International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Accurate and Energy-Efficient Range-Free Localization for Mobile Sensor Networksambitlick
The document summarizes an algorithm called WMCL that improves the sampling efficiency and localization accuracy of existing SMC-based localization algorithms for mobile sensor networks. WMCL achieves higher sampling efficiency by further reducing the size of sensor nodes' bounding boxes, which restrict the scope from which candidate samples are selected, by up to 87%. This improves the sampling efficiency by up to 95%. WMCL also improves localization accuracy by using estimated position information from sensor neighbors, achieving similar accuracy with less communication and computation compared to other algorithms using similar methods.
The document discusses topology issues in wireless sensor networks. It defines two main categories of topology issues - topology control problems and topology awareness problems. Topology control problems involve maintaining sensor coverage topology and sensor connectivity topology. Approaches for sensor coverage include controlling node density and positioning for static, mobile, and hybrid sensor networks. Sensor connectivity approaches use power control and power management mechanisms. Topology awareness problems involve geographic routing to efficiently route packets based on node locations and addressing sensor holes that can disrupt routing. The document provides a taxonomy of topology issues and surveys various approaches studied in the literature to optimize wireless sensor network topology.
Collaborative Re-Localization Method in Mobile Wireless Sensor Network Based ...CSCJournals
Localization in Mobile Wireless Sensor Networks (WSNs), particularly in areas like surveillance applications, necessitates triggering re-localization in different time periods in order to maintain accurate positioning. Further, the re-localization process should be designed for time and energy efficiency in these resource constrained networks. In this paper, an energy and time efficient algorithm is proposed to determine the optimum number of localized nodes that collaborate in the re-localization process. Four different movement methods (Random Waypoint Pattern, Modified Random Waypoint pattern, Brownian motion and Levy walk) are applied to model node movement. In order to perform re-localization, a server/head/anchor node activates the optimal number of localized nodes in each island/cluster. A Markov Decision Process (MDP) based algorithm is proposed to find the optimal policy to select those nodes in better condition to cooperate in the re-localization process. The simulation shows that the proposed MDP algorithm decreases the energy consumption in the WSN between 0.6% and 32%.
Effective range free localization scheme for wireless sensor networkijmnct
Location aware sensors can be used in many areas such as military and civilian applications. Wireless
Sensor Networks help to identify the accurate location of the event. In this paper a cost effective schema for
localization has been proposed. It uses two beacon nodes to identify the location of unknown nodes. It
also uses flooding and estimating method to accurately identify the location of other nodes. Available area
is divided into zones and beacons are provided for each zone. Beacon nodes are placed in appropriate
locations normally two in a zone to provide location information. Using the two nodes location of unknown
nodes can be calculated accurately.
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
IGeekS Technologies is a company located in Bangalore, India. We have being recognized as a quality provider of hardware and software solutions for the student’s in order carry out their academic Projects. We offer academic projects at various academic levels ranging from graduates to masters (Diploma, BCA, BE, M. Tech, MCA, M. Sc (CS/IT)). As a part of the development training, we offer Projects in Embedded Systems & Software to the Engineering College students in all major disciplines.
Reconstruction of Objects with VSN M.Priscilla - UG Scholar,
B.Nandhini - UG Scholar,
S.Manju - UG Scholar,
S.Shafiqa Shalaysha – UG Scholar,
Christo Ananth - Assistant Professor,
Department of ECE,
Francis Xavier Engineering College, Tirunelveli, India
This document summarizes research on topology control techniques in wireless sensor networks. It first discusses how topology control aims to reduce energy consumption while maintaining network connectivity by regulating nodes' transmission power. It then reviews several existing topology control algorithms proposed in other papers. These algorithms distribute transmission power control to maximize network lifetime. Finally, the document concludes that many topology control algorithms have been developed to achieve energy efficient routing, but implementing them on real-world testbeds poses challenges.
Accurate and Energy-Efficient Range-Free Localization for Mobile Sensor Networksambitlick
The document summarizes an algorithm called WMCL that improves the sampling efficiency and localization accuracy of existing SMC-based localization algorithms for mobile sensor networks. WMCL achieves higher sampling efficiency by further reducing the size of sensor nodes' bounding boxes, which restrict the scope from which candidate samples are selected, by up to 87%. This improves the sampling efficiency by up to 95%. WMCL also improves localization accuracy by using estimated position information from sensor neighbors, achieving similar accuracy with less communication and computation compared to other algorithms using similar methods.
The document discusses topology issues in wireless sensor networks. It defines two main categories of topology issues - topology control problems and topology awareness problems. Topology control problems involve maintaining sensor coverage topology and sensor connectivity topology. Approaches for sensor coverage include controlling node density and positioning for static, mobile, and hybrid sensor networks. Sensor connectivity approaches use power control and power management mechanisms. Topology awareness problems involve geographic routing to efficiently route packets based on node locations and addressing sensor holes that can disrupt routing. The document provides a taxonomy of topology issues and surveys various approaches studied in the literature to optimize wireless sensor network topology.
Collaborative Re-Localization Method in Mobile Wireless Sensor Network Based ...CSCJournals
Localization in Mobile Wireless Sensor Networks (WSNs), particularly in areas like surveillance applications, necessitates triggering re-localization in different time periods in order to maintain accurate positioning. Further, the re-localization process should be designed for time and energy efficiency in these resource constrained networks. In this paper, an energy and time efficient algorithm is proposed to determine the optimum number of localized nodes that collaborate in the re-localization process. Four different movement methods (Random Waypoint Pattern, Modified Random Waypoint pattern, Brownian motion and Levy walk) are applied to model node movement. In order to perform re-localization, a server/head/anchor node activates the optimal number of localized nodes in each island/cluster. A Markov Decision Process (MDP) based algorithm is proposed to find the optimal policy to select those nodes in better condition to cooperate in the re-localization process. The simulation shows that the proposed MDP algorithm decreases the energy consumption in the WSN between 0.6% and 32%.
Effective range free localization scheme for wireless sensor networkijmnct
Location aware sensors can be used in many areas such as military and civilian applications. Wireless
Sensor Networks help to identify the accurate location of the event. In this paper a cost effective schema for
localization has been proposed. It uses two beacon nodes to identify the location of unknown nodes. It
also uses flooding and estimating method to accurately identify the location of other nodes. Available area
is divided into zones and beacons are provided for each zone. Beacon nodes are placed in appropriate
locations normally two in a zone to provide location information. Using the two nodes location of unknown
nodes can be calculated accurately.
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
IGeekS Technologies is a company located in Bangalore, India. We have being recognized as a quality provider of hardware and software solutions for the student’s in order carry out their academic Projects. We offer academic projects at various academic levels ranging from graduates to masters (Diploma, BCA, BE, M. Tech, MCA, M. Sc (CS/IT)). As a part of the development training, we offer Projects in Embedded Systems & Software to the Engineering College students in all major disciplines.
Reconstruction of Objects with VSN M.Priscilla - UG Scholar,
B.Nandhini - UG Scholar,
S.Manju - UG Scholar,
S.Shafiqa Shalaysha – UG Scholar,
Christo Ananth - Assistant Professor,
Department of ECE,
Francis Xavier Engineering College, Tirunelveli, India
This document summarizes research on topology control techniques in wireless sensor networks. It first discusses how topology control aims to reduce energy consumption while maintaining network connectivity by regulating nodes' transmission power. It then reviews several existing topology control algorithms proposed in other papers. These algorithms distribute transmission power control to maximize network lifetime. Finally, the document concludes that many topology control algorithms have been developed to achieve energy efficient routing, but implementing them on real-world testbeds poses challenges.
