This document proposes a method for optimizing the performance of multiple interconnected heterogeneous sensor networks through collaborative information sharing. The key points are:
1) Individual sensor networks can optimize their energy consumption and sensing quality by taking into account information reported by other sensor networks, rather than operating autonomously.
2) An architecture is proposed to allow sensor networks to find and connect with other information sources. Individual networks can then make localized decisions to optimize performance based on their own data and external reports.
3) Two performance parameters - energy consumption and sensing quality - can potentially be improved by adjusting the sensor reporting interval and number of active sensors in response to critical or uncritical values reported by other networks.
This document summarizes a distributed localization algorithm for wireless sensor networks. It begins by introducing wireless sensor networks and noting that localization of sensor nodes is an important first task. It then discusses the benefits of distributed algorithms over centralized algorithms for localization, including scalability, reliability, and energy efficiency. The document goes on to describe a basic distributed localization algorithm that works in a discrete model where the operating region is divided into cells and nodes can communicate with others within a certain number of cells. The goals are to design a distributed algorithm, analyze its complexity and errors, and determine an optimal number of "known nodes" to minimize errors.
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications.
1) The document discusses implementing mobile target detection in wireless sensor networks. It focuses on using techniques like Tabu Search heuristics and Sequential Probability Ratio Tests to detect mobile targets and replicas.
2) A key challenge discussed is detecting when a mobile node has been replicated, which the proposed approach attempts to do using SPRT to analyze node speeds and determine if two nodes with the same identity are present.
3) The paper also examines using game theory approaches to maximize network lifetime and weighted intrusion detection in wireless sensor networks for mobile target detection. Experimental results show the SPRT-based replica detection works rapidly with no false positives or negatives.
COMPRESSIVE DATA GATHERING TECHNIQUE BY AVOIDING CORRELATED DATA IN WSNpharmaindexing
This document proposes a technique for compressive data gathering in wireless sensor networks using mobile data collectors. It involves identifying correlated sensor data within clusters near polling points and defining a tour plan for mobile collectors that avoids visiting these correlated sensors. This is done using a spatial correlation method. The goal is to identify new optimal polling points by avoiding correlated sensors, which reduces the tour length of mobile collectors and the number of polling points needed. This extends the lifetime of the wireless sensor network. The document provides background on related work using mobile data collectors and discusses how the proposed approach improves upon prior methods.
Analysis of Cluster Based Anycast Routing Protocol for Wireless Sensor NetworkIJMER
A wireless sensor network is a collection of nodes organized into a cooperative network.
Each node consists of processing capability, may contain multiple types of memory, have a RF
transceiver, have a power source, and accommodate various sensors and actuators. The nodes
communicate wirelessly and often self-organize after being deployed in an ad hoc fashion.
Routing protocols for wireless sensor networks are responsible for maintaining the routes in the
network and have to ensure reliable multi-hop communication .The performance of the network is
greatly influenced by the routing techniques. Routing is to find out the path to route the sensed data to
the base station. In this paper the features of WSNs are introduced and routing protocols are reviewed
for Wireless Sensor Network.
Spatial Correlation Based Medium Access Control Protocol Using DSR & AODV Rou...IOSR Journals
The document summarizes a proposed spatial correlation-based medium access control protocol for wireless sensor networks that aims to improve energy efficiency. It discusses how sensor nodes are spatially distributed and correlated in detecting events. An iterative node selection algorithm is used to select a minimum set of representative sensor nodes based on a distortion constraint, in order to reduce redundant transmissions. The protocol uses vector quantization to calculate distances between nodes and a mobile element. It then evaluates the performance of using the DSR and AODV routing protocols with this spatial correlation-based MAC protocol in terms of energy consumption and packet drop ratio through simulations. The simulation results show that the protocol with AODV routing performs better than with DSR routing.
A MULTI-PATH ROUTING DETERMINATION METHOD FOR IMPROVING THE ENERGY EFFICIENCY...ijwmn
A selective forwarding attack in mobile wireless sensor networks is an attack that selectively drops or delivers event packets as the compromised node moves. In such an attack, it is difficult to detect the compromised node compared with the selective forwarding attack occurring in the wireless sensor network
because all sensor nodes move. In order to detect selective forwarding attacks in mobile wireless sensor networks, a fog computing-based system for a selective forwarding detection scheme has been proposed. However, since the proposed detection scheme uses a single path, the energy consumption of the sensor node for route discovery when the sensor node moves is large. To solve this problem, this paper uses fuzzy
logic to determine the number of multi-paths needed to improve the energy efficiency of sensor networks. Experimental results show that the energy efficiency of the sensor network is improved by 9.5737% compared with that of the existing scheme after 200 seconds when using the proposed scheme
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
This document summarizes a distributed localization algorithm for wireless sensor networks. It begins by introducing wireless sensor networks and noting that localization of sensor nodes is an important first task. It then discusses the benefits of distributed algorithms over centralized algorithms for localization, including scalability, reliability, and energy efficiency. The document goes on to describe a basic distributed localization algorithm that works in a discrete model where the operating region is divided into cells and nodes can communicate with others within a certain number of cells. The goals are to design a distributed algorithm, analyze its complexity and errors, and determine an optimal number of "known nodes" to minimize errors.
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications.
1) The document discusses implementing mobile target detection in wireless sensor networks. It focuses on using techniques like Tabu Search heuristics and Sequential Probability Ratio Tests to detect mobile targets and replicas.
2) A key challenge discussed is detecting when a mobile node has been replicated, which the proposed approach attempts to do using SPRT to analyze node speeds and determine if two nodes with the same identity are present.
3) The paper also examines using game theory approaches to maximize network lifetime and weighted intrusion detection in wireless sensor networks for mobile target detection. Experimental results show the SPRT-based replica detection works rapidly with no false positives or negatives.
COMPRESSIVE DATA GATHERING TECHNIQUE BY AVOIDING CORRELATED DATA IN WSNpharmaindexing
This document proposes a technique for compressive data gathering in wireless sensor networks using mobile data collectors. It involves identifying correlated sensor data within clusters near polling points and defining a tour plan for mobile collectors that avoids visiting these correlated sensors. This is done using a spatial correlation method. The goal is to identify new optimal polling points by avoiding correlated sensors, which reduces the tour length of mobile collectors and the number of polling points needed. This extends the lifetime of the wireless sensor network. The document provides background on related work using mobile data collectors and discusses how the proposed approach improves upon prior methods.
Analysis of Cluster Based Anycast Routing Protocol for Wireless Sensor NetworkIJMER
A wireless sensor network is a collection of nodes organized into a cooperative network.
Each node consists of processing capability, may contain multiple types of memory, have a RF
transceiver, have a power source, and accommodate various sensors and actuators. The nodes
communicate wirelessly and often self-organize after being deployed in an ad hoc fashion.
Routing protocols for wireless sensor networks are responsible for maintaining the routes in the
network and have to ensure reliable multi-hop communication .The performance of the network is
greatly influenced by the routing techniques. Routing is to find out the path to route the sensed data to
the base station. In this paper the features of WSNs are introduced and routing protocols are reviewed
for Wireless Sensor Network.
Spatial Correlation Based Medium Access Control Protocol Using DSR & AODV Rou...IOSR Journals
The document summarizes a proposed spatial correlation-based medium access control protocol for wireless sensor networks that aims to improve energy efficiency. It discusses how sensor nodes are spatially distributed and correlated in detecting events. An iterative node selection algorithm is used to select a minimum set of representative sensor nodes based on a distortion constraint, in order to reduce redundant transmissions. The protocol uses vector quantization to calculate distances between nodes and a mobile element. It then evaluates the performance of using the DSR and AODV routing protocols with this spatial correlation-based MAC protocol in terms of energy consumption and packet drop ratio through simulations. The simulation results show that the protocol with AODV routing performs better than with DSR routing.
A MULTI-PATH ROUTING DETERMINATION METHOD FOR IMPROVING THE ENERGY EFFICIENCY...ijwmn
A selective forwarding attack in mobile wireless sensor networks is an attack that selectively drops or delivers event packets as the compromised node moves. In such an attack, it is difficult to detect the compromised node compared with the selective forwarding attack occurring in the wireless sensor network
because all sensor nodes move. In order to detect selective forwarding attacks in mobile wireless sensor networks, a fog computing-based system for a selective forwarding detection scheme has been proposed. However, since the proposed detection scheme uses a single path, the energy consumption of the sensor node for route discovery when the sensor node moves is large. To solve this problem, this paper uses fuzzy
logic to determine the number of multi-paths needed to improve the energy efficiency of sensor networks. Experimental results show that the energy efficiency of the sensor network is improved by 9.5737% compared with that of the existing scheme after 200 seconds when using the proposed scheme
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
This document discusses approaches to improve reliability in wireless sensor networks. It proposes a dynamic sectoring scheme where the network area is divided into sectors with a sensor node assigned as sector head for each. When an event occurs, only that sector is activated, reducing congestion and energy use. This is expected to enhance packet delivery ratio and reduce losses. Prior work on using data fusion and opportunistic flooding algorithms to improve reliability is also reviewed. The dynamic sectoring approach aims to reliably transmit data with low congestion and energy usage.
Routing techniques in wireless sensor networks a surveyAmandeep Sohal
This document provides an overview of routing techniques in wireless sensor networks. It discusses the key challenges in wireless sensor network routing due to constraints such as limited energy, bandwidth and processing resources. It classifies routing protocols into three categories based on network structure - flat, hierarchical and location-based routing. It also categorizes routing protocols based on their operation such as multipath, query-based, negotiation-based and QoS-based routing. The document concludes by highlighting various routing paradigms and identifying possible areas for future research.
Distribution of maximal clique size underijfcstjournal
In this paper, we analyze the evolution of a small-world network and its subsequent transformation to a
random network using the idea of link rewiring under the well-known Watts-Strogatz model for complex
networks. Every link u-v in the regular network is considered for rewiring with a certain probability and if
chosen for rewiring, the link u-v is removed from the network and the node u is connected to a randomly
chosen node w (other than nodes u and v). Our objective in this paper is to analyze the distribution of the
maximal clique size per node by varying the probability of link rewiring and the degree per node (number
of links incident on a node) in the initial regular network. For a given probability of rewiring and initial
number of links per node, we observe the distribution of the maximal clique per node to follow a Poisson
distribution. We also observe the maximal clique size per node in the small-world network to be very close
to that of the average value and close to that of the maximal clique size in a regular network. There is no
appreciable decrease in the maximal clique size per node when the network transforms from a regular
network to a small-world network. On the other hand, when the network transforms from a small-world
network to a random network, the average maximal clique size value decreases significantly.
