Regularized Least Square Multi-Hops Localization Algorithm for Wireless Sensor Networks

Abstract: Position awareness is very important for many sensor network applications. However, the use of Global Positioning System receivers to every sensor node is very costly. Therefore, anchor based localization techniques are proposed. The lack of anchors in some Wireless Sensor Networks lead to the appearance of multi-hop localization, which permits to localize nodes even if they are far from anchors. One of the well-known multi-hop localization algorithms is the Distance Vector-Hop algorithm (DV-Hop). Although its simplicity, DV-Hop presents some deficiencies in terms of localization accuracy. Therefore, to deal with this issue, we propose in this paper an improvement of DV-Hop algorithm, called Regularized Least Square DV-Hop Localization Algorithm for multi-hop wireless sensors networks. The proposed solution improves the location accuracy of sensor nodes within their sensing field in both isotropic and anisotropic networks. We used the double Least Square localization method and the statistical filtering optimization strategy, which is the Regularized Least Square method. Simulation results prove that the proposed algorithm outperforms the original DV-Hop algorithm with up to 60%, as well as other related works, in terms of localization accuracy

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

Cross-layer energy optimisation of routing protocols in wireless sensor networks

Recent technological developments in embedded systems have led to the emergence of a new class of networks, known asWireless Sensor Networks (WSNs), where individual nodes cooperate wirelessly with each other with the goal of sensing and interacting with the environment.Many routing protocols have been developed tomeet the unique and challenging characteristics of WSNs (notably very limited power resources to sustain an expected lifetime of perhaps years, and the restricted computation, storage and communication capabilities of nodes that are nonetheless required to support large networks and diverse applications). No standards for routing have been developed yet for WSNs, nor has any protocol gained a dominant position among the research community. Routing has a significant influence on the overall WSN lifetime, and providing an energy efficient routing protocol remains an open problem. This thesis addresses the issue of designing WSN routing methods that feature energy efficiency. A common time reference across nodes is required in mostWSN applications. It is needed, for example, to time-stamp sensor samples and for duty cycling of nodes. Alsomany routing protocols require that nodes communicate according to some predefined schedule. However, independent distribution of the time information, without considering the routing algorithm schedule or network topology may lead to a failure of the synchronisation protocol. This was confirmed empirically, and was shown to result in loss of connectivity. This can be avoided by integrating the synchronisation service into the network layer with a so-called cross-layer approach. This approach introduces interactions between the layers of a conventional layered network stack, so that the routing layer may share information with other layers. I explore whether energy efficiency can be enhanced through the use of cross-layer optimisations and present three novel cross-layer routing algorithms. The first protocol, designed for hierarchical, cluster based networks and called CLEAR (Cross Layer Efficient Architecture for Routing), uses the routing algorithm to distribute time information which can be used for efficient duty cycling of nodes. The second method - called RISS (Routing Integrated Synchronization Service) - integrates time synchronization into the network layer and is designed to work well in flat, non-hierarchical network topologies. The third method - called SCALE (Smart Clustering Adapted LEACH) - addresses the influence of the intra-cluster topology on the energy dissipation of nodes. I also investigate the impact of the hop distance on network lifetime and propose a method of determining the optimal location of the relay node (the node through which data is routed in a two-hop network). I also address the problem of predicting the transition region (the zone separating the region where all packets can be received and that where no data can be received) and I describe a way of preventing the forwarding of packets through relays belonging in this transition region. I implemented and tested the performance of these solutions in simulations and also deployed these routing techniques on sensor nodes using TinyOS. I compared the average power consumption of the nodes and the precision of time synchronization with the corresponding parameters of a number of existing algorithms. All proposed schemes extend the network lifetime and due to their lightweight architecture they are very efficient on WSN nodes with constrained resources. Hence it is recommended that a cross-layer approach should be a feature of any routing algorithm for WSNs

