Boundary node selection algorithms in WSNs

Physical damage and/or node power exhaustion may lead to coverage holes in WSNs. Coverage holes can be directly detected by certain proximate nodes known as boundary nodes (B-nodes). Due to the sensor nodes' redundant deployment and autonomous fault detection, holes are surrounded by a margin of B-nodes (MB-nodes). If all B-nodes in the margin take part in the hole recovery processes, either by increasing their transmission power or by relocating towards region of interest (ROI), the probability of collision, interference, disconnection, and isolation may increase affecting the rest of the network's performance and QoS. Thus, distributed boundary node selection algorithms (BNS-Algorithms) are proposed to address these issues. BNS-algorithms allow B-nodes to self-select based on available 1-hop information extracted from nodes' simple geometrical and statistical features. Our results show that the performance of the proposed distributed BNS-algorithms approaches that of their centralized counterparts. © 2011 IEEE

Challenges and Solutions for Location-based Routing in Wireless Sensor Networks with Complex Network Topology

Complex Network Topologies (CNTs)–network holes and cuts–often occur in practical WSN deployments. Many researchers have acknowledged that CNTs adversely affect the performance of location-based routing and proposed various CNT- aware location-based routing protocols. However, although they aim to address practical issues caused by CNTs, many proposed protocols are either based on idealistic assumptions, require too much resources, or have poor performance. Additionally, proposed protocols are designed only for a single routing primitive–either unicast, multicast, or convergecast. However, as recent WSN applications require diverse traffic patterns, the need for an unified routing framework has ever increased. In this dissertation, we address these main weaknesses in the research on location- based routing. We first propose efficient algorithms for detecting and abstracting CNTs in the network. Using these algorithms, we present our CNT-aware location- based unicast routing protocol that achieves the guaranteed small path stretch with significantly reduced communication overhead. We then present our location-based multicast routing protocol that finds near optimal routing paths from a source node to multicast member nodes, with efficient mechanisms for controllable packet header size and energy-efficient recovery from packet losses. Our CNT-aware convergecast routing protocol improves the network lifetime by identifying network regions with concentrated network traffic and distributing the traffic by using the novel concept of virtual boundaries. Finally, we present the design and implementation details of our unified routing framework that seamlessly integrates proposed unicast, multicast, and convergecast routing protocols. Specifically, we discuss the issues regarding the implementation of our routing protocols on real hardware, and the design of the framework that significantly reduces the code and memory size to fit in a resource constrained sensor mote. We conclude with a proactive solution designed to cope with CNTs, where mobile nodes are used for “patching” CNTs to restore the network connectivity and to optimize the network performance

Guided wave-based condition assessment of in situ timber utility poles using machine learning algorithms

This paper presents a machine-learning-based approach for the structural health monitoring (SHM) of in-situ timber utility poles based on guided wave (GW) propagation. The proposed non-destructive testing method combines a new multi-sensor testing system with advanced statistical signal processing techniques and state-of-the-art machine learning algorithms for the condition assessment of timber utility poles. Currently used pole inspection techniques have critical limitations including the inability to assess the underground section. GW methods, on the other hand, are techniques potentially capable of evaluating non-accessible areas and of detecting internal damage. However, due to the lack of solid understanding on the GW propagation in timber poles, most methods fail to fully interpret wave patterns from field measurements. The proposed method utilises an innovative multi-sensor testing system that captures wave signals along a sensor array and it applies machine learning algorithms to evaluate the soundness of a pole. To validate the new method, it was tested on eight in-situ timber poles. After the testing, the poles were dismembered to determine their actual health states. Various state-of-the-art machine learning algorithms with advanced data pre-processing were applied to classify the poles based on the wave measurements. It was found that using a support vector machine classifier, with the GW signals transformed into autoregressive coefficients, achieved a very promising maximum classification accuracy of 95.7±3.1% using 10-fold cross validation on multiple training and testing instances. Using leave-one-out cross validation, a classification accuracy of 93.3±6.0% for bending wave and 85.7±10.8% for longitudinal wave excitation was achieved. © The Author(s) 2014

Cooperative mobility maintenance techniques for information extraction from mobile wireless sensor networks

