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 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.
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.
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.
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.
An Approach of Mobile Wireless Sensor Network for Target Coverage and Network...IRJET Journal
This document discusses using mobile sensors to improve target coverage and network connectivity in wireless sensor networks. It first defines key concepts in mobile wireless sensor networks like coverage, connectivity, and network lifetime. It then reviews existing literature on algorithms for mobile sensor deployment to optimize target coverage and network connectivity with minimum movement. The proposed method aims to deploy mobile sensors with no movement needed to achieve both target coverage and network connectivity. The goal is to minimize energy consumption from sensor movement.
3D Localization Algorithms for Wireless Sensor NetworksIOSR Journals
This document discusses localization algorithms for 3D wireless sensor networks. It begins by explaining that localization in 3D spaces poses unique challenges compared to 2D, as strategies used in 2D do not directly extend to 3D. It then reviews common range-based localization methods like received signal strength and time-based methods, as well as range-free methods like centroid and DV-HOP algorithms. The document aims to address the problem of localization for sensor networks deployed in 3D surfaces.
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.
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 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.
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.
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.
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.
An Approach of Mobile Wireless Sensor Network for Target Coverage and Network...IRJET Journal
This document discusses using mobile sensors to improve target coverage and network connectivity in wireless sensor networks. It first defines key concepts in mobile wireless sensor networks like coverage, connectivity, and network lifetime. It then reviews existing literature on algorithms for mobile sensor deployment to optimize target coverage and network connectivity with minimum movement. The proposed method aims to deploy mobile sensors with no movement needed to achieve both target coverage and network connectivity. The goal is to minimize energy consumption from sensor movement.
3D Localization Algorithms for Wireless Sensor NetworksIOSR Journals
This document discusses localization algorithms for 3D wireless sensor networks. It begins by explaining that localization in 3D spaces poses unique challenges compared to 2D, as strategies used in 2D do not directly extend to 3D. It then reviews common range-based localization methods like received signal strength and time-based methods, as well as range-free methods like centroid and DV-HOP algorithms. The document aims to address the problem of localization for sensor networks deployed in 3D surfaces.
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.
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.
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
Indoor Localization Using Local Node Density In Ad Hoc WSNsjoaquin_gonzalez
Presentation for Master Thesis "Indoor Localization Using Local Node Density In Ad Hoc WSNs", research supported by Free University Berlin. Coordinators: Freddy Lopez Villafuerte, Gianluca Cornetta.
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.
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.
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.
Neural Network Algorithm for Radar Signal RecognitionIJERA Editor
Nowadays, the traditional recognition method could not match the development of radar signals. In this paper, based on fractal theory and Neural Network, a new radar signal recognition algorithm is presented. The relevant point is extracted as the input of neutral network, and then it will recognize and classify the signals. Simulation results show that, this algorithm has a distinguish effect on classification under the condition of low SNR.
Wireless Sensor Network Based Clustering Architecture for Cooperative Communi...ijtsrd
1. The document proposes a cluster-based cooperative communication architecture for wireless sensor networks. It uses clustering as an underlying system to help with stable routing and cooperative transmission.
2. The architecture has three components: an underlying clustering structure, cluster-based cooperative transmission, and analysis of cooperative transmission opportunities.
3. It evaluates the proposed cooperative transmission approach through simulation and finds it achieves at least 12 times higher energy efficiency than non-cooperative transmission.
The document proposes a new localization method called A2L (Angle to Landmark) for wireless sensor networks. A2L uses angle of arrival measurements between sensor nodes and a subset of nodes equipped with GPS (landmarks) to determine the positions of non-landmark nodes. Compared to previous methods like APS and AHLoS that also use angle and distance measurements, simulations show that A2L can locate a greater number of nodes with higher accuracy while requiring fewer connections between nodes. The method is also low-cost since it does not require each node to have GPS or other expensive equipment.
Localization with mobile anchor points in wireless sensor networksHabibur Rahman
The document describes a range-free localization scheme that uses mobile anchor points equipped with GPS to periodically broadcast their positions. Sensor nodes calculate their own positions based on the localization information from at least three mobile anchors without needing additional interactions. Simulation results showed the approach achieved fine-grained accuracy and was distributed, scalable, effective and power efficient. It performed better than other range-free localization mechanisms.
Fault Diagonosis Approach for WSN using Normal Bias TechniqueIDES Editor
In wireless sensor and actor networks (WSAN), the
sensor nodes have a limitation on lifetime as they are equipped
with non-chargeable batteries. The failure probability of the
sensor node is influenced by factors like electrical dynamism,
hardware disasters, communication inaccuracy and undesired
environment situations, etc. Thus, fault tolerant is a very
important and critical factor in such networks. Fault tolerance
also ensures that a system is available for use without any
interruption in the presence of faults. In this paper an
improved fault tolerance scheme is proposed to find the
probability of correctly identifying a faulty node for three
different types of faults based on normal bias. The nodes fault
status is declared based on its confidence score that depends
on the threshold valve. The aim is to find the Correct
Recognition Rate (CRR) and the False Fear Rate (FFR) with
respect to the different error probability (pe) introduced. The
techniques, neighboring nodes, fault calculations, range and
CRR for existing algorithm and proposed algorithm is also
presented.
This document summarizes an evaluation of ad-hoc routing protocols for wireless sensor networks. It analyzes the performance of three protocols - DSDV, AODV, and DSR - through simulation. The results show that AODV has the best performance with less degradation and packet loss compared to DSDV and DSR as node mobility increases. AODV is therefore identified as the most suitable routing protocol for use in mobile wireless sensor networks based on its ability to handle topology changes from node movement.
EFFECTIVE AND SECURE DATA COMMUNICATION IN WSNs CONSIDERING TRANSFER MODULE O...IJEEE
A Bio-inspired clustering algorithm based on BFO has been proposed and investigation on energy efficient clustering algorithms related to WSNs has been done in this paper. The contribution of this paper related to use of Bacteria foraging algorithm firstly for WSNs for enhancing network lifetime of sensor nodes.
This document discusses localization techniques in wireless sensor networks. It begins with introducing wireless sensor networks and their components. It then discusses the need for localization to track objects within sensor networks. There are two main types of localization schemes - range-based which uses distance or angle measurements, and range-free which uses approximate distance estimates. Examples of range-based techniques include time of arrival, time difference of arrival, received signal strength indicator, and angle of arrival. Range-free techniques include proximity and distance-based localization using hop counts. The document compares the advantages and disadvantages of different localization methods.
The document is a presentation on implementing node localization for wireless sensor networks. It discusses wireless sensor networks and their applications. It then covers issues related to wireless sensor networks, including localization. Localization aims to estimate the position of sensor nodes that are randomly deployed. The presentation reviews several papers on localization systems and algorithms. It discusses approaches like using beacon nodes, distance estimation techniques, and localization algorithms that involve trilateration, triangulation or Voronoi diagrams. The conclusion covers challenges in localization and potential areas of future work.
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.
This document summarizes a research paper that proposes using network coding to improve the efficiency of dynamic source routing in wireless sensor networks. The paper describes how typical sensor networks rely on a central processing station that causes congestion. It then discusses network coding and how it allows intermediate nodes to encode packets before forwarding. The paper proposes a scheme where some sensor nodes act as aggregators that apply network coding on received packets from neighboring sensors if the data is significantly different. Simulation results show this approach reduces total transmissions for networks with up to 75 nodes, improving efficiency, but performance degrades for larger networks potentially due to increased collisions.
IRJET- An Introduction to Wireless Sensor Networks, its Challenges and SecurityIRJET Journal
- Wireless sensor networks (WSNs) are composed of small, battery-powered sensor nodes that collect data from the environment and transmit it to each other and a base station. They face challenges related to node mobility, energy efficiency, and lifetime.
- The document discusses clustering in WSNs, which involves organizing nodes into clusters with cluster heads to improve stability and reduce energy consumption. It also covers security issues and applications of WSNs in various fields like healthcare, the environment, and more.
A HYBRID FUZZY SYSTEM BASED COOPERATIVE SCALABLE AND SECURED LOCALIZATION SCH...ijwmn
Localization entails position estimation of sensor nodes by employing different techniques and mathematical computations. Localizable sensors also form an inherent part in the functioning of IoT devices and robotics. In this article, the author extends1 a novel scheme for node localization implemented using a hybrid fuzzy logic system to trace the node locations inside the deployment region, presented by the
Abhishek Kumar et. al. The results obtained were then optimized using Gauss Newton Optimization to improve the localization accuracy by 50% to 90% vis-à-vis weighted centroid and other fuzzy based localization algorithms. This article attempts to scale the proposed scheme for large number of sensor nodes to emulate somewhat real world scenario by introducing cooperative localization in previous presented work. The study also analyses the effectiveness of such scaling by comparing the localization accuracy. In next section, the article incorporates security in the proposed cooperative localization approach to detect malicious nodes/anchors by mutual authentication using El Gamel digital Signature scheme. A detailed study of the impact of incorporating security and scaling on average processing time and localization coverage has also been performed. The processing time increased by a factor of 2.5s for 500 nodes (can be attributed to more number of iterations and computations and large deployment area with small radio range of nodes) and coverage remained almost equal, albeit slightly low by a factor of 1% to 2%. Apart from these, the article also discusses the impact of adding extra functionalities in the proposed hybrid fuzzy system based localization scheme on processing time and localization accuracy.Lastly, this study also briefs about how the proposed scalable, cooperative and secure localization scheme tackles the type of attacks that pose threat to localization.
Wireless sensor networks localization algorithms a comprehensive surveyIJCNCJournal
Wireless sensor networks (WSNs) have recently gained a lot of attention by scientific community. Small
and inexpensive devices with low energy consumption and limited computing resources are increasingly
being adopted in different application scenarios including environmental monitoring, target tracking and
biomedical health monitoring. In many such applications, node localization is inherently one of the system
parameters. Localization process is necessary to report the origin of events, routing and to answer
questions on the network coverage ,assist group querying of sensors. In general, localization schemes are
classified into two broad categories: range-based and range-free. However, it is difficult to classify hybrid
solutions as range-based or range-free. In this paper we make this classification easy, where range-based
schemes and range-free schemes are divided into two types: fully schemes and hybrid schemes. Moreover,
we compare the most relevant localization algorithms and discuss the future research directions for
wireless sensor networks localization schemes.
