| Abstract |
In this era of sophisticated technology for smart cities, when communication between smart things is crucial, Internet of Everything (IoE) networks play a key role in merging the cyber and physical worlds. IoE networks are used in a range of applications, including smart agriculture, smart housing, and smart medical services, thanks to the implementation of smart sensor networks (SSN). However, if human administration of the IoE network is impossible due to unforeseen reasons, the installed IoE network's life cycle is critical. The ability to reduce the amount of energy consumed by an IoE network is crucial for extending the network's life cycle. The objective of this work is to tackle the tough task of reducing IoE network energy usage (EU) on a wide scale. This study proposed an energy-efficient IoE network deployment problem for SSN, unmanned aerial vehicles (UAVs), and low earth orbit (LEO) satellites to achieve the aim of energy-efficient utilization of the stored UAVs' energy. For managing the EU of the IoE network, the proposed problem is a mixed-integer linear programming (MILP) optimization problem which is NP-hard in nature. To address this challenge, we use genetic algorithms (GA) to solve the task of minimizing EU while maintaining a low level of complexity. The proposed solution to the EU problem is flexible and effective, and it contributes to the IoE network's goal of a low EU and a long system lifespan. © 2022 IEICE. |