Doctor of Philosophy

dissertationIn wireless sensor networks, knowing the location of the wireless sensors is critical in many remote sensing and location-based applications, from asset tracking, and structural monitoring to geographical routing. For a majority of these applications, received signal strength (RSS)-based localization algorithms are a cost effective and viable solution. However, RSS measurements vary unpredictably because of fading, the shadowing caused by presence of walls and obstacles in the path, and non-isotropic antenna gain patterns, which affect the performance of the RSS-based localization algorithms. This dissertation aims to provide efficient models for the measured RSS and use the lessons learned from these models to develop and evaluate efficient localization algorithms. The first contribution of this dissertation is to model the correlation in shadowing across link pairs. We propose a non-site specific statistical joint path loss model between a set of static nodes. Radio links that are geographically proximate often experience similar environmental shadowing effects and thus have correlated shadowing. Using a large number of multi-hop network measurements in an ensemble of indoor and outdoor environments, we show statistically significant correlations among shadowing experienced on different links in the network. Finally, we analyze multihop paths in three and four node networks using both correlated and independent shadowing models and show that independent shadowing models can underestimate the probability of route failure by a factor of two or greater. Second, we study a special class of algorithms, called kernel-based localization algorithms, that use kernel methods as a tool for learning correlation between the RSS measurements. Kernel methods simplify RSS-based localization algorithms by providing a means to learn the complicated relationship between RSS measurements and position. We present a common mathematical framework for kernel-based localization algorithms to study and compare the performance of four different kernel-based localization algorithms from the literature. We show via simulations and an extensive measurement data set that kernel-based localization algorithms can perform better than model-based algorithms. Results show that kernel methods can achieve an RMSE up to 55% lower than a model-based algorithm. Finally, we propose a novel distance estimator for estimating the distance between two nodes a and b using indirect link measurements, which are the measurements made between a and k, for k ? b and b and k, for k ? a. Traditionally, distance estimators use only direct link measurement, which is the pairwise measurement between the nodes a and b. The results show that the estimator that uses indirect link measurements enables better distance estimation than the estimator that uses direct link measurements

Self-organising an indoor location system using a paintable amorphous computer

This thesis investigates new methods for self-organising a precisely defined pattern of intertwined number sequences which may be used in the rapid deployment of a passive indoor positioning system's infrastructure.A future hypothetical scenario is used where computing particles are suspended in paint and covered over a ceiling. A spatial pattern is then formed over the covered ceiling. Any small portion of the spatial pattern may be decoded, by a simple camera equipped device, to provide a unique location to support location-aware pervasive computing applications.Such a pattern is established from the interactions of many thousands of locally connected computing particles that are disseminated randomly and densely over a surface, such as a ceiling. Each particle has initially no knowledge of its location or network topology and shares no synchronous clock or memory with any other particle.The challenge addressed within this thesis is how such a network of computing particles that begin in such an initial state of disarray and ignorance can, without outside intervention or expensive equipment, collaborate to create a relative coordinate system. It shows how the coordinate system can be created to be coherent, even in the face of obstacles, and closely represent the actual shape of the networked surface itself. The precision errors incurred during the propagation of the coordinate system are identified and the distributed algorithms used to avoid this error are explained and demonstrated through simulation.A new perimeter detection algorithm is proposed that discovers network edges and other obstacles without the use of any existing location knowledge. A new distributed localisation algorithm is demonstrated to propagate a relative coordinate system throughout the network and remain free of the error introduced by the network perimeter that is normally seen in non-convex networks. This localisation algorithm operates without prior configuration or calibration, allowing the coordinate system to be deployed without expert manual intervention or on networks that are otherwise inaccessible.The painted ceiling's spatial pattern, when based on the proposed localisation algorithm, is discussed in the context of an indoor positioning system

Robustness analysis of graph-based machine learning

Graph-based machine learning is an emerging approach to analysing data that is or can be well-modelled by pairwise relationships between entities. This includes examples such as social networks, road networks, protein-protein interaction net- works and molecules. Despite the plethora of research dedicated to designing novel machine learning models, less attention has been paid to the theoretical proper- ties of our existing tools. In this thesis, we focus on the robustness properties of graph-based machine learning models, in particular spectral graph filters and graph neural networks. Robustness is an essential property for dealing with noisy data, protecting a system against security vulnerabilities and, in some cases, necessary for transferability, amongst other things. We focus specifically on the challenging and combinatorial problem of robustness with respect to the topology of the underlying graph. The first part of this thesis proposes stability bounds to help understand to which topological changes graph-based models are robust. Beyond theoretical results, we conduct experiments to verify the intuition this theory provides. In the second part, we propose a flexible and query-efficient method to perform black-box adversarial attacks on graph classifiers. Adversarial attacks can be considered a search for model instability and provide an upper bound between an input and the decision boundary. In the third and final part of the thesis, we propose a novel robustness certificate for graph classifiers. Using a technique that can certify in- dividual parts of the graph at varying levels of perturbation, we provide a refined understanding of the perturbations to which a given model is robust. We believe the findings in this thesis provide novel insight and motivate further research into both understanding stability and instability of graph-based machine learning models

