. With the dynamic versions, it adds up to eight behavior is due to the distances between the neighbors. In
different methods to compare (PEDAP, LMST, RNG, SPT, low-density case, the distances are long, and since the
PEDAP-D, LMST-D, RNG-D, and SPT-D). overhead because of the distance is exponential, the lifetime
Since the most informative parameter for our scenarios is is far from being optimal. As d increases, the average distance
d, we try to investigate the performances on different values among the nodes becomes closer to the optimal distance—
of d. However, there are three ways of changing d: for each which may be the same as given in [12], [28]. After some point,
of the parameters N; R, and l, we can keep two of them however, the decrease in distances has a negative effect due to
fixed and change the third one. One important point is that the constant cost of wireless transmission. So, we can
in the rest of simulations, we give the results normalized to conclude that using too many nodes is not always very
Fig. 3. Effect of number of nodes on network lifetime for various data gathering schemes. (a) Normalized FNF timings versus (N; d). (b) Normalized
NPT timings versus (N; d).
10. 498 IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 22, NO. 3, MARCH 2011
Fig. 4. Effect of transmission radius on network lifetime for various data gathering schemes. (a) Normalized FNF timings versus (R; d).
(b) Normalized NPT timings versus (R; d).
effective in providing longer network lifetime. If we compare the alternatives also increases. So, the nodes will tend to
RNG- and LMST-based approaches, RNG gives very close send to long distances as the residual energy of the neighbor
results with LMST, but LMST performs always slightly better nodes decreases, and this will cause a decrease in FNF. So,
than RNG. At their best, both PEDAP-D and LMST-D achieve we can say that increasing the radius above some point has
90 percent of the upper bound. an inverse effect on lifetime for our dynamic approach. The
In Fig. 3b, the lifetimes in terms of network partition time dynamic versions may give the best performance when R is
are given, normalized to the values given in Table 3. Again, chosen equal to the same optimal distance mentioned
as expected, the lifetime improves with increasing d in static above. One important point here is that the upper bound
versions of the protocols. However, for the power-aware for the lifetime is always the same in this scenario, since
(dynamic) methods, the increase is smaller. This is increasing R does not affect the MST topology. On the other
explained by the fact that in order to provide longer hand, similar results are observed for the network partition
lifetime in terms of FNF, the system uses more resources. times as in the previous case (Fig. 4b).
The lifetimes for different R values are given in Figs. 4a Another scenario that changes the density is increasing
and 4b. As can be seen in Fig. 4a, increased transmission the area size while keeping graph with parameters N and R
radius dramatically reduces the lifetime of the dynamic the same. Fig. 5a shows the normalized simulations results
methods after some point. The maximum value is achieved for this case. The upper bounds of each specific case are
when R ¼ 25 m. This can be explained by the effect of the given in Table 4. The optimal value of the same graph is
distance to parent. With increasing R, although there exist decreasing with increasing area size, since the average
more alternative nodes to choose, the average distance of distance among the nodes is increasing. However, if we
Fig. 5. Effect of area size on network lifetime for various data gathering schemes. (a) Normalized FNF timings versus (l; d). (b) Normalized NPT
timings versus (l; d).
11. TAN ET AL.: COMPUTING LOCALIZED POWER-EFFICIENT DATA AGGREGATION TREES FOR SENSOR NETWORKS 499
Although in this work, we have used 100 rounds to
TABLE 4
Upper Bound for FNF—N:100, R:20 recompute the aggregation tree as in PEDAP-PA, it is worth
to mention that the period of the recomputation is an
important factor for achieving long lifetimes. With a small
period, we can achieve a good balance among the nodes,
whereas we have larger overhead due to control packets.
Determination of the optimal recomputation period needs
normalize the lifetime, we observe that for the static complex mathematical analysis, and it is beyond the scope
methods, the normalized lifetime is slightly increasing with of this work. An example of changing recomputation period
dynamically in a centralized solution can be found in [23].