Wireless Sensor Network (WSN) consists of sensor nodes which interact with each other through physical parameters like sunlight, wind, vibration, humidity etc. Routing protocols provide an optimal data transmission route from sensor nodes to sink node to save energy of nodes. From Base Station (BS) Sensor node sends and receives data to or from wireless stations. Clustering mechanism is one of the popular routing mechanisms used in WSN for optimizing the problem in sensor nodes. There are two types of clustering schemes known as homogeneous schemes and heterogeneous schemes. In Homogeneous scheme initial energy is same for each node but in heterogeneous scheme initial energy is different for each node and also used to determine the efficiency of sensor networks. Enhanced Modified LEACH (EMODLEACH) is a reactive protocol which is implemented for homogeneous network model. We have implemented the concept of Efficient Cluster head Replacement scheme and Dual transmitting power level scheme of MODLEACH along with the concept of Efficient Intra Cluster transmission Scheme of TEEN in LEACH. We analyze the PEGASIS protocol and modified the exiting protocol called improved energy balanced routing protocol (IEBRP).This IEBRP is based on cluster formation, cluster routing and other aspects of LEACH protocol.
Energy Efficient Modeling of Wireless Sensor Networks using Random Graph Theoryidescitation
This paper deals with the discussion of an innovative and a design for the
efficient power management and power failure diagnosis in the area of wireless sensors
networks. A Wireless Network consists of a web of networks where hundreds of pairs are
connected to each other wirelessly. A critical issue in the wireless sensor networks in the
present scenario is the limited availability of energy within network nodes. Therefore,
making good use of energy is necessary in modeling a sensor network. In this paper we have
tried to propose a new model of wireless sensors networks on a three-dimensional plane
using the percolation model, a kind of random graph in which edges are formed between the
neighbouring nodes. An algorithm has been described in which the power failure diagnosis
is made and solved. The concepts of Electromagnetics, Wave Duality, Energy model of an
atom is linked with wireless networks. A model is prepared in which the positioning of
nodes of sensors are decided. Also the model is made more efficient regarding the energy
consumption, power delivery etc. using the concepts of graph theory concepts, probability
distribution.
Security based Clock Synchronization technique in Wireless Sensor Network for...iosrjce
This document proposes a secure clock synchronization technique for wireless sensor networks used in event-driven measurement applications. The technique aims to 1) provide high synchronization accuracy around detected events, 2) ensure long network lifetime, and 3) provide secure packet transmission. It divides nodes into an improved synchronization subset (ISS) with high accuracy around events, and a default synchronization subset (DSS) with lower accuracy elsewhere. When an event is detected, neighboring nodes in the ISS exchange synchronization packets more frequently for better accuracy. Authentication is used to securely transmit packets and identify intercepted messages. Simulation results show the technique accurately records event occurrence times while maintaining network lifetime through efficient energy usage.
A Survey on Topology Control and Maintenance in Wireless Sensor Networksijeei-iaes
This document summarizes and compares several topology control and maintenance algorithms for wireless sensor networks: A3, A3-Cov, Simple Tree, and Just Tree. It finds that A3 has lower computational complexity and energy consumption than A3-Cov but provides less coverage area. Just Tree is suited for homogeneous networks while Simple Tree can handle heterogeneous networks but has higher complexity. Overall, the paper analyzes the tradeoffs between these algorithms in terms of energy efficiency, coverage area, response time, and other metrics to provide insights into optimizing their use in different wireless sensor network scenarios.
The popularity of Wireless Sensor Networks (WSN) have increased rapidly and tremendously due to the vast potential of the sensor networks to connect the physical world with the virtual world. Since sensor devices rely on battery power and node energy and may be placed in hostile environments, so replacing them becomes a difficult task. Thus, improving the energy of these networks i.e. network lifetime becomes important. The thesis provides methods for clustering and cluster head selection to WSN to improve energy efficiency using fuzzy logic controller. It presents a comparison between the different methods on the basis of the network lifetime. It compares existing ABC optimization method with BFO algorithm for different size of networks and different scenario. It provides cluster head selection method with good performance and reduced computational complexity. In addition it also proposes BFO as an algorithm for clustering of WSN which would result in improved performance with faster convergence.
Energy efficient node deployment for target coverage in wireless sensor networkGaurang Rathod
Network lifetime plays an integral role in setting up an efficient wireless sensor network. Coverage in a network needs to guarantee that the region is monitored with the required degree of reliability. Locations of sensor nodes constitute the basic input for the algorithms that examine coverage of the network. Coverage problems can be broadly classified as area coverage problem and target coverage problem. Area coverage focuses on monitoring the entire region of interest, whereas target coverage concerns monitoring only certain specific points in a given region. Target coverage can be categorized as simple coverage, k-coverage and Q-coverage.
Lower coverage level (simple coverage) is enough for environmental or habitat monitoring or applications like home security. Higher degree of coverage (k-coverage) will be required for some applications like target tracking to track the targets accurately, or if sensors work in a hostile environment such as battle fields or chemically polluted areas. More reliable results are produced for higher degree of coverage which requires multiple sensor nodes to monitor the region/targets. An example of Q-coverage is video surveillance systems deployed for monitoring hostile territorial area where some sensitive targets like a nuclear plant may need more sensors cooperate to ensure source redundancy for precise data. Sensor nodes deterministically deployed by using artificial bee colony algorithm, so as to achieve the required target coverage level and maximize the network lifetime.
AN IMPROVED DECENTRALIZED APPROACH FOR TRACKING MULTIPLE MOBILE TARGETS THROU...ijwmn
Target localization and tracking problems in WSNs have received considerable attention recently, driven
by the requirement to achieve high localization accuracy, with the minimum cost possible. In WSN based
tracking applications, it is critical to know the current location of any sensor node with the minimum
energy consumed. This paper focuses on the energy consumption issue in terms of communication
between nodes whenever the localization information is transmitted to a sink node. Tracking through
WSNs can be categorized into centralized and decentralized systems. Decentralized systems offer low
power consumption when deployed to track a small number of mobile targets compared to the centralized
tracking systems. However, in several applications, it is essential to position a large number of mobile
targets. In such applications, decentralized systems offer high power consumption, since the location of
each mobile target is required to be transmitted to a sink node, and this increases the power consumption
for the whole WSN. In this paper, we propose a power efficient decentralized approach for tracking a
large number of mobile targets while offering reasonable localization accuracy through ZigBee network
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.
A fuzzy based congestion controller for control and balance congestion in gri...csandit
A Wireless Sensor Network (WSN) is deployed with a large number of sensors with limited
power supply in a wide geographically area. These sensors collect information depending on
application. The sensors transmit the data towards a base station called sink. Due to the
relatively high node density and source-to-sink communication pattern, congestion is a critical
issue in WSN. Congestion not only causes packet loss, but also leads to excessive energy
consumption as well as delay. To address this problem, in this paper we propose a new fuzzy
logic based mechanism to detect and control congestion in WSN. In the proposed approach, a
Monitor Node for each grid in congestion candidate region performs a fuzzy control to avoid
increasing congestion. Fuzzy controller’s inputs are continually fetched from the network by the
Monitor Node. Simulation results show that our approach has higher packet delivery ratio and
lower packet loss than existing approaches.
This document proposes an energy efficient framework for data collection in wireless sensor networks using prediction. The framework uses clustering, where sensor nodes are organized into clusters with a cluster head. The cluster head can enable or disable local prediction at sensor nodes to reduce data transmission. When prediction is enabled, sensors only transmit data if the value differs from the predicted value by more than a threshold. Sensors can also sleep when not transmitting to save energy. The document evaluates the performance of this framework through simulations, finding it reduces energy consumption compared to alternatives by integrating prediction with sleep/awake cycles.
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.
Data-Centric Routing Protocols in Wireless Sensor Network: A surveyAli Habeeb
This document summarizes several data-centric routing protocols for wireless sensor networks. It begins by outlining the challenges of routing in WSNs, including energy consumption, scalability, addressing, robustness, topology, and application-specific needs. It then describes several data-centric routing protocols, including flooding, directed flooding, constrained flooding, gossiping, fuzzy gossiping, location-based gossiping, and others. It notes advantages and disadvantages of these protocols for efficiently routing data in wireless sensor networks while minimizing energy consumption.
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.
Key Establishment using Selective Repeat Automatic Repeat Request Mechanism f...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
El documento explica cómo formar el futuro en español. Se puede usar "ir + a + infinitivo" o agregar las terminaciones "-é", "-ás", "-á", "-emos", "-éis" y "-án" al infinitivo del verbo. Algunos verbos irregulares como "poder", "querer" y "decir" cambian su forma antes de agregar las terminaciones. El documento proporciona ejemplos para practicar la formación del futuro.
The document provides materials for evaluating the performance of an inventory supervisor, including:
- A 4-page performance evaluation form covering rating scales, performance factors, employee strengths/areas for improvement, and signatures.
- Links to additional online resources for performance appraisals like phrases, forms, and tips.
- 3 pages of sample performance review phrases for an inventory supervisor, covering attitudes, creativity/innovation, and decision-making.
Corning Willow Glass is a new flexible glass material that has the potential to enable many new applications. It is thin, strong, and flexible enough to be rolled up, allowing for continuous roll-to-roll manufacturing processes. This could significantly reduce costs and enable new form factors like curved or flexible displays. Corning Willow Glass retains the high strength and heat resistance of glass while gaining flexibility. The National Renewable Energy Laboratory has used it to create flexible solar cells that could potentially be integrated into roof shingles to lower the installation costs of solar power.
Los cuatro personajes, dos ratones llamados Fisgón y Escurridizo y dos liliputienses llamados Hem y Haw, vivían en un laberinto buscando queso para alimentarse. Mientras que Fisgón y Escurridizo continuaron con su rutina de búsqueda del queso, Hem y Haw se volvieron perezosos y confiados. Un día, Fisgón y Escurridizo descubrieron que el depósito de queso estaba vacío, pero pronto encontraron una nueva reserva de queso. Hem y Haw, sin embargo
This document discusses approaches to improve reliability in wireless sensor networks. It proposes a dynamic sectoring scheme where the network area is divided into sectors with a sensor node assigned as sector head for each. When an event occurs, only that sector is activated, reducing congestion and energy use. This is expected to enhance packet delivery ratio and reduce losses. Prior work on using data fusion and opportunistic flooding algorithms to improve reliability is also reviewed. The dynamic sectoring approach aims to reliably transmit data with low congestion and energy usage.
Routing techniques in wireless sensor networks a surveyAmandeep Sohal
This document provides an overview of routing techniques in wireless sensor networks. It discusses the key challenges in wireless sensor network routing due to constraints such as limited energy, bandwidth and processing resources. It classifies routing protocols into three categories based on network structure - flat, hierarchical and location-based routing. It also categorizes routing protocols based on their operation such as multipath, query-based, negotiation-based and QoS-based routing. The document concludes by highlighting various routing paradigms and identifying possible areas for future research.
