Power scheduling of distributed estimation in sensor networks with repetition coding

This paper considers the optimal power scheduling for the distributed estimation of a source parameter using quantized samples of noisy sensor observations in a wireless sensor network (WSN). Repetition codes are used to transmit quantization bits of sensor observations to achieve unequal error protection, and a quasi-best linear unbiased estimate is constructed to estimate the source parameter at the fusion center (FC). Based on the adopted distributed estimation scheme (DES), we optimize the power scheduling among sensors to minimize the L^1-norm of the power vector subject to the desired tolerance, which implies minimizing the total transmission power. Since the optimization problem is not convex, we propose a low-complexity alternative, which minimizes the L^2-norm of the power vector while insuring the desired tolerance. We derive the closed form solution of the L^2-norm power scheduling scheme. Simulation results show that the total power consumption of the L^2-norm power scheduling scheme is close to that of the L^1-norm power scheduling scheme, while complexity analysis demonstrates that the L^2-norm power scheduling scheme has very low complexity

Distributed intelligence and data fusion for sensor systems

Sensor systems can monitor any type of parameter which is ideal for gathering information from the real world. The next step is to promote the research of distribute intelligence in sensor systems in order to let the sensors be smart and take their own decisions, and propose new data fusion techniques and methods in order to obtain the appropriate data and accurate results. In this paper we summarise a Special Issue that tackles the distributed intelligence and data fusion for sensor systems from the perspective of coding and channel allocation, data aggregation and data storage, routing, security, mobility and distributed services. This paper introduces the reader to those main hot topics and summarises the recent advances provided by the papers published in this Special Issue. © 2011 The Institution of Engineering and Technology.Shu, L.; Lloret, J.; Rodrigues, JJPC.; Chen, M. (2011). Distributed intelligence and data fusion for sensor systems. IET Communications. 5(12):1633-1636. doi:10.1049/iet-com.2011.0534S16331636512Zhang, L., Cui, T., Ho, T., & Zhang, X.-D. (2010). Power scheduling of distributed estimation in sensor networks with repetition coding. Signal Processing, 90(3), 946-951. doi:10.1016/j.sigpro.2009.07.032Lin, K. (2010). Multi-Attribute Data Fusion for Energy Equilibrium Routing in Wireless Sensor Networks. KSII Transactions on Internet and Information Systems, 5-24. doi:10.3837/tiis.2010.01.001Bass, T. (2000). Intrusion detection systems and multisensor data fusion. Communications of the ACM, 43(4), 99-105. doi:10.1145/332051.332079Lim, A. (2001). Distributed services for information dissemination in self-organizing sensor networks. Journal of the Franklin Institute, 338(6), 707-727. doi:10.1016/s0016-0032(01)00020-