Evaluation and Analysis of Node Localization Power Cost in Ad-Hoc Wireless Sensor Networks with Mobility

One of the key concerns with location-aware Ad-hoc Wireless Sensor Networks (AWSNs) is how sensor nodes determine their position. The inherent power limitations of an AWSN along with the requirement for long network lifetimes makes achieving fast and power-efficient localization vital. This research examines the cost (in terms of power) of network irregularities on communications and localization in an AWSN. The number of data bits transmitted and received are significantly affected by varying levels of mobility, node degree, and network shape. The concurrent localization approach, used by the APS-Euclidean algorithm, has significantly more accurate position estimates with a higher percentage of nodes localized, while requiring 50% less data communications overhead, than the Map-Growing algorithm. Analytical power models capable of estimating the power required to localize are derived. The average amount of data communications required by either of these algorithms in a highly mobile network with a relatively high degree consumes less than 2.0% of the power capacity of an average 560mA-hr battery. This is less than expected and contrary to the common perception that localization algorithms consume a significant amount of a node\u27s power

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.

Node localization in underwater sensor networks (UWSN)

This dissertation focuses on node localization in underwater wireless sensor networks (UWSNs) where anchor nodes have knowledge of their own locations and communicate with sensor nodes in acoustic or magnetic induction (MI) means. The sensor nodes utilize the communication signals and the locations of anchor nodes to locate themselves and propagate their locations through the network. For UWSN using MI communications, this dissertation proposes two localization methods: rotation matrix (RM)-based method and the distance-based method. Both methods require only two anchor nodes with arbitrarily oriented tri-directional coils to locate one sensor node in the 3-D space, thus having advantages in a sparse network. Simulation studies show that the RM-based method achieves high localization accuracy, while the distance-based method exhibits less computational complexity. For UWSN using acoustic communications, this dissertation proposes a novel multi-hop node localization method in the 2-D and 3-D spaces, respectively. The proposed method estimates Euclidean distances to anchor nodes via multi-hop propagations with the help of angle of arrival (AoA) measurements. Simulation results show that the proposed method achieves better localization accuracy than existing multi-hop methods, with high localization coverage. This dissertation also investigates the hardware implementation of acoustic transmitter and receiver, and conducted field experiments with the hardware to estimate ToA using single pseudo-noise (PN) and dual PN(DPN) sequences. Both simulation and field test results show that the DPN sequences outperform the single PNs in severely dispersive channels and when the carrier frequency offset (CFO) is high --Abstract, page iv

A GPS-Less Localization and Mobility Modelling (LMM) System for Wildlife Tracking

Existing wildlife tracking solutions typically use sensor nodes with specialised facilities, such as long-range radio, solar array of cells and Global Positioning System (GPS). This introduces additional manufacturing cost, increased energy and memory consumptions and increased sensor node weight. This paper proposes a novel Localization and Mobility Modelling (LMM) system, that can carry out wildlife tracking by merely using low-cost, lightweight sensor nodes and using short-range peer-to-peer communication facilities only, i.e. without the need for any specialised facilities. This is done by using two computationally simple operations, which are: (i) aggregated data collections from sensor nodes via peer-to-peer communications in a distributed manner, and (ii) estimation of sensor nodes' movement traces using trilateration. The computational load placed on each sensor node is just that of data collection and aggregation, whereas movement traces estimation is carried out on a backend server, separated from the sensor nodes. In the design of the LMM system, we have: (i) carried out an empirical evaluation of different parameter value settings for data collection to develop a Multi-Zone Multi-Hierarchy (MZMH) communication structure, (ii) demonstrated a novel use of an Aggregation based Topology Learning (ATL) protocol for collecting sensor nodes' topology data using peer-to-peer multi-hop communications, and (iii) used a novel Location Estimation (LE) method for estimating sensor nodes' movement traces from the collected topology data. The evaluation results show that the LMM system can accurately estimate sensor nodes' movement traces but with significantly less energy and memory costs, demonstrating its cost-efficiency as compared to the related wildlife tracking solutions. © 2020 IEEE

Density-aware hop-count localization (DHL) algorithm in unevenly distributed wireless sensor networks

Master'sMASTER OF ENGINEERIN

Anchor-Free Localization in Mixed Wireless Sensor Network Systems

Recent technological advances have fostered the emergence of Wireless Sensor Networks (WSNs), which consist of tiny, wireless, battery-powered nodes that are expected to revolutionize the ways in which we understand and construct complex physical systems. A fundamental property needed to use and maintain these WSNs is ``localization\u27\u27, which allows the establishment of spatial relationships among nodes over time. This dissertation presents a series of Geographic Distributed Localization (GDL) algorithms for mixed WSNs, in which both static and mobile nodes can coexist. The GDL algorithms provide a series of useful methods for localization in mixed WSNs. First, GDL provides an approximation called ``hop-coordinates\u27\u27, which improves the accuracy of both hop-counting and connectivity-based measurement techniques. Second, GDL utilizes a distributed algorithm to compute the locations of all nodes in static networks with the help of the hop-coordinates approximation. Third, GDL integrates a sensor component into this localization paradigm for possible mobility and as a result allows for a more complex deployment of WSNs as well as lower costs. In addition, the development of GDL incorporated the possibility of manipulated communications, such as wormhole attacks. Simulations show that such a localization system can provide fundamental support for security by detecting and localizing wormhole attacks. Although several localization techniques have been proposed in the past few years, none currently satisfies our requirements to provide an accurate, efficient and reliable localization for mixed WSNs. The contributions of this dissertation are: (1) our measurement technique achieves better accuracy both in measurement and localization than other methods; (2) our method significantly improves the efficiency of localization in updating location in mixed WSNs by incorporating sensors into the method; (3) our method can detect and locate the communication that has been manipulated by a wormhole in a network without relying on a central server

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

Estimating distances via connectivity in wireless sensor networks

Distance estimation is vital for localization and many other applications in wireless sensor networks. In this paper, we develop a method that employs a maximum-likelihood estimator to estimate distances between a pair of neighboring nodes in a static wireless sensor network using their local connectivity information, namely the numbers of their common and non-common one-hop neighbors. We present the distance estimation method under a generic channel model, including the unit disk (communication) model and the more realistic log-normal (shadowing) model as special cases. Under the log-normal model, we investigate the impact of the log-normal model uncertainty; we numerically evaluate the bias and standard deviation associated with our method, which show that for long distances our method outperforms the method based on received signal strength; and we provide a Cramér-Rao lower bound analysis for the problem of estimating distances via connectivity and derive helpful guidelines for implementing our method. Finally, on implementing the proposed method on the basis of measurement data from a realistic environment and applying it in connectivity-based sensor localization, the advantages of the proposed method are confirmed. Copyright © 2012 John Wiley & Sons, Ltd