Distribution of maximal clique size underijfcstjournal
In this paper, we analyze the evolution of a small-world network and its subsequent transformation to a
random network using the idea of link rewiring under the well-known Watts-Strogatz model for complex
networks. Every link u-v in the regular network is considered for rewiring with a certain probability and if
chosen for rewiring, the link u-v is removed from the network and the node u is connected to a randomly
chosen node w (other than nodes u and v). Our objective in this paper is to analyze the distribution of the
maximal clique size per node by varying the probability of link rewiring and the degree per node (number
of links incident on a node) in the initial regular network. For a given probability of rewiring and initial
number of links per node, we observe the distribution of the maximal clique per node to follow a Poisson
distribution. We also observe the maximal clique size per node in the small-world network to be very close
to that of the average value and close to that of the maximal clique size in a regular network. There is no
appreciable decrease in the maximal clique size per node when the network transforms from a regular
network to a small-world network. On the other hand, when the network transforms from a small-world
network to a random network, the average maximal clique size value decreases significantly.
Wireless Sensor Network (WSN) consists of sensor nodes which interact with each other through physical parameters like sunlight, wind, vibration, humidity etc. Routing protocols provide an optimal data transmission route from sensor nodes to sink node to save energy of nodes. From Base Station (BS) Sensor node sends and receives data to or from wireless stations. Clustering mechanism is one of the popular routing mechanisms used in WSN for optimizing the problem in sensor nodes. There are two types of clustering schemes known as homogeneous schemes and heterogeneous schemes. In Homogeneous scheme initial energy is same for each node but in heterogeneous scheme initial energy is different for each node and also used to determine the efficiency of sensor networks. Enhanced Modified LEACH (EMODLEACH) is a reactive protocol which is implemented for homogeneous network model. We have implemented the concept of Efficient Cluster head Replacement scheme and Dual transmitting power level scheme of MODLEACH along with the concept of Efficient Intra Cluster transmission Scheme of TEEN in LEACH. We analyze the PEGASIS protocol and modified the exiting protocol called improved energy balanced routing protocol (IEBRP).This IEBRP is based on cluster formation, cluster routing and other aspects of LEACH protocol.
Energy Efficient Modeling of Wireless Sensor Networks using Random Graph Theoryidescitation
This paper deals with the discussion of an innovative and a design for the
efficient power management and power failure diagnosis in the area of wireless sensors
networks. A Wireless Network consists of a web of networks where hundreds of pairs are
connected to each other wirelessly. A critical issue in the wireless sensor networks in the
present scenario is the limited availability of energy within network nodes. Therefore,
making good use of energy is necessary in modeling a sensor network. In this paper we have
tried to propose a new model of wireless sensors networks on a three-dimensional plane
using the percolation model, a kind of random graph in which edges are formed between the
neighbouring nodes. An algorithm has been described in which the power failure diagnosis
is made and solved. The concepts of Electromagnetics, Wave Duality, Energy model of an
atom is linked with wireless networks. A model is prepared in which the positioning of
nodes of sensors are decided. Also the model is made more efficient regarding the energy
consumption, power delivery etc. using the concepts of graph theory concepts, probability
distribution.
Security based Clock Synchronization technique in Wireless Sensor Network for...iosrjce
This document proposes a secure clock synchronization technique for wireless sensor networks used in event-driven measurement applications. The technique aims to 1) provide high synchronization accuracy around detected events, 2) ensure long network lifetime, and 3) provide secure packet transmission. It divides nodes into an improved synchronization subset (ISS) with high accuracy around events, and a default synchronization subset (DSS) with lower accuracy elsewhere. When an event is detected, neighboring nodes in the ISS exchange synchronization packets more frequently for better accuracy. Authentication is used to securely transmit packets and identify intercepted messages. Simulation results show the technique accurately records event occurrence times while maintaining network lifetime through efficient energy usage.
A Survey on Topology Control and Maintenance in Wireless Sensor Networksijeei-iaes
This document summarizes and compares several topology control and maintenance algorithms for wireless sensor networks: A3, A3-Cov, Simple Tree, and Just Tree. It finds that A3 has lower computational complexity and energy consumption than A3-Cov but provides less coverage area. Just Tree is suited for homogeneous networks while Simple Tree can handle heterogeneous networks but has higher complexity. Overall, the paper analyzes the tradeoffs between these algorithms in terms of energy efficiency, coverage area, response time, and other metrics to provide insights into optimizing their use in different wireless sensor network scenarios.
The popularity of Wireless Sensor Networks (WSN) have increased rapidly and tremendously due to the vast potential of the sensor networks to connect the physical world with the virtual world. Since sensor devices rely on battery power and node energy and may be placed in hostile environments, so replacing them becomes a difficult task. Thus, improving the energy of these networks i.e. network lifetime becomes important. The thesis provides methods for clustering and cluster head selection to WSN to improve energy efficiency using fuzzy logic controller. It presents a comparison between the different methods on the basis of the network lifetime. It compares existing ABC optimization method with BFO algorithm for different size of networks and different scenario. It provides cluster head selection method with good performance and reduced computational complexity. In addition it also proposes BFO as an algorithm for clustering of WSN which would result in improved performance with faster convergence.
Energy efficient node deployment for target coverage in wireless sensor networkGaurang Rathod
Network lifetime plays an integral role in setting up an efficient wireless sensor network. Coverage in a network needs to guarantee that the region is monitored with the required degree of reliability. Locations of sensor nodes constitute the basic input for the algorithms that examine coverage of the network. Coverage problems can be broadly classified as area coverage problem and target coverage problem. Area coverage focuses on monitoring the entire region of interest, whereas target coverage concerns monitoring only certain specific points in a given region. Target coverage can be categorized as simple coverage, k-coverage and Q-coverage.
Lower coverage level (simple coverage) is enough for environmental or habitat monitoring or applications like home security. Higher degree of coverage (k-coverage) will be required for some applications like target tracking to track the targets accurately, or if sensors work in a hostile environment such as battle fields or chemically polluted areas. More reliable results are produced for higher degree of coverage which requires multiple sensor nodes to monitor the region/targets. An example of Q-coverage is video surveillance systems deployed for monitoring hostile territorial area where some sensitive targets like a nuclear plant may need more sensors cooperate to ensure source redundancy for precise data. Sensor nodes deterministically deployed by using artificial bee colony algorithm, so as to achieve the required target coverage level and maximize the network lifetime.
AN IMPROVED DECENTRALIZED APPROACH FOR TRACKING MULTIPLE MOBILE TARGETS THROU...ijwmn
Target localization and tracking problems in WSNs have received considerable attention recently, driven
by the requirement to achieve high localization accuracy, with the minimum cost possible. In WSN based
tracking applications, it is critical to know the current location of any sensor node with the minimum
energy consumed. This paper focuses on the energy consumption issue in terms of communication
between nodes whenever the localization information is transmitted to a sink node. Tracking through
WSNs can be categorized into centralized and decentralized systems. Decentralized systems offer low
power consumption when deployed to track a small number of mobile targets compared to the centralized
tracking systems. However, in several applications, it is essential to position a large number of mobile
targets. In such applications, decentralized systems offer high power consumption, since the location of
each mobile target is required to be transmitted to a sink node, and this increases the power consumption
for the whole WSN. In this paper, we propose a power efficient decentralized approach for tracking a
large number of mobile targets while offering reasonable localization accuracy through ZigBee network
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.
A fuzzy based congestion controller for control and balance congestion in gri...csandit
A Wireless Sensor Network (WSN) is deployed with a large number of sensors with limited
power supply in a wide geographically area. These sensors collect information depending on
application. The sensors transmit the data towards a base station called sink. Due to the
relatively high node density and source-to-sink communication pattern, congestion is a critical
issue in WSN. Congestion not only causes packet loss, but also leads to excessive energy
consumption as well as delay. To address this problem, in this paper we propose a new fuzzy
logic based mechanism to detect and control congestion in WSN. In the proposed approach, a
Monitor Node for each grid in congestion candidate region performs a fuzzy control to avoid
increasing congestion. Fuzzy controller’s inputs are continually fetched from the network by the
Monitor Node. Simulation results show that our approach has higher packet delivery ratio and
lower packet loss than existing approaches.
This document proposes an energy efficient framework for data collection in wireless sensor networks using prediction. The framework uses clustering, where sensor nodes are organized into clusters with a cluster head. The cluster head can enable or disable local prediction at sensor nodes to reduce data transmission. When prediction is enabled, sensors only transmit data if the value differs from the predicted value by more than a threshold. Sensors can also sleep when not transmitting to save energy. The document evaluates the performance of this framework through simulations, finding it reduces energy consumption compared to alternatives by integrating prediction with sleep/awake cycles.
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.
Data-Centric Routing Protocols in Wireless Sensor Network: A surveyAli Habeeb
This document summarizes several data-centric routing protocols for wireless sensor networks. It begins by outlining the challenges of routing in WSNs, including energy consumption, scalability, addressing, robustness, topology, and application-specific needs. It then describes several data-centric routing protocols, including flooding, directed flooding, constrained flooding, gossiping, fuzzy gossiping, location-based gossiping, and others. It notes advantages and disadvantages of these protocols for efficiently routing data in wireless sensor networks while minimizing energy consumption.
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.
Key Establishment using Selective Repeat Automatic Repeat Request Mechanism f...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
El documento explica cómo formar el futuro en español. Se puede usar "ir + a + infinitivo" o agregar las terminaciones "-é", "-ás", "-á", "-emos", "-éis" y "-án" al infinitivo del verbo. Algunos verbos irregulares como "poder", "querer" y "decir" cambian su forma antes de agregar las terminaciones. El documento proporciona ejemplos para practicar la formación del futuro.
The document provides materials for evaluating the performance of an inventory supervisor, including:
- A 4-page performance evaluation form covering rating scales, performance factors, employee strengths/areas for improvement, and signatures.
- Links to additional online resources for performance appraisals like phrases, forms, and tips.
- 3 pages of sample performance review phrases for an inventory supervisor, covering attitudes, creativity/innovation, and decision-making.
Corning Willow Glass is a new flexible glass material that has the potential to enable many new applications. It is thin, strong, and flexible enough to be rolled up, allowing for continuous roll-to-roll manufacturing processes. This could significantly reduce costs and enable new form factors like curved or flexible displays. Corning Willow Glass retains the high strength and heat resistance of glass while gaining flexibility. The National Renewable Energy Laboratory has used it to create flexible solar cells that could potentially be integrated into roof shingles to lower the installation costs of solar power.
