Minimizing Energy Expenditures in Multi-Hop Transmissions with Genetic Algorithm

Wireless Sensor Networks (WSN) are placed in a specific field to gather, analyse, and collect data before sending it to the base station via various communication techniques and application layer protocols. However, a bottleneck in internet-of-things-based wireless sensor networks deployment is their limited bandwidth, battery power, and computing power capability. The major reason for low energy consumption and efficiency requirement is that WSNs are deployed in remote inaccessible locations and left alone and hence are difficult to recharge. The second reason is wireless sensors or Internet of Things (IoT) sensors are inherently power limited, as designed by manufacturers. This reduces the network lifetime and makes it a critical issue along with energy management. There are lots of algorithms that have been researched like Low Power Adaptive Clustering Hierarchy (LEACH), and LEACH variants such as Low Power Adaptive Clustering Hierarchy-Centralized (LEACH-C), Hybrid Energy Efficient Distributed (HEED), Power Efficient Gathering in Sensor Information Systems (PEGASIS), etc. In this study, existing literature are reviewed, and several routing algorithms are analysed to see how Cluster Head (CH) selection varies between them. An overview of methods influenced by genetics and nature-inspired algorithms and game theory routing protocol implementations are also included. The results are studied and compared on the state of the art for routing algorithms and an algorithm that presents an improvement over the previous reviewed works is proposed. The results show that genetic algorithm LEACH (GA-LEACH) outperformed LEACH and modified LEACH (MOD LEACH) in terms of performance. Comparing with LEACH and MODLEACH, GALEACH greatly enhanced the network lifetime and improved energy consumption by 34.75% and 14.61% respectively.