AN OPTIMIZED WEIGHT BASED CLUSTERING ALGORITHM IN HETEROGENEOUS WIRELESS SENS...cscpconf
The last few years have seen an increased interest in the potential use of wireless sensor networks (WSNs) in various fields like disastermanagementbattle field surveillance, and border security surveillance. In such applications, a large number of sensor nodes are deployed, which are often unattended and work autonomously. The process of dividing the network into interconnected substructures is called clustering and the interconnected substructures are called clusters. The cluster head (CH) of each cluster act as a coordinator within the substructure. Each CH acts as a temporary base station within its zone or cluster. It also communicates with other CHs. Clustering is a key technique used to extend the lifetime of a sensor network by reducing energy consumption. It can also increase network scalability. Researchers in all fields of wireless sensor network believe that nodes are homogeneous, but
some nodes may be of different characteristics to prolong the lifetime of a WSN and its reliability. We have proposed an algorithm for better cluster head selection based on weights for different parameter that influence on energy consumption which includes distance from base station as a new parameter to reduce number of transmissions and reduce energy consumption by sensor nodes. Finally proposed algorithm compared with the WCA, IWCA algorithm in terms of number of clusters and energy consumption.
LOCALIZATION ALGORITHM USING VARYING SPEED MOBILE SINK FOR WIRELESS SENSOR NE...ijasuc
Localization of sensor nodes is important in many aspects in wireless sensor networks. The known
location of sensor node helps in determining the event of interest. A mobile sink is introduced to track the
event driven sensor nodes in the path of the event, thus conserving energy and time. We present a novel
range based localization algorithm which helps the mobile sink to compute the location of the sensor
nodes efficiently. The data transfer from the mobile sink and the sensor nodes is used to estimate the
sensor location. The sensor nodes do not need to spend energy on neighbouring interaction for
localization. The localization mechanism has been implemented in TOSSIM. The simulation results show
that our scheme performed better than other range-based schemes.
An Efficient Approach for Multi-Target Tracking in Sensor Networks using Ant ...ijsrd.com
This document proposes an approach for multi-target tracking in wireless sensor networks using ant colony optimization. The approach uses both static and mobile sensor nodes. Mobile nodes are used to optimize target tracking by moving to locations that improve tracking quality, while minimizing energy consumption. Static nodes ensure full network coverage. Target positions are estimated and predicted, then mobile nodes are assigned new locations to improve tracking using the ant colony optimization algorithm. Experimental results show the approach performs better than alternatives without target prediction, reducing the minimum distance traveled by mobile nodes.
This document presents an implementation of an ant colony optimization adaptive network-on-chip routing framework using a network information region. The proposed method combines backward ant mechanism with a network information region framework to improve network performance, area efficiency, and reduce congestion. Simulation results show that updating routing tables is faster with the proposed method, leading to improved network performance and area efficiency while reducing congestion compared to other approaches.
The Expansion of 3D wireless sensor network Bumps localizationIJERA Editor
Bump localization of wireless sensor network is a hot topic, but present algorithms of 3D wireless sensor node localization arenot accurate enough. In this paper, the DR-MDS algorithm is proposed, DR-MDS algorithm mainly calibrates the coordinatesof nodes and the ranging of nodes based on multidimensional scaling, it calculates the distance between any nodes exactlyaccording to the hexahedral measurement, introducing a modification factor to calibrate the measuring distance by ReceivedSignal Strength Indicator (RSSI). Results of simulation show that DR-MDS algorithm has significant improvement inlocalization accuracy compare with MDS-MAP algorithm.
This document summarizes several energy-efficient routing protocols for wireless sensor networks. It begins by introducing the basic components and architecture of wireless sensor networks. It then categorizes routing protocols based on network structure (flat, hierarchical, location-based) and operation (multipath, query-based, etc.). The majority of the document focuses on reviewing hierarchical protocols, including LEACH, PEGASIS, Hierarchical PEGASIS, and HEED. It provides brief overviews of how these protocols work to reduce energy consumption and extend network lifetime through clustering and data aggregation approaches.
Novel Position Estimation using Differential Timing Information for Asynchron...IJCNCJournal
Positioning techniques have been a common objective since the early development of wireless networks. However, current positioning methods in cellular networks, for instance, are still primarily focused on the use of the Global Navigation Satellite System (GNSS), which has several limitations, like high power drainage and failure in indoor scenarios. This study introduces a novel approach employing standard LTE signaling in order to provide high accuracy positioning estimation. The proposed technique is designed in analogy to the human sound localization system, eliminating the need of having information from three spatially diverse Base Stations (BSs). This is inspired by the perfect human 3D sound localization with two ears. A field study is carried out in a dense urban city to verify the accuracy of the proposed technique, with more than 20 thousand measurement samples collected. The achieved positioning accuracy is meeting the latest Federal Communications Commission (FCC) requirements in the planner dimension.
Iaetsd improving the location of nodes in wireless adIaetsd Iaetsd
The document proposes an Improvised LAL approach to localize nodes in wireless ad hoc and sensor networks. It aims to identify and convert non-localizable nodes to localizable nodes in a single round. The approach has three main modules: 1) Identifying localizable and non-localizable nodes, 2) Analyzing the network structure using a distance graph, and 3) Making adjustments to distinctively convert non-localizable nodes. Additional techniques like add-heuristic and geographical routing are used. The approach decomposes the distance graph and manages components as a tree to efficiently make adjustments along paths from roots to leaves. This allows all nodes to be localized using standard localization algorithms.
Distributed MAC Protocol for Cognitive Radio Networks: Design, Analysis, and ...Polytechnique Montreal
In this paper, we investigate the joint optimal sensing and distributed Medium Access Control (MAC) protocol design problem for cognitive radio (CR) networks. We consider both scenarios with single and multiple channels. For each scenario, we design a synchronized MAC protocol for dynamic spectrum sharing among multiple secondary users (SUs), which incorporates spectrum sensing for protecting active primary users (PUs). We perform saturation throughput analysis for the corresponding proposed MAC protocols that explicitly capture the spectrum-sensing performance. Then, we find their optimal configuration by formulating throughput maximization problems subject to detection probability constraints for PUs. In particular, the optimal solution of the optimization problem returns the required sensing time for PUs' protection and optimal contention window to maximize the total throughput of the secondary network. Finally, numerical results are presented to illustrate developed theoretical findings in this paper and significant performance gains of the optimal sensing and protocol configuration.
Optimization of Cognitive Radio spectrum and
1. To optimise maximum throughput and SNIR of secondary user’s w.r.t Primary user’s.
2. To calculate throughput w.r.t no of slots by varying time slots and channel bandwidth.
3. To study the performance characteristics achieved through Greedy Algorithm and Optimal algorithm.
ADAPTIVE SENSOR SENSING RANGE TO MAXIMISE LIFETIME OF WIRELESS SENSOR NETWORK IJCNCJournal
Wireless Sensor Network (WSN) is commonly used to collect information from a remote area and one of the most important challenges associated with WSN is to monitor all targets in a given area while maximizing network lifetime. In wireless communication, energy consumption is proportional to the breadth of sensing range and path loss exponent. Hence, the energy consumption of communication can be minimized by varying the sensing range and decreasing the number of messages being sent. Sensing energy can be optimized by reducing the repeated coverage target. In this paper, an Adaptive Sensor Sensing Range (ASSR) technique is proposed to maximize the WSN Lifetime. This work considers a sensor network with an adaptive sensing range that are randomly deployed in the monitoring area. The sensor is adaptive in nature and can be modified in order to save power while achieving maximum time of monitoring to increase the lifetime of WSN network. The objective of ASSR is to find the best sensing range for each sensor to cover all targets in the network, which yields maximize the time of monitoring of all targets and eliminating double sensing for the same target. Experiments were conducted using an NS3 simulator to verify our proposed technique. Results show that ASSR is capable to improve the network lifetime by 20% as compared to other recent techniques in the case of a small network while achieving an 8% improvement for the case of a large networks.
Ca mwsn clustering algorithm for mobile wireless senor network [graphhoc
This paper proposes a centralized algorithm for cluster-head-selection in a mobile wireless sensor network.
Before execution of algorithm in each round, Base station runs centralized localization algorithm whereby
sensors update their locations to base station and accordingly Base station performs dynamic clustering.
Afterwards Base station runs CA-MWSN for cluster-head-selection. The proposed algorithm uses three
fuzzy inputs Residual energy, Expected Residual Energy and Mobility to find Chance of nodes to be elected
as Cluster-head. The node with highest Chance is declared as a Cluster-head for that particular cluster.
