Fast and Efficient Classification, Tracking, and Simulation in Wireless Sensor Networks

Wireless sensor networks are composed of large numbers of resource-lean sensors that collect low-level inputs from the physical world. The applications present challenges for programmers. On the one hand, lightweight algorithms are required given the limited capacity of the constituent devices. On the other, the algorithms must be scalable to accommodate large networks. In this thesis, we focus on the design and implementation of fast and lean (yet scalable) algorithms for classification, simulation, and target tracking in the context of wireless sensor networks. We briefly consider each of these challenges in turn. The first challenge is to achieve high precision classification of high-level events in-network using limited computational and energy resources. We present in-network implementations of a Bayesian classifier and a condensed kd-tree classifier for identifying events of interest on resource-lean embedded sensors. The first approach uses preprocessed sensor readings to derive a multi-dimensional Bayesian classifier used to classify sensor data in real-time. The second introduces an innovative condensed kd-tree to represent preprocessed sensor data and uses a fast nearest-neighbor search to determine the likelihood of class membership for incoming samples. Both classifiers consume limited resources and provide high precision classification. To evaluate each approach, two case studies are considered, in the contexts of human movement and vehicle navigation, respectively. The classification accuracy is above 85% for both classifiers across the two case studies. The second challenge is to achieve high performance parallel simulation of sensor network hardware. This is achieved by reducing the synchronization overhead among distributed simulation processes. Traditional parallel simulation strategies introduce significant synchronization overhead, reducing the simulation speed. We present an optimistic simulation algorithm with support for backtracking and re-execution. The algorithm reduces the number of synchronization cycles to the number of transmissions in the network under test. Concretely, we implement SnapSim, an extension to the popular Avrora simulator, based on this algorithm. The experimental results show that our prototype system improves the performance of Avrora by 2 to 10 times for typical network-centric sensor network applications, and up to three orders of magnitude for applications that use the radio infrequently. The third challenge is to efficiently track a moving target in a network. The difficulty again lies in the conflict between the limited resource capacity of typical sensors and the significant processing requirements of typical tracking algorithms. We introduce an in-network object tracking framework for tracking mobile objects using resource-lean sensors. The framework is based on a distributed, dynamically scoped tracking algorithm which adaptively scopes the event detection region based on object speed. A leader node records the samples across an event region (without the aid of time synchronization) and estimates the object\u27s location in situ. To minimize the number of radio transmissions, the location snapshotting rate is also adjusted based on the object speed. In this dissertation, focusing on the above challenges, we present the design, implementation, and evaluation of classification, simulation, and tracking contributions

Bibliographic Review on Distributed Kalman Filtering

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area

Collaborative signal and information processing for target detection with heterogeneous sensor networks

In this paper, an approach for target detection and acquisition with heterogeneous sensor networks through strategic resource allocation and coordination is presented. Based on sensor management and collaborative signal and information processing, low-capacity low-cost sensors are strategically deployed to guide and cue scarce high performance sensors in the network to improve the data quality, with which the mission is eventually completed more efficiently with lower cost. We focus on the problem of designing such a network system in which issues of resource selection and allocation, system behaviour and capacity, target behaviour and patterns, the environment, and multiple constraints such as the cost must be addressed simultaneously. Simulation results offer significant insight into sensor selection and network operation, and demonstrate the great benefits introduced by guided search in an application of hunting down and capturing hostile vehicles on the battlefield

Wireless communication, identification and sensing technologies enabling integrated logistics: a study in the harbor environment

In the last decade, integrated logistics has become an important challenge in the development of wireless communication, identification and sensing technology, due to the growing complexity of logistics processes and the increasing demand for adapting systems to new requirements. The advancement of wireless technology provides a wide range of options for the maritime container terminals. Electronic devices employed in container terminals reduce the manual effort, facilitating timely information flow and enhancing control and quality of service and decision made. In this paper, we examine the technology that can be used to support integration in harbor's logistics. In the literature, most systems have been developed to address specific needs of particular harbors, but a systematic study is missing. The purpose is to provide an overview to the reader about which technology of integrated logistics can be implemented and what remains to be addressed in the future

Localisation of mobile nodes in wireless networks with correlated in time measurement noise.

Wireless sensor networks are an inherent part of decision making, object tracking and location awareness systems. This work is focused on simultaneous localisation of mobile nodes based on received signal strength indicators (RSSIs) with correlated in time measurement noises. Two approaches to deal with the correlated measurement noises are proposed in the framework of auxiliary particle filtering: with a noise augmented state vector and the second approach implements noise decorrelation. The performance of the two proposed multi model auxiliary particle filters (MM AUX-PFs) is validated over simulated and real RSSIs and high localisation accuracy is demonstrated