Moving-baseline localization for mobile wireless sensor networks

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2009.Includes bibliographical references (leaves 93-98).The moving-baseline localization (MBL) problem arises when a group of nodes moves through an environment in which no external coordinate reference is available. When group members cannot see or hear one another directly, each node must employ local sensing and inter-device communication to infer the spatial relationship and motion of all other nodes with respect to itself. We consider a setting in which nodes move with piecewise-linear velocities in the plane, and any node can exchange noisy range estimates with certain sufficiently nearby nodes. We develop a distributed solution to the MBL problem in the plane, in which each node performs robust hyperbola fitting, trilateration with velocity constraints, and subgraph alignment to arrive at a globally consistent view of the network expressed in its own "rest frame." Changes in any node's motion cause deviations between observed and predicted ranges at nearby nodes, triggering revision of the trajectory estimates computed by all nodes. We implement and analyze our algorithm in a simulation informed by the characteristics of a commercially available ultra-wideband (UWB) radio, and show that recovering node trajectories, rather than just locations, requires substantially less computation at each node. Finally, we quantify the minimum ranging rate and local network density required for the method's successful operation.by Jun-geun Park.S.M

A supporting infrastructure for Wireless Sensor Networks in Critical Industrial Environments

Tese de doutoramento no Programa de Doutoramento em Ciências e Tecnologias da Informação apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra.As Redes de Sensores Sem Fios (RSSFs) têm uma aplicabilidade muito elevada nas mais diversas áreas, como na indústria, nos sistemas militares, na saúde e nas casas inteligentes. No entanto, continuam a existir várias limitações que impedem que esta tecnologia tenha uma utilização extensiva. A fiabilidade é uma destas principais limitações que tem atrasado a adopção das RSSFs em ambientes industriais, principalmente quando sujeitos a elevadas interferências e ruídos. Por outro lado, a interoperabilidade é também um dos principais requisitos a cumprir nomeadamente com o avanço para o paradigma da Internet of Things. A determinação da localização dos nós, principalmente dos nós móveis, é, também ele, um requisito crítico em muitas aplicações. Esta tese de doutoramento propõe novas soluções para a integração e para a localização de RSSFs que operem em ambientes industriais e críticos. Como os nós sensores são, na maioria das vezes, instalados e deixados sem intervenção humana durante longos períodos de tempo, isto é, meses ou mesmo anos, é muito importante oferecer processos de comunicação fiável. No entanto, muitos problemas ocorrem durante a transmissão dos pacotes, nomeadamente devido a ruídos, interferências e perda de potência do sinal. A razão das interferências deve-se à existência de mais do que uma rede ou ao espalhamento espectral que ocorre em determinadas frequências. Este tipo de problemas é mais severo em ambientes dinâmicos nos quais novas fontes de ruído pode ser introduzidas em qualquer instante de tempo, nomeadamente com a chegadas de novos dispositivos ao meio. Consequentemente, é necessário que as RSSFs tenham a capacidade de lidar com as limitações e as falhas nos processos de comunicação. O protocolo Dynamic MAC (DunMAC) proposto nesta dissertação utiliza técnicas de rádio cognitivo (CR) para que a RSSF se adapte, de forma dinâmica, a ambientes instáveis e ruidosos através da selecção automática do melhor canal durante o período de operação. As RSSFs não podem operar em isolação completa do meio, e necessitam de ser monitoradas e controladas por aplicações externas. Apesar de ser possível adicionar a pilha protocolar IP aos nós sensores, este procedimento não é adequado para muitas aplicações. Para estes casos, os modelos baseados em gateway ou proxies continuam a apresentar-se preferíveis para o processo de integração. Um dos desafios existentes para estes processos de integração é a sua adaptabilidade, isto é, a capacidade da gateway ou do proxy poder ser reutilizado sem alterações por outras aplicações. A razão desta limitação deve-se aos consumidores finais dos dados serem aplicações e não seres humanos. Logo, é difícil ou mesmo impossível criar normas para as estruturas de dados dada a infinidade de diferentes formatos. É então desejável encontrar uma solução que permita uma integração transparente de diferentes RSSFs e aplicações. A linguagem Sensor Traffic Description Language (STDL) proposta nesta dissertação propõe uma solução para esta integração através de gateways e proxies flexíveis e adaptados à diversidade de aplicações, e sem recorrer à reprogramação. O conhecimento da posição dos nós sensores é, também ele, crítico em muitas aplicações industriais como no controlo da deslocação dos objectos ou trabalhadores. Para além do mais, a maioria dos valores recolhidos dos sensores só são úteis quando acompanhados pelo conhecimento do local onde esses valores foram recolhidos. O Global Positioning Systems (GPS) é a mais conhecida solução para a determinação da localização. No entanto, o recurso ao GPS em cada nó sensor continua a ser energeticamente ineficiente e impraticável devido aos custos associados. Para além disso, os sistemas GPS não são apropriados para ambientes