An energy efficient fusing data gathering protocol in wireless sensor networks

Abstract

The Internet of Things (IoT) is rapidly developing as a promising technology for today’s digital world. Wireless sensor networks (WSN) are a crucial component of IoT, and one of the major problems faced is energy constraints. The clustering protocol is an effective energy saving solution. In this paper, we propose an energy efficient fusing data gathering protocol via distributed second level clustering and deep learning-based data fusion (EEFDG) for WSN. EEFDG divides the WSN into several second-level clusters by a novel distributed cluster algorithm. The transmission distance and the number of neighbors are jointly considered to select an energy-efficient parent node in each second-level cluster. A new cluster head weight function is proposed for intra-cluster parent node rotation. To minimize redundant data transmission, a multivariate convolutional neural network and long short-term memory network (MVCLNet) are designed for data fusion. MVCLNet is deployed hierarchically into the second-level clustered structure to extract features of the multi-sensor data. The EEFDG is an effective solution to the energy limitation problem of WSN. The proposed second-level clustering and data fusion algorithm based on MVCLNet typically decreases communication distances and the amount of data, thereby minimizing communication overhead and energy consumption. Simulation results show that EEFDG significantly prolongs the network lifetime in terms of the first node dead, compared to existing protocols including MR-LEACH, BPDA, CNNDA, FDEAM, DCNN, and LPLL-LEACH, achieving improvements of 181.3%, 42.2%, 48.2%, 19.6%, 62.0%, and 30.2%, respectively. The EEFDG enables highly accurate data fusion and has superior performance in terms of remaining energy, network lifetime, and data collection.