Editorial

EURASIP Journal on Wireless Communications and Networking, Sep 2005

Biao Chen, Wendi B Heinzelman, Mingyan Liu

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Editorial

EURASIP Journal on Wireless Communications and Networking Biao Chen 0 1 2 3 Wendi B. Heinzelman 0 1 2 3 Mingyan Liu 0 1 2 3 Andrew T. Campbell 0 Department of Electrical Engineering and Computer Science, University of Michigan , Ann Arbor, MI 48109-2122 , USA 1 Department of Electrical and Computer Engineering, University of Rochester , Rochester, NY 14627 , USA 2 Department of Electrical Engineering and Computer Science, Syracuse University , Syracuse, NY 13244 , USA 3 Department of Electrical Engineering and Center for Telecommunications Research, Columbia University , NY 10027 , USA - Recent advances in integrated circuits and in digital wireless communication technologies have enabled the design of wireless sensor networks (WSN) to facilitate the joint processing of spatially and temporally distributed information. Such networks immensely enhance our ability to understand and evaluate complex systems and environments. Using wireless connections for sensor networks offers increased flexibility in deployment and reconfiguration of the networks and reduces infrastructure cost. These advantages enable WSN applications in areas ranging from battlefield surveillance to environment monitoring and control to telemedicine. Enormous challenges in the understanding of sensor networks presently impede deployment of many of the envisaged applications. In particular, for WSN that employ in situ unattended sensors, physical constraints, including those of power, bandwidth, and cost, have presented significant challenges as well as research opportunities in the field. Of particular interest to this special issue are topics related to the communications and networking aspects of WSN. Indeed, one of the major concerns in sensor networks is maintaining connectivity and networking functions with geographically dispersed sensor nodes under stringent resource constraints. This is further exacerbated by the volume of data generated by the sensors, which is disproportionately large compared with the network capacity. The papers in this special issue are reflections of some of these issues. Sensor networks are typically built to perform some system-wide missions, that is, collective inference tasks that involve all sensor nodes. Examples include detection of an event and estimation of a parameter or a process. The first three papers are concerned with designing such WSN. The first paper, coauthored by Niu and Varshney, considers the detection of an event in sensor networks with a random number of sensors. High network density and limited bandwidth impose a severe constraint on the number of bits each sensor can transmit, and the authors treat the extreme case where a single bit is sent from each sensor. Under the assumption of a Poisson model on the number of sensors, a simple counting rule is proposed at the fusion center to strike a balance between performance and requirement on a priori information. This work demonstrates that for large-scale heterogeneous sensor networks, heuristics based on intuition often trump theoretically optimal processing that is typically too demanding in its requirement. Under the same network architecture, that is, a number of sensors communicating with a single fusion center, the second paper, by Krasnopeev et al. treats an estimation problem where the unknown signal is corrupted by spatially correlated additive noises. Again, bandwidth constraints dictate that each sensor sends a finite number of bits to the fusion center. By exploiting the spatial correlation of the noise in terms of its covariance matrix, the minimum energy quantizer design is reformulated as a convex optimization problem and hence can be solved efficiently EURASIP Journal on Wireless Communications and Networking using standard convex programming. Taking the problem one step further, the third paper, coauthored by Del Ser et al. deals with the estimation of a random process. Specifically, two binary sources, whose correlation is modeled by a hidden Markov process, are transmitted and the receiver is assumed to reliably recover one of them. This then serves as side information for the decoding of the other. It was demonstrated that the hidden Markov model parameters and the transmitted source can be jointly recovered via iterative decoding. A perennial problem encountered in large-scale sensor networks is medium access control (MAC): the lack of a central node and the stringent bandwidth and other resource constraints make it an extremely difficult problem. In the paper by Yang et al., the authors consider information retrieval and processing problems in the SENMA (sensor networks with mobile access) network architecture, where data generated by ground sensors are collected by mobile access points (e.g., unmanned aerial vehicles). Three MAC protocols are proposed to produce desired data-retrieval patterns, so as to minimize the reconstruction distortion. These MAC protocols integrate random access by the sensor nodes a (...truncated)


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Biao Chen, Wendi B Heinzelman, Mingyan Liu. Editorial, EURASIP Journal on Wireless Communications and Networking, 2005, pp. 321260, Volume 2005, Issue 4, DOI: 10.1155/WCN.2005.459