Abstract-This article describes information-based approaches to processing and organizing spatially distributed, multi-modal sensor data in a sensor network. Energy constrained networked sensing systems must rely on collaborative signal and information processing (CSIP) to dynamically allocate resources, maintain multiple sensing foci, and attend to new stimuli of interest, all based on task requirements and resource constraints. Target tracking is an essential capability for sensor networks and is used as a canonical problem for studying information organization problems in CSIP. After formulating a CSIP tracking problem in a distributed constrained optimization framework, the paper describes IDSQ and other techniques for tracking individual targets as well as combinatorial tracking problems such as counting targets. Results from simulations and experimental implementations have demonstrated that these information based approaches are scalable and make efficient use of scarce sensing and communication resources. Keywords-Sensor networks, distributed sensing, collaborative signal and information processing, information utility, constrained optimization, target tracking. I. Sensor Network Applications, Constraints, and Challenges Networked sensing offers unique advantages over traditional centralized approaches. Dense networks of distributed networked sensors can improve perceived signalto-noise ratio (SNR) by decreasing average distances from sensor to target. Increased energy efficiency in communications is enabled by the multi-hop topology of the network [22] . Moreover, additional relevant information from other sensors can be aggregated during this multi-hop transmission through in-network processing [13] . But perhaps the greatest advantages of networked sensing are in improved robustness and scalability. A decentralized sensing system is inherently more robust against individual sensor node or link failures, because of redundancy in the network. Decentralized algorithms are also far more scalable in practical deployment, and may be the only way to achieve the large scales needed for some applications. A sensor network is designed to perform a set of highlevel information processing tasks such as detection, track- ing, or classification. Measures of performance for these tasks are well defined, including detection, false alarms or misses, classification errors, and track quality. Commercial and military applications include environmental monitoring (e.g., traffic, habitat, security), industrial sensing and diagnostics (e.g., factory, appliances), infrastructure protection (e.g., power grid, water distributions), and battlefield awareness (e.g., multi-target tracking). Unlike a centralized system, however, a sensor network is subject to a unique set of resource constraints such as limited on-board battery power and limited network communication bandwidth. In a typical sensor network, each sensor node operates untethered and has a microprocessor and limited amount of memory for signal processing and task scheduling. Each node also is equipped with one or more of acoustic microphone arrays, video or still cameras, IR, seismic, or magnetic sensing devices. Each sensor node communicates wirelessly with a small number of local nodes within the radio communication range. The current generation of wireless sensor hardware ranges from shoe-box sized Sensoria WINS NG sensors [20] with an SH-4 microprocessor to matchbox sized Berkeley motes with an 8-bit microcontroller [12] . It is well-known that communicating one bit over the wireless medium consumes far more energy than processing the bit. For the Sensoria sensors and Berkeley motes, the ratio of energy consumption for communication and computation is in the range of 1,000-10,000. Despite the advances in silicon fabrication technologies, wireless communication will continue to dominate the energy consumption of embedded networked systems for the foreseeable future [8] . Thus, minimizing the amount and range of communication as much as possible, for example, through local collaboration, data compression, or invoking only the nodes that are relevant to a given task, can significantly prolong the lifetime of a sensor network and leave nodes free to support multi-user operations. Traditional signal processing approaches have focused on optimizing estimation quality for a fixed set of available resources. However, for power-limited and multi-user decentralized systems, it becomes critical to carefully select the embedded sensor nodes that participate in the sensor collaboration, balancing the information contribution of each against its resource consumption or potential utility for other users. This approach is especially important in dense networks, where many measurements may be highly redundant, and communication throughput severely limited. We use the term "collaborative signal and information processing" (CSIP) to refer to signal and information processing problems dominated by this issue of selecting embedded
Zhao et. al. described collaborative signal processing REF to retrieve more accurate information from sensor data and achieve better target tracking performance.
Collaborative Signal and Information Processing: An Information Directed Approach
Proceedings of the IEEE
Computer Science