OntoRLE: an ontological-based compression algorithm for improving energy efficiency and memory utilization in 5G WSNs

5G wireless sensor networks (WSNs) are expanding their roots in the technological domain day by day. WSNs employ sensor nodes that are equipped with small batteries that have limited power capacity. The primary focus in WSNs is to ensure efficient power management in order to prolong the network’s lifespan. To achieve energy conservation, it is imperative to employ a proficient data compression methodology for compressing data transmitted from sensor nodes to submerged networks. In this paper, an ontological-based algorithm is proposed that utilizes some of the features of the run length encoding (RLE) data compression algorithm. The performance of the proposed algorithm is validated based on metrics such as compression ratio (CR), compression factor (CF), compression percentage (CP), network lifetime, and stability period of sensor nodes. The results show that the proposed ontological-based compression algorithm efficiently outperforms the conventional compression algorithms, thereby improving energy efficiency and memory utilization in WSNs.