Performance Analysis of Fault Detection in Round Trip Delay and Path Wireless...Editor IJMTER
In recent years, wsns detect to the fault sensor node based on round trip delay using path
in wireless sensor networks. Portable sensor node is low cost in Wsns . Measured in the round trip
delay time and number of sensor node. Existing method is used to large value of sensor node,
identification of sensor node time and distance . it is used to linear selection path, disadvantages are
data loss, more number of path, complexity. in this proposed method using distributed autonomous
sensor software implementation in NS2.it is detected fault sensor node and malfunction ,in this
analysis time and path using discrete Rtp. real time applicability in received signal strength ,separate
wavelength for end of the node avoid the data loss and complexity. Hardware implementation using
ZigBee and Microcontroller .Equal to the hardware and software implementation. It is overcomes to
the data loss. comparing the threshold and Rtd time. Finally, the algorithm is tested under different
number of faulty sensors in the same area. Our Simulation results demonstrate that the time
consumed to find out the faulty nodes in our proposed algorithm is relatively less with a large
number of faulty sensors existing in the network.
Este documento introduce los conceptos básicos de control de versiones y cómo Git funciona como un sistema de control de versiones distribuido. Compara el control de versiones con guardar juegos de video y explica cómo Git permite que los usuarios guarden y compartan versiones de proyectos de manera distribuida sin necesidad de un servidor central. También aborda conceptos como clonar repositorios y ramas para permitir que los usuarios trabajen de manera independiente y colaboren fácilmente.
Binghamton Research features insights and innovations from faculty members at Binghamton University. This year's theme is Earth on Our Minds, and it focuses on sustainability across numerous disciplines.
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
Indoor Localization Using Local Node Density In Ad Hoc WSNsjoaquin_gonzalez
Presentation for Master Thesis "Indoor Localization Using Local Node Density In Ad Hoc WSNs", research supported by Free University Berlin. Coordinators: Freddy Lopez Villafuerte, Gianluca Cornetta.
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.
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.
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.
Neural Network Algorithm for Radar Signal RecognitionIJERA Editor
Nowadays, the traditional recognition method could not match the development of radar signals. In this paper, based on fractal theory and Neural Network, a new radar signal recognition algorithm is presented. The relevant point is extracted as the input of neutral network, and then it will recognize and classify the signals. Simulation results show that, this algorithm has a distinguish effect on classification under the condition of low SNR.
Wireless Sensor Network Based Clustering Architecture for Cooperative Communi...ijtsrd
1. The document proposes a cluster-based cooperative communication architecture for wireless sensor networks. It uses clustering as an underlying system to help with stable routing and cooperative transmission.
2. The architecture has three components: an underlying clustering structure, cluster-based cooperative transmission, and analysis of cooperative transmission opportunities.
3. It evaluates the proposed cooperative transmission approach through simulation and finds it achieves at least 12 times higher energy efficiency than non-cooperative transmission.
The document proposes a new localization method called A2L (Angle to Landmark) for wireless sensor networks. A2L uses angle of arrival measurements between sensor nodes and a subset of nodes equipped with GPS (landmarks) to determine the positions of non-landmark nodes. Compared to previous methods like APS and AHLoS that also use angle and distance measurements, simulations show that A2L can locate a greater number of nodes with higher accuracy while requiring fewer connections between nodes. The method is also low-cost since it does not require each node to have GPS or other expensive equipment.
Localization with mobile anchor points in wireless sensor networksHabibur Rahman
The document describes a range-free localization scheme that uses mobile anchor points equipped with GPS to periodically broadcast their positions. Sensor nodes calculate their own positions based on the localization information from at least three mobile anchors without needing additional interactions. Simulation results showed the approach achieved fine-grained accuracy and was distributed, scalable, effective and power efficient. It performed better than other range-free localization mechanisms.
Fault Diagonosis Approach for WSN using Normal Bias TechniqueIDES Editor
In wireless sensor and actor networks (WSAN), the
sensor nodes have a limitation on lifetime as they are equipped
with non-chargeable batteries. The failure probability of the
sensor node is influenced by factors like electrical dynamism,
hardware disasters, communication inaccuracy and undesired
environment situations, etc. Thus, fault tolerant is a very
important and critical factor in such networks. Fault tolerance
also ensures that a system is available for use without any
interruption in the presence of faults. In this paper an
improved fault tolerance scheme is proposed to find the
probability of correctly identifying a faulty node for three
different types of faults based on normal bias. The nodes fault
status is declared based on its confidence score that depends
on the threshold valve. The aim is to find the Correct
Recognition Rate (CRR) and the False Fear Rate (FFR) with
respect to the different error probability (pe) introduced. The
techniques, neighboring nodes, fault calculations, range and
CRR for existing algorithm and proposed algorithm is also
presented.
This document summarizes an evaluation of ad-hoc routing protocols for wireless sensor networks. It analyzes the performance of three protocols - DSDV, AODV, and DSR - through simulation. The results show that AODV has the best performance with less degradation and packet loss compared to DSDV and DSR as node mobility increases. AODV is therefore identified as the most suitable routing protocol for use in mobile wireless sensor networks based on its ability to handle topology changes from node movement.
EFFECTIVE AND SECURE DATA COMMUNICATION IN WSNs CONSIDERING TRANSFER MODULE O...IJEEE
A Bio-inspired clustering algorithm based on BFO has been proposed and investigation on energy efficient clustering algorithms related to WSNs has been done in this paper. The contribution of this paper related to use of Bacteria foraging algorithm firstly for WSNs for enhancing network lifetime of sensor nodes.
This document discusses localization techniques in wireless sensor networks. It begins with introducing wireless sensor networks and their components. It then discusses the need for localization to track objects within sensor networks. There are two main types of localization schemes - range-based which uses distance or angle measurements, and range-free which uses approximate distance estimates. Examples of range-based techniques include time of arrival, time difference of arrival, received signal strength indicator, and angle of arrival. Range-free techniques include proximity and distance-based localization using hop counts. The document compares the advantages and disadvantages of different localization methods.
The document is a presentation on implementing node localization for wireless sensor networks. It discusses wireless sensor networks and their applications. It then covers issues related to wireless sensor networks, including localization. Localization aims to estimate the position of sensor nodes that are randomly deployed. The presentation reviews several papers on localization systems and algorithms. It discusses approaches like using beacon nodes, distance estimation techniques, and localization algorithms that involve trilateration, triangulation or Voronoi diagrams. The conclusion covers challenges in localization and potential areas of future work.
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.
This document summarizes a research paper that proposes using network coding to improve the efficiency of dynamic source routing in wireless sensor networks. The paper describes how typical sensor networks rely on a central processing station that causes congestion. It then discusses network coding and how it allows intermediate nodes to encode packets before forwarding. The paper proposes a scheme where some sensor nodes act as aggregators that apply network coding on received packets from neighboring sensors if the data is significantly different. Simulation results show this approach reduces total transmissions for networks with up to 75 nodes, improving efficiency, but performance degrades for larger networks potentially due to increased collisions.
IRJET- An Introduction to Wireless Sensor Networks, its Challenges and SecurityIRJET Journal
- Wireless sensor networks (WSNs) are composed of small, battery-powered sensor nodes that collect data from the environment and transmit it to each other and a base station. They face challenges related to node mobility, energy efficiency, and lifetime.
- The document discusses clustering in WSNs, which involves organizing nodes into clusters with cluster heads to improve stability and reduce energy consumption. It also covers security issues and applications of WSNs in various fields like healthcare, the environment, and more.
A HYBRID FUZZY SYSTEM BASED COOPERATIVE SCALABLE AND SECURED LOCALIZATION SCH...ijwmn
Localization entails position estimation of sensor nodes by employing different techniques and mathematical computations. Localizable sensors also form an inherent part in the functioning of IoT devices and robotics. In this article, the author extends1 a novel scheme for node localization implemented using a hybrid fuzzy logic system to trace the node locations inside the deployment region, presented by the
Abhishek Kumar et. al. The results obtained were then optimized using Gauss Newton Optimization to improve the localization accuracy by 50% to 90% vis-à-vis weighted centroid and other fuzzy based localization algorithms. This article attempts to scale the proposed scheme for large number of sensor nodes to emulate somewhat real world scenario by introducing cooperative localization in previous presented work. The study also analyses the effectiveness of such scaling by comparing the localization accuracy. In next section, the article incorporates security in the proposed cooperative localization approach to detect malicious nodes/anchors by mutual authentication using El Gamel digital Signature scheme. A detailed study of the impact of incorporating security and scaling on average processing time and localization coverage has also been performed. The processing time increased by a factor of 2.5s for 500 nodes (can be attributed to more number of iterations and computations and large deployment area with small radio range of nodes) and coverage remained almost equal, albeit slightly low by a factor of 1% to 2%. Apart from these, the article also discusses the impact of adding extra functionalities in the proposed hybrid fuzzy system based localization scheme on processing time and localization accuracy.Lastly, this study also briefs about how the proposed scalable, cooperative and secure localization scheme tackles the type of attacks that pose threat to localization.
Wireless sensor networks localization algorithms a comprehensive surveyIJCNCJournal
Wireless sensor networks (WSNs) have recently gained a lot of attention by scientific community. Small
and inexpensive devices with low energy consumption and limited computing resources are increasingly
being adopted in different application scenarios including environmental monitoring, target tracking and
biomedical health monitoring. In many such applications, node localization is inherently one of the system
parameters. Localization process is necessary to report the origin of events, routing and to answer
questions on the network coverage ,assist group querying of sensors. In general, localization schemes are
classified into two broad categories: range-based and range-free. However, it is difficult to classify hybrid
solutions as range-based or range-free. In this paper we make this classification easy, where range-based
schemes and range-free schemes are divided into two types: fully schemes and hybrid schemes. Moreover,
we compare the most relevant localization algorithms and discuss the future research directions for
wireless sensor networks localization schemes.
Performance Analysis of Fault Detection in Round Trip Delay and Path Wireless...Editor IJMTER
In recent years, wsns detect to the fault sensor node based on round trip delay using path
in wireless sensor networks. Portable sensor node is low cost in Wsns . Measured in the round trip
delay time and number of sensor node. Existing method is used to large value of sensor node,
identification of sensor node time and distance . it is used to linear selection path, disadvantages are
data loss, more number of path, complexity. in this proposed method using distributed autonomous
sensor software implementation in NS2.it is detected fault sensor node and malfunction ,in this
analysis time and path using discrete Rtp. real time applicability in received signal strength ,separate
wavelength for end of the node avoid the data loss and complexity. Hardware implementation using
ZigBee and Microcontroller .Equal to the hardware and software implementation. It is overcomes to
the data loss. comparing the threshold and Rtd time. Finally, the algorithm is tested under different
number of faulty sensors in the same area. Our Simulation results demonstrate that the time
consumed to find out the faulty nodes in our proposed algorithm is relatively less with a large
number of faulty sensors existing in the network.
