Mobile Edge Computing (MEC) has emerged as a viable means to improve processing efficiency by reducing distance between computation and Internet of Things (IoT) devices, results in lower latency and energy expenditure. Although MEC appears to be a good candidate for improving task distribution efficiency, there remain issues surrounding task offloading, high computational latencies, and the heterogeneity of IoT devices that make centralized resource allocation seamless. This research presents an improved task offloading paradigm, with an emphasis on reducing latency and improving efficiency in an IoT-MEC context. Incoming tasks are classified into four categories based on levels of complexity. Type 1 tasks can be executed on the IoT device, while Type 4 task are fully offloaded into a cloud. Type 2 and Type 3 tasks utilize a parallel execution approach, where tasks are partially executed on the IoT device and partially satisfies the MEC server. The model in this study incorporates matching and queuing theory to improve task allocation from the device to the server. An additional branch based heuristic method is also introduced to further reduce task processing efficiency. Algorithm used was implemented using Python libraries through simulations and real-world cases. Comparative analysis with baseline task offloading methods demonstrates substantial performance improvements, achieving reductions in task computation latency of 40%−70%. These results demonstrate that the proposed methodology is efficient and effective in optimizing the overall execution of task in MEC-enabled IoT networks.

The Wireless Sensor Network (WSN) is a network that is constructed in regions that are inaccessible to human beings. The widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military environments. They make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the area. The data have to be picked up by the sensor, and then sent to the sink node where they may be processed. The nodes of the WSNs are powered by batteries, therefore they eventually run out of power. This energy restriction has an effect on the network life span and environmental sustainability. The objective of this study is to further improve the Engroove Leach (EL) protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of energy. The lifespan of WSNs is being extended often using clustering and routing strategies. The Meta Inspired Hawks Fragment Optimization (MIHFO) system, which is based on passive clustering, is used in this study to do clustering. The cluster head is chosen based on the nodes’ residual energy, distance to neighbors, distance to base station, node degree, and node centrality. Based on distance, residual energy, and node degree, an algorithm known as Heuristic Wing Antfly Optimization (HWAFO) selects the optimum path between the cluster head and Base Station (BS). They examine the number of nodes that are active, their energy consumption, and the number of data packets that the BS receives. The overall experimentation is carried out under the MATLAB environment. From the analysis, it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput, packet delivery and drop ratio, and average energy consumption.