Abstract
The development of the Internet of Things (IoT) network has greatly benefited from the expansion of sensing technologies. These networks interconnect with wireless systems and collaborate with other devices using multi-hop communication. Besides data sensing, these devices also perform other operations such as compression, aggregation, and transmission. Recently, many solutions have been proposed to overcome the various research challenges of wireless sensor networks; however, energy efficiency with optimized intelligence is still a burning research problem that needs to be tackled. Thus, this paper presents an energy-efficient enabled edge optimization embedded system using graph theory for increasing performance in terms of network lifetime and scalability. First, minimum spanning trees are extracted using artificial intelligence techniques to improve the embedded system for response time and latency performance. Second, the extracted routes are provided with full protection against anonymous access in a two-tiered system. Third, the IoT systems collaborate with mobile sinks, and they need to be authenticated using lightweight techniques for the involvement in routing sensed information. Moreover, edge networks further provide the timely delivery of data to mobile sinks with less overhead on IoT devices. Finally, the proposed system is verified using simulations, revealing its significance to existing approaches.