Los cuatro personajes, dos ratones llamados Fisgón y Escurridizo y dos liliputienses llamados Hem y Haw, vivían en un laberinto buscando queso para alimentarse. Mientras que Fisgón y Escurridizo continuaron con su rutina de búsqueda del queso, Hem y Haw se volvieron perezosos y confiados. Un día, Fisgón y Escurridizo descubrieron que el depósito de queso estaba vacío, pero pronto encontraron una nueva reserva de queso. Hem y Haw, sin embargo
Requisitos y formatos educacion jose santos chocano choropamapaquinuamayo
El documento presenta 9 cuadros con información sobre el rendimiento académico, recursos humanos y físicos, criterios de evaluación docente, y costos de operación y mantenimiento de la Institución Educativa Secundaria José Santos Chocano. Los cuadros muestran que la mayoría de los alumnos tienen un rendimiento básico o suficiente, la institución cuenta solo con 6 docentes, carece de recursos físicos operativos, y los costos mensuales de operación ascienden a 6,860 nuevos soles.
The document discusses the importance of reciting Soorat Al-Kahf based on several Hadith. It states that the Prophet Muhammad said anyone who memorizes the first 10 verses of Soorat Al-Kahf will be protected from the Dajjaal. It also mentions that reciting the last 10 verses of the Soorah will prevent the Dajjaal from having power over someone if he appears after they recite it. The document then provides a brief overview of the stories and lessons contained in Soorat Al-Kahf before emphasizing the importance of reading it every Friday based on these Hadith and understanding its lessons regarding trials in this life.
This document discusses eTextbooks and new approaches being taken by CourseSmart, an eTextbook platform provider. It covers:
1) The challenging environment in higher education with changing technologies, increased competition, and student demands.
2) An overview of CourseSmart and the services it provides for eTextbooks, including an interactive eReader, integration with learning management systems, and analytics on textbook usage.
3) Examples of new approaches CourseSmart is taking including an interactive eReader using EPUB3, integration to improve efficiency and user experience, and flexible business models enabled through integration.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Este documento es la sentencia de un caso judicial en la Audiencia Nacional de España. Se juzgó a un hombre por un delito de injurias contra la corona por un artículo publicado en un periódico digital. La sentencia declara probado que el acusado escribió el artículo e injurió al Rey de España llamándolo borracho, putero y otros insultos. El tribunal determina que esto constituye un delito de injurias pero no el delito agravado propuesto por la fiscalía. Se le condena a una multa.
Design Issues and Applications of Wireless Sensor Networkijtsrd
Efficient design and implementation of wireless sensor networks has become a hot area of research in recent years, due to the vast potential of sensor networks to enable applications that connect the physical world to the virtual world. By networking large numbers of tiny sensor nodes, it is possible to obtain data about physical phenomena that was difficult or impossible to obtain in more conventional ways. In future as advances in micro-fabrication technology allow the cost of manufacturing sensor nodes to continue to drop, increasing deployments of wireless sensor networks are expected, with the networks eventually growing to large numbers of nodes.Potential applications for such large-scale wireless sensor networks exist in a variety of fields, including medical monitoring, environmental monitoring, surveillance, home security, military operations, and industrial machine monitoring etc. G. Swarnalatha | R. Srilalitha"Design Issues and Applications of Wireless Sensor Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd4688.pdf http://www.ijtsrd.com/engineering/computer-engineering/4688/design-issues-and-applications-of-wireless-sensor-network/g-swarnalatha
A Review On Reactive And Proactive Wireless Sensor Networks ProtocolsMary Montoya
This document reviews and summarizes reactive and proactive wireless sensor network protocols. It begins with an abstract discussing the importance of energy utilization in wireless sensor networks (WSNs) due to resource-constrained battery-powered sensor nodes. The document then provides background on WSNs, describing their origin, characteristics, and applications. It discusses routing protocols for WSNs, categorizing them as proactive (table-driven) or reactive (on-demand). Specific protocols discussed include Low Energy Adaptive Clustering Hierarchy (LEACH) and Stable Cluster Head Election (SCHE) Protocol, which are cluster-based routing protocols aimed at improving energy efficiency and extending network lifetime.
1) The document proposes implementing an efficient K-means clustering algorithm to enhance connectivity and lifetime in wireless sensor networks.
2) It compares the proposed K-means algorithm to an existing Jumper Firefly algorithm based on energy consumption, network lifetime, and end-to-end delay.
3) Simulation results show the proposed K-means algorithm improves performance by reducing energy consumption from 16 to 12 Joules, increasing network lifetime by 96% compared to 83% for the existing algorithm, and lowering end-to-end delay from 3.7 to 2.7 seconds.
This document discusses energy efficient data acquisition in wireless sensor networks. It proposes using mobile sinks to reduce energy consumption compared to static sinks. The network model includes sensor nodes deployed randomly to monitor an area and send data to sink nodes. Two cases are examined: one with a static sink and one with a mobile sink that moves along shortest paths to cluster heads. Simulation results show the mobile sink reduces energy consumption by 30% and improves packet delivery ratio and reduces delay compared to the static sink. The mobile sink approach enhances network lifetime by more efficiently utilizing energy in data transmission from sensors to sinks.
DATA TRANSMISSION IN WIRELESS SENSOR NETWORKS FOR EFFECTIVE AND SECURE COMMUN...IJEEE
Data transmission occurs from transmitting node to sink node, which communicate each other via large number of intermediate nodes or directly to an external base station. A network consists of numbers of nodes with one as a source and one or more as a destination node.
Performance Evaluation of Wireless Sensor Networks Communication Overhead and...ijtsrd
Powerful actors and resource constrained sensors are joined in wireless networks to form Wireless Sensor and Actor Networks WSANs . The lifespan of a sensor network may be effectively increased by clustering. The method of clustering involves breaking up sensor networks into smaller, more nimble groups of individuals with a cluster head. In hierarchically organised wireless sensor networks, clustering algorithms must choose the ideal number of clusters. In this study, we examine the effectiveness of cluster based wireless sensor networks for various wireless sensor network communication patterns WSNs . By utilising the self organizing map SOM based clustering approach, we concentrate on their performances in terms of Communication overhead and Energy consumption in WSN with varied velocities for the cluster based protocol. Mangukiya Hiteshkumar Bhupatbhai "Performance Evaluation of Wireless Sensor Networks' Communication Overhead and Energy Consumption using the Self-Organizing Map Method" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-6 , October 2022, URL: https://www.ijtsrd.com/papers/ijtsrd51936.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/51936/performance-evaluation-of-wireless-sensor-networks-communication-overhead-and-energy-consumption-using-the-selforganizing-map-method/mangukiya-hiteshkumar-bhupatbhai
IRJET- Studies on Lifetime Enhancement Techniques for Wireless Sensor NetworkIRJET Journal
1) Wireless sensor networks consist of small autonomous sensor nodes that monitor environmental conditions and communicate wirelessly. Battery power is limited so increasing node lifetime is important.
2) Techniques to enhance lifetime include clustering nodes so cluster heads can aggregate data and reduce transmissions, relocating the data sink to balance energy usage, and using energy-efficient routing protocols.
3) Neural networks can also be used to help manage the fuzzy nature of wireless sensor networks and account for various factors that influence node operations.
An Efficient Approach for Data Gathering and Sharing with Inter Node Communi...cscpconf
In today’s era Wireless sensor networks (WSNs) have emerged as a solution for a wide range of
applications. Most of the traditional WSN architectures consist of static nodes which are densely deployed
over a sensing area. Recently, several WSN architectures based on mobile elements (MEs) have been
proposed. Most of them exploit mobility to address the problem of data collection in WSNs. The common
drawback among them is to data sharing between interconnected nodes. In this paper we propose an
Efficient Approach for Data Gathering and Sharing with Inter Node Communication in Mobile-Sink. Our
algorithm is divided into seven parts: Registration Phase, Authentication Phase, Request and Reply Phase,
Setup Phase, Setup Phase (NN), Data Gathering, and Forwarding to Sink. Our approach provides an
efficient way to handle data in between the intercommunication nodes. By the above approach we can
access the data from the node which is not in the list, by sharing the data from the node which is
approachable to the desired node. For accessing and sharing we need some security so that the data can
be shared between authenticated nodes. For this we use two way security approach one for the accessing
node and other for the sharing.
Computational Analysis of Routing Algorithm for Wireless Sensor NetworkIRJET Journal
This document discusses an energy-efficient routing algorithm for wireless sensor networks (WSNs) proposed by the authors. It begins with background on WSNs and challenges related to limited energy. Then, it discusses prior work on routing protocols like LEACH and proposes a new algorithm. The key contributions are formulating control node selection as an optimization problem considering energy and distance, and using particle swarm optimization to solve this problem. This aims to improve energy efficiency for multi-tasking in software-defined WSNs compared to traditional protocols.
Node Deployment in Homogeneous and Heterogeneous Wireless Sensor NetworkIJMTST Journal
Optimal sensor deployment is necessary condition in homogeneous and heterogeneous wireless sensor
network. Effective deployment of sensor nodes is a major point of concern as performance and lifetime of any
WSN. Proposed sensor deployment in WSN explore every sensor node sends its data to the nearest sink node
of the WSN. In addition to that system proposes a hexagonal cell based sensor deployment which leads to
optimal sensor deployment for both homogeneous and heterogeneous sensor deployment. Wireless sensor
networks are receiving significant concentration due to their potential applications ranging from surveillance
to tracking domains. In limited communication range, a WSN is divided into several disconnected sub-graphs
under certain conditions. We deploy sensor nodes at random locations so that it improves performance of the
network.This paper aims to study, discuss and analyze various node deployment strategies and coverage
problems for Homogeneous and Heterogeneous WSN.
DESIGN ISSUES ON SOFTWARE ASPECTS AND SIMULATION TOOLS FOR WIRELESS SENSOR NE...IJNSA Journal
In this paper, various existing simulation environments for general purpose and specific purpose WSNs are discussed. The features of number of different sensor network simulators and operating systems are compared. We have presented an overview of the most commonly used operating systems that can be used in different approaches to address the common problems of WSNs. For different simulation environments there are different layer, components and protocols implemented so that it is difficult to compare them. When same protocol is simulated using two different simulators still each protocol implementation differs, since their functionality is exactly not the same. Selection of simulator is purely based on the application, since each simulator has a varied range of performance depending on application.
