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The development of wireless sensor networks (WSNs) has been particularly notable in the context of smart computing, where various application areas have been identified. These networks consist of self-configured, small sensor nodes with battery power. However, sensors have limited energy and resources at their disposal. When unbalanced nodes exist within the network, it adversely affects the network's lifetime due to increased power consumption. Designing efficient routing for wireless sensor networks remains a challenging task, particularly in terms of energy efficiency. Optimal solutions to address these challenges involve reducing node energy consumption through the implementation of clustering techniques. The current clustering schemes do not take into account node energy balancing, node density, and scalability when selecting low-energy nodes as cluster heads. The existing system is limited to exploring global opportunities within specified search zones. To enhance the performance of WSNs, a hybrid approach combining artificial bee colony (ABC) and ant colony optimization (ACO) has been developed. This approach helps in selecting an ideal cluster head from a group of terminals. Several factors are considered in the cluster head election, including residual energy at the nodes, distance to neighbors, distance to the base station, node degree, and node centrality. ACO determines the path between the cluster leader and the base station (BS) by selecting the most efficient route in terms of distance, remaining power, and node degrees. The performance of this proposed methodology has been analyzed in terms of energy consumption, network lifetime, and data packets at the base station. A comparison has been made between the outputs of the proposed methods and traditional benchmarking methods. The results reveal a substantial improvement in the average network lifetime, showcasing an increase of 40.50%, 33.17%, 25.00%, and 15.49% when compared to LEACH, Beecluster, iABC, and BeeSensor, respectively. In terms of alive nodes, our solution surpasses LEACH, Beecluster, iABC, and BeeSensor by 28.7%, 22.51%, 20.95%, and 12.47%, respectively. Additionally, the energy consumption of our approach proves to be significantly lower than LEACH, Beecluster, iABC, and BeeSensor, recording reductions of 47.25%, 32.40%, 27.38%, and 22.20%, respectively. [ABSTRACT FROM AUTHOR]

Hierarchical architecture, in combination with static structure, is a productive approach to Wireless Sensor Networks (WSNs) scalability as well as efficiency. Sensor nodes, a dual-layer framework, are extensively explored for several scenarios. This paper demonstrates the proposed novel clustering methodology representing it as the Minimum Weight Low Energy Adaptive Clustering Hierarchy (MW-LEACH). The Cluster Heads (CHs) are selected in MW-LEACH depending upon the surplus energy and the distances among them. With the proposed scheme, from the initial set, the nodes are chosen through surplus energy around the centre of the density, thus leading to the composition of the primary set of CH candidates. These candidates further accumulate data from their members by travelling in multiple directions forwarding that particular data to the BS. This specific methodology has lesser complications when it comes to message and time and is speedy and guarantees an appropriate fault tolerance level. In Matlab simulations and evaluations during the experimental phase, the proposed approach exceeds the advanced trendy protocols in reliance on performance metrics of throughput, packet delivery, energy intake, network lifespan (duration), and delays. [ABSTRACT FROM AUTHOR]