Este documento introduce los conceptos básicos de control de versiones y cómo Git funciona como un sistema de control de versiones distribuido. Compara el control de versiones con guardar juegos de video y explica cómo Git permite que los usuarios guarden y compartan versiones de proyectos de manera distribuida sin necesidad de un servidor central. También aborda conceptos como clonar repositorios y ramas para permitir que los usuarios trabajen de manera independiente y colaboren fácilmente.
Binghamton Research features insights and innovations from faculty members at Binghamton University. This year's theme is Earth on Our Minds, and it focuses on sustainability across numerous disciplines.
The document is a progress report on New York City's Digital Leadership Roadmap from 2013. It summarizes achievements in increasing access to broadband and digital technologies for New Yorkers. Key points include:
1) Through programs like Connected Learning and Connected Communities, nearly 300,000 low-income residents now have access to subsidized broadband Internet through public technology centers.
2) Free public Wi-Fi has been expanded through partnerships and is now available in over 50 city parks and several commercial districts, with over 100,000 New Yorkers able to access the network in Chelsea.
3) Library systems have increased public access computers by 89% and now offer over 6,800 computers across Brooklyn, New
Final Project Report - The Evaluation and Expansion of the Solar Disinfection...Kristine Lilly
The document summarizes Phase I of a research project evaluating the use of solar disinfection (SODIS) to treat residential greywater in the United States. In Phase I, students standardized a laboratory greywater solution and cultured a chemically resistant strain of E. coli to test SODIS prototype vessels. Testing of prototypes like glass and acrylic tubes demonstrated at least a 4-log (99.99%) reduction in E. coli viability, showing the potential for SODIS to safely treat greywater for reuse. Phase II of the project is proposed to further optimize vessel designs and expand testing of the SODIS method for residential greywater treatment and recycling.
A Survey on Localization of Wireless SensorsKarthik Mohan
The document summarizes localization techniques for wireless sensor nodes. It discusses several common localization methods including known location-based using GPS, proximity-based using signal strength, angle-based using angle of arrival, and range-based using time of arrival or time difference of arrival. It also covers some challenges with each approach like accuracy limitations and environmental factors. Finally, it provides a brief comparison of the localization techniques and their typical accuracy ranges from 1-15 meters depending on the method.
This document discusses localization techniques in wireless sensor networks (WSNs). It begins with an introduction to WSNs and their applications that require location information. While GPS could provide location data, it is not practical for WSNs due to cost and physical constraints. The document then categorizes localization methods as range-based, which use distance or angle measurements, and range-free, which do not directly measure distance. Specific techniques like time of arrival, received signal strength, and DV-Hop localization are described. The document concludes with classifications of localization methods and topics for future work.
This document provides a student housing analysis of the Southeast Keene neighborhood in New Hampshire. It begins with acknowledging the geography department at Keene State College and others who assisted with the project. It then discusses the context of neighborhoods and how they are defined. Neighborhood change is examined, including the stages of decline and revitalization. Issues related to "town and gown" relationships between college students and local residents in college towns are also reviewed. The document presents the central questions and hypotheses that will be examined regarding student housing trends and impacts on the Southeast Keene neighborhood.
The document discusses the roles and impacts of engineering. It provides definitions of engineering from prominent engineers, noting that engineering applies science to improve everyday life and address human needs. The document also lists 14 major challenges that 21st century engineers will need to address, such as developing carbon storage, providing clean water, preventing nuclear terror, and advancing health technologies.
The document presents a novel three-dimensional adaptive localization (T-Dial) algorithm for wireless sensor networks. The proposed algorithm works in four primary phases: 1) Neighbor formation where nodes broadcast information to nodes within transmission range to form neighbor tables; 2) Group formation where anchor nodes connect to nearby nodes to divide the network into manageable groups; 3) Edge node marking where edge nodes on the network boundary are detected and marked; and 4) Localization error correction where missing nodes from initial setup are rediscovered and corrected. Simulation results show the proposed algorithm improves localization rate, reduces localization error, and increases positioning rate compared to existing algorithms.
A Novel Three-Dimensional Adaptive Localization (T-Dial) Algorithm for Wirele...iosrjce
The document presents a novel three-dimensional adaptive localization (T-Dial) algorithm for wireless sensor networks. The proposed algorithm works in four primary phases: 1) Neighbor formation where nodes broadcast information to nodes within transmission range to form neighbor tables; 2) Group formation where anchor nodes connect to non-anchor nodes to divide the network into smaller manageable groups; 3) Edge node marking where edge nodes on the network boundary are detected and marked; and 4) Localization error correction where missing nodes from initial setup are rediscovered and corrected. Simulation results show the proposed algorithm improves localization rate, reduces localization error, and increases positioning rate compared to existing algorithms.
As Wireless Sensor Networks are penetrating into the industrial domain, many research opportunities are emerging. One such essential and challenging application is that of node localization. A feed-forward neural network based methodology is adopted in this paper. The Received Signal Strength Indicator (RSSI) values of the anchor node beacons are used. The number of anchor nodes and their configurations has an impact on the accuracy of the localization system, which is also addressed in this paper. Five different training algorithms are evaluated to find the training algorithm that gives the best result. The multi-layer Perceptron (MLP) neural network model was trained using Matlab. In order to evaluate the performance of the proposed method in real time, the model obtained was then implemented on the Arduino microcontroller. With four anchor nodes, an average 2D localization error of 0.2953 m has been achieved with a 12-12-2 neural network structure. The proposed method can also be implemented on any other embedded microcontroller system.
5.a robust frame of wsn utilizing localization technique 36-46Alexander Decker
This document discusses localization techniques for wireless sensor networks. It begins by defining localization as identifying a sensor node's position and explains that localization is a fundamental challenge for wireless sensor networks. It then describes two main categories of localization techniques: range-based and range-free. Range-based techniques use distance or angle measurements between nodes to determine positions but require expensive hardware. Range-free techniques estimate positions based on neighboring node information and are less expensive but less accurate. The document reviews several specific localization algorithms from previous research and discusses their advantages and limitations.
11.0005www.iiste.org call for paper.a robust frame of wsn utilizing localizat...Alexander Decker
This document discusses localization techniques for wireless sensor networks. It begins by defining localization as identifying a sensor node's position and explains how accuracy is important. It then describes two main categories of localization techniques: range-based and range-free. Range-based uses distance or angle measurements between nodes for higher accuracy but requires expensive hardware. Range-free relies on information from nearby nodes and is less accurate but cheaper. The document reviews several specific localization algorithms from previous research and their limitations. It concludes by stating that energy efficiency is critical for wireless sensor networks due to limited battery life.
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.
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.
The document discusses energy efficient routing protocols for clustered wireless sensor networks. It provides an overview of wireless sensor networks and discusses how clustering is commonly used to improve energy efficiency and scalability. The document reviews several existing clustering-based routing protocols and analyzes their approaches for prolonging network lifetime by minimizing energy consumption in wireless sensor networks.
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.
DESIGN OF ENERGY EFFICIENT ROUTING ALGORITHM FOR WIRELESS SENSOR NETWORK (WSN...cscpconf
Development of energy efficient Wireless Sensor Network (WSN) routing protocol is nowadays main area of interest amongst researchers. This research is an effort in designing energy efficient Wireless Sensor Network (WSN) routing protocol under certain parameters consideration. Research report discusses various existing WSN routing protocols and propose a new WSN energy efficient routing protocol. Results show a significant improvement in life cycle of the nodes and enhancement in energy efficiency of WSN. In this paper, an attempt has been made to design a wireless sensor network involving the extraction of Pascal Graph features. The standard task involves designing a suitable topology using Pascal Graph. As per the definition of interconnection network it is equivalent that a suitable graph can represent the different computer network topologies very efficiently. Different characteristics of Pascal Graph Topology has been discovered and used in network topology design. Since Pascal Graph gives
better result in terms of finding the dependable and reliable nodes in topology, it has been considered for network analysis. Moreover, we propose a methodology that involves the Pascal
Graph Topology for wireless sensor network which can analyse and represent the network and help in routing.
Data Security and Data Dissemination of Distributed Data in Wireless Sensor N...IJERA Editor
The document discusses a data dissemination protocol called seDrip for wireless sensor networks. seDrip allows multiple authorized network users to simultaneously distribute data items directly to sensor nodes, without relying on a central sink node. It implements authentication using digital signatures to provide security and prevent unauthorized access. The protocol is analyzed and shown to satisfy security requirements like authenticity, integrity, and resistance to denial-of-service attacks. RSA encryption is used to encode data for confidentiality.
Grid-Based Multipath with Congestion Avoidance Routing Protocolijtsrd
This document summarizes routing techniques for wireless sensor networks. It discusses traditional techniques like flooding and gossiping and current techniques including flat, hierarchical, and location-based routing. For hierarchical routing, it describes protocols like LEACH, PEGASIS and TEEN in detail. LEACH uses clustering to reduce energy consumption and extends network lifetime by randomly selecting cluster heads. It compares LEACH to other protocols like SPIN and directed diffusion, showing LEACH provides better performance. The document also discusses issues like uneven energy consumption in LEACH that new protocols try to address.
Reliable and Efficient Data Acquisition in Wireless Sensor NetworkIJMTST Journal
The sensors in the WSN sense the surrounding, collects the data and transfers the data to the sink node. It
has been observed that the sensor nodes are deactivated or damaged when exposed to certain radiations or
due to energy problems. This damage leads to the temporary isolation of the nodes from the network which
results in the formation of the holes. These holes are dynamic in nature and can grow and shrink depending
upon the factors causing the damage to the sensor nodes. So a solution has been presented in the base paper
where the dual mode i.e. Radio frequency and the Acoustic mode are considered so that the data can be
transferred easily. Based on this a survey has been done where several factors are studied so that the
performance of the system can be increased.
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.
Hybrid Target Tracking Scheme in Wireless Sensor NetworksIRJET Journal
1) The document discusses a hybrid target tracking scheme for wireless sensor networks that combines range-free and range-based localization techniques to more accurately determine the location of unknown nodes.
2) It describes some of the challenges with localization in wireless sensor networks, including environmental factors that can impact variables used for localization like transmission range and signal strength.
3) The hybrid approach determines the exact location of an unknown node using a combination of range-free and range-based methods to improve accuracy compared to using either method alone.
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.