Dynamic clustering provides uniform and significant distribution of energy in a non-uniform distribution of
sensors. CA-MWSN guarantees completion of the round.
GPS-less Localization Protocol for Underwater Acoustic NetworksCSCJournals
The problem of underwater positioning is increasingly crucial due to the emerging importance of sub-sea activities. Knowledge of node location is essential for many applications for which sensor networks can be used. At the surface, positioning problems have been resolved by the extended use of GPS, which is straightforward and effective. Unfortunately, using GPS in the sub-sea environment is impossible and positioning requires the use of special systems. One of the major challenges in the underwater acoustic networks (UANs) area of research is the development of a networking protocol that can cope with the management of a dynamic sub-sea network. We propose a scheme to perform node discovery, using only one seed node (primary seed) in a known position. The discovery protocol can be divided into two parts: First, building up the relative co-ordinate system. Second, involving more remote nodes becoming seed nodes for further discoveries. Four different algorithms have been investigated; (i) Farthest/Farthest Algorithm, (ii) Farthest/Nearest Algorithm, (iii) Nearest/Farthest Algorithm and (iv) Nearest/Nearest Algorithm. We investigated the performances of random and fixed (grid) network topologies. Different locations of primary seed node were exercised and statistics for node discovery will be reported.
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.
This document proposes and analyzes several algorithms for blind spectrum sensing of OFDM signals in cognitive radio systems. It first shows that the existing cyclic prefix correlation coefficient (CPCC)-based detection algorithm is a special case of the constrained generalized likelihood ratio test (GLRT) in the absence of multipath. It then develops a new multipath-based constrained GLRT (MP-based C-GLRT) algorithm that exploits multipath correlation and outperforms CPCC-based detection in multipath environments. Combining CPCC- and MP-based C-GLRT algorithms provides further performance improvement. The document also develops a GLRT-based detection algorithm for unsynchronized OFDM signals that achieves near-synchron
Current issue- International Journal of Advanced Smart Sensor Network Systems...ijassn
With the availability of low cost, short range sensor technology along with advances in wireless networking, sensor networks has become a hot topic of discussion. The International Journal of Advanced Smart Sensor Network Systems is an open access peer-reviewed journal which focuses on applied research and applications of sensor networks. While sensor networks provide ample opportunities to provide various services, its effective deployment in large scale is still challenging due to various factors. This journal provides a forum that impacts the development of high performance computing solutions to problems arising due to the complexities of sensor network systems. It also acts as a path to exchange novel ideas about impacts of sensor networks research.
Location predictionin cellular network using neural networkIAEME Publication
1. The document discusses using neural networks for location prediction in cellular networks to improve location management and reduce costs.
2. It proposes using a backpropagation multilayer neural network trained on subscriber movement patterns to predict a subscriber's new location instead of traditional location management schemes.
3. The results show over 69% correct prediction for the random walk mobility pattern, which can help reduce location management costs by knowing a subscriber's location without paging all cells.
This document summarizes localization algorithms in wireless sensor networks. It discusses how node localization is an important challenge for wireless sensor networks. It reviews different approaches to node localization, including centralized and distributed algorithms. Centralized algorithms migrate data to a central station, while distributed algorithms perform computations locally. Specific algorithms discussed include MDS-MAP, simulated annealing approaches, beacon-based, relaxation-based, and coordinate system stitching approaches. The document also discusses hybrid localization techniques and future challenges in improving localization.
This document summarizes and compares various routing protocols for wireless sensor networks. It categorizes routing protocols into three types: data-centric, hierarchical, and location-based. For each type, it describes some representative protocols, outlines their advantages and disadvantages, and discusses their application domains. The document concludes that routing in wireless sensor networks faces challenges due to constraints like limited energy, bandwidth and memory in sensor nodes, and more research is still needed to develop efficient and adaptive routing techniques.
Mobility and Propagation Models in Multi-hop Cognitive Radio Networksszhb
This document discusses how mobility affects the performance of multi-hop cognitive radio networks under different propagation models. It summarizes the results of simulations run with the Network Simulator 2 (NS2) that tested free space, two-ray ground, and shadowing propagation models carrying MPEG4 video traffic over a cognitive radio network with mobile nodes. The key findings are: 1) Free space propagation provided the best throughput and lowest loss ratio compared to two-ray ground and shadowing models; 2) All propagation models performed significantly worse with mobile nodes compared to stationary nodes; 3) Mobility reduced average performance by disrupting signal strength and increasing noise as nodes moved.
The Key Metric forEvaluation Localizationin Wireless Sensor Networks via Dist...CSEIJJournal
Wireless sensor network localization is an importantarea that attracted significant research interest..
Hence, localization schemes for wireless sensorAlthough mobility would appear to make localization more
difficult, in this paper We present a new method bywhich a sensor node can determine its location by
listening to wireless transmissions from three or more fixed beacon nodes and argue that it can exploit
mobility to improve the accuracy and precision of localization. Our approach does not require additional
hardware on the nodes and works even when the movement of seeds and nodes is uncontrollable. The
proposed method is based on aDistance/ Angle- Estimation technique that does not increase the complexity
or cost of construction of the localization sensor nodes. It determines how the available information will be
manipulated to enable all of the nodes of the WSN to estimate their positions. It is a distributed and usually
multi-hop algorithm
This document discusses the minimum cost localization problem in wireless sensor networks. The problem aims to localize all sensors in a network using the minimum number or total cost of anchor nodes given distance measurements between nodes. Existing localization methods try to localize as many nodes as possible without guaranteeing all can be localized and assume enough anchor nodes are available. The proposed system detects wheel structures to identify more localizable nodes than simple trilateration, but the document notes there is a counter-example where nodes in a wheel structure cannot be uniquely localized due to a possible flip.
AN OPTIMIZED WEIGHT BASED CLUSTERING ALGORITHM IN HETEROGENEOUS WIRELESS SENS...cscpconf
The last few years have seen an increased interest in the potential use of wireless sensor networks (WSNs) in various fields like disastermanagementbattle field surveillance, and border security surveillance. In such applications, a large number of sensor nodes are deployed, which are often unattended and work autonomously. The process of dividing the network into interconnected substructures is called clustering and the interconnected substructures are called clusters. The cluster head (CH) of each cluster act as a coordinator within the substructure. Each CH acts as a temporary base station within its zone or cluster. It also communicates with other CHs. Clustering is a key technique used to extend the lifetime of a sensor network by reducing energy consumption. It can also increase network scalability. Researchers in all fields of wireless sensor network believe that nodes are homogeneous, but
some nodes may be of different characteristics to prolong the lifetime of a WSN and its reliability. We have proposed an algorithm for better cluster head selection based on weights for different parameter that influence on energy consumption which includes distance from base station as a new parameter to reduce number of transmissions and reduce energy consumption by sensor nodes. Finally proposed algorithm compared with the WCA, IWCA algorithm in terms of number of clusters and energy consumption.
LOCALIZATION ALGORITHM USING VARYING SPEED MOBILE SINK FOR WIRELESS SENSOR NE...ijasuc
Localization of sensor nodes is important in many aspects in wireless sensor networks. The known
location of sensor node helps in determining the event of interest. A mobile sink is introduced to track the
event driven sensor nodes in the path of the event, thus conserving energy and time. We present a novel
range based localization algorithm which helps the mobile sink to compute the location of the sensor
nodes efficiently. The data transfer from the mobile sink and the sensor nodes is used to estimate the
sensor location. The sensor nodes do not need to spend energy on neighbouring interaction for
localization. The localization mechanism has been implemented in TOSSIM. The simulation results show
that our scheme performed better than other range-based schemes.
An Efficient Approach for Multi-Target Tracking in Sensor Networks using Ant ...ijsrd.com
This document proposes an approach for multi-target tracking in wireless sensor networks using ant colony optimization. The approach uses both static and mobile sensor nodes. Mobile nodes are used to optimize target tracking by moving to locations that improve tracking quality, while minimizing energy consumption. Static nodes ensure full network coverage. Target positions are estimated and predicted, then mobile nodes are assigned new locations to improve tracking using the ant colony optimization algorithm. Experimental results show the approach performs better than alternatives without target prediction, reducing the minimum distance traveled by mobile nodes.
