A Cognitive Knowledged Energy-Efficient Path Selection Using Centroid and Ant-Colony Optimized Hybrid Protocol for WSN-Assisted IoT

In a WSN-assisted IoT environment, the sensors are resource constrained. The energy, computing and storage resources of deployed sensors in the sensing area are limited. A hybrid protocol named as an Energy Efficient Centroid-based Ant colony Optimization (EECAO) hybrid protocol is proposed in this paper to improve the performance of the sensor network in WSN-assisted IoT environment. This protocol uses a concept of centroid based clustering to gather the information of local clusters and ant colony optimization to relay the same to the base station. The energy level of deployed cognitive sensors is considered as a key parameter for defining the position of centroid in this protocol. The proposed protocol has a new distributed cluster formation design which includes multiple clustering factors such as energy cost, channel consistency and cognitive sensor throughput to select cluster heads. In the proposed protocol, the selection of the super cluster head is based on the energy centroid position for a defined coverage area. The path optimization between the super cluster heads and the base station is carried out using an ant routing model. Our simulation results indicate that the proposed protocol performs better when benchmarked against existing ETSP and EECRP protocols. Also, it suits well for the sensor networks that requires long lifetime when the base station is placed at either center, border or outside the network.