CODE AWARE DYNAMIC SOURCE ROUTING FOR DISTRIBUTED SENSOR NETWORKIJNSA Journal
Sensor network facilitates monitoring and controlling of physical environments. These wireless networks consist of dense collection of sensors capable of collection and dissemination of data. They have application in variety of fields such as military purposes, environment monitoring etc. Typical deployment of sensor network assumes central processing station or a gateway to which all other nodes route their data using dynamic source routing (DSR). This causes congestion at central station and thus reduces the efficiency of the network. In this work we will propose a better dynamic source routing technique using network coding to reduce total number of transmission in sensor networks resulting in better efficiency.
The document proposes an improved data routing protocol for wireless sensor networks. It aims to address deficiencies in existing chain-based routing protocols like Chiron and PEGASIS that can cause longer transmission delays and redundant paths. The key aspects of the proposed protocol are:
1) The sensing area is divided into fan-shaped groups using beamforming from the base station, instead of concentric clusters. Shorter chains are formed within each group for data transmission.
2) The node with maximum residual energy in each chain is elected as the chain leader, rather than taking turns, to aggregate and transmit data to the base station.
3) Transmission between chain leaders is optimized to avoid longer distances and redundant paths.
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.
This document summarizes research on algorithms for proximity estimation in sensor networks. It discusses using sensor networks to detect events observed by nodes within a certain distance of each other. It proposes an algorithm that utilizes a distributed routing index maintained by nodes in the network to process multiple proximity queries involving different event types. The document reviews several related works on localization algorithms, data-centric sensor networks, geographic routing protocols, and node localization techniques. It evaluates different wireless sensor network simulators and deployment schemes.
The chapter Lifelines of National Economy in Class 10 Geography focuses on the various modes of transportation and communication that play a vital role in the economic development of a country. These lifelines are crucial for the movement of goods, services, and people, thereby connecting different regions and promoting economic activities.
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
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How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
1. Network Protocols and Algorithms
ISSN 1943-3581
2010, Vol. 2, No. 1
Localization Algorithms in Wireless Sensor Networks:
Current Approaches and Future Challenges
Amitangshu Pal
Department of Electrical and Computer Engineering
The University of North Carolina at Charlotte
9201 University City Blvd, Charlotte, North Carolina 28223-0001
apal@uncc.edu
Tel: 1-980-229-3383
Abstract
Recent advances in radio and embedded systems have enabled the proliferation of wireless
sensor networks. Wireless sensor networks are tremendously being used in different
environments to perform various monitoring tasks such as search, rescue, disaster relief,
target tracking and a number of tasks in smart environments. In many such tasks, node
localization is inherently one of the system parameters. Node localization is required to report
the origin of events, assist group querying of sensors, routing and to answer questions on the
network coverage. So, one of the fundamental challenges in wireless sensor network is node
localization. This paper reviews different approaches 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. Future research
directions and challenges for improving node localization in wireless sensor networks are
also discussed.
Keywords: Centralized Localization, Distributed Localization, Beacon-based distributed
algorithms, Relaxation-based distributed algorithms, Coordinate system stitching based
distributed algorithms, Diffusion, Bounding Box, Gradient, Wireless Sensor Networks.
45 www.macrothink.org/npa
2. Network Protocols and Algorithms
ISSN 1943-3581
2010, Vol. 2, No. 1
1. Introduction
The massive advances of microelectromechanical systems (MEMS), computing and
communication technology have fomented the emergence of massively distributed, wireless
sensor networks consisting of hundreds and thousands of nodes. Each node is able to sense
the environment, perform simple computations and communicate with its other sensors or to
the central unit. One way of deploying the sensor networks is to scatter the nodes throughout
some region of interest. This makes the network topology random. Since there is no a priori
communication protocol, the network is ad hoc. These networks are tremendously being
implemented to perform a number of tasks, ranging from environmental and natural habitat
monitoring to home networking, medical applications and smart battlefields. Sensor network
can signal a machine malfunction to the control centre in a factory or it can warn about
smoke on a remote forest hill indicating that a forest fire is about to start. On the other hand
wireless sensor nodes can be designed to detect the ground vibrations generated by silent
footsteps of a burglar and trigger an alarm.
Since most applications depend on a successful localization, i.e. to compute their
positions in some fixed coordinate system, it is of great importance to design efficient
localization algorithms. In large scale ad hoc networks, node localization can assist in routing
[1], [2], [3]. In the smart kindergarten [4] node localization can be used to monitor the
progress of the children by tracking their interaction with toys and also with each other. It can
also be used in hospital environments to keep track of equipments, patients, doctors and
nurses [1].
For these advantages precise knowledge of node localization in ad hoc sensor networks is
an active field of research in wireless networking. Unfortunately, for a large number of sensor
nodes, straightforward solution of adding GPS to all nodes in the network is not feasible
because:
• In the presence of dense forests, mountains or other obstacles that block the
line-of-sight from GPS satellites, GPS cannot be implemented.
• The power consumption of GPS will reduce the battery life of the sensor nodes and
also reduce the effective lifetime of the entire network.
• In a network with large number of nodes, the production cost factor of GPS is an
important issue.
• Sensor nodes are required to be small. But the size of GPS and its antenna increases
the sensor node form factor.
For these reasons an alternate solution of GPS is required which is cost effective, rapidly
deployable and can operate in diverse environments.
The paper is organized as follows. Section 2 presents the formulation of localization
problem in wireless sensor networks. Related work has been discussed in section 3. In section
4, presents different location discovery approaches to solve the problem of localization in
wireless sensor networks (WSN). In section 5 different proposals to improve localization in
WSN are discussed. Section 6 states the summary of all proposals. Section 7 concludes the
paper where the future challenges and directions to improve localization in WSN technology
are described.
46 www.macrothink.org/npa
3. Network Protocols and Algorithms
ISSN 1943-3581
2010, Vol. 2, No. 1
2. Problem Definition
Consider the case when we have deployed a sensor network consist of N sensors at
locations S = {S1, S2,…….,SN}. Let Sxi refer to the x-coordinate of the location of sensor i
and let Syi and Szi refer to the y and z coordinates, respectively. Constraining Szi to be 0
suffices the 2D version of this problem. Determining these locations constitutes the
localization problem. Some sensor nodes are aware of their own positions, these nodes are
known as anchors or beacons. All the other nodes localize themselves with the help of
location references received from the anchors. So, mathematically the localization problem
can be formulated as follows: given a multihop network, represented by a graph G = (V, E),
and a set of beacon nodes B, their positions {xb, yb} for all b ε B, we want to find the position
{xu, yu} for all unknown nodes u ε U.
3. Related Work
Localization in WSN is an active area of research and so there are some existing literature
surveys [23], [24] on this topic. In these literatures the authors discuss most important
localization techniques and critique those techniques. But there are some existing techniques
which use two localization techniques such as multidimensional scaling (MDS) and
proximity based map (PDM) [16] or MDS and Ad-hoc Positioning System (APS) [17]. These
techniques have not been mentioned in any literature but these techniques give new directions
in WSN localization as these schemes give high accuracy in low communication and
computation cost. On the other hand interferometric ranging based localization has been
proposed in [18], [19], [20] which have not been discussed by any existing literature.
Moreover due to channel fading and noise corruption error propagation comes in picture. To
suppress this error propagation a localization scheme has been proposed in [21] which was
not been discussed by any literature. This literature gives comprehensive summary of these
techniques along with other existing localization schemes. At the same time this paper also
compares all localization techniques and also provides future research directions in this area.
4. Different Location Discovery Approaches
Existing location discovery approaches basically consists of two basic phases: (1)
distance (or angle) estimation and (2) distance (or angle) combining. The most popular
methods for estimating the distance between two nodes are described below:
Received Signal Strength Indicator (RSSI): RSSI measures the power of the signal at the
receiver and based on the known transmit power, the effective propagation loss can be
calculated. Next by using theoretical and empirical models we can translate this loss into a
distance estimate. This method has been used mainly for RF signals. RSSI is a relatively
cheap solution without any extra devices, as all sensor nodes are likely to have radios. The
performance, however, is not as good as other ranging techniques due to the multipath
propagation of radio signals. In [26], the authors characterize the limits of a variety of
approaches to indoor localization using signal strengths from 802.11 routers. They also
suggest that adding additional hardware or altering the model of the environment is the only
47 www.macrothink.org/npa
4. Network Protocols and Algorithms
ISSN 1943-3581
2010, Vol. 2, No. 1
alternative to improve the localization performance.
Time based methods (ToA, TDoA): These methods record the time-of-arrival (ToA) or
time-difference-of-arrival (TDoA). The propagation time can be directly translated into
distance, based on the known signal propagation speed. These methods can be applied to
many different signals, such as RF, acoustic, infrared and ultrasound. TDoA methods are
impressively accurate under line-of-sight conditions. But this line-of-sight condition is
difficult to meet in some environments. Furthermore, the speed of sound in air varies with air
temperature and humidity, which introduce inaccuracy into distance estimation. Acoustic
signals also show multi-path propagation effects that may impact the accuracy of signal
detection.
Angle-of-Arrival (AoA): AoA estimates the angle at which signals are received and use
simple geometric relationships to calculate node positions. Generally, AoA techniques
provide more accurate localization result than RSSI based techniques but the cost of
hardware of very high in AoA.
For the combining phase, the most popular alternatives are:
Hyperbolic trilateration: The most basic and intuitive method is called hyperbolic
trilateration. It locates a node by calculating the intersection of 3 circles as shown in Fig. 1(a).
Triangulation: This method is used when the direction of the node instead of the distance is
estimated, as in AoA systems. The node positions are calculated in this case by using the
trigonometry laws of sines and cosines (shown in Fig. 1(b)).
Maximum Likelihood (ML) estimation: ML estimation estimates the position of a node by
minimizing the differences between the measured distances and estimated distances (shown
in Fig. 1(c)).
5. Different Proposals For Network Management And Control Issues
This section presents different proposals put forward by the research community in the
areas of localization in wireless sensor networks and critiques their contributions.
Research on localization in wireless sensor networks can be classified into two broad
categories.
(a) (b) (c)
Fig.1. Localization techniques a) Hyperbolic trilateration b) Triangulation c) Maximum Likelihood Estimation
Centralized Localization: Centralized localization is basically migration of inter-node
ranging and connectivity data to a sufficiently powerful central base station and then the
migration of resulting locations back to respective nodes. The advantage of centralized
algorithms are that it eliminates the problem of computation in each node, at the same time
the limitations lie in the communication cost of moving data back to the base station. As
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representative proposals in this category [5], [6], [7] are explained in greater detail.
