Modified Cuckoo Search for Resource Allocation on Social Internet-of-Things

The fundamental requirement for communication and computation across distinct application areas on Social Internet of Things (SIoT) is the resource discovery that demands appropriate reasoning for the optimal selection. With exponential growth of resources and their produced huge amount of heterogeneous data, various activities face challenges due to interoperability. In order to eliminate the challenge, the paper focuses on to propose an optimal resource selection technique namely, Modified Cuckoo Search (MCSA). The technique helps in reducing traffic congestion on network by selecting optimal resources in less time. The technique is tested on random dataset. The obtained results show that MCSA outperforms 22% approximately in comparison to nature-inspired, meta heuristic based machine learning algorithms i.e., Particle Swarm Optimization and Binary Genetic Algorithm.