Otsuka Ochiai Energy Efficient and Probabilistic Extreme Load Balancing for WSN Assisted IoT

In the current era, both Internet of things (IoT) and Wireless Sensor Networks (WSNs) are said to be the paramount operational coercion for technology advancements. However, when load conditions dynamically fluctuate, an imbalance load between sensors. In this paper, we propose an energy-efficient data aggregation and load balancing for reliable WSN assisted IoT communication called, Otsuka Ochiai Energy-efficient and Probabilistic Extreme Load Balancing (OOE-PELB). Otsuka-Ochiai Energy-efficient Data Aggregation is applied for performing optimal clustering and selecting the energy-efficient cluster head to reduce data aggregation time. Next, Probabilistic Extreme Learning Machine Load Balancing model is applied for precise and reliable WSN assisted IoT communications. The performance of the proposed method is evaluated by extensive simulations. The simulation results reveal that it outperforms the existing state-of-the-art methods in terms of energy efficiency, data aggregation time and network lifetime.