Distributed Localization: In Distributed localizations all the relevant computations are
done on the sensor nodes themselves and the nodes communicate with each other to get their
positions in a network. Distributed localizations can be categorized into three classes.
• Beacon-based distributed algorithms: Beacon-based distributed algorithms start with
some group of beacons and nodes in the network to obtain a distance measurement to
a few beacons, and then use these measurements to determine their own location.
Some of the proposals [8], [9], [10], [11], in this category are described below.
• Relaxation-based distributed algorithms: In relaxation-based distributed algorithms
use a coarse algorithm to roughly localize nodes in the network. This coarse algorithm
is followed by a refinement step, which typically involves each node adjusting its
position to approximate the optimal solution. Some of the proposals [12], [13] in this
category are discussed in greater details.
• Coordinate system stitching based distributed algorithms: In Coordinate system
stitching the network is divided into small overlapping subregions, each of which
creates an optimal local map. Next the scheme merges the local maps into a single
global map. Some approaches [14], [15] of this category are examined in the next
section.
• Hybrid localization algorithms: Hybrid localization schemes use two different
localization techniques such as : multidimensional scaling (MDS) and proximity
based map (PDM) or MDS and Ad-hoc Positioning System (APS) to reduce
communication and computation cost. Such kinds of approaches are depicted in [16],
[17].
• Interferometric ranging based localization: Radio interferometric positioning exploits
interfering radio waves emitted from two locations at slightly different frequencies to
obtain the necessary ranging information for localization. Such types of localization
techniques are proposed in [18], [19] and [20].
• Error propagation aware localization: When sensors communicate with each other,
error propagation can be caused due to the undesirable wireless environment, such as
channel fading and noise corruption. To suppress error propagation [21] has proposed
a scheme called error propagation aware (EWA) algorithm.
A classification of various schemes is shown in Fig. 2.
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Fig.2. Classification of various proposals for Localization in WSN
4.1. Centralized Localization
4.1.1. MDS-MAP
In [5] the authors present a centralized algorithm called MDS-MAP (mentioned in
Appendix A) which basically consists of three steps.
1. First the scheme computes shortest paths between all pairs of nodes in the region of
consideration by the use of all pair shortest path algorithm such as Dijkstra’s or Floyd’s
algorithm. The shortest path distances are used to construct the distance matrix for MDS.
2. Next the classical MDS is applied to the distance matrix, retaining the first 2 (or 3)
largest eigenvalues and eigenvectors to construct a 2-D (or 3-D) relative map that gives a
location for each node. Although these locations may be accurate relative to one another, the
entire map will be arbitrarily rotated and flipped relative to the true node positions.
3. Based on the position of sufficient anchor nodes (3 or more for 2-D, 4 or more for 3-D),
transform the relative map to an absolute map based on the absolute positions of anchors
which includes scaling, rotation, and reflection. The goal is to minimize the sum of squares of
the errors between the true positions of the anchors and their transformed positions in the
MDS map.
The advantage of this scheme is that it does not need anchor or beacon nodes to start with.
It builds a relative map of the nodes even without anchor nodes and next with three or more
anchor nodes, the relative map is transformed into absolute coordinates. This method works
well in situations with low ratios of anchor nodes. A drawback of MDS-MAP is that it
requires global information of the network and centralized computation.
4.1.2. Localize node based on Simulated Annealing
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In [6] the authors propose an innovative approach based on Simulated Annealing to
localize the sensor nodes in a centralized manner. Since the algorithm is centralized, it enjoys
the access to estimated locations and neighbourhood information of all localizable nodes in
the system. Let us consider a sensor network of m anchor nodes with known locations and
n-m sensor nodes with unknown locations. As the proposed algorithm is implemented in a
centralized architecture, it has access to estimated locations and neighborhood information of
all localizable nodes in the system. The proposed scheme is based on two stages. In the first
stage simulated annealing is used to obtain an estimate of location of the localizable sensor
nodes using distance constraints. Let us define the set Ni as a set containing all one hop
neighbors of node i. The localization problem can be formulated as:
Min ∑i=m+1 to n∑j€Ni (d^ij – dij)2 (1)
In equation (1), dij is the measured distance between node i and its neighbor j; dˆij = √{(xˆi
− xˆj)2 + (yˆi − yˆj)2} is the estimated distance; (x^i, y^i) and (x^j,y^j) are the estimated
coordinates of node i and its one hop neighbor j respectively and the cost function CF =
∑i=m+1 to n∑j€Ni (d^ij – dij)2. Then according to Simulated Annealing coordinate estimate (x^i, y^i)
of any chosen node i is given a small displacement in a random direction and the new value
of the cost function is calculated for the new location estimate. If Δ(CF) ≤ 0, (Δ(CF) = CFnew
− CFold ) then the perturbation is accepted and the new location estimate is used as the
starting point of the next step. Otherwise the probability that the displacement is accepted is
P(Δ(CF)) = exp(−Δ(CF)/T ). Here T is a control parameter and P is a monotonically
increasing function of T.
In the next stage of the algorithm the authors eliminate the error caused by flip ambiguity.
Flip ambiguity occurs when a node’s neighbors are placed in positions such that they are
approximately on the same line, this node can be reflected across the line of best fit produced
by its neighbors with essentially no change in the cost function. In Fig. 3, the neighbors of
node A are nodes B, C, D and E which are almost collinear and the node A could be flipped
across the line of best fit of nodes B, C, D and E to location A/ with almost no change in the
cost function. But we should note from Fig. 3 that the flipped position A/ has gone into the
wrong neighborhood of nodes H and I. Based on this observation the authors define a
complement set comp(Ni) of the set Ni as a set containing all nodes which are not neighbors
of node i. If R is the transmission range of the sensor node and the estimated coordinate of
node j comp(Ni) is such that dˆij < R, then the node j has been placed in the wrong
neighborhood of node i, resulting in both nodes i and j having each other as wrong neighbors.
So the minimum error due to the flip is dˆij – R and the new localization problem can be
formulated as in equation (2).
Min ∑i=m+1 to n ( ∑j€Ni (d^ij - dij)2 + ∑ (d^ij – R)2) (2)
The paper presented a novel simulated annealing based localization algorithm which
mitigates the flip ambiguity problem. By simulations the authors the authors show that the
proposed algorithm gives better accuracy than the semi-definite programming localization.
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Disk Model Of Node H
H
F
I
B A/
G
C
Disk Model Of Node I
D
A E
Fig.3. Illustration of Flip Ambiguity [6]
They show that the proposed algorithm does not propagate error in localization. The
proposed flip ambiguity mitigation method is based on neighborhood information of nodes
and it works well in a sensor network with medium to high node density. However when the
node density is low, it is possible that a node is flipped and still maintains the correct
neighborhood. In this situation, the proposed algorithm fails to identify the flipped node.
4.1.3. A RSSI-based centralized localization technique
In [7] the authors propose a scheme which localizes nodes through RF attenuation in
Electromagnetic waves. The scheme basically consists of three stages:
1) RF mapping of the network: It is obtained by conveying short packets at different
power levels through the network and by storing the average RSSI value of the
received packets in memory tables.
2) Creation of the ranging model: All the tuples recorded between the two anchors are
processed at the central unit to compensate the non linearity and calibrate the model.
Let a generic tuple (i, j, Ptx, Prx) comes from the RF mapping characterizing stage,
where i is the transmitting node and j is the receiving node. Now first the algorithm
corrects the received power as Prx/ =f(Prx, Ptx), f() is a function which takes into
account the modularity effects. So, the estimated distance between the nodes will be
rij0 = m-1(Prx/)
3) Centralized localization model: An optimization problem is solved and provides the
position of the nodes. The final result can be obtained by minimizing the function
E=∑i=1 to n∑j=1 to n (ki,jai,j ( rij-rij 0)2) , rij = d(i, j) when i and j are anchors.
Where N is the number of nodes, ai,j is 1 when the link is present and 0 otherwise.
Once the distance between the nodes rij can be expressed in terms of their coordinates
(x, y)i and (x, y)j the authors solve the minimizing problem by sequential quadratic
programming (SQP) method.
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The advantage of this scheme is that it is a practical, self-organizing scheme that allows
addressing any outdoor environments. The limitation of this scheme is that the scheme is
power consuming because it requires extensive generation and need to forward much
information to the central unit.
4.2. Distributed Localization
4.2.1. Beacon based distributed localization
Beacon based approaches can be categorized in Diffusion, Bounding Box and Gradient
which are described as follows:
4.2.1.1. Diffusion
In diffusion the most likely position of the node is at the centroid of its neighboring
known nodes.
APIT: In [8] the authors describe a novel area-based range free localization scheme,
called APIT which requires a heterogeneous network of sensing devices where some devices
are equipped with high-powered transmitters and location information. These devices are
known as anchors. In this approach the location information is performed by isolating the
environment into triangular regions between beaconing nodes. An unknown node chooses
three anchors from all audible anchors and tests whether it is inside the triangle formed by
connecting these three anchors. APIT repeats this tests with different audible anchor
combinations until all combinations are exhausted or the required accuracy is achieved. At
this point, APIT calculates the centre of gravity of the intersection of all triangles in which
the unknown node resides to determine the estimated position.
The advantage of APIT lies in its simplicity and ease of implementation. But APIT requires a
high ratio of beacons to nodes and longer range beacons to get a good position estimate. For
low beacon density this scheme will not give accurate results.
4.2.1.2. Bounding Box
Bounding box forms a bounding region for each node and then tries to refine their
positions.
4.2.1.2.1. Collaborative Multilateration
In [9] the authors present a collaborative multilateration approach that consists of a set of
mechanisms that enables nodes found several hops away from location aware beacon nodes
collaborate with each other to estimate their locations with high accuracy. Collaborative
multilateration consists of three phases:
• Forming Collaborative sub trees: A computation sub tree constitutes a configuration
of unknowns and beacons for which the solution of the position estimates of the
unknown can be uniquely determined. The requirement of one-hop multilateration for
an unknown node is that it is within the range of at least three beacons (see Fig 4(a)).
A two hop multilateration represents the case where the beacons are not always
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directly connected to the nodes but they are within a two hop radius from the
unknown node (see Fig. 4(b)).
