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

Cooperative localisation in underwater robotic swarms for ocean bottom seismic imaging.

Spatial information must be collected alongside the data modality of interest in wide variety of sub-sea applications, such as deep sea exploration, environmental monitoring, geological and ecological research, and samples collection. Ocean-bottom seismic surveys are vital for oil and gas exploration, and for productivity enhancement of an existing production facility. Ocean-bottom seismic sensors are deployed on the seabed to acquire those surveys. Node deployment methods used in industry today are costly, time-consuming and unusable in deep oceans. This study proposes the autonomous deployment of ocean-bottom seismic nodes, implemented by a swarm of Autonomous Underwater Vehicles (AUVs). In autonomous deployment of ocean-bottom seismic nodes, a swarm of sensor-equipped AUVs are deployed to achieve ocean-bottom seismic imaging through collaboration and communication. However, the severely limited bandwidth of underwater acoustic communications and the high cost of maritime assets limit the number of AUVs that can be deployed for experiments. A holistic fuzzy-based localisation framework for large underwater robotic swarms (i.e. with hundreds of AUVs) to dynamically fuse multiple position estimates of an autonomous underwater vehicle is proposed. Simplicity, exibility and scalability are the main three advantages inherent in the proposed localisation framework, when compared to other traditional and commonly adopted underwater localisation methods, such as the Extended Kalman Filter. The proposed fuzzy-based localisation algorithm improves the entire swarm mean localisation error and standard deviation (by 16.53% and 35.17% respectively) at a swarm size of 150 AUVs when compared to the Extended Kalman Filter based localisation with round-robin scheduling. The proposed fuzzy based localisation method requires fuzzy rules and fuzzy set parameters tuning, if the deployment scenario is changed. Therefore a cooperative localisation scheme that relies on a scalar localisation confidence value is proposed. A swarm subset is navigationally aided by ultra-short baseline and a swarm subset (i.e. navigation beacons) is configured to broadcast navigation aids (i.e. range-only), once their confidence values are higher than a predetermined confidence threshold. The confidence value and navigation beacons subset size are two key parameters for the proposed algorithm, so that they are optimised using the evolutionary multi-objective optimisation algorithm NSGA-II to enhance its localisation performance. Confidence value-based localisation is proposed to control the cooperation dynamics among the swarm agents, in terms of aiding acoustic exteroceptive sensors. Given the error characteristics of a commercially available ultra-short baseline system and the covariance matrix of a trilaterated underwater vehicle position, dead reckoning navigation - aided by Extended Kalman Filter-based acoustic exteroceptive sensors - is performed and controlled by the vehicle's confidence value. The proposed confidence-based localisation algorithm has significantly improved the entire swarm mean localisation error when compared to the fuzzy-based and round-robin Extended Kalman Filter-based localisation methods (by 67.10% and 59.28% respectively, at a swarm size of 150 AUVs). The proposed fuzzy-based and confidence-based localisation algorithms for cooperative underwater robotic swarms are validated on a co-simulation platform. A physics-based co-simulation platform that considers an environment's hydrodynamics, industrial grade inertial measurement unit and underwater acoustic communications characteristics is implemented for validation and optimisation purposes

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Genetic Programming for Smart Phone Personalisation

Personalisation in smart phones requires adaptability to dynamic context based on user mobility, application usage and sensor inputs. Current personalisation approaches, which rely on static logic that is developed a priori, do not provide sufficient adaptability to dynamic and unexpected context. This paper proposes genetic programming (GP), which can evolve program logic in realtime, as an online learning method to deal with the highly dynamic context in smart phone personalisation. We introduce the concept of collaborative smart phone personalisation through the GP Island Model, in order to exploit shared context among co-located phone users and reduce convergence time. We implement these concepts on real smartphones to demonstrate the capability of personalisation through GP and to explore the benefits of the Island Model. Our empirical evaluations on two example applications confirm that the Island Model can reduce convergence time by up to two-thirds over standalone GP personalisation.Comment: 43 pages, 11 figure

