Minimizing the Localization Error in Wireless Sensor Networks Using Multi-Objective Optimization Techniques

When it comes to remote sensing applications, wireless sensor networks (WSN) are crucial. Because of their small size, low cost, and ability to communicate with one another, sensors are finding more and more applications in a wide range of wireless technologies. The sensor network is the result of the fusion of microelectronic and electromechanical technologies. Through the localization procedure, the precise location of every network node can be determined. When trying to pinpoint the precise location of a node, a mobility anchor can be used in a helpful method known as mobility-assisted localization. In addition to improving route optimization for location-aware mobile nodes, the mobile anchor can do the same for stationary ones. This system proposes a multi-objective approach to minimizing the distance between the source and target nodes by employing the Dijkstra algorithm while avoiding obstacles. Both the Improved Grasshopper Optimization Algorithm (IGOA) and the Butterfly Optimization Algorithm (BOA) have been incorporated into multi-objective models for obstacle avoidance and route planning. Accuracy in localization is enhanced by the proposed system. Further, it decreases both localization errors and computation time when compared to the existing systems

무선 센서 네트워크 상에서의 효율적인 위치 추정 알고리즘 연구

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 김성철.In this dissertation, efficient localization algorithms for wireless sensor networks are represented. Localization algorithms are widely used in commercial systems and application. The localization techniques are anticipated to be developed for various environments and reduce the localization error for accurate location information because the user demands for more accurate positioning systems for medical care, home networks, and monitoring applications in personal range environments. A well-known localization system is GPS, with applications such as mobile navigation. The GPS shows good performance on road or roughly finding location system in outdoor environments but limited in indoor environments. Due to the development of handsets like smart phone, the users can easily receive the GPS signals and other RF signals including 3G/4G/5G signals, WLAN (Wireless Local Area Networks) signals, and the signals from other sensors. Thus, the various systems using localization schemes are developed, especially, the WSNs (Wireless Sensor Networks) localization system is actively studied in indoor environment without GPS. In this dissertation, the range-free localization algorithm and the range-based localization algorithm are reported for WSNs localization system. The range-free localization algorithms are proposed before to estimate location using signal database, called signal map, or the anchor nodes of antenna patterns, or ID configuration of the linked anchor nodes, etc. These algorithms generally need to additional hardware or have low accuracy due to low information for location estimation. The range-based algorithms, equal to distance-based algorithms, are based on received signal strength, RSSI, or time delay, TOA and TDOA, between the anchor nodes and a target node. Although the TOA and TDOA are very accurate distance estimation schemes, these scheme have the critical problem, the time synchronization. Although RSSI is very simple to setup the localization system with tiny sensors, the signal variation causes severe distance estimation error. The angle estimation, AOA, provides additional information to estimation the location. However, AOA needs additional hardware, the antenna arrays, which is not suitable for tiny sensors. In this dissertation, range-free and range-based localization algorithms are analyzed and summarized for WSNs with tiny sensors. The WSNs localization systems are generally used range-based algorithm. The range-based algorithms have major source of distance estimation error, and the distance estimation error causes severe localization error. In this dissertation, the localization error mitigation algorithms are proposed in two dimensional environments and three dimensional environments for WSNs. The mitigation algorithms in two dimensional environments consist of several steps, which are distance error mitigation algorithm, location error mitigation algorithm, and bad condition detection algorithm. The each algorithm is effective to reduce the localization error, but the accuracy of location estimation is the best when they are combined. The performance of proposed algorithms is examined with variation of received signal strength and it is confirmed that the combined proposed algorithm has the best performance rather than that of conventional scheme and each proposed algorithms. The three dimensional localization uses Herons formula of tetrahedron to calculate the target height, then transforms a two dimensional location computed by LLSE into a three dimensional estimated location. Simulation results validate the accuracy of the proposed scheme.Contents Chapter 1 Introduction...........................................................1 Chapter 2 Location estimation for wireless sensor networks.................................................................................................4 2.1 Introduction..................................................................................4 2.2 Range-free location estimation ...................................................7 2.2.1 Cell-ID location estimation .........................................................7 2.2.2 Fingerprint location estimation ...................................................8 2.2.3 Other range-free location estimation.........................................10 2.3 Range-based location estimation ..............................................12 2.3.1 Time delay based distance estimation.......................................12 2.3.2 Received signal strength based distance estimation .................16 2.3.3 Angle of arrival based location estimation................................18 2.4 Summary.......................................................................................20 Chapter 3 Two dimensional location estimation for wireless sensor networks......................................................................22 3.1 Introduction................................................................................22 3.2 Tri-lateration ..................................................................24 3.2.1 Linear least square estimation ..................................................24 3.2.2 The cases of tri-lateration .........................................................26 3.3 Geometric mitigation algorithm …............................................27 3.3.1 Motivation .................................................................................27 3.3.2 Algorithm explanation ..............................................................28 3.3.3 Simulation .................................................................................29 3.3.4 Conclusion ................................................................................34 3.4 Coordinate shift algorithm ..........................................................35 3.4.1 Motivation .................................................................................35 3.4.2 Algorithm explanation...............................................................36 3.4.3 Simulation .................................................................................41 3.4.4 Conclusion ................................................................................43 3.5 Bad condition detection algorithm ...............................................44 3.5.1 Motivation .................................................................................44 3.5.2 Algorithm explanation...............................................................45 3.5.3 Simulation .................................................................................51 3.5.4 Conclusion ................................................................................54 3.6 Conclusion..................................................................................55 Chapter 4 Three dimensional location estimation for wireless sensor networks .....................................................................56 4.1 Introduction................................................................................56 4.2 Motivation.....................................................................................57 4.2.1 Singular matrix problem…........................................................57 4.2.2 Short range location estimation.................................................59 4.3 Algorithm explanation....................................................................60 4.4 Simulation........................................................................................68 4.5 Conclusion..................................................................................72 Bibliography....................................................................................73 Abstract in Korean.....................................................................................78Docto

