An Energy-Efficient Information Aggregation Protocol With Optimized Trilevel K-Means Clustering for IoT-Based WSN Framework: A Case Study on Smart Agriculture

Abstract

In agriculture production, Internet of Things–based wireless sensor network (IoT-based WSN) technology is used to observe yield conditions and automatic precision agriculture through different sensors. As a part of intelligent farming decisions, sensors are used in the agricultural field to collect information regarding plants, crops, measurements, temperature, humidity, and irrigation systems. IoT-based WSNs are proposed for smart agriculture that have different levels of design. Initially, significant information is captured by agricultural sensors. Then, an optimized trilevel K-means clustering (KMA) is introduced to cluster the agriculture data. The trilevel KMA is effectively used to extract knowledge in the agriculture field. After that, the cluster head is selected using the arithmetic optimization algorithm (AOA) by considering the general factors, including energy, delay, and distance, improving the lifetime of the nodes. This method proposes a new chain-based routing method for optimizing information transmission in IoT-based WSN frameworks. A comparison between the proposed and existing methods is performed to prove their effectiveness. The experimental results show that the proposed protocol outperforms the other existing methods in terms of throughput (96 kbps), packet delivery ratio (37%), network latency (0.028 ms), and average energy consumption (0.52 J).