Recent advances in the development of microprocessors, microsensors, ad-hoc wireless networking and information fusion algorithms led to increasingly capable Wireless Sensor Networks (WSNs). Besides severe resource constraints, sensor nodes mobility is considered a fundamental characteristic of WSNs. Information Extraction (IE) is a key research area within WSNs that has been characterised in a variety of ways, ranging from a description of its purposes to reasonably abstract models of its processes and components. The problem of IE is a challenging task in mobile WSNs for several reasons including: the topology changes rapidly; calculation of trajectories and velocities is not a trivial task; increased data loss and data delivery delays; and other context and application specific challenges. These challenges offer fundamentally new research problems. There is a wide body of literature about IE from static WSNs. These approaches are proved to be effective and efficient. However, there are few attempts to address the problem of IE from mobile WSNs. These attempts dealt with mobility as the need arises and do not deal with the fundamental challenges and variations introduced by mobility on the WSNs. The aim of this thesis is to develop a solution for IE from mobile WSNs. This aim is achieved through the development of a middle-layer solution, which enables IE approaches that were designed for the static WSNs to operate in the presence of multiple mobile nodes. This thesis contributes toward the design of a new self-stabilisation algorithm that provides autonomous adaptability against nodes mobility in a transparent manner to both upper network layers and user applications. In addition, this thesis proposes a dynamic network partitioning protocol to achieve high quality of information, scalability and load balancing. The proposed solution is flexible, may be applied to different application domains, and less complex than many existing approaches. The simplicity of the solutions neither demands great computational efforts nor large amounts of energy conservation. Intensive simulation experiments with real-life parameters provide evidence of the efficiency of the proposed solution. Performance experimentations demonstrate that the integrated DNP/SS protocol outperforms its rival in the literature in terms of timeliness (by up to 22%), packet delivery ratio (by up to 13%), network scalability (by up to 25%), network lifetime (by up to 40.6%), and energy consumption (by up to 39.5%). Furthermore, it proves that DNP/SS successfully allows the deployment of static-oriented IE approaches in hybrid networks without any modifications or adaptations

Prohibitive-link Detection and Routing Protocol

Abstract In this paper we investigate the limits of routing according to left-or righthand rule (LHR). Using LHR, a node upon receipt of a message will forward to the neighbour that sits next in counter-clockwise order in the network graph. When used to recover from greedy routing failures, LHR guarantees success if implemented over planar graphs. We note, however, that if planarity is violated then LHR is only guaranteed to eventually return to the point of origin. Our work seeks to understand why. An enumeration and analysis of possible intersections leads us to propose the Prohibitive-link Detection and Routing Protocol (PDRP) that can guarantee delivery over non-planar graphs. As the name implies, the protocol detects and circumvents the 'bad' links that hamper LHR. Our implementation of PDRP in TinyOS reveals the same level of service as face-routing protocols despite preserving most intersecting links in the network

Visual region understanding: unsupervised extraction and abstraction

The ability to gain a conceptual understanding of the world in uncontrolled environments is the ultimate goal of vision-based computer systems. Technological societies today are heavily reliant on surveillance and security infrastructure, robotics, medical image analysis, visual data categorisation and search, and smart device user interaction, to name a few. Out of all the complex problems tackled by computer vision today in context of these technologies, that which lies closest to the original goals of the field is the subarea of unsupervised scene analysis or scene modelling. However, its common use of low level features does not provide a good balance between generality and discriminative ability, both a result and a symptom of the sensory and semantic gaps existing between low level computer representations and high level human descriptions. In this research we explore a general framework that addresses the fundamental problem of universal unsupervised extraction of semantically meaningful visual regions and their behaviours. For this purpose we address issues related to (i) spatial and spatiotemporal segmentation for region extraction, (ii) region shape modelling, and (iii) the online categorisation of visual object classes and the spatiotemporal analysis of their behaviours. Under this framework we propose (a) a unified region merging method and spatiotemporal region reduction, (b) shape representation by the optimisation and novel simplication of contour-based growing neural gases, and (c) a foundation for the analysis of visual object motion properties using a shape and appearance based nearest-centroid classification algorithm and trajectory plots for the obtained region classes. 1 Specifically, we formulate a region merging spatial segmentation mechanism that combines and adapts features shown previously to be individually useful, namely parallel region growing, the best merge criterion, a time adaptive threshold, and region reduction techniques. For spatiotemporal region refinement we consider both scalar intensity differences and vector optical flow. To model the shapes of the visual regions thus obtained, we adapt the growing neural gas for rapid region contour representation and propose a contour simplication technique. A fast unsupervised nearest-centroid online learning technique next groups observed region instances into classes, for which we are then able to analyse spatial presence and spatiotemporal trajectories. The analysis results show semantic correlations to real world object behaviour. Performance evaluation of all steps across standard metrics and datasets validate their performance