Beacon Node Unknown Node
(a) (b)
Fig.4. (a) One-hop Multilateration (b) Two-hop Multilaretation
• Obtaining initial estimates: This phase is explained by the help of Fig. 5. In Fig. 5 A
and B are beacons where C is the unknown node. If the distance between C and A is a
then the x coordinate of C are bounded by a to the left and to the right of the x
coordinate of A, xA – a and xA + a. Similarly beacon B which is two hops away from
C, bounds the coordinate of C within xB – (b + c) and xB + (b + c). by knowing the
information, C can determine that its x coordinate bounds with respect to beacons A
and B are xB + (b + c) and xA – a. The same operation is applied on the y coordinates.
C then combines its bounds on x and y coordinates, to obtain a bounding box of the
region where it lies.
From [xA – a] to [xB + (b + c)]
a a
A
a
c C
b
B
b+c
B Beacon Node Unknown Node
Fig.5. X coordinates bounds for C using initial estimates [9]
• Position refinement: In the third phase, the initial node positions are refined using
Kalman Filter implementation (mentioned in the Appendix B). Now as most unknown
nodes are not directly connected to beacons, they use the initial estimates of their
neighbours as the reference points for estimating their locations. As soon as an
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unknown node computes a new estimate, it broadcasts this estimate to its neighbours,
and the neighbour use it to update their own position estimates. As shown in Fig. 6
first node 4 computes its location estimate using beacons 1 and 5 and node 3 as
reference. Once node 4 broadcasts its update, node 3 recomputes its own estimate
received from node 4. Next node 3 broadcasts the new estimate and node 4 uses this
to compute a new estimate that is more accurate than its previous estimate.
The collaborative multilateration enables sensor nodes to accurately estimate their
locations by using known beacon locations that are several hops away and distance
measurements to neighboring nodes. At the same time it increases the computational cost
also.
5
2 3
4
Unknown Node
Beacon Node
1
Fig.6. Initial estimates over multiple hops
4.2.1.2.2. Node localization assuming the region as square box
In [10] the authors frame the localization problem as follows. They have assumed that in
a square region Q = [0, s] x [0,s], called region of operations, N nodes S1, ………, SN have
been scattered and each of which is equipped with an RF transceiver with communication
range r > 0. In other words a node Si can communicate with every node which lies in its
communication region, which is the disk with radius r centered at Si. The nodes form an ad
hoc network Ŋ in which there is an edge between Si and Sj if their distance is less than r. They
scheme assume that there are certain positive number of beacons nodes in Q and other are
unknown nodes. Now for any integer n > 0, partition Q into n2 congruent squares called
cells of area (s/n)2 and for every known node S, we know the cell which contains S. To make
the problem tractable the authors assume that communication range is ρ calls where ρ =
[nr/s√2], where [x] denotes the integer part of x, which means that each node S can
communicate with every node lying in the square centered at S and containing (2ρ + 1)2 cells.
Usually n is large and r is much smaller than n. In particular 2ρ + 1 < n. Then for an arbitrary
unknown S in Ŋ the localization algorithm at S can be written as:
Step 1: Initialize the estimate: Ls = Q.
Step 2: Send Hello packets to the neighbours. Each known neighbor sends back (1, a, b),
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where (a, b) is its grid position, while each unknown neighbor sends (0, 0, 0).
Step 3: For each response (1, a, b), update the estimate as shown in equation (3).
Ls = Ls ∩ [a – ρ, a + ρ] x [b – ρ, b + ρ] (3)
Step 4: Stop when all responses are received. The position estimate is Ls.
In this approach an unknown node could query some of its neighbours which reduce
communication cost but increases computations.
4.2.1.3. Gradient
In [11] the authors describe an algorithm for organizing a global coordinate system from
local information. In this approach ad-hoc sensor nodes are randomly distributed on a two
dimensional plane and each sensor communicates with nearby sensors within a fixed distance
r, where r is much smaller than the dimension of the plane. In their algorithm they assume
some set of sensors as “seed” sensors which are identical to other sensors in capabilities
except that they are programmed with their global position. The algorithm consists of two
parts:
• Gradient Algorithm: - Each seed sensor produces a locally propagating gradient that
allows other sensors to estimate the distance from the seed sensors. A seed sensor
initiates a gradient by sending its neighbors a message with its location and a count
set to one. Each recipient remembers the value of the count and forwards the message
to its neighbors with the count incremented by one. Hence a wave of messages
propagates outwards from the seed. Each sensor maintains the minimum counter
value received and ignores messages containing larger values, which prevents the
wave from traveling backwards. If two sensors can communicate with each other
directly then they are considered to be within one communication hop of each other.
The minimum hop count value, hi, that a sensor i maintains will eventually be the
length of the shortest path to the seed in communication hops. In the proposed ad hoc
sensor network, a communication hop has a maximum physical distance of r
associated with it. This implies that a sensor i is at most distance hir from the seed.
However as the average density of sensors increases, sensors with the same hop count
tend to form concentric circular rings, of width approximately r, around the seed
sensor.
• Multilateration Algorithm: - Each sensor uses a multilateral procedure to combine the
distance estimates from all the seed sensors to produce their own positions. After
receiving at least three gradient values, sensors combine the distances from the seeds
to estimate their position relative to the positions of the seed sensors. In particular,
each sensor estimates its coordinates by finding coordinates that minimize the total
squared error between calculated distances and estimated distances. Sensor j's
calculated distance to seed i is:
dji = √ [ (xi - xj)2 + (yi - yj)2 (4)
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and sensor j's total error is:
Ej = ∑i=1to n (dji - dˆji)2 (5)
In equation (4) and equation (5), n is the number of seed sensors and dˆji is the estimated
distance computed through gradient propagation. The coordinates are then incrementally
updated in proportion to the gradient of the total error with respect to that coordinate.
The advantage of this algorithm is that it can be easily adapted to the addition of sensors,
addition of seeds and also death of sensors and seeds. But it requires substantial node density
before its accuracy reaches an acceptable level. Besides this hop count is not reliable in
measurement because environmental obstacles can prevent edges from appearing in the
connectivity graph that otherwise would be present as shown in Fig 7. In Fig 7 the hop count
distance between A and E is four hops due to the obstacle, but the real distance is far lesser
than four values.
C
B D
A E
OBSTACLE
Fig.7. Error in hop count distance matrices in the presence of an obstacle
4.2.2. Relaxation Based Distributed Algorithm
4.2.2.1. Spring Model
In [12] the authors propose an Anchor Free Localization (AFL) algorithm where nodes
start from a random initial coordinate assignment and converge to a consistent solution using
only local node interactions. The algorithm proceeds in two phases and it assumes the nodes
as point masses connected with strings and use force-directed relaxation methods to converge
to a minimum-energy configuration.
The first phase is a heuristic that produces a graph embedding which looks similar to the
original embedding. The authors assume that each node has a unique identifier and the
identifier of node i is denoted by IDi and the hop-count between nodes i ad j is the number of
nodes hi,j along the shortest path between i and j. The algorithm first elects the five reference
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nodes in which four nodes n1, n2, n3 and n4 are selected such that they are on the periphery of
the graph and the pair (n1, n2) is roughly perpendicular to the pair (n3, n4). The node n5 is
elected such that it is in the middle of the graph. At first the node with smallest ID is selected.
Next the reference node n1 is selected to maximize h1,2. After that n3 is selected to minimize |
h1,3 – h2,3 | and the tie-breaking rule is to pick the node that minimizes h1,3 + h2,3. In the next
stage n4 is selected to minimize | h1,4 – h2,4 | and the ties are broken by picking the node that
maximizes h3,4. Next n5 is selected which minimizes | h1,5 – h2,5 | and from contender nodes
pick the node that minimizes | h3,5 – h4,5 |. So node n5 is the center of the graph and node n1,
n2, n3, n4 becomes the periphery of the graph. Now for all nodes ni the heuristics uses the
hop-counts h1,i, h2i, h3,i, h4,i, and h5,i from the chosen reference nodes to approximate the polar
coordinates (ρi, θi) where
ρi = h5,i * R (7)
θi = tan-1[(h1,i – h2,i) / (h3,i – h4,i)] (8)
and R is the maximum radio range. In the first stage when calculating ρk the use of range
R to represent one hop-count results in a graph which is physically larger than the original
graph and this error can be eliminated in the next stage.
In the second phase, each node ni calculates the estimated distance dˆi,j to each neighbours
nj and it also knows the measured distance ri,j to neighbour nj. Now if vˆi,j represent the unit
vector in the direction from pˆi to pˆj ( pˆi and pˆj ate the current estimates of i and j
respectively) then the force Fi,j in the direction vˆi,j is given by
Fi,j = vˆi,j ( dˆi,j – ri,j) (9)
And the resultant force on node i is given by
Fi = ∑i,j Fi,j (10)
The energy Ei,j of nodes ni and nj due to the difference in measured and estimated
distances is the sequence of the magnitude of Fi,j and the total energy of node i is equal to
Ei = ∑j Ei,j = ∑j ( dˆi,j – ri,j)2 (11)
And the total energy of the system E is given by
E = ∑i Ei (12)
Now the energy Ei of each node ni reduces when it moves by an infinitesimal amount in
the direction of force Fi. In the optimization, the magnitude of Fi for each node ni is zero and
the global energy of the system E is also zero and the algorithm converges.
Extensive simulations show that the proposed algorithm outperforms incremental
algorithm by both being able to converge to correct positions and by being significantly more
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robust to errors in local distance estimate [13]. The limitation of this approach is that the
algorithm is susceptible to local minima.
4.2.2.2. Cooperative Ranging Approach
In [13] the authors describe a Cooperative ranging approach which uses Assumption
Based Coordinate (ABC) as its primitive to solve the localization problem. ABC algorithm
determines the location of the unknown nodes by making assumptions when necessary and
compensating the errors through corrections and redundant calculations as more information
becomes available. The algorithm starts with the assumption that node n0 is located at (0, 0,
0). n1 is the first node to establish communication with n0 and is assumed to be located at (r01,
0, 0), where r01 is the RSSI distance between n0 and n1. The location of the next node n2 (x2,
y2, z2) can be obtained on the basis of two assumptions: y2 is positive and z2 = 0, so
x2 = (r012 + r022 + r122)/ 2r01 (13)
y2 = √ ( r022 - x22) (14)
Next location of n3 (x3, y3, z3) can be determined by assuming z3 = 0, so
x3 = (r012 + r032 + r132)/ 2r01 (15)
y3 = (r032 - r232 + x22 + y22 – 2x2x3)/ 2y2 (16)
z3 = √ ( r032 – x32 – y32) (17)
From this point onwards the system of equations used to solve for further is no longer
underdetermined and so the standard algorithm can be applied for each node and its
neighbours.