This document presents an implementation of an ant colony optimization adaptive network-on-chip routing framework using a network information region. The proposed method combines backward ant mechanism with a network information region framework to improve network performance, area efficiency, and reduce congestion. Simulation results show that updating routing tables is faster with the proposed method, leading to improved network performance and area efficiency while reducing congestion compared to other approaches.
The Expansion of 3D wireless sensor network Bumps localizationIJERA Editor
Bump localization of wireless sensor network is a hot topic, but present algorithms of 3D wireless sensor node localization arenot accurate enough. In this paper, the DR-MDS algorithm is proposed, DR-MDS algorithm mainly calibrates the coordinatesof nodes and the ranging of nodes based on multidimensional scaling, it calculates the distance between any nodes exactlyaccording to the hexahedral measurement, introducing a modification factor to calibrate the measuring distance by ReceivedSignal Strength Indicator (RSSI). Results of simulation show that DR-MDS algorithm has significant improvement inlocalization accuracy compare with MDS-MAP algorithm.
This document summarizes several energy-efficient routing protocols for wireless sensor networks. It begins by introducing the basic components and architecture of wireless sensor networks. It then categorizes routing protocols based on network structure (flat, hierarchical, location-based) and operation (multipath, query-based, etc.). The majority of the document focuses on reviewing hierarchical protocols, including LEACH, PEGASIS, Hierarchical PEGASIS, and HEED. It provides brief overviews of how these protocols work to reduce energy consumption and extend network lifetime through clustering and data aggregation approaches.
Novel Position Estimation using Differential Timing Information for Asynchron...IJCNCJournal
Positioning techniques have been a common objective since the early development of wireless networks. However, current positioning methods in cellular networks, for instance, are still primarily focused on the use of the Global Navigation Satellite System (GNSS), which has several limitations, like high power drainage and failure in indoor scenarios. This study introduces a novel approach employing standard LTE signaling in order to provide high accuracy positioning estimation. The proposed technique is designed in analogy to the human sound localization system, eliminating the need of having information from three spatially diverse Base Stations (BSs). This is inspired by the perfect human 3D sound localization with two ears. A field study is carried out in a dense urban city to verify the accuracy of the proposed technique, with more than 20 thousand measurement samples collected. The achieved positioning accuracy is meeting the latest Federal Communications Commission (FCC) requirements in the planner dimension.
Iaetsd improving the location of nodes in wireless adIaetsd Iaetsd
The document proposes an Improvised LAL approach to localize nodes in wireless ad hoc and sensor networks. It aims to identify and convert non-localizable nodes to localizable nodes in a single round. The approach has three main modules: 1) Identifying localizable and non-localizable nodes, 2) Analyzing the network structure using a distance graph, and 3) Making adjustments to distinctively convert non-localizable nodes. Additional techniques like add-heuristic and geographical routing are used. The approach decomposes the distance graph and manages components as a tree to efficiently make adjustments along paths from roots to leaves. This allows all nodes to be localized using standard localization algorithms.
Distributed MAC Protocol for Cognitive Radio Networks: Design, Analysis, and ...Polytechnique Montreal
In this paper, we investigate the joint optimal sensing and distributed Medium Access Control (MAC) protocol design problem for cognitive radio (CR) networks. We consider both scenarios with single and multiple channels. For each scenario, we design a synchronized MAC protocol for dynamic spectrum sharing among multiple secondary users (SUs), which incorporates spectrum sensing for protecting active primary users (PUs). We perform saturation throughput analysis for the corresponding proposed MAC protocols that explicitly capture the spectrum-sensing performance. Then, we find their optimal configuration by formulating throughput maximization problems subject to detection probability constraints for PUs. In particular, the optimal solution of the optimization problem returns the required sensing time for PUs' protection and optimal contention window to maximize the total throughput of the secondary network. Finally, numerical results are presented to illustrate developed theoretical findings in this paper and significant performance gains of the optimal sensing and protocol configuration.
Optimization of Cognitive Radio spectrum and
1. To optimise maximum throughput and SNIR of secondary user’s w.r.t Primary user’s.
2. To calculate throughput w.r.t no of slots by varying time slots and channel bandwidth.
3. To study the performance characteristics achieved through Greedy Algorithm and Optimal algorithm.
ADAPTIVE SENSOR SENSING RANGE TO MAXIMISE LIFETIME OF WIRELESS SENSOR NETWORK IJCNCJournal
Wireless Sensor Network (WSN) is commonly used to collect information from a remote area and one of the most important challenges associated with WSN is to monitor all targets in a given area while maximizing network lifetime. In wireless communication, energy consumption is proportional to the breadth of sensing range and path loss exponent. Hence, the energy consumption of communication can be minimized by varying the sensing range and decreasing the number of messages being sent. Sensing energy can be optimized by reducing the repeated coverage target. In this paper, an Adaptive Sensor Sensing Range (ASSR) technique is proposed to maximize the WSN Lifetime. This work considers a sensor network with an adaptive sensing range that are randomly deployed in the monitoring area. The sensor is adaptive in nature and can be modified in order to save power while achieving maximum time of monitoring to increase the lifetime of WSN network. The objective of ASSR is to find the best sensing range for each sensor to cover all targets in the network, which yields maximize the time of monitoring of all targets and eliminating double sensing for the same target. Experiments were conducted using an NS3 simulator to verify our proposed technique. Results show that ASSR is capable to improve the network lifetime by 20% as compared to other recent techniques in the case of a small network while achieving an 8% improvement for the case of a large networks.
Ca mwsn clustering algorithm for mobile wireless senor network [graphhoc
This paper proposes a centralized algorithm for cluster-head-selection in a mobile wireless sensor network.
Before execution of algorithm in each round, Base station runs centralized localization algorithm whereby
sensors update their locations to base station and accordingly Base station performs dynamic clustering.
Afterwards Base station runs CA-MWSN for cluster-head-selection. The proposed algorithm uses three
fuzzy inputs Residual energy, Expected Residual Energy and Mobility to find Chance of nodes to be elected
as Cluster-head. The node with highest Chance is declared as a Cluster-head for that particular cluster.
Dynamic clustering provides uniform and significant distribution of energy in a non-uniform distribution of
sensors. CA-MWSN guarantees completion of the round.
GPS-less Localization Protocol for Underwater Acoustic NetworksCSCJournals
The problem of underwater positioning is increasingly crucial due to the emerging importance of sub-sea activities. Knowledge of node location is essential for many applications for which sensor networks can be used. At the surface, positioning problems have been resolved by the extended use of GPS, which is straightforward and effective. Unfortunately, using GPS in the sub-sea environment is impossible and positioning requires the use of special systems. One of the major challenges in the underwater acoustic networks (UANs) area of research is the development of a networking protocol that can cope with the management of a dynamic sub-sea network. We propose a scheme to perform node discovery, using only one seed node (primary seed) in a known position. The discovery protocol can be divided into two parts: First, building up the relative co-ordinate system. Second, involving more remote nodes becoming seed nodes for further discoveries. Four different algorithms have been investigated; (i) Farthest/Farthest Algorithm, (ii) Farthest/Nearest Algorithm, (iii) Nearest/Farthest Algorithm and (iv) Nearest/Nearest Algorithm. We investigated the performances of random and fixed (grid) network topologies. Different locations of primary seed node were exercised and statistics for node discovery will be reported.
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.