ENERGY EFFICIENT MULTIHOP QUALITY PATH BASED DATA COLLECTION IN WIRELESS SENS...Editor IJMTER
In recent years there has been an increased focus on the use of sensor networks to sense and measure
the environment. This leads to a wide variety of theoretical and practical issues on appropriate protocols for data
sensing and transfer. Recent work shows sink mobility can improve the energy efficiency in wireless sensor
networks (WSNs). However, data delivery latency often increases due to the speed limit of mobile sink. Most of
them exploit mobility to address the problem of data collection in WSNs. The WSNs with MS (mobile Sink) and
provide a comprehensive taxonomy of their architectures, based on the role of the MS. An overview of the data
collection process in such a scenario, and identify the corresponding issues and challenges. A protocol named
weighted rendezvous planning (WRP) which is a heuristic method that finds a near-optimal traveling tour that
minimizes the energy consumption of sensor nodes. Focus on the path selection problem in delay-guaranteed
sensor networks with a path-constrained mobile sink. Concentrate an efficient data collection scheme, which
simultaneously improves the total amount of data and reduces the energy consumption. The optimal path is chosen
to meet the requirement on delay as well as minimize the energy consumption of entire network. Predictable sink
mobility is exploited to improve energy efficiency of sensor networks.
This document proposes an energy efficient three-level model for query optimization in wireless sensor networks (WSNs). At the three levels are: base station, cluster heads, and sensor nodes. The base station maintains metadata about cluster heads and sensor nodes. When a query is received, it first checks if the result is cached. If not, it checks the status of cluster heads and selects a new cluster head if needed. The query is then disseminated to cluster heads using a modified Bellman-Ford algorithm. Cluster heads aggregate data from relevant sensor nodes and send the result to the base station. This model aims to minimize communication costs during query processing in WSNs.
Single Sink Repositioning Technique in Wireless Sensor Networks for Network L...IRJET Journal
This document presents a technique called single sink repositioning to extend the lifetime of wireless sensor networks. Sensor nodes have limited battery power, so energy consumption must be managed carefully. In typical static sink networks, nodes farther from the sink expend more energy transmitting data and drain their batteries quicker, shortening network lifetime. The proposed approach tracks the distance of each node to the sink and calculates an optimal sink position to minimize distances. It simulates moving the sink to this position using an algorithm in NS-2. Simulation results show repositioning the sink achieves significant energy savings compared to static sinks, helping improve overall network lifetime.
Reliable Data Aggregation Protocol (RDAT) uses functional reputation to improve data reliability in wireless sensor networks. It assigns separate reputation values for sensing, routing, and aggregation actions. Nodes monitor neighbors and exchange reputation tables. Before transmitting data, nodes evaluate aggregators' aggregation reputation to detect compromised ones. Aggregators run the Reliable Data Aggregation algorithm to further ensure integrity by using routing and sensing reputation to identify false reports. Simulation results show RDAT significantly improves data reliability over attacked networks compared to existing trust systems.
The Energy hole problem is a major problem of
data collection in wireless sensor networks. The sensors near the
static sink serve as relays for remote sensors, which reduce their
energy rapidly, causing energy holes in the sensor field. This
project has proposed a customizable mobile sink based adaptive
protected energy efficient clustering protocol (MSAPEEP) for
improvement of the problem of energy holes along with that we
also characterize and made comparison with the previous
existing protocols. A MSAPEEP uses the adaptive protected
method (APM) to discover the best possible number of cluster
heads (CHs) to get better life span and constancy time of the
network. The effectiveness of MSAPEEP is compared with
previous protocols; specifically, low energy adaptive clustering
hierarchy (LEACH) and mobile sink enhanced energy efficient
PEGASIS based routing protocol using network simulator(NS2).
Examples of simulation result show that MSAPEEP is more
reliable and removes the potential of energy hole and enhances
the stability and life span of the wireless sensor network(WSN).
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.
Proactive Data Reporting of Wireless sensor Network using Wake Up Scheduling ...ijsrd.com
In Wireless Sensor Network (WSNs), gather the data by using mobile sinks has become popular. Reduce the number of messages which is used for sink location broadcasting, efficient energy data forwarding, become accustomed to unknown earthly changes are achieved by a protocol which is projected by a SinkTrail. The forecast of mobile sinks’ location are done by using logical coordinate system. When sensor nodes don’t have any data to send, at that time they switch to sleep mode to save the energy and to increase the network lifetime. And due to this reason there is a chance of the involvement of nodes that are in sleeping state between the path sources to the mobile sink which is selected by the SinkTrail protocol. Before become the fully functional and process the information, these sleeping nodes can drop the some information. Due to this reason, it is vital to wake-up the sleeping nodes on the path earlier than the sender can start transferring of sensed data. In this paper, on-demand wake-up scheduling algorithm is projected which is used to activates sleeping node on the path before data delivery. Here, in this work the multi-hop communication in WSN also considers. By incorporating wake-up scheduling algorithm to perk up the dependability and improve the performance of on-demand data forwarding extends the SinkTrail solution in our work. This projected algorithm improves the quality of service of the network by dishonesty of data or reducing the loss due to sleeping nodes. The efficiency and the effectiveness projected solution are proved by the evaluation results.
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.
A Fault tolerant system based on Genetic Algorithm for Target Tracking in Wir...Editor IJCATR
In this paper, we explored the possibility of using Genetic Algorithm (GA) being used in Wireless Sensor Networks in general with
specific emphasize on Fault tolerance. In Wireless sensor networks, usually sensor and sink nodes are separated by long communication
distance and hence to optimize the energy, we are using clustering approach. Here we are employing improved K-means clustering algorithm to
form the cluster and GA to find optimal use of sensor nodes and recover from fault as quickly as possible so that target detection won’t be
disrupted. This technique is simulated using Matlab software to check energy consumption and lifetime of the network. Based on the
simulation results, we concluded that this model shows significant improvement in energy consumption rate and network lifetime than other
method such as Traditional clustering or Simulated Annealing
A Fault tolerant system based on Genetic Algorithm for Target Tracking in Wir...
SPal_Cooperative_WSNs
1. Journal of Ambient Intelligence and Smart Environments 0 (0) 1 1
IOS Press
Performance Optimization of Multiple
Interconnected Heterogeneous Sensor
Networks via Collaborative Information
Sharing
Sougata Pal a, Simon Oechsner a Boris Bellalta a,∗ Miquel Oliver a
a
Department of Information and Communication Technologies. Universitat Pompeu Fabra, Barcelona.
e-mail:{name.surname}@upf.edu
Abstract. Interconnecting multiple sensor networks is a relatively new research field which has emerged in the Wireless Sensor
Network domain. Wireless Sensor Networks (WSNs) have typically been seen as logically separate, and few works have consid-
ered interconnection and interaction between them. Interconnecting multiple heterogeneous sensor networks therefore opens up
a new field besides more traditional research on, e.g., routing, self organization, or MAC layer development. Up to now, some
approaches have been proposed for interconnecting multiple sensor networks with goals like information sharing or monitoring
multiple sensor networks. In this paper, we propose to utilize inter-WSN communication to enable Collaborative Performance
Optimization, i.e., our approach aims to optimize the performance of individual WSNs by taking into account measured in-
formation from others. The parameters to be optimized are energy consumption on the one hand and sensing quality on the
other.
Keywords: Interconnecting Wireless Sensor Networks, Performance Optimization, P2P Overlay, Collaborative Information
Sharing
1. Introduction
Wireless Sensor Networks (WSNs) [1] are wireless
networks whose primary task is information gathering
by monitoring parameters of the environment. They
consist of a number of sensor nodes each gathering in-
formation and reporting it back to their sink. The ob-
served parameters can take a variety of forms, such as
temperature, humidity, pollution, traffic, volcano and
earthquake activities, structural deficiencies, bird flight
path patterns or even enemy movements on battle-
fields. Due to this rich and dynamic field of applica-
tions WSNs have become an active research field in
the area of wireless communications.
*Corresponding author. E-mail: boris.bellalta@upf.edu
The main research focus for WSNs is on creating
smarter, cheaper and more intelligent sensors, as well
as more energy efficient networks. But even although
there has been tremendous progress, there remain nu-
merous unsolved issues. Moreover, due to their vary-
ing application scenarios, individual sensor networks
differ from each other, e.g, in terms of their individ-
ual protocol stack or network functionality. This is par-
tially due to the fact that different vendors are manu-
facturing different kinds of sensor network nodes, so
every sensor network has its own MAC, routing and
transport protocols. Due to this heterogeneity, sensor
networks are typically considered as individual entities
being logically separated from each other.
As a result, research on WSNs focuses more on
internal issues of single networks, such as routing,
energy management or MAC protocol development.
1876-1364/0-1900/$17.00 c 0 – IOS Press and the authors. All rights reserved
2. 2 S. Pal, et al., / Perf. Opt. of Mult. Interconnected Heterogeneous Sensor Networks via Collaborative Information Sharing
Extensive work has already been done in those ar-
eas, and development continues. However, the inter-
action between multiple heterogeneous spatially dis-
tributed sensor networks is an issue that has not re-
ceived much attention. Therefore, sharing information
between multiple sensor networks, inter WSN commu-
nication, and connecting multiple sensor networks via
a feasible architecture have been started to be investi-
gated recently.
In this work, we describe a novel approach to utilize
an inter-WSN communication architecture to optimize
the performance of individual WSNs. It can therefore
be seen as a complementary work to all performance
optimizations focused on single networks without any
additional information, e.g., MAC or routing proto-
cols, or cross layer optimizations for the sensor net-
work protocol stack. In our approach, we consider the
heterogeneous nature of different types of sensor net-
works and do not try to generate a global, centrally
controlled optimization strategy. We believe it is nei-
ther feasible nor practical to propose a common MAC
or routing protocol for every sensor network, or to have
a common cross layer optimization method for the en-
tire architecture to optimize the performance.
Instead, we propose that individual sensor networks
improve their performance by utilizing reports from
other sensor networks, taking into account all relevant
information to take the best decision locally. To this
end, we propose both a global architecture that allows
to find and establish contact with these additional in-
formation sources, as well as a method how to use this
information to reduce energy consumption and to in-
crease the sensing quality of individual networks. To
the best of our knowledge, this approach of using re-
ported information from other WSNs to optimize local
performance has not been considered before.
The remainder of this paper is organized as follows:
Section 2 describes the basic idea and model for im-
proving the performance of a WSN using external in-
formation. Section 3 provides the description of our
proposed architecture, while Section 4 contains realis-
tic use cases for our architecture. Section 5 gives the
an overview on the related work, and finally Section 6
summarizes our conclusions and provides an outlook
on our future work.
2. Benefits of Collaboration
In this section we describe how we aim to improve
energy consumption and sensing quality by sharing in-
formation between WSNs. To this end, we develop a
basic model, and then apply it to both of these perfor-
mance indices.