Localization performance evaluation of extended kalman filter in wireless sensors network

This paper evaluates the positioning and tracking performance of Extended Kalman Filter (EKF) in wireless sensors network. The EKF is a linear approximation of statistical Kalman Filter (KF) and has the capability to work efficiently in non-linear systems. The EKF is based on an iterative process of estimating current state information from the previously estimated state. Its working is based on the linearization of observation model around the mean of current state information. The EKF has small computation complexity and requires low memory compared to other Bayesian algorithms which makes it very suitable for low powered mobile devices. This paper evaluates the localization and tracking performance of EKF for (i) Position (P) model, (ii) Position-Velocity (PV) model and (iii) Position-Velocity-Acceleration (PVA) model. The EKF processes distance measurements from cricket sensors that are acquired through time difference of arrival between ultrasound and Radio Frequency (RF) signals. Further, localization performance under varying number of beacons/sensors is also evaluated in this paper. © 2014 Published by Elsevier B.V.Peer ReviewedPostprint (published version

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

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

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

Localization Context-Aware Models for Wireless Sensor Network

Wireless sensor networks (WSNs) are emerging as the key technology to support the Internet of Things (IoT) and smart objects. Small devices with low energy consumption and limited computing resources have wide use in many applications and different fields. Nodes are deployed randomly without a priori knowledge of their location. However, location context is a fundamental feature necessary to provide a context-aware framework to information gathered from sensors in many services such as intrusion detection, surveillance, geographic routing/forwarding, and coverage area management. Nevertheless, only a little number of nodes called anchors are equipped with localization components, such as Global Positioning System (GPS) chips. Worse still, when sensors are deployed in an indoor environment, GPS serves no purpose. This chapter surveys a variety of state-of-the-art existing localization techniques and compares their characteristics by detailing their applications, strengths, and challenges. The specificities and enhancements of the most popular and effective techniques are as well reported. Besides, current research directions in localization are discussed

Energy efficient coordinate establishment in wireless sensor networks

Wireless Sensor Networks (WSNs) refer to a group of spatially deployed devices which are used to monitor or detect phenomena, and have the ability to relay sensed data and signalling wirelessly. Positioning information in WSNs is absolutely crucial to perform tasks such as intelligent routing, data aggregation and data collection optimally. A need exists for localisation algorithms which are scalable, distributed, energy efficient and easy to deploy. This research proposes a beaconless Cluster-based Radial Coordinate Establishment (CRCE) positioning algorithm to locate sensor nodes relative to a local coordinate system. The system does not make use of Global Positioning System (GPS) or any other method to provide apriori position information for a set of nodes prior to the CRCE process. The objective of CRCE is to reduce energy consumption while providing a scalable coordinate establishment method for use in WSNs. To reduce energy consumption during the node positioning process, the research focuses on minimising the number of message exchanges in the network by implementing a cluster-based network topology and utilising the potential of geographically distributed processors. A radial coordinate convergence process is proposed to achieve scalability as the number of sensors in the network increases. Three other localisation algorithms are investigated and compared to CRCE to identify the one best suited for coordinate establishment in WSNs. Two of these comparison algorithms are published in the literature and the other is a modified version of one of the published algorithms. The results show a significant decrease in the number of messages that are necessary to establish a network-wide coordinate system successfully, ultimately making it more scalable and energy efficient. In addition, position based algorithms, such as location based routing, can be deployed on top of CRCE.Dissertation (MEng (Computer Engineering))--University of Pretoria, 2006.Electrical, Electronic and Computer Engineeringunrestricte