This document proposes and analyzes several algorithms for blind spectrum sensing of OFDM signals in cognitive radio systems. It first shows that the existing cyclic prefix correlation coefficient (CPCC)-based detection algorithm is a special case of the constrained generalized likelihood ratio test (GLRT) in the absence of multipath. It then develops a new multipath-based constrained GLRT (MP-based C-GLRT) algorithm that exploits multipath correlation and outperforms CPCC-based detection in multipath environments. Combining CPCC- and MP-based C-GLRT algorithms provides further performance improvement. The document also develops a GLRT-based detection algorithm for unsynchronized OFDM signals that achieves near-synchron
Current issue- International Journal of Advanced Smart Sensor Network Systems...ijassn
With the availability of low cost, short range sensor technology along with advances in wireless networking, sensor networks has become a hot topic of discussion. The International Journal of Advanced Smart Sensor Network Systems is an open access peer-reviewed journal which focuses on applied research and applications of sensor networks. While sensor networks provide ample opportunities to provide various services, its effective deployment in large scale is still challenging due to various factors. This journal provides a forum that impacts the development of high performance computing solutions to problems arising due to the complexities of sensor network systems. It also acts as a path to exchange novel ideas about impacts of sensor networks research.
Location predictionin cellular network using neural networkIAEME Publication
1. The document discusses using neural networks for location prediction in cellular networks to improve location management and reduce costs.
2. It proposes using a backpropagation multilayer neural network trained on subscriber movement patterns to predict a subscriber's new location instead of traditional location management schemes.
3. The results show over 69% correct prediction for the random walk mobility pattern, which can help reduce location management costs by knowing a subscriber's location without paging all cells.
This document summarizes localization algorithms in wireless sensor networks. It discusses how node localization is an important challenge for wireless sensor networks. It reviews different approaches to node localization, including centralized and distributed algorithms. Centralized algorithms migrate data to a central station, while distributed algorithms perform computations locally. Specific algorithms discussed include MDS-MAP, simulated annealing approaches, beacon-based, relaxation-based, and coordinate system stitching approaches. The document also discusses hybrid localization techniques and future challenges in improving localization.
This document summarizes and compares various routing protocols for wireless sensor networks. It categorizes routing protocols into three types: data-centric, hierarchical, and location-based. For each type, it describes some representative protocols, outlines their advantages and disadvantages, and discusses their application domains. The document concludes that routing in wireless sensor networks faces challenges due to constraints like limited energy, bandwidth and memory in sensor nodes, and more research is still needed to develop efficient and adaptive routing techniques.
Mobility and Propagation Models in Multi-hop Cognitive Radio Networksszhb
This document discusses how mobility affects the performance of multi-hop cognitive radio networks under different propagation models. It summarizes the results of simulations run with the Network Simulator 2 (NS2) that tested free space, two-ray ground, and shadowing propagation models carrying MPEG4 video traffic over a cognitive radio network with mobile nodes. The key findings are: 1) Free space propagation provided the best throughput and lowest loss ratio compared to two-ray ground and shadowing models; 2) All propagation models performed significantly worse with mobile nodes compared to stationary nodes; 3) Mobility reduced average performance by disrupting signal strength and increasing noise as nodes moved.
The Key Metric forEvaluation Localizationin Wireless Sensor Networks via Dist...CSEIJJournal
Wireless sensor network localization is an importantarea that attracted significant research interest..
Hence, localization schemes for wireless sensorAlthough mobility would appear to make localization more
difficult, in this paper We present a new method bywhich a sensor node can determine its location by
listening to wireless transmissions from three or more fixed beacon nodes and argue that it can exploit
mobility to improve the accuracy and precision of localization. Our approach does not require additional
hardware on the nodes and works even when the movement of seeds and nodes is uncontrollable. The
proposed method is based on aDistance/ Angle- Estimation technique that does not increase the complexity
or cost of construction of the localization sensor nodes. It determines how the available information will be
manipulated to enable all of the nodes of the WSN to estimate their positions. It is a distributed and usually
multi-hop algorithm
This document discusses the minimum cost localization problem in wireless sensor networks. The problem aims to localize all sensors in a network using the minimum number or total cost of anchor nodes given distance measurements between nodes. Existing localization methods try to localize as many nodes as possible without guaranteeing all can be localized and assume enough anchor nodes are available. The proposed system detects wheel structures to identify more localizable nodes than simple trilateration, but the document notes there is a counter-example where nodes in a wheel structure cannot be uniquely localized due to a possible flip.
NOVEL POSITION ESTIMATION USING DIFFERENTIAL TIMING INFORMATION FOR ASYNCHRON...IJCNCJournal
Positioning techniques have been a common objective since the early development of wireless networks. However, current positioning methods in cellular networks, for instance, are still primarily focused on the use of the Global Navigation Satellite System (GNSS), which has several limitations, like high power drainage and failure in indoor scenarios. This study introduces a novel approach employing standard LTE signaling in order to provide high accuracy positioning estimation. The proposed technique is designed in analogy to the human sound localization system, eliminating the need of having information from three spatially diverse Base Stations (BSs). This is inspired by the perfect human 3D sound localization with two ears. A field study is carried out in a dense urban city to verify the accuracy of the proposed technique, with more than 20 thousand measurement samples collected. The achieved positioning accuracy is meeting the latest Federal Communications Commission (FCC) requirements in the planner dimension.
CA-MWSN: CLUSTERING ALGORITHM FOR MOBILE WIRELESS SENOR NETWORKFransiskeran
This paper proposes a centralized clustering algorithm (CA-MWSN) for selecting cluster heads in mobile wireless sensor networks. The base station first performs dynamic clustering based on sensor location updates. It then runs CA-MWSN, which uses fuzzy logic to calculate each node's "chance" of being elected cluster head based on residual energy, expected residual energy, and mobility. The node with the highest chance is selected as the cluster head for that round. Dynamic clustering and CA-MWSN aim to provide uniform energy distribution in mobile sensor networks where nodes may become densely or sparsely distributed.
Localization of wireless sensor networkIRJET Journal
This document summarizes a range-free localization algorithm for wireless sensor networks called TSBMCL (Temporary-Seed Based Monte Carlo Localization) that uses the Monte Carlo method. It discusses how TSBMCL works in two main parts: 1) voting for temporary anchor nodes from localized nodes, and 2) using the temporary anchors to aid localization of other nodes. The algorithm is shown to improve localization accuracy over the MCB algorithm. Simulation results demonstrate that TSBMCL reduces localization failure rates and requires fewer sampling particles than standard Monte Carlo localization methods. In conclusion, TSBMCL provides an accurate and efficient range-free localization scheme for mobile wireless sensor networks.
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High uncertainty aware localization and error optimization of mobile nodes fo...IAESIJAI
The localization of mobile sensor nodes in a wireless sensor network (WSN) is a key research area for the speedy development of wireless communication and microelectronics. The localization of mobile sensor nodes massively depends upon the received signal strength (RSS). Recently, the least squared relative error (LSRE) measurements are optimized using traditional semidefinite programming (SDP) and the location of the mobile sensor nodes was determined using the previous localization methods like least squared relative error and semidefinite programming (LSRE-SDP), and approximate nonlinear least squares and semidefinite programming (ANLS-SDP). Therefore, in this work, a novel high uncertainty aware-localization error correction and optimization (HUA-LECO) model is employed to minimize the aforementioned problems regarding the localization of mobile sensor nodes and enhance the performance efficiency of root mean square error (RMSE) results. Here, the position of target mobile sensor nodes is evaluated based on the gathered measurements while discarding faulty data. Here, an iterative weight updation approach is utilized to perform localization based on Monte Carlo simulations. Simulation results show significant improvement in terms of RMSE results in comparison with traditional LSRE-SDP and ANLS-SDP methods under high uncertainty.
The document presents a localization technique for wireless sensor networks that uses trilateration based on received signal strength. It begins with an introduction to wireless sensor networks and localization. It then discusses related work on trilateration localization techniques. The document proposes a methodology that uses signal propagation models to estimate distances between nodes from received signal strength, and then applies trilateration to compute node positions in 3D space using four or more anchor nodes. It presents simulation results showing the mean square error decreases and localization accuracy increases as the number of anchor nodes increases. The technique provides more accurate localization than existing fuzzy logic approaches, with average localization errors less than 0.5 units when using 100 anchor nodes.
This document describes different techniques for determining a mobile device's location, including cell-ID, RxPowerLevels, GPS, A-GPS, AOA, TOA, and OTDOA. It proposes a hybrid algorithm that selects the most appropriate technique based on required accuracy, locality, time needed, and cost. The algorithm is being implemented in the LoVEUS project to provide location-based information to users by combining Cell-ID, GPS, and A-GPS based on an application's accuracy needs and power consumption constraints.