Next the authors propose a cooperative ranging approach that exploits the high
connectivity of the network to translate the global positioning challenge into a number of
distributed local positioning problems that iteratively converge to a global solution by
interacting with each other. In the proposed approach, every single node plays the same role
repetitively and concurrently executes the following functions:
• Receive ranging and location information from neighbouring nodes.
• Solve the local localization problem by ABC algorithm.
• Transmit the obtained results to the neighbouring nodes.
After some repetitive iteration the system will converge to a global solution.
The advantage of this approach is that no global resources or communications are needed.
The disadvantage is that convergence may take some time and that nodes with high mobility
may be hard to cover.
4.2.3. Coordinate System Stitching
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C
E
B E
C
E
F
D B F
A
D D
A
C A
B
(a) (b)
Fig.8. (a) Flip ambiguity (b) Discontinuous Flex ambiguity [14]
4.2.3.1. Cluster based Approach
In [14] the authors propose a distributed algorithm for locating nodes in a sensor network
in which the nodes have the ability to estimate the distance to nearby nodes. Before
describing the algorithm we need to know the distinction between non-rigid and rigid graphs.
Non-rigid graphs can be continuously deformed to produce on infinite number of different
realization, while rigid graphs cannot. However, in rigid graphs there are two types of
discontinuous deformations that can prevent a realization from being unique.
• Flip ambiguities occur for a graph in a d-dimensional space when the positions of all
neighbours of some vertex span a (d-1) dimensional subspace. In this case, the
neighbours create a mirror through which the vertex can be reflected. As shown in Fig.
8(a) vertex A can be reflected across the line connecting B and C with no change in
distance constraints.
• Discontinuous flex ambiguities occur when the removal of one edge allows part of the
graph to be flexed to a different configuration and the removal edge reinserted with
the same length. As in Fig. 8(b) first AD is removed and then reinserted, the graph can
flex in the direction of arrow, taking on a different configuration but preserved all
distance constraints.
The algorithm is basically consists of two phases. Phase 1 is cluster localization where
each node becomes the centre of the cluster and estimates the relative location of its
neighbours which can be unambiguously localized. For each cluster, all the robust
quadrilaterals as well as the largest sub graph composed solely of overlapping robust quads
are identified. The authors define robust triangles to be a triangle which satisfies
Bsin2θ > dmin (18)
In equation (18), b is the length of the shortest side and θ is the smallest angle and dmin is
the threshold based on the measurement noise. If a quadrilateral has four robust sub-triangles
then the quadrilateral is a robust quadrilateral. The algorithm starts with a robust quadrilateral
and when two quads have three nodes in common and the first quad is fully localized, the
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second quad can be localized by trilaterating from the three known positions.
10 26 3
1
13 33
15 2 18
16
22
5 4 13
42
19 15
32
6 9
14
22 50
36
10
8 7
40
Fig.9. Network Topology with inter node distances between nodes [15]
In the second phase i.e. cluster transformation, the position of each node in each local
coordinate system are shared. As long as there are at least three non-collinear nodes in
common between the two localizations, the transformation can be computed by rotation,
translation, reflection.
The advantage of this scheme is that cluster based localization supports dynamic node
insertion and mobility. The limitation is that under condition of low node connectivity or high
measurement noise, the algorithm may be unable to localize a useful number of nodes.
4.2.3.2. Construction of Global Coordinate System in a network of Static Computational
Nodes from Inter Node Distance
In [15] the authors propose an algorithm which is based on coordinate system stitching
which constructs a spatial map and a distance matrix and then tries to minimize the
discrepancies between them by translation, rotation and reflection. The distance matrix is
explained with the help of Fig. 9. and Fig. 10. In Fig. 9 a collection of nodes and estimates of
distances between some pairs of these nodes has been shown. A distance matrix of an
individual node may acquire some subset of the distance estimates. So the distance matrix for
node 2 is shown in Fig. 10(a). The distance matrix of two different nodes may overlap as
shown in Fig. 10(b). Now to construct the spatial map from a distance matrix we need to
construct an initial map containing a triangle of three non-collinear pair-wise neighbouring
nodes. Then more nodes are inserted into the map, one at a time, based on distances to nodes
already in the map, in an iterative process so that the node must have at least three
non-collinear neighbour nodes. The process terminates when all nodes are inserted into the
map or when no uninserted node can be inserted.
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1 3
1
2
10 2
26 3 5 4
13 15 5
22
6
19 6
9
8
7
(a) (b)
Fig.10. (a) Distance Matrix of Node 2 (b) Overlapping local maps [15]
Now to compute the initial map we need to find the longest side and denote it’s end node
as p and r and then allign this side with x axis by setting p’s position to (0,0) and r’s position
to (Dpr, 0). Then choose any third node q whose position is (x, y) where x=( Dpq2 + Dpr2+
Dqr2)/2 Dpr and y=√( Dpr2-x2) (as shown in Fig. 11). Next at each iteration, a node with highest
number of neighbours already in the maps is chosen for insertion and the process will stop
when no remaining unmapped node can be found with at least three mapped neighbours that
are non-collinear.
q (x,y)
Dpq Dqr
p (0,0) r (Dpr,0)
x2 + y2 = Dpq2, ( Dpr – x)2 + y2 = Dqr2
Fig.11. Initial map [15]
Next the authors discusses the process of reconciling two maps that have at least some
nodes in common but that differ on the position of those common nodes by rotation,
translation and reflection. When a node has sufficient distance estimates, it locally broadcasts
a map of its neighbourhood. When a node receives a map from a neighbour, it reconciles its
own map with its neighbour’s and broadcasts its own map. In this way, each node should
quickly acquire a map of its neighbourhood. Eventually, this agreement should spread
throughout the network so that a common coordinate system is formed.
The advantage of this scheme is that it does not need anchor or beacon nodes for
localization. But in traditional communication model, where nodes can communicate only
with neighbors, this algorithm may converge quite slowly since a single coordinate system
must propagate from its source across the entire network.
4.2.4. Hybrid Localization
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4.2.4.1. Localization scheme composed MDS and PDS
In [16] the authors present a localization scheme composed of two localization techniques:
multidimensional scaling (MDS) and proximity based map (PDM). At first some anchors are
deployed denoted by primary anchors. In the first phase, some sensors are selected as
secondary anchors which are localized through multidimensional scaling. Nodes which are
neither primary anchors nor secondary one are called normal sensors. In the second phase, the
normal sensors are localized through proximity distance mapping.
In the first stage each primary anchor sends an invitation packet containing its unique ID,
a counter initialized to zero and a value ks controlling the number of secondary anchors, to
one of its neighbors. Normal sensor receiving this packet will perform a Bernoulli trial with a
success rate of p. If the outcome is true, the normal sensor increments the counter by one and
becomes a secondary anchor. The packet will be forwarded to another neighbor until the
counter equals to ks. After sending the invitation packet, each primary anchor sends packets
containing its unique ID and coordinates to all of its neighbors. The packet also bears a field
marking the proximity, i.e. the distance or hop count the packet has travelled. The value is
initialized to be zero. Secondary anchors will also do what primary anchors do, sending out
packets with its unique ID but leaving the coordinate field blank. Every node (including
anchors) receiving a proximity packet from an anchor (either primary or secondary) will store
its ID and the proximity value. If a packet from a particular anchor has been received before,
the node examines the proximity and check whether it is larger than the stored proximity. If it
is larger than the stored value, the packet will be discarded. Otherwise, the stored value and
the proximity field of the packet will be updated and the packet will be forwarded to other
neighbors. Thus the stored proximity always reflects the shortest path distance or hop count
from a particular anchor. After an anchor has discovered its proximities to all anchors, it will
send the proximities it has collected to other anchors and wait for other anchors to do the
same thing. When all anchors distribute the proximities to their counterparts, each anchor
knows the proximity information between every pair of anchors. Each secondary anchor can
now determine its location through classical MDS.
After the first phase, each secondary anchor also knows the position estimates of other
secondary anchors as MDS provides a configuration about the primary and secondary
anchors and calculates the proximity distance mapping. The mapping and the position
estimates of secondary anchors obtained from the first phase are distributed to the normal
nodes nearby. Normal sensor node uses the mapping to process the proximity vector it has
stored when it aided anchors exchanging proximity information. Finally, the node position is
calculated by multilateration with the processed proximity vector and the position
information of primary and secondary anchors.
The main advantage of this scheme is to minimize the computation cost. For classical
MDS, the complexity is О(n3) where n is number of nodes. The complexity for PDM is О(m3)
where m is the number of anchors. But the scheme composed of MDS and PDM has a
complexity of О(mx3) where mx is the total number of primary and secondary anchors. So by
keeping mx as a reasonable number, the complexity can be made similar to the complexity of
PDM. The limitation of this scheme is that it does not perform well when there are only a few
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anchors.
4.2.4.2. Simple Hybrid Absolute-Relative Positioning (SHARP)
In [17] the authors present a localization scheme refers to as: Simple Hybrid
Absolute-Relative Positioning (SHARP) which uses multidimensional scaling (MDS) and
Ad-hoc Positioning System (APS) for localization. The localization scheme consists of three
phases. In the first phase a set of reference nodes are selected randomly or along the outer
perimeter of the network. In the second phase a relative localization method MDS is used to
relatively localize the reference nodes selected in first phase. At first shortest-path distance
between each pair of reference nodes are computed and then MDS is applied to construct the
relative map. The result of first and second phases is a set of nodes with known coordinates
according to some coordinate system. In third phase, an absolute localization method APS is
used to localize the rest of the nodes in the network using the reference nodes as anchors.
Each node uses the shortest-path distance information to estimate its distances to anchors.
Then, it performs multilateration to estimate its position.
SHARP outperforms MDS if both the localization error and the cost are considered. The
limitation of this scheme is that for anisotropic networks SHARP gives poor performance.
4.2.4.3. Localization scheme composed inductive and deductive approach
In [27] the authors present a localization scheme for indoor environment. There are two
main methods to estimate the position in indoor environments. On the one hand, there are the
so-called deductive methods. These take into account the physical properties of signal
propagation. They require a propagation model, topological information about the
environment, and the exact position of the base stations. On the other hand, there are the
so-called inductive methods. These require a previous training phase, where the system learns
the signal strength in each location. The main shortcoming of this approach is that the
training phase can be very expensive. The complex indoor environment makes the
propagation model task very hard. It is difficult to improve deductive methods when there are
many walls and obstacles because deductive methods work estimating the position
mathematically with the real measures taken directly from environment in the training phase.
In [27] the authors present a hybrid location system using a new stochastic approach which is
based on a combination of deductive and inductive methods.
