A Novel Energy Optimization Approach for Artificial Intelligence-enabled Massive Internet of Things
Ali Hassan Sodhro, Mohammad S. Obaidat, Sandeep Pirbhulal, Gul Hassan Sodhro, Noman Zahid, Abhimanyu Rawat · 2019
Emerging trends in Internet of things (IoT) has caught the attention of every domain e.g., industrial, business, and healthcare etc. Sensor-embedded IoT devices are the key drivers for collecting large amount of data. Managing these large datasets is one of the critical challenges to be tackled. Continuous huge information collection through sensorenabled devices is known as the massive IoT (mIoT). Thus, there is a need of self-adaptive artificial intelligence (AI)based strategies to effectively cluster, examine and interpret the entire entities in the system. With increased data volumes and power hungry natured IoT devices it is a dire need to manage their power wisely. To fairly allot the power levels to the tiny portable devices it is important to integrate mIoT with AI-based techniques. To remedy these issues this paper proposes a novel cross-layer based energy optimization algorithm (CEOA) in mIoT system by examining the detailed features and data patterns. Experimental analysis reveals that proposed CEOA outperforms its competing counterpart i.e., Baseline in terms of efficient power management and monitoring.