Dead Reckoning Localization Technique for Mobile Wireless Sensor Networks

Localization in wireless sensor networks not only provides a node with its geographical location but also a basic requirement for other applications such as geographical routing. Although a rich literature is available for localization in static WSN, not enough work is done for mobile WSNs, owing to the complexity due to node mobility. Most of the existing techniques for localization in mobile WSNs uses Monte-Carlo localization, which is not only time-consuming but also memory intensive. They, consider either the unknown nodes or anchor nodes to be static. In this paper, we propose a technique called Dead Reckoning Localization for mobile WSNs. In the proposed technique all nodes (unknown nodes as well as anchor nodes) are mobile. Localization in DRLMSN is done at discrete time intervals called checkpoints. Unknown nodes are localized for the first time using three anchor nodes. For their subsequent localizations, only two anchor nodes are used. The proposed technique estimates two possible locations of a node Using Bezouts theorem. A dead reckoning approach is used to select one of the two estimated locations. We have evaluated DRLMSN through simulation using Castalia simulator, and is compared with a similar technique called RSS-MCL proposed by Wang and Zhu .Comment: Journal Paper, IET Wireless Sensor Systems, 201

Efficient Range-Free Monte-Carlo-Localization for Mobile Wireless Sensor Networks

Das Hauptproblem von Lokalisierungsalgorithmen für WSNs basierend auf Ankerknoten ist die Abhängigkeit von diesen. Mobilität im Netzwerk kann zu Topologien führen, in denen einzelne Knoten oder ganze Teile des Netzwerks temporär von allen Ankerknoten isoliert werden. In diesen Fällen ist keine weitere Lokalisierung möglich. Dies wirkt sich primär auf den Lokalisierungsfehler aus, der in diesen Fällen stark ansteigt. Des weiteren haben Betreiber von Sensornetzwerken Interesse daran, die Anzahl der kosten- und wartungsintensiveren Ankerknoten auf ein Minimum zu reduzieren. Dies verstärkt zusätzlich das Problem von nicht verfügbaren Ankerknoten während des Netzwerkbetriebs. In dieser Arbeit werden zunächst die Vor- und Nachteile der beiden großen Hauptkategorien von Lokalisierungsalgorithmen (range-based und range-free Verfahren) diskutiert und eine Studie eines oft für range-based Lokalisierung genutzten Distanzbestimmungsverfahren mit Hilfe des RSSI vorgestellt. Danach werden zwei neue Varianten für ein bekanntes range-free Lokalisierungsverfahren mit Namen MCL eingeführt. Beide haben zum Ziel das Problem der temporär nicht verfügbaren Ankerknoten zu lösen, bedienen sich dabei aber unterschiedlicher Mittel. SA-MCL nutzt ein dead reckoning Verfahren, um die Positionsschätzung vom letzten bekannten Standort weiter zu führen. Dies geschieht mit Hilfe von zusätzlichen Sensorinformationen, die von einem elektronischen Kompass und einem Beschleunigungsmesser zur Verfügung gestellt werden. PO-MCL hingegen nutzt das Mobilitätsverhalten von einigen Anwendungen in Sensornetzwerken aus, bei denen sich alle Knoten primär auf einer festen Anzahl von Pfaden bewegen, um den Lokalisierungsprozess zu verbessern. Beide Methoden werden durch detaillierte Netzwerksimulationen evaluiert. Im Fall von SA-MCL wird außerdem eine Implementierung auf echter Hardware vorgestellt und eine Feldstudie in einem mobilen Sensornetzwerk durchgeführt. Aus den Ergebnissen ist zu sehen, dass der Lokalisierungsfehler in Situationen mit niedriger Ankerknotendichte im Fall von SA-MCL um bis zu 60% reduziert werden kann, beziehungsweise um bis zu 50% im Fall von PO-MCL.

Localization Algorithm for Mobile Sensor Nodes Using 3D Space in Wireless Sensor Network

On the problem of wireless sensor network localization; few true three - dimensional (3D) methods have been developed to satisfy the practical needs. In this work we proposed range - based 3D localization algorithm that is accurate, anchor - free, scalable and physical position available. A novel combination of distance and direction measurement techniques introduced to estimate ranges between neighbours. Based on this information local coordinate systems are constructed and then converge to form a global network wide coordinate system ,which finally leads to nodes absolute positions. Simulation results have shown that our algorithm achieves good trade - off between localization percentage and precision

Sparse Localization with a Mobile Beacon Based on LU Decomposition in Wireless Sensor Networks

