A Decentralized Architecture for Active Sensor Networks

This thesis is concerned with the Distributed Information Gathering (DIG) problem in which a Sensor Network is tasked with building a common representation of environment. The problem is motivated by the advantages offered by distributed autonomous sensing systems and the challenges they present. The focus of this study is on Macro Sensor Networks, characterized by platform mobility, heterogeneous teams, and long mission duration. The system under consideration may consist of an arbitrary number of mobile autonomous robots, stationary sensor platforms, and human operators, all linked in a network. This work describes a comprehensive framework called Active Sensor Network (ASN) which addresses the tasks of information fusion, decistion making, system configuration, and user interaction. The main design objectives are scalability with the number of robotic platforms, maximum flexibility in implementation and deployment, and robustness to component and communication failure. The framework is described from three complementary points of view: architecture, algorithms, and implementation. The main contribution of this thesis is the development of the ASN architecture. Its design follows three guiding principles: decentralization, modularity, and locality of interactions. These principles are applied to all aspects of the architecture and the framework in general. To achieve flexibility, the design approach emphasizes interactions between components rather than the definition of the components themselves. The architecture specifies a small set of interfaces sufficient to implement a wide range of information gathering systems. In the area of algorithms, this thesis builds on the earlier work on Decentralized Data Fusion (DDF) and its extension to information-theoretic decistion making. It presents the Bayesian Decentralized Data Fusion (BDDF) algorithm formulated for environment features represented by a general probability density function. Several specific representations are also considered: Gaussian, discrete, and the Certainty Grid map. Well known algorithms for these representations are shown to implement various aspects of the Bayesian framework. As part of the ASN implementation, a practical indoor sensor network has been developed and tested. Two series of experiments were conducted, utilizing two types of environment representation: 1) point features with Gaussian position uncertainty and 2) Certainty Grid maps. The network was operational for several days at a time, with individual platforms coming on and off-line. On several occasions, the network consisted of 39 software components. The lessons learned during the system's development may be applicable to other heterogeneous distributed systems with data-intensive algorithms

ANALYSIS AND DEVELOPMENT OF A MATHEMATICAL STRUCTURE TO DESCRIBE ENERGY CONSUMPTION OF SENSOR NETWORKS

Collections of several hundred, thousands, or even millions of small devices scattered or placed throughout an area monitoring the environment called sensor networks have several useful applications. Until recently, the economic cost of development, manufacture, and deployment limited the use of sensor networks to military and government applications. Recent advances in technology provide a means for economical development, deployment, and manufacture of sensor networks.Current methodology designs, then implements and simulates the sensor network, then goes back and redesigns to better meet the specifications. The model developed in this dissertation provides an early indication of what types of solutions will meet the requirements and what types of solutions will not. With this ability, the time required for simulation and proof of concept is reduced, allowing more time and money for design and testing of the real world system. The model developed characterizes the energy consumption of a sensor or RFID network as a whole is extremely beneficial and is needed. The model provides a means to benchmark different types of sensor networks (i.e. different protocols, hardware, software) and to determine which type is the better solution. A model such as this removes the requirement to develop a simulation to compare different types. Using the model reduces the time (and save money) needed to verify the solution and helps with development as multiple designs can be quickly tested and compared possibly at a much earlier stage in the development cycle allowing a thorough investigation of different design alternatives