This document summarizes positioning techniques for wireless sensor networks (WSNs) and Internet of Things (IoT) systems. It discusses both range-based and range-free localization methods. Range-based methods use distance or angle measurements between sensor nodes, including received signal strength indication (RSSI), time of arrival (TOA), time difference of arrival (TDOA), and angle of arrival (AOA). Range-free methods depend on node connectivity and do not require specialized hardware. The document reviews several algorithms and techniques for each category.
This document analyzes node localization in wireless sensor networks. It compares three range-based localization algorithms (TOA, AOA, RSSI) based on their standard deviation of localization error under varying network parameters. Through simulations, it finds that the TOA algorithm generally provides the lowest error compared to the other two algorithms. Specifically, it finds that standard deviation decreases with increasing network density and anchor node density, but first decreases and then increases with network size. It concludes that the TOA algorithm provides the best accuracy for localization based on its analysis of parameter effects.
Wide-band spectrum sensing with convolution neural network using spectral cor...IJECEIAES
Recognition of signals is a spectrum sensing challenge requiring simultaneous detection, temporal and spectral localization, and classification. In this approach, we present the convolution neural network (CNN) architecture, a powerful portrayal of the cyclo-stationarity trademark, for remote range detection and sign acknowledgment. Spectral correlation function is used along with CNN. In two scenarios, method-1 and method-2, the suggested approach is used to categorize wireless signals without any previous knowledge. Signals are detected and classified simultaneously in method-1. In method-2, the sensing and classification procedures take place sequentially. In contrast to conventional spectrum sensing techniques, the proposed CNN technique need not bother with a factual judgment process or past information on the signs’ separating qualities. The method beats both conventional sensing methods and signal-classifying deep learning networks when used to analyze real-world, over-the-air data in cellular bands. Despite the implementation’s emphasis on cellular signals, any signal having cyclo-stationary properties may be detected and classified using the provided approach. The proposed model has achieved more than 90% of testing accuracy at 15 dB.
Optimum Sensor Node Localization in Wireless Sensor Networkspaperpublications3
Abstract: Scientists, engineers, and researchers use wireless sensor networks (WSN) for a wide array of applications. Many of these applications rely on knowledge of the precise position of each node. An optimum localization algorithm can be used for determining the position of nodes in a wireless sensor network. This paper provides an overview of different approach of node localization discovery in wireless sensor networks. The overview of the schemes proposed by different scholars for the improvement of localization in wireless sensor networks is also presented. Experiments were performed in a testbed area containing anchor and blind nodes deployed in it to characterize the pathloss exponent and to determine the localization error of the algorithm. Details regarding the implementation of new algorithm are also discussed in this paper.
A SELECTIVE PAGING SCHEME BASED ON ACTIVITY IN CELLULAR MOBILE NETWORKS FOR R...ijwmn
This document presents a selective paging scheme based on activity for location management in cellular networks. An activity-based mobility model is used where mobile terminals move between cells according to daily activity patterns like going to work or school. A simulation is conducted with 49 cells, 100 mobile terminals, and schedules for moving between home, workplaces, colleges and fitness centers. The proposed scheme applies prediction-based selective paging at reporting centers to reduce paging cost without increasing location update cost. Simulation results are analyzed to compare the location management costs of the conventional and proposed schemes.
This document discusses using a learning automata approach to predict target locations in wireless sensor networks to reduce energy consumption and improve tracking accuracy. It proposes a learning automata based method that uses a target's movement history to predict its next location. Related works on target tracking techniques like tree-based, cluster-based, and prediction-based methods are summarized. Learning automata concepts are introduced. Simulation results are said to show the proposed method improves energy efficiency, reduces missed targets, and decreases transmitted packets compared to other methods.
This document presents a genetic algorithm approach for solving the Minimum Cost Localization Problem (MCLP) in wireless sensor networks. The MCLP aims to determine the minimum number of beacon nodes needed to localize all other nodes in the network.
The document begins with an introduction to localization in wireless sensor networks and discusses previous work on the MCLP, including greedy algorithms. It then formally defines the MCLP. The document proposes a genetic algorithm to improve upon previous greedy approaches. Simulation results show the genetic algorithm approach improves localization performance over the best existing greedy algorithm by up to 50% in some cases.
This document summarizes a research paper that proposes a new cuboid-based localization algorithm for wireless sensor networks. The algorithm aims to minimize localization error and decrease energy consumption by shifting complexity to anchor nodes that have GPS. It works by having anchor nodes broadcast their locations to form triangles around unknown nodes. Distances from unknown nodes to anchors are estimated using RSSI. The algorithm is simulated in a 3D space and shows decreasing localization error as the number of anchor nodes increases, achieving an error of under 1.6m. The paper aims to improve over existing localization methods that have issues like multipath interference affecting RSSI-based techniques.
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PERFORMANCE EVALUATION OF ERGR-EMHC ROUTING PROTOCOL USING LSWTS AND 3DUL LOC...IJCNCJournal
This paper studies the impact of different localization schemes on the performance of location-based
routing for UWSNs. Particularly, LSWTS and 3DUL localization schemes available in the literature are
used to study their effects on the performance of the ERGR-EMHC routing protocol. First, we assess the
performance of two localization schemes by measuring their localization coverage, accuracy, control
packets overhead, and required localization time. We then study the performance of the ERGR-EMHC
protocol using location information provided by the selected localization schemes. The results are
compared with the performance of the routing protocol when using exact nodes’ locations. The obtained
results show that LSWTS outperforms 3DUL in terms of localization accuracy by 83% and localization
overhead by 70%. In addition, the results indicate that the localization error has a significant impact on
the performance of the routing protocol. For instance, ERGR-EMHC with LSWTS is better in delivering
data packets by an average of 175% compared to 3DUL
Performance Evaluation of ERGR-EMHC Routing Protocol using LSWTS and 3DUL Loc...IJCNCJournal
This paper studies the impact of different localization schemes on the performance of location-based routing for UWSNs. Particularly, LSWTS and 3DUL localization schemes available in the literature are used to study their effects on the performance of the ERGR-EMHC routing protocol. First, we assess the performance of two localization schemes by measuring their localization coverage, accuracy, control packets overhead, and required localization time. We then study the performance of the ERGR-EMHC protocol using location information provided by the selected localization schemes. The results are compared with the performance of the routing protocol when using exact nodes’ locations. The obtained results show that LSWTS outperforms 3DUL in terms of localization accuracy by 83% and localization overhead by 70%. In addition, the results indicate that the localization error has a significant impact on the performance of the routing protocol. For instance, ERGR-EMHC with LSWTS is better in delivering data packets by an average of 175% compared to 3DUL.
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International Journal of Computational Engineering Research (IJCER)
1. International Journal of Computational Engineering Research||Vol, 03||Issue, 7||
||Issn 2250-3005 || ||July||2013|| Page 36
An Enhanced Localization Scheme for Mobile Sensor Networks
Dr. Siva Agora Sakthivel Murugan1
, K.Karthikayan2
, Natraj.N.A3
,
Rathish.C.R4
Associate Professor1
, Assistant Professor2,3,4
1,2
DepartmentofEEE, 3
DepartmentofECE,
Sree Sakthi Engineering College1,2
, United Institute of Technology3,4
I. INTRODUCTION
Wireless sensor networks (WSNs) have been used in many fields, including environmental and habitat
monitoring, precision agriculture, animal tracking, and disaster rescue. In many applications, it is essential for
nodes to know their positions. For example, data should be labelled with the positions where they are collected
to help the scientists perform corresponding analysis. Position information of nodes are also necessary in many
network protocols, e.g., clustering and routing which depend on the geographical information of nodes. The
procedure through which the nodes obtain their positions is called localization. In localization, the nodes in a
sensor network can be categorized into two types: beacon nodes which are aware of their positions and sensor
nodes which need to determine their positions using a localization algorithm.
