Dr. Shreyas J
Advancement in technology is improving rapidly in recent years, the Internet of Things (IoT) aims to connect multiple devices for information sharing and intelligent decision making. The rapid increase in connected devices comes along with new challenges. Addressing these challenges using traditional algorithms may not be effective. Swarm intelligence algorithms are self-organized algorithms used to resolve complex and dynamic problems with incomplete information and limited computational capabilities. The aim of this exclusive survey is to provide a summarized brief study of all the existing research on application of swarm intelligence for IoT. The results show that the Field of application of swarm intelligence in IoT is very huge and there is still room for further study. The study shows the benefits of applications of swarm intelligence on IoT in many real time situations. After the mapping study the different types of swarm intelligence algorithms used and their percentages (like Particle Swarm Optimization-25.220%, Ant Colony Optimization-13.040%, Hybrid Intelligence-13.910%, Grey Wolf Optimization-6.090%, Artificial Bee Colony-5.220%, Cuckoo Search-3.480% etc. The detailed study conducted is summarized into year published, problem solved, scope of the paper, benefits of the study, gap area, implementation tool used and the performance analysis.
The gap area in the papers shows that there is requirement of the further study is this field. Swarm algorithms in IoT have immense capability of further development and are prominent in building smart cities and artificial intelligence based connected society.