We consider a set of sensor networks, consisting of
WSN A (SA
) up to WSN N (SN
), with each of these
networks consisting of a different number of sensor
nodes. If we define every individual sensor network SI
as its set of sensors SI
= SI
1 , SI
2 , SI
3 , SI
4 , ....SI
nI
,
which has cardinality nI, we can express those indi-
vidual sensor network sets as
SA
= SA
1 , SA
2 , SA
3 , SA
4 , .....SA
na
SB
= SB
1 , SB
2 , SB
3 , SB
4 , .....SB
nb
. . .
SN
= SN
1 , SN
2 , SN
3 , SN
4 , .....SN
nN
We will now first consider the case of WSN SI
in
a standard, non-cooperative case, i.e., SI
operates au-
tonomously without any additional information. We
model the operation of this network in terms of the
messages sent and received by the sink of SI
. We con-
sider that the sink updates the configuration of the sen-
sors SI
k by sending query messages at time instances
tk, cf. Figure 1.a). Among other things, these queries
configure the time interval tα between two consecutive
reports sent by the sensors to the sink. As well, it con-
figures the number of active nodes |SI
active| in SI
, i.e.,
the subset of SI
which will report its values.
We can assume tα and |SI
active| to remain constant
between two consecutive sink queries at tx and tx+1.
Depending on the WSN, this value may even never be
changed over the lifetime of the network. In any case,
SI
can in the best case only take into account its own
measured information to modify tα and |SI
active| over
time.
In our cooperative scenario, however, we assume
that more information is available, coming from other
WSNs, e.g., SJ
. This additional information will allow
to judge better the necessity for frequent reports and
the number of active sensors. For example, if SJ
re-
ports an incident, SI
can decrease tα. In contrast, if SJ
reports uncritical values, SI
may decrease its level of
operation as well by increasing tα and only requesting
reports from a lower number |SI
active| of its sensors.
Thus, the situation shown in Figure 1.a) changes. SI
may now additionally update the reporting interval ev-
ery time it receives information from SJ
, cf. Figure
3. S. Pal, et al., / Perf. Opt. of Mult. Interconnected Heterogeneous Sensor Networks via Collaborative Information Sharing 3
tα
t
a) Non−cooperative Scenario
t
b) Cooperative Scenario
tα
new query from the sink
new query from the sink
t t
t t
x
x
x+1
x+1
sensor update messages
sensor update messages
Fig. 1. Non-cooperative and cooperative scenario. In the cooperative scenario, the queries are used to change the operation of the WSNs to adapt
to environment based on the information received from other WSNs.
1.b). Using a decision making process described in the
next section, it can take into account its own measured
information plus the information from SJ
. If this pro-
cess returns a changed tα, the sink of SI
will issue an
additional query to its sensors.
In general, we imagine that sensor nodes from both
SI
and SJ
will sense information and forward them
towards their respective sinks. The sinks will then
forward this information towards interested recipient
WSNs, using the architecture described in the next sec-
tion. However, if no information from other networks
is received, the individual WSNs can still operate as in
the non-cooperative scenario.
The information exchange will be useful when the
exchanging WSNs sense the same parameter in the
same area. However, the more interesting case is when
SI
and SJ
sense different parameters that are tightly
correlated in nature, e.g., the traffic and pollution lev-
els in the same city. Then, information about one pa-
rameter, e.g., traffic, can be used to predict the level of
the other, e.g., pollution, to a certain degree.
Our basic assumption is that a sensor network is not
interested in measuring all levels of its sensed parame-
ter with equal interest. Instead, extreme or critical val-
ues have a much higher impact than ’normal’ levels,
and are therefore of more interest. As an example, high
levels of pollution should be sensed with a higher accu-
racy than low levels or a level of 0, due to their health
implications. Similarly, it is more critical to have more
exact information about the location and spread of a
forest fire when it occurs, than measuring with a high
level of accuracy that there is no forest fire. As we
will describe in the following, being able to predict the
level of the sensed value should allow for a better re-
source usage in the affected WSN.
2.1. Energy Consumption
Since every sent and routed information update con-
taining measured values consumes an amount of en-
ergy of the sensor nodes, we see that the energy con-
sumption of the sensor network depends on tα and
|SI
active|. For longer values of tα, less energy is con-
sumed over a longer timespan, for shorter values of
tα, more energy is consumed. Similarly, if the sink re-
quests that a lower number of sensors |SI
active| in its
network reports values, less energy will be consumed
within the complete network, extending its lifetime.
Therefore, if SI
receives information from SJ
and
decides, based on this information, that it can re-
duce its level of operation (increase tα and decrease
|SI
active|), it can reduce its energy consumption during
that period. When a higher level of operation is neces-
sary afterwards, e.g., because SJ
or its own sensors re-
port an important event, SI
can increase |SI
active| and
decrease tα again. Thereby, SI
can adapt its energy
4. 4 S. Pal, et al., / Perf. Opt. of Mult. Interconnected Heterogeneous Sensor Networks via Collaborative Information Sharing
000
000
111
111
00
00
11
11
00
00
00
11
11
11
000000
111111
00
0000
11
1111
00
0000
11
1111
000000
111111
00
00
11
11
00
00
11
11
00
00
11
1100
00
11
11
00
00
11
11
000
000
111
111
Inter−WSNs communication
The Internet
Gateway
Enhanced
WSN3WSN2WSN1
Wireless
Sensor
WSNs deployment area
Fig. 2. System Architecture
consumption better to the current situation in the real
world.
2.2. Sensing Quality
Similarly, SI
can adapt at the same time its sensing
quality (Q). Sensing quality can be defined as the re-
lation between the actual update frequency of sensors
λ = 1
tα
and their number |SI
active| on the one hand,
and the ideal frequency λ∗
and number |SI
active|∗
on
the other. A simple approach for the sensing quality
would therefore be
Q = min 1,
λ
λ∗
|SI
active|
|SI
active|∗
(1)
where the maximum achievable quality is Q = 1.
The values of λ∗
and |SI
active|∗
depend on the cur-
rent circumstances in the real world. For example, un-
der critical circumstances (extremely high levels of
traffic or temperature, an earthquake or forest fire hap-
pening), these values can be expected to be higher than
under non-critical circumstances. Generally, the more
sensor nodes send their sensed information to their
sink, and the higher the frequency of these updates is,
up to the ideal values, the better will be the sensing
quality. However, this has to be paid for in terms of en-
ergy consumption. In our approach, we want therefore
to improve on the utilization of this trade-off utilizing
the information from additional networks.
Note that from Equation 1, it is clear that λ and
|SI
active| should be always equal to λ∗
and |SI
active|∗
to get Q = 1. Then, if λ∗
and |SI
active|∗
change with
time, λ and |SI
active| should also be adapted to those
changes. Higher values than the optimal only result in
a higher energy consumption, but not in a higher qual-
ity.
3. Architecture Description
In this section, we will describe the components and
algorithms of our architecture. These additional build-
ing blocks enable the information exchange between
WSNs and the usage of this information to improve the
performance of individual WSNs.
In order to interconnect multiple sensor networks,
we are introducing a new entity called Enhanced Gate-
ways (EG) in each sensor network, as shown in Fig-
ure 2. Each EG has a direct connectivity to the sink of
its sensor network, and additionally is connected to the
Internet.
In some previous works this kind of entity has been
shown only as Gateways, with the sole purpose of pro-
viding a bridge between the sensor networks and the IP
network. In our scenario, this entity offers more than
gateway functionality, as we will describe in the fol-
5. S. Pal, et al., / Perf. Opt. of Mult. Interconnected Heterogeneous Sensor Networks via Collaborative Information Sharing 5
tα
|Sactive|
Itα
|Sactive|
I
communication
Inter−WSN
data collection
WSN
Reasoning Module
Enhanced Gateway
ReportsMeasured data
Start
New and
Default and
Update WSN operation
from wireless sensors from/to cooperating WSNs
No action
required
Fig. 3. Enhanced Gateway Operation
lowing. Since we are placing EGs on the edge of the
fixed-line part of the Internet, we assume they do not
have any energy or performance constraints.
3.1. Enhanced Gateway
The Enhanced Gateways are the main new archi-
tectural component that our approach adds to WSNs.
They are well-dimensioned machines having a reliable
Internet connection, and are directly connected to their
respective sensor network sink, i.e., there is one EG
per sensor network. The set of all EGs maintains an
overlay between themselves, as described in Section
3.2. This overlay is used to establish communication
between pairs of EGs, of which at least one can benefit
from information exchange.
These pairs of EGs will exchange aggregated infor-
mation from their attached sensor networks in the form
of regular updates. Currently, we envision this to be an
average of the sensed values, but it might also be the
complete set of measurements from all sensors in the
network. The receipt of such an update by an EG leads
to an evaluation of all available information, and pos-
sibly a reconfiguration of the attached sensor network,
as described in Section 2. Moreover, in some specific
scenarios these updates may also trigger a forwarding
towards other concerned entities, e.g., emergency ser-
vices.
In our architecture, we propose to evaluate received
information based on the flow chart shown in Figure
3. Whenever a new update from a remote sensor net-
work or the local sensors is received, the EG Rea-
soning Module considers this new information along
with the latest values stored for all other information
sources. Based on the trust value of all these sources,
cf. Section 3.4, and on the currency of their informa-
tion, it will calculate a new level of operation, i.e.,
tα and SI
active. If the values of these parameters have
changed in comparison to the current sensor network
configuration, the EG will instruct its attached sink to
generate a new query with the updated parameters and
send it to the sensors.
The calculation of the level of operation should,
e.g., take into account if the local sensor network mea-
sured highly variable or extreme values, and whether
reported values for correlated parameters indicate a
change in the measured value soon. To illustrate this,
we will give in Table 1 some example rules that we
will use in a straightforward rule-based approach for
this algorithm for measured values of pollution (P)
and traffic (T).
3.2. Overlay
The EGs EI
of all participating sensor networks SI
will be maintaining a single-hop P2P overlay between
themselves as shown in Figure 4. The primary rea-
6. 6 S. Pal, et al., / Perf. Opt. of Mult. Interconnected Heterogeneous Sensor Networks via Collaborative Information Sharing
Table 1
Operation Level of a WSNs measuring air pollution when the measured road traffic is used to trigger it.
Condition Operation Level
If P ≤ 10µg/m3 && T ≤ 10 veh./min Pollution Network Operation Level = Low
If 10µg/m3 < P ≤ 20µg/m3 && 10 veh./min < T ≤ 50 veh./min Pollution Network Operation Level = Moderate
If 10µg/m3 < P ≤ 45µg/m3 && 10 veh./min < T ≤ 50 veh./min Pollution Network Operation Level = High
P2P overlay
WSN
WSNWSN
NETWORK
EG
EG
EG
Physical Network
Fig. 4. P2P overlay Network
son for selecting a single hop overlay is that we want
to keep the lookup time to locate other corresponding
EGs as low as possible. As in our scenario the nodes
are stable and not changing their locations, the churn
rate will be low, making the use of such an overlay
feasible.
