Wireless sensor networks encounter issues like irregular deployment, node failures, and uneven energy consumption that create coverage holes, leading to a reduction in network lifetime in critical or disaster-based applications. Most existing approaches focus on coverage enhancement during the initial deployment and perform mitigation only at the beginning of the network operation. However, the coverage holes may also occur later due to node failures and energy depletion. To address this issue, a Hybrid Raccoon–Hermit crab optimization algorithm that advocates both initial coverage enhancement and adaptive mitigation due to future coverage holes is proposed. The proposed algorithm uses the global exploration ability of the raccoon optimization algorithm to find optimal cluster heads and the exploitation ability of the Hermit crab optimization to determine the optimal position and to relocate the static nodes logically to mitigate coverage holes. The proposed algorithm is evaluated under different node densities (50, 100, 200, 500, and 1000), with the sink at $(100,100)$. It results in an enhanced network lifetime of $65.20\%$, an improved coverage ratio $(16.94\%)$ from $(77.05\%)$ to $(93.94\%)$, increased throughput by delivering (3,139,293) bits, and a reduced delay of $2.27292$ s for 1000 nodes compared with other existing methods.

Objectives: Consolidating any two dissimilar networks leads to a formation of a heterogeneous wireless network. Vision to achieve distinct networks to get converged such that characterizing of the upcoming wireless networks becomes a reality. This in turn ushers in vertical handoff such that handoff among different technologies is efficient. And at the same time it is so smooth that naming it as seamless is justifiable. Pragmatic consideration of the network characteristics and the dynamics are very essential to choose one of the best available network handoff decisions. Methods: Stating conventionally, when a mobile client is roaming, a single criterion- for example, received signal strength is employed to realize the vertical handoff. But, single criterion consideration is not sufficient and taking into account of other parameters is needed for a proper handoff decision. The present paper puts forth a strong load balancing algorithm M-OPTF that is based on dynamic weight assignment technique for allocation of fresh calls and handoff calls. Findings: In the present scheme assignment of weight factor is done to attachment points basing on the distribution of load on them. In addition it contains a process for handoff decision that takes into account the velocity of mobile node to do triggering of handoff. The put forth M-OPTF algorithm is compared along with Remote Sensing Systems (RSS) and OPT-F algorithm in this paper. Application/Improvements: The simulation results confirm the fact that M-OPTF algorithm apart from balancing the network load it can effectively decrease congestion in network, reduce the number of handoffs that are unnecessary, and increases the battery life time, thus improve the overall performance of the system.