In wireless sensor networks (WSNs), homogeneous or heterogenous sensor nodes are deployed at a certain area to monitor our curious target. The sensor nodes report their observations to the base station (BS), and the BS should implement the parameter estimation with sensors’ data. Best linear unbiased estimation (BLUE) is a common estimator in the parameter estimation. Due to the end-to-end packet delay, it takes some time for the BS to receive sufficient data for the estimation. In some soft real-time applications, we expect that the estimation can be completed before the deadline with a probability. The existing approaches usually guarantee the real-time constraint through reducing the number of hops during data transmission. However, this kind of approaches does not take full advantage of the soft real-time property. In this paper, we proposed an energy-efficient scheduling algorithm especially for the soft real-time estimations in WSNs. Through the proper assignment of sensors’ state, we can achieve an energy-efficient estimation before the deadline with a probability. The simulation results demonstrate the efficiency of our algorithm.

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Published in International Journal of Distributed Sensor Networks, v. 2013, article ID 814807, p. 1-12.

Copyright © 2013 Senlin Zhang et al.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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This work was supported in part by the National Natural Science Foundation of China under Grants 61222310, 61174142, 61071061, 61134012, and 60874050, the Zhejiang Provincial Natural Science Foundation of China under Grants R1100234 and Z1090423, the Program for New Century Excellent Talents (NCET) in University under Grant NCET-10-0692, the Fundamental Research Funds for the Central Universities under Grant 2011QNA4036, the ASFC under Grant 20102076002, the Specialized Research Fund for the Doctoral Program of Higher Education of China (SRFDP) under Grants 20100101110055, and 20120101110115, the Zhejiang Provincial Science and Technology Planning Projects of China under Grants 2012C21044 and the Marine Interdisciplinary Research Guiding Funds for Zhejiang University under Grant 2012HY009B. This work was also supported by the “151 Talent Project” of Zhejiang Province. The work is also partially supported by NSF CNS-1249223 (M. Qiu).