In a one-hop DHT, every EG will hold some state
about every other EG. This state is contained in a
Global Lookup Table (GLT), which will hold a small
number of values for each EG and their sensor net-
work. These values are the overlay node identifier, the
IP address of the corresponding enhanced gateway, the
GPS coordinates of the centre of the sensor network,
and finally the network category, identifying what kind
of information the sensor network is collecting. Thus,
the GLT looks like shown in Table 2.
An EG wishing to join the overlay generates an at-
tach request message and sends it to an already par-
ticipating node, which will respond with its GLT. The
address of a participating node can be obtained using
a globally available information source, such as a web
service. As well, this step may include a registration or
authentication process.
3.3. Cooperating Networks
While the GLT means that an EG knows about the
existence of all other sensor networks willing to ex-
change information, it is neither feasible nor necessary
to actually establish a cooperation with all of them.
Only sensor networks that are roughly in the same
area in the physical space, and that gather information
about related parameters are of interest for communi-
cation. Therefore, a second list of EGs needs to be de-
rived from the GLT, containing the sensor networks
with which the local EG wants to share information.
We will call this secondary list Cooperating Networks
Table (CNT).
The formation of this secondary table is based on the
aim that it should consist of only those networks who
will qualify for cooperation. As a simple algorithm to
implement this functionality, we propose to use the
physical proximity between sensor networks, as dis-
cussed in Section 3.3.1. Apart from this distance calcu-
lation, every EG also should make its selection based
on the network category column of the GLT, which al-
lows to filter for sensor networks measuring a related
parameter.
7. S. Pal, et al., / Perf. Opt. of Mult. Interconnected Heterogeneous Sensor Networks via Collaborative Information Sharing 7
Table 2
Global Lookup Table
Nodes IP Address Node ID Co-ordinate Network Category
EGA 132.187.16.21 CF32A1 N 49 ◦ 47’ 39.4506",
E 9 ◦ 55’ 38.9778"
Pollution
EGB 141.101.126.147 63D80B N 41 ◦ 23’ 16.6416",
E 2 ◦ 10’ 11.715"
Humidity
EGC 193.174.81.220 4248C4 N 49 ◦ 47’ 39.4510",
E 9 ◦ 55’ 38.9703"
Traffic
... ... .... .... ....
... ... .... .... ....
... ... .... .... ....
... ... .... .... ....
EGN 195.251.255.138 815162 N 37 ◦ 58’ 44.8932",
E 23 ◦ 42’ 59.688"
Pollution
While this simple algorithm allows to rule out com-
munication with unsuited networks, not all sensor net-
works included in the CNT will provide the same sup-
port in terms of useful information. Thus, we propose
to assign a trust level to each of these networks, re-
flecting the value of the provided information to the
local performance optimization. A first approximation
of this trust can be made based upon the distances be-
tween the networks, as we describe in Section 3.4. In
Section 3.4.1, we will describe another, experience-
based method to fine-tune the trust level.
This value, along with the currency of received in-
formation, should be taken into account when making
decisions based on information coming from other net-
works, cf. Section 3.1. Thus, the CNT has the form
seen in Table 3. The time interval signifies how fre-
quently updates are received from the other sensor net-
work, while latest value holds the last reported infor-
mation. Finally, the CNT holds the timestamp at which
the last update containing that information arrived.
3.3.1. Calculating Sensor Network Distances
In this section, we will explain how to calculate the
distance between two sensor networks, as this value is
used to form the CNT.
We consider (xI, yI) as the geographical location
coordinates for sensor network SI
. Typically, we as-
sume these coordinates to be the geographical center
of the sensor network. It is one of our assumptions
that while deploying the sensor network, the concerned
person will note these location coordinates.
Since these locations are part of the GLT, the lo-
cal EG forming its CNT can calculate the distance
between its own sensor network center and the co-
ordinates of all other sensor networks. This distance
calculation between two GPS coordinates can be per-
formed using the Haversine distance calculation for-
mula (Equation 2) where d = Distance between 2 ge-
ographical location coordinates, R = Mean radius of
earth(6371 kms), lat = Difference between lat2 &
lat1, long = Difference between long2 & long1.
As an example regarding how this distance calcula-
tion formula will work, lets refer to our above men-
tioned Table 2. Here EGA
& EGC
represents two net-
works one of which is measuring pollution and another
one measuring traffic. Now we assume pollution net-
work wants to collaborate with traffic network to op-
timize its own performance by sharing informations
with the traffic network. As mentioned before the per-
son who will be responsible for deploying the network
should upload the geographical location coordinates of
the centre of the network in the respective EG. As we
can notice the geographical location coordinates for
EGA
& EGC
are respectively N 49 ◦
47’ 39.4506",
E 9 ◦
55’ 38.9778" & N 49 ◦
47’ 39.4510", E 9 ◦
55’
38.9703",EGA
will calculate the distance between the
location coordinates which is coming as 0.02016 kms
in this situation.
Using these distances, one for every sensor network
participating in the global architecture, the local EG
can now filter out all networks that are farther away
than a threshold dI
max. This threshold can depend on
the type of the local sensor network and is therefore
not a globally fixed value. Only networks closer than
this maximum distance are considered for inclusion in
the CNT. In addition, their category must match, as de-
scribed in the following section.
3.3.2. Matching Network Category
After calculating the distance, the EG evaluates the
network category of the remaining networks. This
value will show what parameters those networks are
8. 8 S. Pal, et al., / Perf. Opt. of Mult. Interconnected Heterogeneous Sensor Networks via Collaborative Information Sharing
Table 3
Cooperating Networks Table
Nodes Trust Value Time Interval Latest Value Timestamp
EGA 6 600 35 µg
m3 10:03h
EGC 8 300 10 1
min
10:05h
... ... .... .... ....
... ... .... .... ....
EGJ 2 300 .... 10:07h
a = sin2
(
lat
2
) + cos(lat1) · cos(lat2) · sin2
(
long
2
)
c = 2 · a tan 2(
√
a, (1 − a)) (2)
d = R · c
sensing. Every EG should know with what other kind
of network it can collaborate with, e.g., by pre-defining
it in the EGs’ software. If the category of the remote
sensor network matches, it can be included in the CNT.
3.4. Level of Trust
As described, with the trust value we aim to give
an EG the option to differentiate between information
sources, and to weigh their input. Any EG that is part
of the CNT has a base trust value, since information
from this entity is accepted. However, EGs should be
trusted more the more is known about them and the
quality of their information.
As a first default value for the trust of other EGs,
without any more data, we can use the proximity of
their attached sensor network center to the local sensor
network coordinates. This allows us to linearly scale
the trust value between a maximum initial value, e.g.,
10, for having the exact same coordinates, and a value
of 0 for a distance of dI
max.
If, additionally, the coordinates of the individual
sensors of the two networks are available as well, a
more fine-grained distance could be computed, with an
according higher trust value if this calculation shows a
good match between the two networks.
Finally, we can increase the trust value of remote
networks by keeping track of their reports and how
well they correlate with the measured values in the lo-
cal sensor network, as described in the following.
3.4.1. Correlation Coefficient of Measured
Information
As described, the EG of a sensor network SI
will
regularly receive reports from each cooperating EG,
e.g., SJ
. We define the contained reported value of the
report number y as vJ
y . EGI
can also note the cur-
rent value reported by the local sensor network, vI
y,
and thus create a pair of values for each received re-
port. Over time, it thus can create a combined history
of externally and internally collected information, i.e.,
vI
and vJ
.
Once enough values are collected, EGI
can cal-
culate the correlation coefficient function rvI vJ using
Equation 3, where n is the length of the recorded his-
tory.
The trust value can then be increased if a strong pos-
itive or negative correlation is seen, i.e., by common
definition for |rvI vJ | > 0.7. Whether we see a positive
or negative correlation depends on the scenario, i.e.,
traffic levels and pollution should generally be posi-
tively correlated, while humidity and forest fire proba-
bility should be negatively correlated.
In this experience-based approach, rvI vJ can be up-
dated over time. Thus, an EG can react to changes in
sensor deployments, better coverage, etc. Remote EGs
that report more and more useful information will thus
be able to ’earn’ more trust, and their reports will then
have a higher weight in the local decision making pro-
cess.
4. Use Case Descriptions
In this section, we will briefly describe two sce-
narios where we believe our approach could be ben-
eficial. However, these examples are merely illustra-
tive and not exhaustive, since our approach works for
any combination of sensed information that is corre-
9. S. Pal, et al., / Perf. Opt. of Mult. Interconnected Heterogeneous Sensor Networks via Collaborative Information Sharing 9
rvI vJ =
n vI
vJ
− ( vI
)( vJ
)
( n vI2
− ( vI)2) · ( n vJ2
− ( vJ )2)
(3)
lated in nature, such as humidity and rainfall, tem-
perature and agricultural growth monitoring, or earth-
quakes and structural monitoring.
4.1. Traffic & Pollution
Traffic and pollution are two basic areas where we
can find wide spread usage of sensor networks in
metropolitan cities around the world. Traffic Sensors
are normally widely deployed for traffic management
and monitoring by the civil authorities. These sensors
can measure vehicle quantities passing by during a
fixed period of time, and in addition they may monitor
vehicle classes and velocity. This scenario is depicted
in Figure 5.
Pollution sensors, on the other hand, are used espe-
cially in large cities to keep track of the smog gener-
ated there by the high concentration of industry and
traffic. Since especially the day-to-day air pollution
has health implications, data from these sensor net-
works can be used to warn citizens with a form of pol-
lution scale, or traffic may be managed differently to
reduce the amount of cars in the city.
Since a significant part of the air pollution in large
cities is caused by traffic, we envision that a coopera-
tion between these types of sensor networks could be
beneficial. Since the level of traffic typically does not
depend on the level of pollution, there will be mainly
an information flow from the traffic sensor network
EGs to the pollution sensor network EGs. Each update
contains the currently measured traffic levels. The EG
of the receiving pollution sensor network will there-
fore receive information about rising traffic levels, and
can then increase the level of operations of its network
accordingly to better sense an upcoming peak in pol-
lution. On the other hand, if low levels of traffic are re-
ported, and if the pollution sensor themselves show no
critical values, the EG might decide to increase tα and
decrease |SI
active|.
4.2. Temperature, Wind speed, Humidity & Forest
Fire Probability
The previous scenario illustrates an example for one
type of sensor network using data of a second type.
