Optimisation of moving target's low-power and high-precision monitoring with RSSI based on static and dynamic clustering

Abstract

Independently using the static clustering or the dynamic clustering algorithm can lead to the large amount of transmitted data and high energy consumption. This paper proposes a static and dynamic fusion strategy that includes intelligent selection clustering technology, the sensor node density, the cluster size, etc. Theoretical analysis shows that our fusion strategy can reduce the quantity of data transmission, and decrease the power consumption of the wireless sensor network nodes. Further, we designed an adaptive Kalman filter with the optimisation function. Using the improved Kalman filter algorithm, we establish the moving target monitoring model based on the RSSI and Kalman filter algorithm, and then apply it to the moving targets monitoring in the long narrow environment of higher density of anchor nodes. Simulations and experimental results show that the proposed method significantly improved the monitoring accuracy of the moving targets, as well as the energy consumption of the network.