Editorial
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Editorial Kung Yao Department of Electrical Engineering, University of California, Los Angeles (UCLA), Los Angeles, CA 90095-1594, USA Email: [email protected]
Deborah Estrin Department of Computer Science, University of California, Los Angeles (UCLA), Los Angeles, CA 90095-1596, USA Email: [email protected]
Yu Hen Hu Department of Electrical and Computer Engineering, University of Wisconsin-Madison, 1415 Engineering Drive, Madison, WI 53706-1691, USA Email: [email protected]
Advances in low-cost and low-power wireless communication, microsensor, and microprocessor hardware, as well as progress in ad hoc networking routing and protocols, distributed signal and array processing, pervasive computing, and embedded systems have all made sensor networking a topic of active interest. In recent years, the Internet has been able to provide a large number of users with the ability to move diverse forms of information readily and thus revolutionized business, industry, defense, science, education, research, and human interactions. Sensor networking may, in the long run, be equally significant by providing measurement of the physical phenomena around us, leading to their understanding and ultimately the utilization of this information for a wide range of applications. Potential applications of sensor networking include environmental monitoring, health care monitoring, battlefield surveillance and reconnaissance, modern highway, modern manufacturing, condition-based maintenance of complex systems, and so forth. In order to understand and build sensor networks, diverse technology and technical disciplines are involved. However, in this special issue we deal only with various signal processing aspects of sensor networking. Of the seven papers, four of them deal with source localization, two of them with tracking, and one with sensor network decomposition and organization. Energy-Based Collaborative Source Localization Using Acoustic Microsensor Array, by D. Li and Y. H. Hu, uses acoustic energy measurements to perform source localization. This approach assumes the acoustic source energy decays inversely with the square of the distance. By comparing acoustic sensor energy measurements around the source, the source location can be estimated as the intersection of
multiple hyperspheres. The Fusion of Distributed Microphone Arrays for Sound Localization, by P. Aarabi, also deals with acoustic source localization. The author proposes to use the spatial observability function (SOF), which gives an indication of how well a microphone array perceives events at different spatial position. Each microphone array also has a spatial likelihood function (SLF) which reports the likelihood of a source at each spatial location. SOF and SLF approaches are used together for sound localization. In A Self-Localization Method for Wireless Sensor Networks, by R. L. Moses, D. Krishnamurthy, and R. Patterson, the authors consider the problem of locating and orienting a network of unattended sensors by using a number of known source signals for calibrat
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