Localisation and tracking of people using distributed UWB sensors

In vielen Überwachungs- und Rettungsszenarien ist die Lokalisierung und Verfolgung von Personen in Innenräumen auf nichtkooperative Weise erforderlich. Für die Erkennung von Objekten durch Wände in kurzer bis mittlerer Entfernung, ist die Ultrabreitband (UWB) Radartechnologie aufgrund ihrer hohen zeitlichen Auflösung und Durchdringungsfähigkeit Erfolg versprechend. In dieser Arbeit wird ein Prozess vorgestellt, mit dem Personen in Innenräumen mittels UWB-Sensoren lokalisiert werden können. Er umfasst neben der Erfassung von Messdaten, Abstandschätzungen und dem Erkennen von Mehrfachzielen auch deren Ortung und Verfolgung. Aufgrund der schwachen Reflektion von Personen im Vergleich zum Rest der Umgebung, wird zur Personenerkennung zuerst eine Hintergrundsubtraktionsmethode verwendet. Danach wird eine konstante Falschalarmrate Methode zur Detektion und Abstandschätzung von Personen angewendet. Für Mehrfachziellokalisierung mit einem UWB-Sensor wird eine Assoziationsmethode entwickelt, um die Schätzungen des Zielabstandes den richtigen Zielen zuzuordnen. In Szenarien mit mehreren Zielen kann es vorkommen, dass ein näher zum Sensor positioniertes Ziel ein anderes abschattet. Ein Konzept für ein verteiltes UWB-Sensornetzwerk wird vorgestellt, in dem sich das Sichtfeld des Systems durch die Verwendung mehrerer Sensoren mit unterschiedlichen Blickfeldern erweitert lässt. Hierbei wurde ein Prototyp entwickelt, der durch Fusion von Sensordaten die Verfolgung von Mehrfachzielen in Echtzeit ermöglicht. Dabei spielen insbesondere auch Synchronisierungs- und Kooperationsaspekte eine entscheidende Rolle. Sensordaten können durch Zeitversatz und systematische Fehler gestört sein. Falschmessungen und Rauschen in den Messungen beeinflussen die Genauigkeit der Schätzergebnisse. Weitere Erkenntnisse über die Zielzustände können durch die Nutzung zeitlicher Informationen gewonnen werden. Ein Mehrfachzielverfolgungssystem wird auf der Grundlage des Wahrscheinlichkeitshypothesenfilters (Probability Hypothesis Density Filter) entwickelt, und die Unterschiede in der Systemleistung werden bezüglich der von den Sensoren ausgegebene Informationen, d.h. die Fusion von Ortungsinformationen und die Fusion von Abstandsinformationen, untersucht. Die Information, dass ein Ziel detektiert werden sollte, wenn es aufgrund von Abschattungen durch andere Ziele im Szenario nicht erkannt wurde, wird als dynamische Überdeckungswahrscheinlichkeit beschrieben. Die dynamische Überdeckungswahrscheinlichkeit wird in das Verfolgungssystem integriert, wodurch weniger Sensoren verwendet werden können, während gleichzeitig die Performanz des Schätzers in diesem Szenario verbessert wird. Bei der Methodenauswahl und -entwicklung wurde die Anforderung einer Echtzeitanwendung bei unbekannten Szenarien berücksichtigt. Jeder untersuchte Aspekt der Mehrpersonenlokalisierung wurde im Rahmen dieser Arbeit mit Hilfe von Simulationen und Messungen in einer realistischen Umgebung mit UWB Sensoren verifiziert.Indoor localisation and tracking of people in non-cooperative manner is important in many surveillance and rescue applications. Ultra wideband (UWB) radar technology is promising for through-wall detection of objects in short to medium distances due to its high temporal resolution and penetration capability. This thesis tackles the problem of localisation of people in indoor scenarios using UWB sensors. It follows the process from measurement acquisition, multiple target detection and range estimation to multiple target localisation and tracking. Due to the weak reflection of people compared to the rest of the environment, a background subtraction method is initially used for the detection of people. Subsequently, a constant false alarm rate method is applied for detection and range estimation of multiple persons. For multiple target localisation using a single UWB sensor, an association method is developed to assign target range estimates to the correct targets. In the presence of multiple targets it can happen that targets closer to the sensor induce shadowing over the environment hindering the detection of other targets. A concept for a distributed UWB sensor network is presented aiming at extending the field of view of the system by using several sensors with different fields of view. A real-time operational prototype has been developed taking into consideration sensor cooperation and synchronisation aspects, as well as fusion of the information provided by all sensors. Sensor data may be erroneous due to sensor bias and time offset. Incorrect measurements and measurement noise influence the accuracy of the estimation results. Additional insight of the targets states can be gained by exploiting temporal information. A multiple person tracking framework is developed based on the probability hypothesis density filter, and the differences in system performance are highlighted with respect to the information provided by the sensors i.e. location information fusion vs range information fusion. The information that a target should have been detected when it is not due to shadowing induced by other targets is described as dynamic occlusion probability. The dynamic occlusion probability is incorporated into the tracking framework, allowing fewer sensors to be used while improving the tracker performance in the scenario. The method selection and development has taken into consideration real-time application requirements for unknown scenarios at every step. Each investigated aspect of multiple person localization within the scope of this thesis has been verified using simulations and measurements in a realistic environment using M-sequence UWB sensors

Distributed localisation algorithm for wireless ad hoc networks of moving nodes

Existing ad hoc network localisation solutions rely either on external location references or network-wide exchange of information and centralised processing and computation of location estimates. Without these, nodes are not able to estimate the relative locations of other nodes within their communication range. This thesis defines a new distributed localisation algorithm for ad hoc networks of moving nodes. The Relative Neighbour Localisation (RNL) algorithm works without any external localisation signal or systems and does not assume centralised information processing. The idea behind the location estimates produced by the RNL algorithm is the relationship between the relative locations of two nodes, their mobility parameters and the signal strengths measured between them. The proposed algorithm makes use of the data available to each node to produce a location estimate. The signal strength each node is capable of measuring is used as one algorithm input. The other input is the velocity vector of the neighbouring node, composed of its speed and direction of movement, which each node is assumed to periodically broadcast. The relationship between the signal strength and the mobility parameters on one, and the relative location on the other side can be analytically formulated in an ideal case. The limitations of a realistic scenario complicate this relationship, making it very difficult to formulate analytically. An empirical approach is thus used. The angle and the distance estimates are individually computed, together forming a two-dimensional location estimate. The performance of the algorithm was analysed in detail using simulation, showing a median estimate error of under 10m, and its application was tested through design and evaluation of a distributed sensing coverage algorithm, showing RNL location estimates can provide 90% of the coverage achievable with true locations being known