Distributed Control Architecture

This document describes the development and testing of a novel Distributed Control Architecture (DCA). The DCA developed during the study is an attempt to turn the components used to construct unmanned vehicles into a network of intelligent devices, connected using standard networking protocols. The architecture exists at both a hardware and software level and provides a communication channel between control modules, actuators and sensors. A single unified mechanism for connecting sensors and actuators to the control software will reduce the technical knowledge required by platform integrators and allow control systems to be rapidly constructed in a Plug and Play manner. DCA uses standard networking hardware to connect components, removing the need for custom communication channels between individual sensors and actuators. The use of a common architecture for the communication between components should make it easier for software to dynamically determine the vehicle s current capabilities and increase the range of processing platforms that can be utilised. Implementations of the architecture currently exist for Microsoft Windows, Windows Mobile 5, Linux and Microchip dsPIC30 microcontrollers. Conceptually, DCA exposes the functionality of each networked device as objects with interfaces and associated methods. Allowing each object to expose multiple interfaces allows for future upgrades without breaking existing code. In addition, the use of common interfaces should help facilitate component reuse, unit testing and make it easier to write generic reusable software

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

Scalable wireless sensor networks for dynamic communication environments: simulation and modelling

This thesis explores the deployment of Wireless Sensor Networks (WSNs) on localised maritime events. In particular, it will focus on the deployment of a WSN at sea and estimating what challenges derive from the environment and how they affect communication. This research addresses these challenges through simulation and modelling of communication and environment, evaluating the implications of hardware selection and custom algorithm development. The first part of this thesis consists of the analysis of aspects related to the Medium Access Control layer of the network stack in large-scale networks. These details are commonly hidden from upper layers, thus resulting in misconceptions of real deployment characteristics. Results show that simple solutions have greater advantages when the number of nodes within a cluster increases. The second part considers routing techniques, with focus on energy management and packet delivery. It is shown that, under certain conditions, relaying data can increase energy savings, while at the same time allows a more even distribution of its usage between nodes. The third part describes the development of a custom-made network simulator. It starts by considering realistic radio, channel and interference models to allow a trustworthy simulation of the deployment environment. The MAC and Routing techniques developed thus far are adapted to the simulator in a cross-layer manner. The fourth part consists of adapting the WSN behaviour to the variable weather and topology found in the chosen application scenario. By analysing the algorithms presented in this work, it is possible to find and use the best alternative under any set of environmental conditions. This mechanism, the environment-aware engine, uses both network and sensing data to optimise performance through a set of rules that involve message delivery and distance between origin and cluster hea

Cooperative Vehicle Tracking in Large Environments

Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can be easily achieved by providing vehicles with a constant communication link to a control centre and having the vehicles broadcast their position. The problem changes dramatically when vehicles operate within a large environment of potentially hundreds of square kilometres and in difficult terrain. This thesis presents algorithms for cooperative tracking of vehicles based on a vehicle motion model that incorporates the properties of the working area, and information collected by infrastructure collection points and other mobile agents. The probabilistic motion prediction approach provides long-term estimates of vehicle positions using motion profiles built for the particular environment and considering the vehicle stopping probability. A limited number of data collection points distributed around the field are used to update the position estimates, with negative information also used to improve the estimation. The thesis introduces the concept of observation harvesting, a process in which peer-to-peer communication between vehicles allows egocentric position updates and inter-vehicle measurements to be relayed among vehicles and finally conveyed to the collection points for an improved position estimate. It uses a store-and-synchronise concept to deal with intermittent communication and aims to disseminate data in an opportunistic manner. A nonparametric filtering algorithm for cooperative tracking is proposed to incorporate the information harvested, including the negative, relative, and time delayed observations. An important contribution of this thesis is to enable the optimisation of fleet scheduling when full coverage networks are not available or feasible. The proposed approaches were validated with comprehensive experimental results using data collected from a large-scale mining operation

Cooperative Vehicle Tracking in Large Environments

Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can be easily achieved by providing vehicles with a constant communication link to a control centre and having the vehicles broadcast their position. The problem changes dramatically when vehicles operate within a large environment of potentially hundreds of square kilometres and in difficult terrain. This thesis presents algorithms for cooperative tracking of vehicles based on a vehicle motion model that incorporates the properties of the working area, and information collected by infrastructure collection points and other mobile agents. The probabilistic motion prediction approach provides long-term estimates of vehicle positions using motion profiles built for the particular environment and considering the vehicle stopping probability. A limited number of data collection points distributed around the field are used to update the position estimates, with negative information also used to improve the estimation. The thesis introduces the concept of observation harvesting, a process in which peer-to-peer communication between vehicles allows egocentric position updates and inter-vehicle measurements to be relayed among vehicles and finally conveyed to the collection points for an improved position estimate. It uses a store-and-synchronise concept to deal with intermittent communication and aims to disseminate data in an opportunistic manner. A nonparametric filtering algorithm for cooperative tracking is proposed to incorporate the information harvested, including the negative, relative, and time delayed observations. An important contribution of this thesis is to enable the optimisation of fleet scheduling when full coverage networks are not available or feasible. The proposed approaches were validated with comprehensive experimental results using data collected from a large-scale mining operation

Enhancing wireless sensor networks functionalities

 The main objective of this thesis is to develop solutions for the existing research problems in wireless sensor networks that negatively influence their performances. To achieve that four main research gaps from collecting, aggregating and transferring data with considering different deployment methods of sensor nodes were addressed