2012/10/04 by Swarnendu Kar, Kar, Swarnendu, Pramod K. Varshney +1
Computer Science · #Distributed Sensor Networks and Detection Algorithms #Energy Efficient Wireless Sensor Networks #FOS: Computer and information sciences #Information Theory (cs.IT) #Target Tracking and Data Fusion in Sensor Networks
paper · pdf · doi:10.48550/arxiv.1210.1624
openalex publication_date 2012/10/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider a wireless sensor network consisting of multiple nodes that are\ncoordinated by a fusion center (FC) in order to estimate a common signal of\ninterest. In addition to being coordinated, the sensors are also able to\ncollaborate, i.e., share observations with other neighboring nodes, prior to\ntransmission. In an earlier work, we derived the energy-optimal collaboration\nstrategy for the single-snapshot framework, where the inference has to be made\nbased on observations collected at one particular instant. In this paper, we\nmake two important contributions. Firstly, for the single-snapshot framework,\nwe gain further insights into partially connected collaboration networks\n(nearest-neighbor and random geometric graphs for example) through the analysis\nof a family of topologies with regular structure. Secondly, we explore the\nestimation problem by adding the dimension of time, where the goal is to\nestimate a time-varying signal in a power-constrained network. To model the\ntime dynamics, we consider the stationary Gaussian process with exponential\ncovariance (sometimes referred to as Ornstein-Uhlenbeck process) as our\nrepresentative signal. For such a signal, we show that it is always beneficial\nto sample as frequently as possible, despite the fact that the samples get\nincreasingly noisy due to the power-constrained nature of the problem.\nSimulation results are presented to corroborate our analytical results.\n