in-door. Este trabalho de doutoramento propõe-se actuar nestas áreas. Em particular, é proposto, implementado e avaliado o protocolo DynMAC para oferecer fiabilidade às RSSFs. Para a segunda temática, a linguagem STDL e o seu motor são propostos para suportar a integração de ambientes heterogéneos de RSSFs e aplicações. As soluções propostas não requerem reprogramação e suportam também serviços de localização nas RSSFs. Diferentes métodos de localização foram avaliados para estimar a localização dos nós. Assim, com estes métodos as RSSFs podem ser usadas como componentes para integrar e suportar a Futura Internet. Todas as soluções propostas nesta tese foram implementadas e validadas tanto em simulação com em plataformas práticas, laboratoriais e industriais.The Wireless Sensor Network (WSN) has a countless number of applications in almost all of the fields including military, industrial, healthcare, and smart home environments. However, there are several problems that prevent the widespread of sensor networks in real situations. Among them, the reliability of communication especially in noisy industrial environments is difficult to guarantee. In addition, interoperability between the sensor networks and external applications is also a challenge. Moreover, determining the position of nodes, particularly mobile nodes, is a critical requirement in many types of applications. My original contributions in this thesis include reliable communication, integration, localization solutions for WSNs operating in industrial and critical environments. Because sensor nodes are usually deployed and kept unattended without human intervention for a long duration, e.g. months or even years, it is a crucial requirement to provide the reliable communication for the WSNs. However, many problems arise during packet transmission and are related to the transmission medium (e.g. signal path-loss, noise and interference). Interference happens due to the existence of more than one network or by the spectral spread that happens in some frequencies. This type of problem is more severe in dynamic environments in which noise sources can be introduced at any time or new networks and devices that interfere with the existing one may be added. Consequently, it is necessary for the WSNs to have the ability to deal with the communication failures. The Dynamic MAC (DynMAC) protocol proposed in this thesis employs the Cognitive Radio (CR) techniques to allow the WSNs to adapt to the dynamic noisy environments by automatically selecting the best channel during its operation time. The WSN usually cannot operate in complete isolation, but it needs to be monitored, controlled and visualized by external applications. Although it is possible to add an IP protocol stack to sensor nodes, this approach is not appropriate for many types of WSNs. Consequently, the proxy and gateway approach is still a preferred method for integrating sensor networks with external networks and applications. The problem of the current integration solutions for WSNs is the adaptability, i.e., the ability of the gateway or proxy developed for one sensor network to be reused, unchanged, for others which have different types of applications and data frames. One reason behind this problem is that it is difficult or even impossible to create a standard for the structure of data inside the frame because there are such a huge number of possible formats. Consequently, it is necessary to have an adaptable solution for easily and transparently integrating WSNs and application environments. In this thesis, the Sensor Traffic Description Language (STDL) was proposed for describing the structure of the sensor networks’ data frames, allowing the framework to be adapted to a diversity of protocols and applications without reprogramming. The positions of sensor nodes are critical in many types of industrial applications such as object tracking, location-aware services, worker or patient tracking, etc. In addition, the sensed data is meaningless without the knowledge of where it is obtained. Perhaps the most well-known location-sensing system is the Global Positioning System (GPS). However, equipping GPS sensor for each sensor node is inefficient or unfeasible for most of the cases because of its energy consumption and cost. In addition, GPS is not appropriate in some environments, e.g., indoors. Similar to the original concept of WSNs, the localization solution should also be cheap and with low power consumption. This thesis aims to deal with the above problems. In particular, in order to add the reliability for WSN, DynMAC protocol was proposed, implemented and evaluated. This protocol adds a mechanism to automatically deal with the noisy and changeable environments. For the second problem, the STDL and its engine provide the adaptable capability to the framework for interoperation between sensor networks and external applications. The proposed framework requires no reprogramming when deploying it for new applications and protocols of WSNs. Moreover, the framework also supports localization services for positioning the unknown position sensor nodes in WSNs. The different localization methods are employed to estimate the location of mobile nodes. With the proposed framework, WSNs can be used as plug and play components for integrating with the Future Internet. All the proposed solutions were implemented and validated using simulation and real testbeds in both the laboratory and industrial environments

Smart FRP Composite Sandwich Bridge Decks in Cold Regions

INE/AUTC 12.0