Continuous evaluation of biological and physiological metrics of sports personalities, evaluating general health status, and alerting for life-saving treatments, is supposed to enhance efficiency and healthy performance. Wearable devices with acceptable form factors compact, flexibility, minimal power consumption, etc., are needed for continuous monitoring to avoid affecting everyday operations, thereby retaining functional effectiveness and consumer satisfaction. This research focuses on the acceleration tracker for particularizing the work. Acceleration data is typically collected on battery-powered sensors for activity detection, referring to an exchange between high-precision detection and energy-efficient processing. From a feature selection perspective, the paper explores this trade-off. It suggests an Energy-Efficient Behavior Recognition System with a comprehensive energy utilization model and the Multi-objective Algorithm of Particle Swarm Optimization (EEBRS-MPSO). Therefore, using Random Forest (RF) classifiers, the model and algorithm are tested to measure the precision of identification and obtain the task’s best performance with the lowest energy consumption, among other biologically-inspired algorithms. The findings indicate that energy consumption for data storage and data processing is minimized with magnitude relative to the raw data method by choosing suitable groups of attributes. Thus, the platform allows a scalable range of feature clusters that require the authors to provide an adequate power adjustment for given target use.
Collision Avoidance Geographic P2P-RPL in Multi-Hop Indoor Wireless Networks