Limited range coverage problems

Doutoramento em MatemáticaTal como o título indica, esta tese estuda problemas de cobertura com alcance limitado. Dado um conjunto de antenas (ou qualquer outro dispositivo sem fios capaz de receber ou transmitir sinais), o objectivo deste trabalho é calcular o alcance mínimo das antenas de modo a que estas cubram completamente um caminho entre dois pontos numa região. Um caminho que apresente estas características é um itinerário seguro. A definição de cobertura é variável e depende da aplicação a que se destina. No caso de situações críticas como o controlo de fogos ou cenários militares, a definição de cobertura recorre à utilização de mais do que uma antena para aumentar a eficácia deste tipo de vigilância. No entanto, o alcance das antenas deverá ser minimizado de modo a manter a vigilância activa o maior tempo possível. Consequentemente, esta tese está centrada na resolução deste problema de optimização e na obtenção de uma solução particular para cada caso. Embora este problema de optimização tenha sido investigado como um problema de cobertura, é possível estabelecer um paralelismo entre problemas de cobertura e problemas de iluminação e vigilância, que são habitualmente designados como problemas da Galeria de Arte. Para converter um problema de cobertura num de iluminação basta considerar um conjunto de luzes em vez de um conjunto de antenas e submetê-lo a restrições idênticas. O principal tema do conjunto de problemas da Galeria de Arte abordado nesta tese é a 1-boa iluminação. Diz-se que um objecto está 1-bem iluminado por um conjunto de luzes se o invólucro convexo destas contém o objecto, tornando assim este conceito num tipo de iluminação de qualidade. O objectivo desta parte do trabalho é então minimizar o alcance das luzes de modo a manter uma iluminação de qualidade. São também apresentadas duas variantes da 1-boa iluminação: a iluminação ortogonal e a boa !-iluminação. Esta última tem aplicações em problemas de profundidade e visualização de dados, temas que são frequentemente abordados em estatística. A resolução destes problemas usando o diagrama de Voronoi Envolvente (uma variante do diagrama de Voronoi adaptada a problemas de boa iluminação) é também proposta nesta tese.As the title implies, this thesis studies limited range coverage problems. Given a set of antennas (or any wireless device able to send or receive some sort of signal), the objective of the discussion that follows is to calculate the antennas’ minimum range so that a path between two points within a region is covered by the antennas, a path known as a safe route. The definition of coverage is variable and depends on the applications. In some instances, for example, when monitoring is critical as in the case of fires or military, the definition of coverage necessarily involves the use of multiple antennas to increase the effectiveness of monitoring. However, it is also desirable to extend a network’s lifespan, normally achieved by minimising the antennas’ range. Therefore the focus of this thesis will be the resolution of this dual problem and an affective solution is offered for each case. Although this question has been researched as an issue of coverage, it is also possible to establish a relation between coverage and illumination and visibility, known as Art Gallery problems. To conceptualise coverage problems as Art Gallery problems, all that is needed is to consider a set of lights instead of a set of antennas, which are subject to a similar set of restrictions. The main focus of the Art Gallery problems addressed in this thesis is 1-good illumination. An object is 1-well illuminated if it is fully contained by the convex hull of a set of lights, making this a type of quality illumination. The objective of the discussion that follows is therefore to minimise the lights’ range whilst maintaining a quality illumination. Moreover, two variants of 1-good illumination are also presented: orthogonal good illumination and good ! -illumination. The latter being related to data depth problems and data visualisation that are frequently used in statistics. The resolution of these problems using the Embracing Voronoi diagram (a variant of Voronoi diagrams adapted to good illumination) is also discussed in this thesis

Modelling socio-spatial dynamics from real-time data

This thesis introduces a framework for modelling the social dynamic of an urban landscape from multiple and disparate real-time datasets. It seeks to bridge the gap between artificial simulations of human behaviour and periodic real-world observations. The approach is data-intensive, adopting open-source programmatic and visual analytics. The result is a framework that can rapidly produce contextual insights from samples of real-world human activity – behavioural data traces. The framework can be adopted standalone or integrated with other models to produce a more comprehensive understanding of people-place experiences and how context affects behaviour. The research is interdisciplinary. It applies emerging techniques in cognitive and spatial data sciences to extract and analyse latent information from behavioural data traces located in space and time. Three sources are evaluated: mobile device connectivity to a public Wi-Fi network, readings emitted by an installed mobile app, and volunteered status updates. The outcome is a framework that can sample data about real-world activities at street-level and reveal contextual variations in people-place experiences, from cultural and seasonal conditions that create the ‘social heartbeat’ of a landscape to the arrhythmic impact of abnormal events. By continuously or frequently sampling reality, the framework can become self-calibrating, adapting to developments in land-use potential and cultural influences over time. It also enables ‘opportunistic’ geographic information science: the study of unexpected real-world phenomena as and when they occur. The novel contribution of this thesis is to demonstrate the need to improve understanding of and theories about human-environment interactions by incorporating context-specific learning into urban models of behaviour. The framework presents an alternative to abstract generalisations by revealing the variability of human behaviour in public open spaces, where conditions are uncertain and changeable. It offers the potential to create a closer representation of reality and anticipate or recommend behaviour change in response to conditions as they emerge