Abstract
Energy consumption is the first-class constraint for the battery-powered Internet of Things (IoT) devices and sensors. By moving around a sensor network to gather data, a mobile sink (MS) can greatly save sensor energy for multi-hop communication. To unlock the potential of mobile sinks, we need to carefully plan the path a mobile sink moves within the network for collecting information without compromising its coverage and its battery life. This paper presents a new way to find the optimal information collection path for mobile sinks. We achieve this by formulating the optimization problem as a classical Traveling Salesman Problem with Neighborhoods (TSPN). We then design a novel solver based on the particle multi-dimensional search algorithm to quickly locate a good path schedule in the TSPN optimization space. As a significant departure from prior work which uses multiple particles to explore multiple potential solutions, our method uses only one particle for problem-solving. Doing so significantly reduces the complexity of the algorithm, allowing it to scale to a larger sensor network. To ensure the quality of the chosen solution, we have carefully designed the evolutionary process for problem-solving. We show that our approach finds a solution with similar quality as those given by a multi-particle-based search, but with significantly less time. Simulation results show that our approach can find a high-quality path schedule compared to the state-of-the-art algorithm in a large sensor network.
Supported in part by the Key Research and Development Project of Shaanxi Province under Grant 2018GY-017, 2017GY-191 and 2019GY-146, in part by the Foundation of Education Department of Shaanxi Province Natural Science (15JK1742), and in part by the Foundation of Science Project of Xi’an (201805029YD7CG13(4)).
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Huang, P., Wu, F., Wang, W., Liu, H., Liu, Q. (2021). Finding Good Mobile Sink Information Collection Paths with Quicker Search Time: A Single-Particle Multi-dimensional Search Algorithm-Based Approach. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 370. Springer, Cham. https://doi.org/10.1007/978-3-030-72795-6_32
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