Node localization is the core in wireless sensor network. It can be solved by powerful beacons, which are equipped with global positioning system devices to know their location information. In this article, we present a novel sparse localization approach with a mobile beacon based on LU decomposition. Our scheme firstly translates node localization problem into a 1-sparse vector recovery problem by establishing sparse localization model. Then, LU decomposition pre-processing is adopted to solve the problem that measurement matrix does not meet the re¬stricted isometry property. Later, the 1-sparse vector can be exactly recovered by compressive sensing. Finally, as the 1-sparse vector is approximate sparse, weighted Cen¬troid scheme is introduced to accurately locate the node. Simulation and analysis show that our scheme has better localization performance and lower requirement for the mobile beacon than MAP+GC, MAP-M, and MAP-M&N schemes. In addition, the obstacles and DOI have little effect on the novel scheme, and it has great localization performance under low SNR, thus, the scheme proposed is robust

Localisation in wireless sensor networks for disaster recovery and rescuing in built environments

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyProgress in micro-electromechanical systems (MEMS) and radio frequency (RF) technology has fostered the development of wireless sensor networks (WSNs). Different from traditional networks, WSNs are data-centric, self-configuring and self-healing. Although WSNs have been successfully applied in built environments (e.g. security and services in smart homes), their applications and benefits have not been fully explored in areas such as disaster recovery and rescuing. There are issues related to self-localisation as well as practical constraints to be taken into account. The current state-of-the art communication technologies used in disaster scenarios are challenged by various limitations (e.g. the uncertainty of RSS). Localisation in WSNs (location sensing) is a challenging problem, especially in disaster environments and there is a need for technological developments in order to cater to disaster conditions. This research seeks to design and develop novel localisation algorithms using WSNs to overcome the limitations in existing techniques. A novel probabilistic fuzzy logic based range-free localisation algorithm (PFRL) is devised to solve localisation problems for WSNs. Simulation results show that the proposed algorithm performs better than other range free localisation algorithms (namely DVhop localisation, Centroid localisation and Amorphous localisation) in terms of localisation accuracy by 15-30% with various numbers of anchors and degrees of radio propagation irregularity. In disaster scenarios, for example, if WSNs are applied to sense fire hazards in building, wireless sensor nodes will be equipped on different floors. To this end, PFRL has been extended to solve sensor localisation problems in 3D space. Computational results show that the 3D localisation algorithm provides better localisation accuracy when varying the system parameters with different communication/deployment models. PFRL is further developed by applying dynamic distance measurement updates among the moving sensors in a disaster environment. Simulation results indicate that the new method scales very well

Sequential Monte Carlo Localization Methods in Mobile Wireless Sensor Networks: A Review

The advancement of digital technology has increased the deployment of wireless sensor networks (WSNs) in our daily life. However, locating sensor nodes is a challenging task in WSNs. Sensing data without an accurate location is worthless, especially in critical applications. The pioneering technique in range-free localization schemes is a sequential Monte Carlo (SMC) method, which utilizes network connectivity to estimate sensor location without additional hardware. This study presents a comprehensive survey of stateof-the-art SMC localization schemes. We present the schemes as a thematic taxonomy of localization operation in SMC. Moreover, the critical characteristics of each existing scheme are analyzed to identify its advantages and disadvantages. The similarities and differences of each scheme are investigated on the basis of significant parameters, namely, localization accuracy, computational cost, communication cost, and number of samples. We discuss the challenges and direction of the future research work for each parameter

Geometric Constraint Based Range Free Localization Scheme For Wireless Sensor Networks (WSNs)

