GCP: A multi-strategy improved wireless sensor network model for environmental monitoring

Abstract

Nowadays, smart environmental monitoring devices are widely used in various fields, and one of the most representative tools is the wireless sensor network (WSN). WSNs are easy to deploy and provide real-time information feedback, which is very suitable for environmental monitoring. As we all know, the environmental monitoring network because of the special nature of its work requirements, the need for uninterrupted and real-time transmission of monitoring data, which leads to energy consumption is extremely large, and this cannot meet the needs of its long-term work. Existing traditional routing has problems such as unscientific cluster head election and high redundancy in data transmission, which usually lead to a large amount of energy consumption, which is not conducive to the long-term stable operation of sensor networks. In this paper, we improve the traditional routing protocol and design a cluster head election method based on the genetic algorithm, which proposes a new fitness function in terms of energy, distance, and the number of nodes in the cluster, and performs the selection of cluster head nodes based on this method. In addition, we propose a new grey prediction model, which can realize the real-time update of data queues, and optimize the data transmission process of traditional WSNs based on this prediction model to reduce the amount of intra-cluster data transmission. Combining these improvements, a grey cluster prediction (GCP) model is proposed, and the performance of the model is tested based on real mine and soil data sets. The simulation results show that the model significantly reduces energy loss and extends the network life cycle while ensuring the integrity of data transmission. It can also meet the requirements of long-term stable operation of environmental monitoring equipment.