Optimum Computational Intelligence (OCI) in Wireless Sensor Networks (WSNs)

Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms of Optimum computational intelligence (OCI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. Nevertheless nature has played its ultimate role as well to give an idea of perfection in optimizing the teething issues in any field and so in WSN. Design and deployment, localization, routing and clustering, Quality of Service (QoS) management, security etc are few scenarios in WSN. The optimum computational intelligence paradigm inspired by nature, comprising artificial neural network (ANN), artificial immune system (AIS), evolutionary algorithm (EA), swarm intelligence (SI). These are different flavors for increasing efficiency in terms of memory and computational power which alternatively affects other issues towards perfection i.e. convenience and intelligence. Hybrid and non hybrid algorithms optimizes the issues through OCI paradigms, which are presented in this research article relevant to Wireless Sensor Network. Our findings strongly recommend development of hybrid OCI based algorithms to address combinatorial optimizing issues in WSN.