The advantage of this method covers a hard indoor environment without many base
stations. Besides that, this technique reduces the training phase without losing precision.
4.2.5. Interferometric Ranging Based Localization
The idea behind the Radio Interferometric Positioning System (RIPS) proposed in [18],
[19], [20] is to utilize two transmitters to create the interference signal directly. If the
frequencies of the two emitters are almost the same then the composite signal will have a low
frequency envelope that can be measured by cheap and simple hardware readily available on
a WSN node. But due to the lack of synchronization of the nodes there will be a relative
phase offset of the signal at two receivers which is a function of the relative positions of the
four nodes involved and the carrier frequency. By making multiple measurements it is
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possible to reconstruct the relative location of the nodes in 3D. But localization using
interferometric ranging is an NP-Complete problem [20]. To optimize the solution globally
[18] uses genetic algorithm approach whereas [19] reduces the search space with additional
RSSI readings.
Compared to the more common techniques such as received signal strength, time of
arrival, and angle of arrival ranging, interferometric ranging has the advantage that the
measurement could be highly precise. But localization using interferometric ranging requires
a considerably larger set of measurements which limits their solutions to smaller networks
(16 nodes in [18] and 25 nodes in [19]). To solve this problem an iterative algorithm has been
proposed in [20] which calculates node locations from a set of seeding anchors and gradually
builds a global localization solution. Compared to [18] and [19], which treat localization as a
global optimization problem, the iterative algorithm is a distributed algorithm that is simple
to implement in larger networks.
4.2.6. Error Propagation Aware Localization
An error propagation aware (EPA) algorithm has been proposed in [21] which integrates
the path loss and distance measurement error model. In the start of the algorithm, anchor
nodes broadcast their information which includes their unique ID, global coordinates, and the
position error variance σp2. Each node senses the channel and records the TOA information to
each anchor. The power of the detected direct path is translated to a ranging variance σr2.
After getting σr2 and σp2 the sensor node formulates weighting matrix given by equation (19).
W = Wr + Wp (19)
Wr = diag(σr1 ,………., σrn ) and Wp = diag(σp1 ,………., σpn2)
2 2 2
for n range measurement to anchors. In the next stage the node computes its position by
incorporating its weighting matrix into Weighted Least Square (WLS) algorithm. After
getting its own position the sensor node becomes an anchor and starts broadcasting its ID,
global coordinate and σp2. This process is repeated until all the nodes obtain their position and
transformed into anchors.
The algorithm takes advantage of the ranging and position information obtained from
each involved anchor and so it produces precise estimation than other localization schemes.
5. Summary Of Proposals
The performance of any localization algorithm depends on a number of factors, such as
anchor density, node density, computation and communication costs, accuracy of the scheme
and so on. All approaches have their own merits and drawbacks, making them suitable for
different applications.
Some algorithms require beacons (Diffusion, Bounding Box, Gradient, APIT) and some
do not (MDS-MAP, Relaxation based localization scheme, Coordinate system stitching).
Beaconless algorithms produce relative coordinate system which can optionally be registered
to a global coordinate system. Sometimes sensor networks do not require a global coordinate
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system. In these situations beaconless algorithms suffice.
Certain algorithms are centralized while some are distributed. Centralized algorithms
generally compute more accurate positions and can be applicable to situations where
accuracy is important. Distributed algorithms on the other hand do not depend on large
centralized system and potentially have better scalability.
Beside these factors battery life and communication costs are also important for sensor
networks. Generally centralized algorithms the communication costs are high to move data
back to the base station. But the accuracy is also high in centralized schemes than the
distributed approaches. Moreover some schemes perform well in high anchor density while
some need only few anchors. As shown in [25], multilateration has low computation and
communication cost and performs well when there are many anchors. On the other hand
MDS-MAP has higher computation and communication cost and performs well when there
are few anchor nodes.
The different schemes reviewed in this article are summarized in Table 1.
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Table 1. Summary of proposals for Localization in WSN
Table 1. Summary of proposals for Localization in WSN
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6. Open Problems
There are considerable amount research activities to improve localization in wireless
sensor networks. But there are also some interesting open problems that need further
attention.
Interferomatric ranging based localization that takes error propagation into account:
Interferometric ranging technique has been recently proposed as a possible way to localize
sensor networks as it gives precise measurements than other common techniques. But
simulation results from [20] indicate that error propagation can be a potentially significant
problem in interferometric ranging. In order to localize large networks using interferometric
ranging from a small set of anchors, future localization algorithms need to find a way to
effectively limit the error propagation.
Robust algorithm for mobile sensor networks: Recently there has been a great deal of
research on using mobility in sensor networks to assist in the initial deployment of nodes.
Mobile sensors are useful in this environment because they can move to locations that meet
sensing coverage requirements. New localization algorithms will need to be developed to
accommodate these moving nodes. So, devising a robust localization algorithm for next
generation mobile sensor networks is an open problem in future.
Attack the challenges of Information Asymmetry: WSNs are often used for military
applications like landmine detection, battlefield surveillance, or target tracking. In such
unique operational environments, an adversary can capture and compromise one or more
sensors physically. The adversary can now tamper with the sensor node by injecting
malicious code, forcing the node to malfunction, extracting the cryptographic information
held by the node to bypass security hurdles like authentication and verification, so on and so
forth. In a beacon-based localization model, since sensor nodes are not capable of
determining their own location, they have no way of determining which beacon nodes are
being truthful in providing accurate location information. There could be malicious beacon
nodes that give false location information to sensor nodes compelling them to compute
incorrect location. This situation, in which one entity has more information than the other, is
referred to as information asymmetry. To solve this problem, in [22] the authors propose a
Distributed Reputation-based Beacon Trust System (DRBTS), which aimed to provide a
method by which beacon nodes can monitor each other and provide information so that
unknown nodes can choose who to trust, but future research work is needed in this field.
Finding the minimum number of Beacon locations: Beacon based approaches requires of
a set of beacon nodes, with known locations. So, an optimal as well as robust scheme will be
to have a minimum number of beacons in a region. Further work is needed to find the
minimum number of locations where beacons must be placed so the whole network can be
localized with a certain level of accuracy.
Finding localization algorithms in three dimensional space: WSNs are physical
impossible to be deployed into the area of absolute plane in the context of real-world
applications. For all kinds of applications in WSNs accurate location information is crucial.
So, a good localization schemes for accurate localization of sensors in three dimensional
space can be a good area of future work.
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So these are the few problems for future research work to improve localization in wireless
sensor technology.
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Appendix: A. Multidimensional Scaling
In Multidimensional Scaling (MDS) a set of points whose position is unknown and
measured distances between each pair of points are given. It can be used to localize sensor
nodes in a network. Without anchors or GPS, MDS can solve for the relative coordinates of a
group of sensor nodes with resilience to measurement error and rather high accuracy.
Let there be n sensors in a network, with positions Xi, i = 1 . . . n, and let X =
[X1,X2, . . . ,Xn]T . X is nxm, where m is the dimensionality of X.
Let D = [dij ] be the nxn matrix of pairwise distance measurements, where dij is the
measured distance between Xi and Xj for i ≠ j, and dii = 0 for all i. The distance measurements
dij must obey the triangular inequality: dij + dik ≥ djk for all (i, j, k).
Classical metric multidimensional scaling is derived from the Law of Cosines, which
states that given two sides of a triangle dij, dik and the angle between them θjik, the third side
can be computed using the formula:
d2jk = d2ij + d2ik − 2dijdik cos θjik (A.1)
→ dijdik cos θjik = 1/2(d2ij + d2ik − d2jk) (A.2)
→ (Xj − Xi) · (Xk − Xi) = 1/2(d2ij + d2ik − d2jk) (A.3)
Next choose some X0 from X to be the origin of a coordinate system, and construct a
matrix B(n−1)x(n−1) as follows:
bij = 1/2(d20i + d20j − d2ij) (A.4)
Let X/(n−1)xm the matrix X where each of the Xi’s is shifted to have its origin at X0: X/i =
Xi − X0. Then, using equations (A.3) and (A.4):
X/X/T = B (A.5)
We can solve for X/ by taking an eigen decomposition of B into an orthonormal matrix of
eigenvectors and a diagonal matrix of matching eigenvalues:
B = X/X/T = UVUT (A.6)
X/ = UV ½ (A.7)
The problem is that X/ has too many columns and we need to find X in 2-space or
3-space. To do this, we throw away all but the two or three largest eigenvalues from V ,
leaving a 2x2 or 3x3 diagonal matrix, and throw away the matching eigenvectors (columns)
of U, leaving U(n−1)x2 or U(n−1)x3. Then X/ has the proper dimensionality.
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Appendix: B. Kalman Filter
The Kalman filter is a recursive estimator. This means that only the estimated state from
the previous time step and the current measurement are needed to compute the estimate for
the current state. In what follows, the notation x^n׀m represents the estimate of x at time n
given observations up to and including time m.
The state of the filter is represented by two variables:
• x^k׀k, the estimate of the state at time k given observations up to and including time k;
• Pk׀k, the error covarience matrix (a measure of the estimated accuracy of the state
estimate).
The Kalman filter has two distinct phases: Predict and Update. The predict phase uses
the state estimate from the previous timestep to produce an estimate of the state at the current
timestep. In the update phase, measurement information at the current timestep is used to
refine this prediction to arrive at a new more accurate state estimate, again for the current
timestep.
Predict:
Predicted state is given by x^k׀k = Fk x^k-1׀k-1 + Bkuk and Predicted estimate
covarience is given by Pk׀k-1 = Fk Pk-1׀k-1FTk + Qk-1 where
• Fk is the state transition model which is applied to the previous state xk-1;
• Bk is the control-input model which is applied to the control vector uk;
• Wk is the process noise which is assumed to be drawn from a zero mean
multivariate normal distribution with covariance Qk i.e. wk ~ N(0, Qk).
Update:
Innovation or measurement residual y˜k = zk - Hk x^k׀k-1 where at time k an
observation (or measurement) zk of the true state xk is made according to
zk = Hkxk + vk
where Hk is the observation model which maps the true state space into the
observed space and vk is the observation noise which is assumed to be zero mean
Gaussian white noise with covariance Rk i.e. vk ~ N( 0, Rk).
Innovation (or residual) covariance Sk = Hk Pk׀k-1HTk +Rk
Kalman Filter gain Kk = Pk׀k-1HTkSk-1
Updated state estimate x^k׀k = x^k׀k-1 + Kk y˜k
Updated estimate covariance Pk׀k = (I –KkHk) Pk׀k-1
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