Coverage and connectivity management in wireless sensor networks

Ph.DDOCTOR OF PHILOSOPH

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

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

Target Enumeration via Euler Characteristic Integrals

We solve the problem of counting the total number of observable targets (e.g., persons, vehicles, landmarks) in a region using local counts performed by a network of sensors, each of which measures the number of targets nearby but neither their identities nor any positional information. We formulate and solve several such problems based on the types of sensors and mobility of the targets. The main contribution of this paper is the adaptation of a topological sheaf integration theory — integration with respect to Euler characteristic — to yield complete solutions to these problems

A pragmatic approach to area coverage in hybrid wireless sensor networks

Success of Wireless Sensor Networks (WSN) largely depends on whether the deployed network can provide desired area coverage with acceptable network lifetime. In hostile or harsh environments such as enemy territories in battlefields, fire or chemical spills, it is impossible to deploy the sensor nodes in a predeter- mined regular topology to guarantee adequate coverage. Random deployment is thus more practical and feasible for large target areas. On the other hand, random deployment of sensors is highly susceptible to the occurrence of coverage holes in the target area. A potential solution for enhancing the existing coverage achieved by random deployments involves the use of mobility capable sensors that would help fill the coverage holes. This thesis seeks to address the problem of determining the current coverage achieved by the non-deterministic deployment of static sensor nodes and subsequently enhancing the coverage using mobile sensors. The main contributions of this dissertation are the design and evaluation of MAPC (Mobility Assisted Probabilistic Coverage), a distributed protocol for ensuring area coverage in hybrid wireless sensor networks. The primary contribution is a pragmatic approach to sensor coverage and maintenance that we hope would lower the technical barriers to its field deployment. Most of the assumptions made in the MAPC protocol are realistic and implementable in real-life applications e.g., practical boundary estimation, coverage calculations based on a realistic sensing model, and use of movement triggering thresholds based on real radio characteristics etc. The MAPC is a comprehensive three phase protocol. In the first phase, the static sensors calculate the area coverage using the Probabilistic Coverage Algorithm (PCA). This is a deviation from the idealistic assumption used in the binary detection model, wherein a sensor can sense accurately within a well defined (usually circular) region. Static sensors execute the PCA algorithm, in a distributed way, to identify any holes in the coverage. In the second phase, MAPC scheme moves the mobile nodes in an optimal manner to fill these uncovered locations. For different types of initial deployments, the proposed movement algorithms consume only 30-40% of the energy consumed by the basic virtual force algorithm. In addition, this thesis addresses the problem of coverage loss due to damaged and energy depleted nodes. The problem has been formulated as an Integer Linear Program and implementable heuristics are developed that perform close to optimal solutions. By replacing in-operational nodes in phase three, MAPC scheme ensures the continuous operation of the WSN. Experiments with real mote hardware were conducted to validate the boundary and coverage estimation part of the MAPC protocol. Extensive discrete event simulations (using NS2) were also performed for the complete MAPC protocol and the results demonstrate that MAPC can enhance and maintain the area coverage by efficiently moving mobile sensor nodes to strategic positions in the uncovered area

Spatiotemporal Wireless Sensor Network Field Approximation with Multilayer Perceptron Artificial Neural Network Models

As sensors become increasingly compact and dependable in natural environments, spatially-distributed heterogeneous sensor network systems steadily become more pervasive. However, any environmental monitoring system must account for potential data loss due to a variety of natural and technological causes. Modeling a natural spatial region can be problematic due to spatial nonstationarities in environmental variables, and as particular regions may be subject to specific influences at different spatial scales. Relationships between processes within these regions are often ephemeral, so models designed to represent them cannot remain static. Integrating temporal factors into this model engenders further complexity. This dissertation evaluates the use of multilayer perceptron neural network models in the context of sensor networks as a possible solution to many of these problems given their data-driven nature, their representational flexibility and straightforward fitting process. The relative importance of parameters is determined via an adaptive backpropagation training process, which converges to a best-fit model for sensing platforms to validate collected data or approximate missing readings. As conditions evolve over time such that the model can no longer adapt to changes, new models are trained to replace the old. We demonstrate accuracy results for the MLP generally on par with those of spatial kriging, but able to integrate additional physical and temporal parameters, enabling its application to any region with a collection of available data streams. Potential uses of this model might be not only to approximate missing data in the sensor field, but also to flag potentially incorrect, unusual or atypical data returned by the sensor network. Given the potential for spatial heterogeneity in a monitored phenomenon, this dissertation further explores the benefits of partitioning a space and applying individual MLP models to these partitions. A system of neural models using both spatial and temporal parameters can be envisioned such that a spatiotemporal space partitioned by k-means is modeled by k neural models with internal weightings varying individually according to the dominant processes within the assigned region of each. Evaluated on simulated and real data on surface currents of theGulf ofMaine, partitioned models show significant improved results over single global models

Coverage Assessment and Target Tracking in 3D Domains

Recent advances in integrated electronic devices motivated the use of Wireless Sensor Networks (WSNs) in many applications including domain surveillance and mobile target tracking, where a number of sensors are scattered within a sensitive region to detect the presence of intruders and forward related events to some analysis center(s). Obviously, sensor deployment should guarantee an optimal event detection rate and should reduce coverage holes. Most of the coverage control approaches proposed in the literature deal with two-dimensional zones and do not develop strategies to handle coverage in three-dimensional domains, which is becoming a requirement for many applications including water monitoring, indoor surveillance, and projectile tracking. This paper proposes efficient techniques to detect coverage holes in a 3D domain using a finite set of sensors, repair the holes, and track hostile targets. To this end, we use the concepts of Voronoi tessellation, Vietoris complex, and retract by deformation. We show in particular that, through a set of iterative transformations of the Vietoris complex corresponding to the deployed sensors, the number of coverage holes can be computed with a low complexity. Mobility strategies are also proposed to repair holes by moving appropriately sensors towards the uncovered zones. The tracking objective is to set a non-uniform WSN coverage within the monitored domain to allow detecting the target(s) by the set of sensors. We show, in particular, how the proposed algorithms adapt to cope with obstacles. Simulation experiments are carried out to analyze the efficiency of the proposed models. To our knowledge, repairing and tracking is addressed for the first time in 3D spaces with different sensor coverage schemes