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

Joint position estimation, packet routing and sleep scheduling in wireless sensor networks

Wireless Sensor Network (WSN) is an important research field in Computer Science with applications that span multiple domains. Due to the limitation of sensor nodes, network lifetime is a critical issue that needs to be addressed. Therefore, in this thesis I propose the Energy-aware Connected k-Neighbourhood (ECKN), a joint position estimation, packet routing, and sleep scheduling solution that combines some overlap- ping features. I propose a localization algorithm that performs trilateration using the position of a mobile sink and of neighbour nodes to estimate the position of a sensor node with no GPS module. I introduce a routing protocol based on the well-known Greedy Geographic Forwarding (GGF). Similarly to GGF, my protocol takes into consideration the position of neighbours to decide the best forwarding node, however it also considers the residual energy in order to guarantee that the forwarding node will deliver the packet. The concept of bridges is also introduced, in which the sink compares its current position with previous positions and calculates whether there is a shortest path in order to create a bridge that will reduce the number of hops a packet has to travel through. Lastly, a sleep scheduler is proposed in order to extend the network lifetime, it is based on the Connected k-Neighbourhood (CKN) algorithm, which aids in the decision of what nodes goes to sleep while maintaining the network connected. My sleep scheduler maintains the network denser in the area close to the sink, since this region receives packets from the whole network to forward to the sink. An extensive set of performance evaluation experiments is conducted and results show that ECKN can extend network lifetime, while sustaining acceptable packet delivery ratio and reducing network overhead

Vision Based Calibration and Localization Technique for Video Sensor Networks

The recent evolutions in embedded systems have now made the video sensor networks a reality. A video sensor network consists of a large number of low cost camera-sensors that are deployed in random manner. It pervades both the civilian and military fields with huge number of applications in various areas like health-care, environmental monitoring, surveillance and tracking. As most of the applications demand the knowledge of the sensor-locations and the network topology before proceeding with their tasks, especially those based on detecting events and reporting, the problem of localization and calibration assumes a significance far greater than most others in video sensor network. The literature is replete with many localization and calibration algorithms that basically rely on some a-priori chosen nodes, called seeds, with known coordinates to help determine the network topology. Some of these algorithms require additional hardware, like arrays of antenna, while others require having to regularly reacquire synchronization among the seedy so as to calculate the time difference of the received signals. Very few of these localization algorithms use vision based technique. In this work, a vision based technique is proposed for localizing and configuring the camera nodes in video wireless sensor networks. The camera network is assumed randomly deployed. One a-priori selected node chooses to act as the core of the network and starts to locate some other two reference nodes. These three nodes, in turn, participate in locating the entire network using tri-lateration method with some appropriate vision characteristics. In this work, the vision characteristics that are used the relationship between the height of the image in the image plane and the real distance between the sensor node and the camera. Many experiments have been simulated to demonstrate the feasibility of the proposed technique. Apart from this work, experiments are also carried out to locate any other new object in the video sensor network. The experimental results showcase the accuracy of building up one-plane network topology in relative coordinate system and also the robustness of the technique against the accumulated error in configuring the whole network

An Algorithmic View on Sensor Networks - Surveillance, Localization, and Communication

This thesis focuses on scalability issues of diverse problems on sensor networks and presents efficient solutions. First, we show that it is NP-hard to find optimal activation schedules for monitoring areas and provide an EPTAS algorithm. Second, we present a distributed algorithm for the detection of network boundaries that only requires local connectivity information. Finally, we introduce an FPTAS for computing shortest paths and describe an algorithm for determining alternative routes

Efficient Optimization Algorithms for Nonlinear Data Analysis

Identification of low-dimensional structures and main sources of variation from multivariate data are fundamental tasks in data analysis. Many methods aimed at these tasks involve solution of an optimization problem. Thus, the objective of this thesis is to develop computationally efficient and theoretically justified methods for solving such problems. Most of the thesis is based on a statistical model, where ridges of the density estimated from the data are considered as relevant features. Finding ridges, that are generalized maxima, necessitates development of advanced optimization methods. An efficient and convergent trust region Newton method for projecting a point onto a ridge of the underlying density is developed for this purpose. The method is utilized in a differential equation-based approach for tracing ridges and computing projection coordinates along them. The density estimation is done nonparametrically by using Gaussian kernels. This allows application of ridge-based methods with only mild assumptions on the underlying structure of the data. The statistical model and the ridge finding methods are adapted to two different applications. The first one is extraction of curvilinear structures from noisy data mixed with background clutter. The second one is a novel nonlinear generalization of principal component analysis (PCA) and its extension to time series data. The methods have a wide range of potential applications, where most of the earlier approaches are inadequate. Examples include identification of faults from seismic data and identification of filaments from cosmological data. Applicability of the nonlinear PCA to climate analysis and reconstruction of periodic patterns from noisy time series data are also demonstrated. Other contributions of the thesis include development of an efficient semidefinite optimization method for embedding graphs into the Euclidean space. The method produces structure-preserving embeddings that maximize interpoint distances. It is primarily developed for dimensionality reduction, but has also potential applications in graph theory and various areas of physics, chemistry and engineering. Asymptotic behaviour of ridges and maxima of Gaussian kernel densities is also investigated when the kernel bandwidth approaches infinity. The results are applied to the nonlinear PCA and to finding significant maxima of such densities, which is a typical problem in visual object tracking.Siirretty Doriast