Localization of the wireless sensor networks (WSNs) is an emerging area of research. The accurate localization is essential to support extended network lifetime, better covering, geographical routing, and congested free network. In this thesis, we proposed four distributed range-free localization schemes. The proposed schemes are based on the analytical geometry, where an arc is used as the geometric primitive shape. The simulation and experimental validation are performed to evaluate the performance of the proposed schemes. First, we have proposed a mobile beacon based range-free localization scheme (MBBRFLS). The proposed scheme resolved the two underlying problems of the constraint area based localization: (i) localization accuracy depends on the size of the constraint area, and (2) the localization using the constraint area averaging. In this scheme, the constraint area is used to derive the geometric property of an arc. The localization begins with an approximation of the arc parameters. Later, the approximated parameters are used to generate the chords. The perpendicular bisector of the chords estimate the candidate positions of the sensor node. The valid position of the sensor node is identified using the logarithmic path loss model. The performance of proposed scheme is compared with Ssu and Galstyan schemes. From the results, it is observed that the proposed scheme at varying DOI shows 20.7% and 11.6% less localization error than Ssu and Galstyan schemes respectively. Similarly, at the varying beacon broadcasting interval the proposed scheme shows 18.8% and 8.3% less localization error than Ssu and Galstyan schemes respectively. Besides, at the varying communication range, the proposed scheme shows 18% and 9.2% less localization error than Ssu and Galstyan schemes respectively. To further enhance the localization accuracy, we have proposed MBBRFLS using an optimized beacon points selection (OBPS). In MBBRFLS-OBPS, the optimized beacon points minimized the constraint area of the sensor node. Later, the reduced constraint area is used to differentiate the valid or invalid estimated positions of the sensor node. In this scheme, we have only considered the sagitta of a minor arc for generating the chords. Therefore, the complexity of geometric calculations in MBBRFLS-OBPS is lesser than MBBRFLS. For localization, the MBBRFLS-OBPS use the perpendicular bisector of the chords (corresponding to the sagitta of minor arc) and the approximated radius. The performance of the proposed MBBRFLS-OBPS is compared with Ssu, Galstyan, and Singh schemes. From the results, it is observed that the proposed scheme using CIRCLE, vii SPIRAL, HILBERT, and S-CURVE trajectories shows 74.68%, 78.3%, 73.9%, and 70.3% less localization error than Ssu, Galstyan, and Singh schemes respectively. Next, we have proposed MBBRFLS using an optimized residence area formation (ORAF). The proposed MBBRFLS-ORAF further improves the localization accuracy. In this scheme, we have used the adaptive mechanism corresponding to the different size of the constraint area. The adaptive mechanism defines the number of random points required for the different size of the constraint area. In this scheme, we have improved the approximation accuracy of the arc parameters even at the larger size of the constraint area. Therefore, the localization accuracy is improved. The previous scheme MBBRFLS-OBPS use the residence area of the two beacon points for approximation. Therefore, the larger size of the constraint area degrades the approximation accuracy. In the MBBRFLS-ORAF, we have considered the residence area of the three non-collinear beacon points, which further improves the localization accuracy. The performance of the proposed scheme is compared with Ssu, Lee, Xiao, and Singh schemes. From the results, it is observed that the proposed MBBRFLS-ORAF at varying communication range shows 73.2%, 48.7%, 33.2%, and 20.7% less localization error than Ssu, Lee, Xiao, and Singh schemes respectively. Similarly, at the different beacon broadcasting intervals the proposed MBBRFLS-ORAF shows 75%, 53%, 38%, and 25% less localization error than Ssu, Lee, Xiao, and Singh schemes respectively. Besides, at the varying DOI the proposed MBBRFLS-ORAF shows 76.3%, 56.8%, 52%, and 35% less localization error than Ssu, Lee, Xiao, and Singh schemes respectively. Finally, we have proposed a localization scheme for unpredictable radio environment (LSURE). In this work, we have focused on the radio propagation irregularity and its impact on the localization accuracy. The most of the geometric constraint-based localization schemes suffer from the radio propagation irregularity. To demonstrate its impact, we have designed an experimental testbed for the real indoor environment. In the experimental testbed, the three static anchor nodes assist a sensor node to perform its localization. The impact of radio propagation irregularity is represented on the constraint areas of the sensor node. The communication range (estimated distance) of the anchor node is derived using the logarithmic regression model of RSSI-distance relationship. The additional error in the estimated distances, and the different placement of the anchor nodes generates the different size of the constraint areas. To improve the localization accuracy, we have used the dynamic circle expansion technique. The performance of the proposed LSURE is compared with APIT and Weighted Centroid schemes using the various deployment scenarios of the anchor nodes. From the results, it is observed that the proposed LSURE at different deployment scenarios of anchor nodes shows 65.94% and 73.54% less localization error than APIT and Weighted Centroid schemes

Localization in Wireless Sensor Networks

The technique of nding physical co-ordinates of a node is known as localization. Importance of localization arises from the need to tag the sensed data and associate events with their location of occurrence. Location information of a sensor node can be obtained by using GPS. But, installing GPS in every node is not a feasible solution. This is because: (i) sensor nodes are deployed in a very large number. Installing GPS at every node will increase the cost as well as size, (ii) GPS consume power, which will effect the network lifetime. Moreover, location cannot be pre-programmed as it is un-known where nodes will be deployed during their operational phase. In this thesis, we have made an attempt to address localization in static as well as mobile sensor networks. For static network we have proposed two distributed range based localization techniques called (i ) Localization using a single anchor node (LUSA), (ii ) Dis- tributed binary node localization estimation (DBNLE). Both the techniques are proposed for grid environment. In LUSA, we have identied three types of node: anchor, special and unknown node. For every anchor node there exists two special node and they are placed perpendicular to the anchor node. Localization in LUSA is achieved by a single anchor node and two special nodes. Localization occurs in two steps. First special nodes are localized and then the unknown nodes. We have compared LUSA with a closely related localization technique called Multi-duolateration (MDL). It is observed that the localization error and localization time is lesser in LUSA. In DBNLE a node is localized with only two location aware nodes instead of three nodes in most localization techniques. This not only reduces the localization time but also the dependency. For mobile WSNs, we have proposed a distributed localization technique called dead reckoning localization in mobile sensor networks (DRLMSN). In DRLMSN, localization is done at discrete time intervals called checkpoint. Unknown nodes are localized for the rst time using three anchor nodes. In their subsequent localization, only two anchor nodesare used. Using Bezouts theorem, we estimate two possible locations of a node. A dead reckoning approach is used to select one among the two estimated locations. We have used Castalia simulator to evaluate the performance of the schemes