A straightforward method for localization in WSNs is to use existing localization techniques, e.g.,
attaching a Global Positioning System (GPS) receiver on every sensor node. However, as the scale of sensor
networks becomes larger and larger, these methods become infeasible because of their high cost or
inconvenience. In some recently emerging applications such as animal monitoring and tracking sensor nodes
may move after deployment. These nodes form mobile sensor networks in contrast to traditional static sensor
networks in which sensor nodes remain stationary after deployment. The motion of sensor nodes makes most
existing localization algorithms designed for static sensor networks inapplicable to mobile sensor networks.
There are some localization algorithms specially designed for mobile sensor networks, All of them are based on
the Sequential Monte Carlo (SMC) method. This is because the SMC method provides simple simulation-based
approaches in estimating the location. Previous SMC-based localization algorithms either suffer from low
sampling efficiency or require high beacon density to achieve high localization accuracy. The major problem of
most existing SMC-based localization algorithms is that they only rely on increasing beacon density to improve
localization accuracy. However, beacon nodes are usually more expensive than sensor nodes. Because there are
much more sensor nodes than beacon nodes in a sensor network, it will be very beneficial if sensor nodes can be
used to improve the localization accuracy.In this paper, we propose an efficient algorithm which addresses both
aforementioned issues. The algorithm is based on the sequential Monte Carlo Localization (MCL) algorithm
named as Improved MCL (WMCL). IMCL achieves high sampling efficiency and achieves high localization
accuracy even when the beacon density is low using bounding box technique and weight computation
ABSTRACT
Localization in mobile sensor networks is more challenging than in static sensor networks
because mobility increases the uncertainty of nodes positions. The localization algorithms used in the
Mobile sensor networks (MSN)are mainly based on Sequential Monte Carlo (SMC) method. The
existing SMC based localization algorithms commonly rely on increasing beacon density in order to
improve localization accuracy and suffers from low sampling efficiency and also sampling in those
algorithms are static and have high energy consumption. Those algorithms cannot able to localize
sensor nodes in some circumstances.The main reason for that is in some time slots the sensor node
cannot hear any beaconnode. This results in localization failure. The Improved Monte Carlo
Localization (IMCL) algorithm achieves high sampling efficiency, high localization accuracy even in
the case when there is a low beacon density. This can be achieved using bounding box and weight
computation techniques. This algorithm also uses time series forecasting and dynamic sampling
method for solving the problem of localization failure. Simulation results showed that the proposed
method has a better performance in sparse networks in comparison with previous existing method.
KEYWORDS : Mobile Sensor Networks, Localization,Sequential Monte Carlo methods.
2. An Enhanced Localization Scheme For Mobile…
||Issn 2250-3005 || ||July||2013|| Page 37
methods.Despite the above technique haves good localization accuracy, sampling in these
techniquesare static and they have high energy consumption. Also the existingalgorithms are not able to localize
sensor nodes in somecircumstances. The main reason is that in some time slots the node cannot hear any seed
node. TheImproved Monte Carlo Localization (IMCL) algorithm uses forecasting and dynamic sampling
method forlocalization. This method has the ability of nodes localization in those conditions and it is an energy
efficient method.The paper is organized as follows. The Section 2 deals with related work in localization of
Wireless Sensor Networks. Section 3 deals with the proposed Improved Monte Carlo Localization (IMCL)
algorithm. Section 4 is devoted to extensive performance analysis. Section 5 deals with the Conclusion and
future directions.
II. RELATED WORK
Extensive research has been done on localization for wireless networks. A general survey is done
focusing only on localization techniques suitable for ad hoc sensor networks. The approaches taken to achieve
localization in sensor networks differ in their assumptions about the network deployment and the hardware’s
capabilities. Centralized localization techniques depend on sensor nodes transmitting data to a central location,
where computation is performed to determine the location of each node. Doherty, Pister and Ghaoui developed a
centralized technique using convex optimization to estimate positions based only on connectivity constraints
given some nodes with known positions. MDS-MAP technique improves on these results by using a
multidimensional scaling approach, but still requires centralized computation. Requiring central computation
would be infeasible for mobile applications because of the high communication costs and inherent delay, hence
we focus on distributed localization techniques.Distributed localization methods do not require centralized
computation, and rely on each node determining its location with only limited communication with nearby
nodes. These methods can be classified as range-based and range-free. Range-based techniques use distance
estimates or angle estimates in location calculations, while a range-free solution depends only on the contents of
received messages. Range-based approaches have exploited time of arrival, received signal strength, time
difference of arrival of two different signals (TDOA), and angle of arrival (AOA). Though they can reach fine
resolution, either the required hardware is expensive (ultrasound device for TDOA, antenna arrays for AOA) or
the results depend on other unrealistic assumptions about signal propagation (for example, the actual received
signal strengths of radio signals can vary when the surrounding environment changes). Because of the hardware
limitations of sensor devices, range-free localization algorithms are a cost effective alternative to more
expensive range-based approaches.Monte Carlo localization (MCL) method is developed for use in robotics
localization for use in mobile sensor network applications. MCL is a particle filter combined with probabilistic
models of robot perception and motion. It outperforms other proposed localization algorithms in both accuracy
and computational efficiency. The key idea of MCL is to represent the posterior distribution of possible
locations using a set of weighted samples. Each step is divided into a prediction phase and an update phase. In
the prediction phase, the robot makes a movement and the uncertainty of its position increases. In the update
phase, new measurements (such as observations of new landmarks) are incorporated to filter and update data.
The process repeats and the robot continually updates its predicted location.
However, there are substantial differences between robot localization and node localization for sensor
networks. While robot localization locates a robot in a predefined map, localization in sensor networks works in
a free space or unmapped terrain. Second, a robot has relatively good control and probabilistic knowledge of its
movement in a predefined map. A sensor node typically has little or no control of its mobility, and is unaware of
its speed and direction. Third, a robot can obtain precise ranging information from landmarks, but a sensor node
can only learn that it is within radio range. Finally, in robot localization, the individual measurements are
integrated multiplicatively, assuming conditional independence between them, and the weights of samples need
to be normalized after updating. In MCL, due to the constraints in computing and memory power, a filtering
approach is adopted in which each measurement can be considered independently, and the weight of each
sample is either 0 or 1. There are some other variants of MCL, for example, the dual and Mixture MCL,
Multihop-based Monte CarloLocalization (MMCL), and Range-based MCL. The dual and Mixture MCL
improves the localization accuracy of MCL by exchanging the probability functions used in the sampling step
and in the filtering step. It incurs higher computational cost than MCL. MMCL and Range-based MCL use
multihop sensor-beacon distances to improve the localization accuracy and to reduce the number of needed
beacons. Compared with them, Our WMCL algorithm doesn’t use multihop sensor-beacon distances so incurs
much less communication cost and it uses weight computation methods to minimize localization error.
The previous existing methods have two major weak points which are not focused. The first problem is using of
constant number of samples for localization. Second is that all nodes in all time slots cannot be localized. The
proposed methods will overcome those drawbacks and the methods are discussed below.
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III. THE PROPOSED IMCL ALGORITHM
A. Introduction
In IMCL algorithm, the network model is introduced and then the five main parts of IMCL are
described as follows:Bounding-box construction, Dynamic sampling, Time series forecasting, Samples weights
computing, maximum possible localization error computing.
B. Building the Bounding Box
There are two areas involved in bounding box of IMCL: the candidate samples area and the valid
samples area. The candidate samples area is used to draw new candidate samples and the valid samples area is
used to filter out invalid samples. When the candidate samples area is large and the valid samples area is small,
candidate samples drawn in the sampling step have high probability to be filtered out in the filtering step.figure1
shows the construction of bounding box in IMCL algorithm.
Figure1:Building the Bounding-box
In IMCL, the possible locations of a sensor node after move lie in a disk with radius . So the size
of the candidate samples area will increase when increases. On the other hand, when sd increases, the size
of the valid samples areas will decrease. Denote by Vt the total number of candidate samples drawn in the
sampling step in time unit t and define the sampling efficiency in t as,
et = (1)
then in WMCL the sampling efficiency will decrease when or Sd increases, which will cause high
computational cost accordingly. Two-Hop beacon neighbours are also used to reduce the size of the bounding-
box by replacing r with 2r. The candidate samples are chosen from the bounding box.
