Machine Learning Based Power Efficient Optimized Communication Ensemble Model with Intelligent Fog Computing for WSNs

Wireless sensor networks have evolved in recent years. Energy consumption is one of the major parameters to access the performance of the network. This study proposes an algorithm which is based on Ant lion optimization along with three different clustering techniques. This study is motivated from fog computing in which Ant lion optimization is applied in the bottom layer and an ensemble model of k-means, SOM and Fuzzy c-means techniques is used for clustering in FOG layer. To access model’s performance, results are compared to the traditional methods. This model has shown significant improvement in terms of energy consumption as compared to traditional model ECCM. Proposed model has also shown improvement in terms of load balancing.