ABSTRACT
Applications for the internet of things (IoT) have rapidly expanded, posing a number of difficulties in terms of quality of service (QoS), delay, latency, and disconnections. These difficulties are still there even if fog computing has emerged. An innovative resource allocation strategy is presented for fog computing’s latency problems. The proposed approach includes allocating requests and assigning jobs to improve technical capabilities. It uses a queuing system with 3 recommended lists (a) block, (b)wait, and (c) scheduling – that is on the basis of loaded criteria to choose available nodes. The Cuckoo search (CS) approach, which significantly lowers latency, is developed to enhance resource allocation by calculating the distance between fog nodes and users and selecting the nearest along with the available nodes for request processing. By contrasting latency measures with and without the CS algorithm, the given evaluation shows the value of strategy. The results show a striking drop in latency, with the method having the distance and decreasing overall latency. The cuckoo search method is integrated with the suggested resource allocation mechanism to produce measurable latency reductions and improves the general performance of fog computing systems to enhance quality of life.
