Hybrid approach for evaluating dynamic scheduling mechanisms in IoT cognitive sensors for enhanced network performance

The Internet of Things is an intelligent network of cognitive sensors that are embedded into nearby objects and appliances to create a connected environment. It collects, processes, and transmits data for automation and decision-making while providing ubiquitous services. Sensors create primarily redundant data, with several repeating bit streams and matching patterns. Due to resource restrictions, sensors are carefully scheduled to activate and deactivate sensing operations. As a result, it is highly recommended to create schemes that can handle large volumes of data while maintaining a careful strategy for data collection. This article has identified and analyzed various approaches for comprehensively scheduling cognitive sensors and then evaluated the efficiency of the three schemes, CADS, EASS, and EDASS. Based on these schemes, a hybrid approach is proposed. The hybrid scheme, "Hybrid of CADS, EASS, and EDASS" (HCEE), is recommended to be used in those areas where each scheme is implemented and evaluated for applicability individually. Using the best features of these schemes, one combined scheme can be used to provide better results than those three in their individual forms. The basic three systems were evaluated with HCEE, for communication overhead and latency, node processing and buffering, the number of messages in the network, and energy usage in various scenarios. Every scheme has its primary goal, based on specific factors and a unique set of applications. Each of them exhibits unique behavior and has a variety of uses, but using one scheme in all these scenarios has achieved better results than any of these schemes. In performance analysis, CADS and EASS are more energy efficient with 67.58 and 64.25 mean values, respectively. HCEE has an extra message overhead of 6.61% compared to the average of the three schemes. HCEE has a transmission delay of 60.07% compared to other systems, and requires less buffer than CADS but more than EASS and EDASS.