Energy-Efficient Soft Real-Time Scheduling for Parameter Estimation in WSNs

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.