However, our architecture allows a more general coop-
eration, i.e., the collection and utilization of data from
a number of sources of different kinds. An example
for this is a forest fire detection sensor network, as de-
picted in Figure 6.
Geographical information datasets have shown that
the probability for forest fires is directly proportional
to specific environmental conditions, such as very high
temperature, extremely low relative humidity and very
high wind-speed [15]. As an example we can take the
Black Saturday Bush Fire of 2009 in Australia. Ac-
cording to records, this incident was made possible by
the presence of all of the above mentioned favorable
conditions. Temperatures reached 46.4 degrees Cel-
sius, while humidity levels as low as 6% were reported,
and wind speeds of 100 km/h were measured [14]. All
three of these metrics can be and are monitored by sen-
sor networks, e.g., for meteorological purposes.
Thus, we believe the information gathered by these
types of sensor networks can be used to adapt the level
of operation of a forest fire detection sensor network.
In periods with low temperatures and high humidity,
the level of operation of the detection network can be
decreased because the probability for a fire is very low.
On the other hand, if high temperatures and a low hu-
midity are reported in the general area for a longer pe-
riod, and if in addition high wind speeds are measured,
then the EG should let more sensors report in shorter
intervals to be able to detect a fire quickly in this criti-
cal situation.
5. Other Initiatives
As we have mentioned, traditionally research in
WSNs has been focused on internal issues like routing,
self organization, new MAC or routing protocol devel-
opment, and energy consumption. Due to the numer-
ous approaches in these fields, WSNs show a heteroge-
neous usage of protocols and mechanisms. Integrating
these heterogeneous networks with the IP world has
become a new research direction recently. In this field,
some work has been done regarding architectures for
interconnecting WSNs.
To interconnect multiple spatially distributed het-
erogeneous sensor networks, two basic approaches
exist, namely a client/server-based approach and a
10. 10 S. Pal, et al., / Perf. Opt. of Mult. Interconnected Heterogeneous Sensor Networks via Collaborative Information Sharing
0011
01
Traffic Sensor
Pollution
Sensor
EG EG
PollutionTraffic
4:00 8:00 12:00 16:00 20:00
time
4:00 8:00 12:00 16:00 20:00
time
Security
Threshold
Internet
Data
Communication
(WSNs)
24:00
24:00
Traffic
Pollution
Fig. 5. Traffic & Pollution Scenario
0
0
1
1
01
01
0
0
1
1
0000111101
0011
01
00001111
01
4:00 8:00 12:00 16:00 20:00 24:00 4:00 8:00 12:00 16:00 20:00 24:00
4:00 8:00 12:00 16:00 20:00 24:00
4:00 8:00 12:00 16:00 20:00 24:00
EG EG EG
Temperature Humidity Fire
EG
Internet
Tree
time
no
yes
Wind Speed
Temperature Humidity
time
time
time
Fire
Wind Speed
Communication
Data
(WSNs)
Fire sensor
Humidity sensor
Temperature sensor
Wind sensor
Fig. 6. Temperature, Wind speed, Humidity & Forest Fire Probability
11. S. Pal, et al., / Perf. Opt. of Mult. Interconnected Heterogeneous Sensor Networks via Collaborative Information Sharing 11
P2P approach. As in other application fields, both ap-
proaches have their advantages and disadvantages, i.e.,
the more centralized system offers a higher level of
control, but at the price of introducing a central point
of failure and scalability issues. Due to the fact that our
approach belongs to the class of P2P architectures, we
will primarily discuss these systems.
IrisNet [2] was one of the first large scale overlay-
based architectures. It was primarily meant to intercon-
nect various multimedia sensors which are distributed
world-wide and which are capable of running rich ap-
plication suites. Sharing infrastructure resources is the
most important design principle of the IrisNet archi-
tecture.
Hourglass [3] is an Internet-based software architec-
ture to connect various sensor networks, their services
and applications. Hourglass consists of an overlay net-
work of dedicated connected machines which provides
service registration, node discovery and routing of data
from sensors to clients application.
SharedSense [4] is mainly a peer to peer environ-
ment for connecting multiple heterogeneous sensor
networks and monitoring them. It is based upon JXTA
[5], which is a Java based P2P substrate, having a dif-
ferent set of rules and regulations in comparison to
other popular P2P overlays [6].
The authors of [7] proposed a mobile P2P sensor
networks overlay which was based upon 3G mobile
networks. This overlay was also based upon JXTA.
P2PBridge [8,9] was another initiative with the idea to
have an inter WSN-communication and interoperabil-
ity. Again, JXTA is used as the P2P substrate.
The MetroSense Project [10] is a general purpose
heterogeneous architecture based on people-centric
sensing applications. GSN [11], or the Global Sen-
sor Network platform is a flexible middleware ap-
proach which was envisioned to provide a common
platform to connect multiple widely deployed sensor
networks and create a global Sensor Internet. GSWSN
[12], or Global Scale Wireless Sensor Networks is an-
other Internet-based overlay architecture to intercon-
nect globally dispersed heterogeneous wireless sensor
networks. Finally, in [13], a hierarchically structured
worldwide sensor web architecture has been proposed
to retrieve data from multiple heterogeneous sensor
networks.
All these architectures, and comparable server-
based approaches, have in common that they in-
terconnect different and heterogeneous sensor net-
works. However, the functionality of these architec-
tures mainly consists in enabling a global manage-
ment and data evaluation. Information is generally en-
visioned to flow from sensor networks towards users or
external systems. In contrast, our architecture has the
primary goal of enabling communication and coopera-
tion between the sensor networks themselves, with the
specific goal of performance optimization for individ-
ual WSNs.
6. Conclusion and Open Research Directions
In this paper, we have proposed a new concept to
improve the performance of WSNs based on informa-
tion received from other sensor networks, opening new
research directions in both the WSN and Networking
community. We described the internal WSN mecha-
nisms used for this optimization and our reasoning for
their beneficial effect. In addition, we presented an ar-
chitecture and algorithms enabling this information ex-
change and the processing of received data.
To further develop the proposed concept, there are
several topics which require special attention:
– Create a complete list with the WSNs combina-
tions (scenarios) that can benefit from our pro-
posed architecture. The details for each combined
scenario have to include the types of sensor net-
works that will be sharing information between
themselves, the criteria to be met for any kind
of information sharing to be practically feasible,
the different operation levels in every individual
sensor network, the kind of communication mes-
sages to be exchanged, etc.
– Develop new reasoning models based on the cor-
relation of the sensed values from each individ-
ual WSN collaborating. This relationship model
should be constructed based upon real-life geo-
graphical data. It has to provide the understand-
ing about how the measured values of those
environmental parameters do actually relate to
each other, and therefore form the basis for the
decision-making process in the EGs.
– Evaluate the functioning and the performance of
this kind of collaborative architecture, as well as
the protocols that compose it. Through our de-
scription in Section 2, we have shown how can we
achieve our target of performance optimization
of WSNs by controlling their energy consump-
tion and level of sensing quality, but quantitative
works are required.
12. 12 S. Pal, et al., / Perf. Opt. of Mult. Interconnected Heterogeneous Sensor Networks via Collaborative Information Sharing
Acknowledgements
This work has been partially supported by the
Spanish Government under projects TEC2008-06055
(Plan Nacional I+D), CSD2008-00010 (Consolider-
Ingenio Program) and by the Catalan Government
(SGR2009#00617).
References
[1] J. Yick; B. Mukherjee; D. Ghosal. "Wireless Sensor Network
Survey". Computer Networks, April, 2008, 52, 2292-2330.
[2] J.B Gibbons; B. Karp; Y. Ke; S. Seshan. "IrisNet: An architec-
ture for a worldwide sensor Web". Pervasive Computing, 2003,
vol.2,pp. 22-23.
[3] J. Shneidman; P. Pietzuch; J. Ledlie; M. Roussopoulos; M.
Seltzer; M. Welsh. "Hourglass: An Infrastructure for Connecting
Sensor Networks and Applications". Technical Report TR-21-04
Harvard University, EECS, 2004
[4] A. Antoniou; I. Chatzigiannakis; A. Kinalis; G. Mylonas; S.
Nikoletseas; A. Papageorgiou. "A Peer-to-Peer Environment for
Monitoring Multiple Wireless Sensor Networks". in Proceed-
ings of the 10th ACM/IEEE Symposium on Modeling, Analysis
and Simulation of Wireless and Mobile Systems, Greece, 2007
[5] JXTA, http://jxta.kenai.com/
[6] E.K Lua; J. Crowcroft; M. Pias; R. Sharma; S. Lim. "A Survey
and Comparison of PEER to Peer Overlay Network Schemes".
IEEE Communications Surveys, Second Quarter 2005, Vol 7,
No.2
[7] S. Krco; D. Cleary; D. Parker. "P2P Mobile Sensor Networks".
in Proceedings of the 38th Hawaii International Conference on
System Sciences, 2005, pp. 324-324.
[8] M. Isomura; C. Decker; M. Beigl. "Generic Communication
Structure to Integrate Widely Distributed Wireless Sensor Nodes
by P2P Technology". in Proceedings of the seventh Interna-
tional conference on Ubiquitous Computing (Ubicomp 2005),
Tokyo September, 2005
[9] M. Isomura; C. Decker; M. Beigl; T. Reidel; H.A.H.H. Horiuchi.
"Sharing sensor networks". in Proceedings of the 26th IEEE In-
ternational Conference Workshops on Distributed Computing
Systems, Lisboa, Portugal, 2006
[10] B.S Eisenman; D.N Lane; E. Miluzzo; A.R Peterson; G. Ahn;
T.A Campbell. "MetroSense Project: People-Centric Sensing at
Scale". In Workshop on World-Sensor-Web (WSW), Boulder, Oc-
tober, 2006
[11] K. Aberer; M. Hauswirth; A. Salehi. "Infrastructure for Data
Processing in Large-Scale Interconnected Sensor Networks". in
Proceedings of the 8th International Conference on Mobile Data
Management, Mannheim, Germany, May 7-11, 2007
[12] D.W Phillips; R. Sanker. "An Internet Overlay Architecture for
Global Scale Wireless Sensor Networks". Wireless Telecommu-
nication Symposium, April, 2010
[13] I. Rhead; M. Merabti; H. Mokhtar; P. Fergus. "A Hierarchically
Structured Worldwide Sensor Web Architecture". International
Conference on Advanced Information Networking and Applica-
tions Workshops, 2009
[14] Government of Victoria. "Final Report Report of 2009
Victorian Bushfires Royal Commission: Chapter 1".
http://www.royalcommission.vic.gov.au/Documents/PF-
Chapters/Chapter1_PF.pdf, 2010.
[15] European Forest Fire Information System,
http://effis.jrc.ec.europa.eu/