Smart camera networks have recently emerged as a new class of sensor network infrastructure that is capable of supporting high-power in-network signal processing and enabling a wide range of applications. In this article, we provide an exposition of our efforts to build a low-bandwidth wireless camera network platform, called CITRIC, and its applications in smart camera networks. The platform integrates a camera, a microphone, a frequency-scalable (up to 624 MHz) CPU, 16 MB FLASH, and 64 MB RAM onto a single device. The device then connects with a standard sensor network mote to form a wireless camera mote. With reasonably low power consumption and extensive algorithmic libraries running on a decent operating system that is easy to program, CITRIC is ideal for research and applications in distributed image and video processing. Its capabilities of in-network image processing also reduce communication requirements, which has been high in other existing camera networks with centralized processing. Furthermore, the mote easily integrates with other low-bandwidth sensor networks via the IEEE 802.15.4 protocol. To justify the utility of CITRIC, we present several representative applications. In particular, concrete research results will be demonstrated in two areas, namely, distributed coverage hole identification and distributed object recognition.

Many of the routing protocols that have been designed for wireless ad-hoc networks focus on energy-efficiency and guaranteeing high throughput in a non-adversarial setting. However, given that ad-hoc and sensor networks are deployed and left unattended for long periods of time, it is crucial to design secure routing protocols for these networks. Over the past few years, attacks on the routing protocols have been studied and a number of secure routing protocols have been designed for wireless sensor networks. However, there has not been a comprehensive study of how these protocols compare in terms of achieving security goals and maintaining high throughput. In this paper, we focus on the problem of analyzing the inherent security of routing protocols with respect to two categories: multi-path and single-path routing. Within each category, we focus on deterministic vs. probabilistic mechanisms for setting up the routes. We consider the scenario in which an adversary has subverted a subset of the nodes, and as a result, the paths going through these nodes are compromised. We present our findings through simulation results.

This paper considers the problem of pursuit evasion games (PEGs), where the objective of a group of pursuers is to chase and capture a group of evaders in minimum time with the aid of a sensor network. The main challenge in developing a real-time control system using sensor networks is the inconsistency in sensor measurements due to packet loss, communication delay, and false detections. We address this challenge by developing a real-time hierarchical control system, namedLochNess, which decouples the estimation of evader states from the control of pursuers via multiple layers of data fusion. The multiple layers of data fusion convert noisy, inconsistent, and bursty sensor measurements into a consistent set of fused measurements. Three novel algorithms are developed forLochNess: multisensor fusion, hierarchical multitarget tracking, and multiagent coordination algorithms. The multisensor fusion algorithm converts correlated sensor measurements into position estimates, the hierarchical multitarget tracking algorithm based on Markov chain Monte Carlo data association (MCMCDA) tracks an unknown number of targets, and the multiagent coordination algorithm coordinates pursuers to chase and capture evaders using robust minimum-time control. The control systemLochNessis evaluated in simulation and successfully demonstrated using a large-scale outdoor sensor network deployment.

On August 30, 2005, we successfully demonstrated a large-scale, real-time, surveillance and control application on a wireless sensor network. The task was to track multiple human targets walking through a 5041 square meter sensor field and dispatch simulated pursuers to capture them. We employed a multi-target tracking algorithm that was a combination of a multi-sensor fusion algorithm for fusing binary detections and a Markov chain Monte Carlo data association (MCMCDA) algorithm that can initiate and terminates tracks autonomously and is robust to a high level of false alarms and missing measurements, a common problem in sensor networks. The tracks were used by a multi-agent coordination and control algorithm to capture the evaders. We were able to demonstrate successful pursuit of two crossing targets and successful tracking of three targets moving through a 144 node sensor field. To the authors' best knowledge, this experiment is the largest demonstration to date of a real-time tracking and control system on a wireless sensor network that does not use classification information