A New Decision-Making Method for Service Discovery and Selection in the Internet of Things Using Flower Pollination Algorithm

Abstract

The Internet of Things (IoT) enables intelligent and heterogeneous things to access the Internet and subsequently interact and share info. A service management methodology is required by growing IoT applications and the number of services supplied by various objects. Nevertheless, making decisions, finding, and choosing a service is complex. Therefore, numerous techniques are explored in this regard. This paper employed Flower Pollination Algorithm (FPA) for service discovery and selection in IoT. The FPA is a nature-inspired algorithm that mimics flowering plant pollination behavior. Through a hand-over probability, it is possible to adjust the balance between local and global search properly. The survival of the fittest and the optimal reproducing plants regarding numbers are parts of an optimum plant reproduction strategy. These elements are optimization-oriented and constitute the FPA's basics. The suggested methodology has an excellent performance in minimizing data access time, energy usage and optimizing cost according to simulation findings.