Optimizing Lifetime of Internet-of-Things Networks with Dynamic Scanning

• Published in: Mathematics (Basel) Vol. 11; no. 23; p. 4768
• Main Authors: Choi, Seung-Kyu, Kim, Woo Hyun, Sohn, Illsoo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-12-2023
• Subjects: Algorithms; Bluetooth; Bluetooth Low Energy; Bluetooth technology; Communication; Complexity; connectionless communication; Energy consumption; Finite element method; Floods; Internet of Things; Maximization; mesh network; network lifetime; Network reliability; Optimization; Search algorithms; Smart buildings; Wireless communications; Wireless telecommunications equipment; China
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math11234768

With the development of Internet-of-Things (IoT) technology, industries such as smart agriculture, smart health, smart buildings, and smart cities are attracting attention. As a core wireless communication technology, Bluetooth Low Energy (BLE) is gaining a lot of interest as a highly reliable low-power communication technology. In particular, BLE enables a connectionless mesh network that propagates data in a flooding manner using advertising channels. In this paper, we aim to optimize the energy consumption of the network by minimizing the scanning time while preserving the reliability of the network. Maximizing network lifetime requires various optimizing algorithms, including exhaustive searching and gradient descent searching. However, they are involved with excessive computational complexity and high implementation costs. To reduce computational complexity of network optimization, we mathematically model the energy consumption of BLE networks and formulate maximizing network lifetime as an optimization problem. We first present an analytical approach to solve the optimization problem and show that finding the minima from the complicated objective function of the optimization problem does not guarantee a valid solution to the problem. As a low-complexity solution, we approximate the complicated objective function into a convex form and derive a closed-form expression of the suboptimal solution. Our simulation results show that the proposed suboptimal solution provides almost equivalent performance compared to the optimal solution in terms of network lifetime. With very low computational complexity, the proposed suboptimal solution can extensively reduce implementation costs.