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Aerial Network Assistance Systems for Post-Disaster Scenarios : Topology Monitoring and Communication Support in Infrastructure-Independent Networks

Communication anytime and anywhere is necessary for our modern society to function. However, the critical network infrastructure quickly fails in the face of a disaster and leaves the affected population without means of communication. This lack can be overcome by smartphone-based emergency communication systems, based on infrastructure-independent networks like Delay-Tolerant Networks (DTNs). DTNs, however, suffer from short device-to-device link distances and, thus, require multi-hop routing or data ferries between disjunct parts of the network. In disaster scenarios, this fragmentation is particularly severe because of the highly clustered human mobility behavior. Nevertheless, aerial communication support systems can connect local network clusters by utilizing Unmanned Aerial Vehicles (UAVs) as data ferries. To facilitate situation-aware and adaptive communication support, knowledge of the network topology, the identification of missing communication links, and the constant reassessment of dynamic disasters are required. These requirements are usually neglected, despite existing approaches to aerial monitoring systems capable of detecting devices and networks. In this dissertation, we, therefore, facilitate the coexistence of aerial topology monitoring and communications support mechanisms in an autonomous Aerial Network Assistance System for infrastructure-independent networks as our first contribution. To enable system adaptations to unknown and dynamic disaster situations, our second contribution addresses the collection, processing, and utilization of topology information. For one thing, we introduce cooperative monitoring approaches to include the DTN in the monitoring process. Furthermore, we apply novel approaches for data aggregation and network cluster estimation to facilitate the continuous assessment of topology information and an appropriate system adaptation. Based on this, we introduce an adaptive topology-aware routing approach to reroute UAVs and increase the coverage of disconnected nodes outside clusters. We generalize our contributions by integrating them into a simulation framework, creating an evaluation platform for autonomous aerial systems as our third contribution. We further increase the expressiveness of our aerial system evaluation, by adding movement models for multicopter aircraft combined with power consumption models based on real-world measurements. Additionally, we improve the disaster simulation by generalizing civilian disaster mobility based on a real-world field test. With a prototypical system implementation, we extensively evaluate our contributions and show the significant benefits of cooperative monitoring and topology-aware routing, respectively. We highlight the importance of continuous and integrated topology monitoring for aerial communications support and demonstrate its necessity for an adaptive and long-term disaster deployment. In conclusion, the contributions of this dissertation enable the usage of autonomous Aerial Network Assistance Systems and their adaptability in dynamic disaster scenarios

Bandwidth scaling behavior in wireless systems : theory, experimentation, and performance analysis

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 167-174).The need for ubiquitous wireless services has prompted the exploration of using increasingly larger transmission bandwidths often in environments with harsh propagation conditions. However, present analyses do not capture the behavior of systems in these channels as the bandwidth changes. This thesis: describes the development of an automated measurement apparatus capable of characterizing wideband channels up to 16 GHz; formulates a framework for evaluating the performance of wireless systems in realistic propagation environments; and applies this framework to sets of channel realizations collected during a comprehensive measurement campaign. In particular, the symbol error probability of realistic wideband subset diversity (SSD) systems, as well as improved lower bounds on time-of-arrival (TOA) estimation are derived and evaluated using experimental data at a variety of bandwidths. These results provide insights into how the performance of wireless systems scales as a function of bandwidth. Experimental data is used to quantify the behavior of channel resolvability as a function of bandwidth. The results show that there are significant differences in the amount of energy captured by a wideband SSD combiner under different propagation conditions. In particular, changes in the number of combined paths affect system performance more significantly in non-line-of-sight conditions than in line-of-sight conditions. Results also indicate that, for a fixed number of combined paths, lower bandwidths may provide better performance because a larger portion of the available energy is captured at those bandwidths. The expressions for lower bounds on TOA estimation, developed based on the Ziv-Zakai bound (ZZB), are able to account for the a priori information about the TOA as well as statistical information regarding the multipath phenomena. The ZZB, evaluated using measured channel realizations, shows the presence of an ambiguity region for moderate signal-to-noise ratios (SNRs). It is shown that in a variety of propagation conditions, this ambiguity region diminishes as bandwidth increases. Results indicate that decreases in the root mean square error for TOA estimation were significant for bandwidths up to approximately 8 GHz for SNRs in this region.by Wesley M. Gifford.Ph.D