decreasing density. If the dynamic versions are examined,
The area size and the maximum transmission range are
above some density, the PEDAP-D and LMST-D methods
usually set by the application itself. It is an interesting open
can achieve 90 percent of the upper bound. With decreasing
problem to theoretically derive the optimum number of
density, after some point, the lifetime decreases dramati-
nodes for given R and l. Also, based on this result, one
cally. This is expected since when there are more alternative could combine our method with some sort of sleep
neighbors to choose, our dynamic version can balance the scheduling algorithm to get a performance increase on
load among the nodes. If the density is low, the number of high-density networks. So, if a sleep scheduling algorithm
alternative routing trees becomes also small. This fact [29] recomputes the roles of the nodes periodically, the
combined with the distance effect reduces the system same period can be used to recompute the routing tree
lifetime considerably on wide networks. The reason of the spanning only the active nodes with our protocols. More-
decrease in lifetime on high-density networks is that as the over, with the advantage of using periodic recomputations,
area size becomes smaller, the effect of the distance gets our dynamic methods can be used efficiently in such a
smaller. Similar to the first scenario, the degree plays more scenario. One can also investigate the application of
important role to determine the lifetime. So, as in the first connected dominating sets (CDSs) [30] to limit internal tree
case, the maximum degree is increased slightly and the nodes to such a set, and rotating periodically these sets.
overall lifetime decreases. Tree computation via broadcasting is possible only via
We can observe similar result also for the NPT timings nodes in CDS, and leaves can even sleep temporarily while
(Fig. 5b). As the area enlarges, connectivity decreases, and data are being gathered.
distances get longer. This leads to a decrease in NPT timings. We did not measure the cost of setup and maintenance.
However, our motivation is exactly to address this setup
cost and maintenance cost by proposing localized solutions.
7 CONCLUSION Almost all existing papers do ignore these costs by
In this paper, we presented a new energy-efficient routing describing centralized solutions, without even mentioning
approach that combines the desired properties of minimum the communication overhead involved in gathering needed
spanning tree and shortest path tree-based routing schemes. information. In our study, measuring this cost would be
The proposed scheme uses the advantages of the powerful even counterproductive. This cost in our approach is
localized structures such as RNG and LMST and provides negligible compared to the same cost in existing algorithms
simple solutions to the known problems in route setup and which are centralized. By ignoring this cost, we were able to
maintenance because of its distributed nature. The pro- conclude that our localized solutions perform almost as
posed algorithm is robust, scalable, and self-organizing. The well as centralized, and with over 90 percent.
algorithm is appropriate for systems where all the nodes are
not in direct communication range of each other. We show ACKNOWLEDGMENTS
through simulations that our algorithm outperforms the
shortest weighted path-based approaches, and can achieve This research is partially supported by NSERC Discovery
90 percent of the upper bound on lifetime. grant and NSERC Strategic Grant on sensor and actuator
One important contribution of this paper is the easily networks STPGP 336406-07. This work is also supported in
computable theoretical upper bound. By using this value, we part by the European Commission in the framework of the
can see how good a data aggregation protocol is. The FP7 Network of Excellence in Wireless COMmunications
simulation results showed that the SWP over LMST approach NEWCOM++ (contract no. 216715).
is the best among our new family of protocols and by using
this approach, one can achieve almost the same performance
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Vector Routing,” Proc. Second IEEE Workshop Mobile Computing College Park. Since 2002, he has been an
Systems and Applications, p. 90, 1999. assistant professor in the Computer Engineering
[16] C. Intanagonwiwat, R. Govindan, and D. Estrin, “Directed Department, Bilkent University. Prior to that, he
Diffusion: A Scalable and Robust Communication Paradigm for worked in several research and development
Sensor Networks,” Proc. Int’l Conf. Mobile Computing and Network- companies including IBM T.J. Watson Research
ing, pp. 56-67, 2000. Center, Ericsson, Bell Laboratories, and Bell-
[17] K. Kalpakis, K. Dasgupta, and P. Namjoshi, “Maximum Lifetime core. He received Turkish Scientific and Technological Research
Data Gathering and Aggregation in Wireless Sensor Networks,” Council (TUBITAK) Young Investigator Career Award in 2004. In
Proc. 2002 IEEE Int’l Conf. Networking (ICN ’02), pp. 685-696, Aug. 2006, he received Bilkent University Distinguished Teaching Award,
2002. and in 2009, IBM Faculty Award. His research interests include
[18] S. Lindsey and C.S. Raghavendra, “Pegasis: Power-Efficient computer networks and systems. His current research focus is on
Gathering in Sensor Information Systems,” Proc. IEEE Aerospace wireless ad hoc and sensor networks, wireless mesh networks, and P2P
Conf., Mar. 2002. networks. He is a member of the IEEE and the IEEE Computer Society.