B. Dynamic sampling method
As previously mentioned, instead of taking a fixed number of samples like 50 for localization, we can
determine this number dynamically based on the size of the sampling area. It is clear that for a large anchor box,
a large number of samples are needed to estimate nodes location accurately. While in the case of small anchor
box, we will focus on a small area. For a small area, small number of samples is needed to accurately estimate
nodes position.
If created anchor box have the coordinates of (Xmin, Ymin) and (Xmax, Ymax), so area size of this box is
determined and we will specify the number of samples based on this area size. Noting to the standard number of
samples that is 50, this number will be used for an anchor box with maximum area size. An anchor box is
maximized when the node hears only a one-hop anchor node. In this case the box size will be equal to a square
of size 2Vmax. So we will consider 50 samples for this box and use equation (5) for other box sizes.
Sample Number=50*((xmax-xmin ) (ymax-ymin))/4Vmax
2
(2)
When the anchor box has the maximum area, numerator and denominator of the fraction in the equation (2) will
be equal and so the number of samples will be equal to 50.
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For the cases when the anchor box size is more than 4Vmax
2
, we consider the number of samples equal
to 50. Example of such cases is when the sensor node hears only one two-hop anchor node.
C. Linear prediction using time series : Linear prediction method is a powerful technique for predicting time
series in a time-varying environment. Thismethod is expressed in equation(6) and is a recursive method
y(t + T) = a1y(t)+a1y(t-T)+ ... +amy(t-(m-I)T) (3)
Estimated value at time t as a linear function of previous values in the times "t-T, t-2T. .. t-mT" has been
produced is obtained. In equation(3) a1, a2, ..., am are the linear prediction coefficients, 'm' is the model degree,
'T' is the sampling time, y(t+T) is the next observation estimation and y(t), y(t-T), ..., y(t-mT) are the present and
past observations. The prediction error which is the difference between the predicted and the real locations
(Equation (4))must be minimized.
Error(%)= { }* 100% (4)
In order to estimate the coefficients of linear prediction model we use the least squares error method and rewrite
equation (3) with considering modelling error in equation (4):
y(t) = a1y(t)+a1y(t-T)+ ... +amy(t-(m-I)T) +e (t) (5)
The error e(t) is generated because of not adopting the linear prediction model to the real value. So to find the
coefficients, a1, a2, …… am in equation(5), we use the sum least squares error and set of linear functions
presented in equation(6)
(6)
Y=ɸ+A+E (7)
Elements in the matrix A are the coefficients which can be found by least squares error method in equation (8):
A=(ɸT
ɸ)-1
ɸT
Y (8)
In equation (8), ɸT
is the transpose of the matrix ɸ and (ɸT
ɸ)-1
is the inverse of matrix. After obtaining
the coefficients a1, a2 ... am, the nodes location in the next time slot predicted using equation (3). If the node do
not localized using WMCL algorithm with dynamic sampling, we will use this predicted location instead. Then
the weights are computed for predicted samples.
D. Weighting the Samples : After a sample candidate is chosen, its weight is computed using 1-hop and 2-
hop (anchor and common) neighbour nodes. Figure2 shows the phenomenon of weight computation.
Figure 2:Weight Computation Method
In WMCL, Ot = S T US. So the weight of a candidate sample is computed as
= p(Ot│lt) = (12)
When s or s T, P(s│lt) can be easily computed
If s , then
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P(s│lt) = [d(lt,s) ≤ r] (13)
If s , then
P(s│lt) = [r < d(lt,s) ≤ 2r] (14)
The weights of samples are computed. The samples with zero weights are rejected and samples with
high weights are taken for error computation.
E. Error Computation
After obtaining N valid samples, a sensor node computes the weighted average of these samples as its
position estimation. Using the position estimation and the bounding- box, a sensor node can compute its ERx
and ERy, as illustrated in Figure3. A more riskily method is to use the smallest rectangle enclosing all of p’s
valid samples to compute ERx and ERy. This method can improve localization accuracy a lot. However, when
using this method the procedure of constructing the bounding-box should be carefully manipulated. In this case
the inequality p causes some inconsistence in the computation.
Figure 3:Computing maximum localization error
For example, it is possible that xmin is larger than xmax and consequently the bounding-box cannot be
built. After p gets (xe,ye) and ERx, ERy, it broadcasts them to its neighbors. Its neighbors will use this
information to compute their position estimation in the next time unit.Algorithm of the proposed method has
been presented in figure (4).
Figure 4: IMCL Algorithm
IV. RESULTS AND DISCUSSION
The simulation is carried out in NS-2 simulator under Linux platform with simulation area of 1000 x
1000m and 150 mobile nodes.The node is randomly placed from there onwards the node mobility occurs in
random direction. The simulation time is 100 seconds.
A. Sampling efficiency
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Figure 5: Samples Vs Beacon nodes
Figure 6: Samples Vs Sensor nodes
The sampling efficiency is a very import metric in SMC-based localization algorithms because higher
sampling means less candidate samples generation and consequently less computational cost. Figure (5) & (6)
shows the number of candidate samples obtained forlocalization in the WMCL and IMCL algorithms with
varying number of beacon and sensor nodes. Thenumber of samples in WMCL algorithm is fixed andeach
unknown node during each time slot uses 50 samples to do localization. But in the IMCL algorithm samples
number isdetermined dynamically. Simulation results show that with using IMCL algorithm, less candidate
samples are chosen thereby obtaining high sampling efficiency.each unknown node uses fewer samples than
other methods.
B. Sampling Attempts
Figure 6: Sampling attempts
Most of computational energy consumption for these algorithms is related to the number of used
samples and the number of sampling attempts to produce acceptable samples. Also the response time depends
on the number of sampling attempts for production of required samples. Figure (6) showssimulation results for
the number of sampling attempts to produce enough valid samples.
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C. Dynamic Sampling
Figure 7: Dynamic sampling
The Dynamic sampling is performed in mobile nodes. Figure (7) shows the number of valid samples
obtained for localization in the WMCL and IMCL algorithms. The number of samples in WMCL algorithm is
fixed and each unknown node during each time slot uses 50 samples to do localization. But in the IMCL
algorithm samples number is determined dynamically. Simulation results show that with using IMCL algorithm,
each unknown node uses fewer samples than WMCL method.
D. Localization Accuracy
Localization accuracy is the most important metric in evaluating localization algorithms. The
localization accuracy is determined from the estimated value of localization error. The localization error is noted
for different time periods. The localization error is also determined by varying number of beacon and sensor
nodes.
Figure 8: Localization errorVs Beacon nodes
Figure 9: Localization error Vs sensor nodes
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Figure(7) & (8) shows the localization error for varying number of beacon and sensor nodes. The graph
shows that the localization error in IMCL algorithm is reduced compared with WMCL algorithm and thereby
having high localization accuracy.
E. Energy backlog
Figure 10: Energy backlog
The dynamic sampling method and TSF method used in IMCL algorithm will reduce the energy
consumed in localization process by minimizing the number of sampling operation. Fig 10shows the amount of
energy consumed in IMCL algorithm is very much reduced than the WMCL algorithm.
V. CONCLUSION
The Improved Monte Carlo Localization (IMCL) algorithm achieved high sampling efficiency, high
localization accuracy even in the case when there is a low beacon density using the bounding box and weight
computation methods. The localization accuracy is improved by using the estimated position information of
sensor nodes. The proposed IMCL algorithm used dynamic sampling based on the size of sampling area to
estimate the sensor nodes position. Also the proposed algorithm uses TSF method to predict sensors position
when the sensor nodes do not hear any anchor nodes. The proposed algorithm is suitable for mobile sensor
networks with low anchor node density. This algorithm has less implementation costs in comparison with
previous method.Simulation results showed that the proposed algorithm providesbetter performance than the
similar method in the sparse sensor networks.
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