[19] C. Hua and T.-S.P. Yum, “Optimal Routing and Data Aggregation
for Maximizing Lifetime of Wireless Sensor Networks,” IEEE/ Ivan Stojmenovic received the PhD degree in
ACM Trans. Networking, vol. 16, no. 4, pp. 892-903, Aug. 2008. mathematics. He held regular and visiting posi-
[20] S. Upadhyayula and S.K.S. Gupta, “Spanning Tree Based tions in Serbia, Japan, USA, Canada, France,
Algorithms for Low Latency and Energy Efficient Data Aggrega- Mexico, Spain, United Kingdom (as the chair in
tion Enhanced Convergecast (DAC) in Wireless Sensor Net- Applied Computing at the University of Birming-
works,” Ad Hoc Networking, vol. 5, no. 5, pp. 626-648, 2007. ham), Hong Kong, and Brazil, and is a full
professor at the University of Ottawa, Canada.
[21] I.S.-J. Park and I.-R. Sivakumar, “Energy Efficient Correlated Data
He published more than 250 different papers,
Aggregation for Wireless Sensor Networks,” Int’l J. Distributed
and edited four books on wireless, ad hoc and
Sensor Networks, vol. 4, no. 1, pp. 13-27, 2008.
sensor networks, and applied algorithms with
[22] M.D. Penrose, “The Longest Edge of the Random Minimal
Wiley/IEEE. He is an editor of over dozen journals, the editor in chief of
Spanning Tree,” The Annals of Applied Probability, vol. 7, no. 2,
the IEEE Transactions on Parallel and Distributed Systems (in January
pp. 340-361, 1997.
2010), and the founder and editor in chief of three journals (Multiple-
[23] S. Hussain and O. Islam, “An Energy Efficient Spanning Tree Valued Logic and Soft Computing, Parallel, Emergent and Distributed
Based Multi-Hop Routing in Wireless Sensor Networks,” Proc. Systems, and Ad Hoc Sensor Networks). He has h-index 35 and
IEEE Wireless Comm. and Networking Conf. (WCNC ’07), pp. 4383- 6,000 citations. One of his articles was recognized as the Fast
4388, Mar. 2007. Breaking Paper, for October 2003 (as the only one for all of computer
[24] Y. Wu, S. Fahmy, and N.B. Shroff, “On the Construction of a science), by Thomson ISI Essential Science Indicators. He is the
Maximum-Lifetime Data Gathering Tree in Sensor Networks: Np- recipient of the Royal Society Research Merit Award, United Kingdom.
completeness and Approximation Algorithm,” Proc. IEEE INFO- He is a fellow of the IEEE (Communications Society, class 2008), and is
COM, pp. 356-360, 2008. the recipient of Excellence in Research Award of the University of
[25] F.J. Ovalle-Martinez, I. Stojmenovic, F. Garcia-Nocetti, and J. Ottawa 2008-09. He chaired and/or organized 50 workshops and
Solano-Gonzalez, “Finding Minimum Transmission Radii for conferences, and served in more than 100 program committees. Among
Preserving Connectivity and Constructing Minimal Spanning others, he was/is the program co/vice chair at the IEEE PIMRC 2008,
Trees in Ad Hoc and Sensor Networks,” J. Parallel and Distributed IEEE AINA-07, IEEE MASS-0407, EUC-0508, WONS-05, MSN-
Computing, vol. 65, no. 2, pp. 132-141, 2005. 0506, and ISPA-0507; founded workshop series at IEEE MASS,
[26] O. Escalante, T. Perez, J. Solano, and I. Stojmenovic, “Rng- IEEE ICDCS, and IEEE DCOSS; and the workshop chair at the IEEE
Based Searching and Broadcasting Algorithms over Internet MASS-09, ACM Mobicom/Mobihoc-07, and Mobihoc-08. He presented
Graphs and Peer-to-Peer Computing Systems,” Proc. ACS/IEEE more than dozen tutorials.
2005 Int’l Conf. Computer Systems and Applications (AICCSA ’05),
p. 17-I, 2005.
[27] T. Perez, J. Solano-Gonzalez, and I. Stojmenovic, “Lmst-Based . For more information on this or any other computing topic,
Searching and Broadcasting Algorithms over Internet Graphs and please visit our Digital Library at www.computer.org/publications/dlib.
Peer-to-Peer Computing Systems,” Proc. IEEE Int’l Conf. Signal
Processing and Comm. (ICSPC ’07), pp. 1227-1230, Nov. 2007.
[28] M. Bhardwaj, A. Chandrakasan, and T. Garnett, “Upper Bounds
on the Lifetime of Sensor Networks,” Proc. IEEE Int’l Conf. Comm.,
pp. 785-790, 2001.