Version 1
: Received: 6 November 2024 / Approved: 6 November 2024 / Online: 6 November 2024 (17:14:14 CET)
How to cite:
Kósa, B.; Bukovinszki, M.; Michaletzky, T. V.; Tihanyi, V. Combined Barrier-Target Coverage for Directional Sensor Network. Preprints2024, 2024110437. https://doi.org/10.20944/preprints202411.0437.v1
Kósa, B.; Bukovinszki, M.; Michaletzky, T. V.; Tihanyi, V. Combined Barrier-Target Coverage for Directional Sensor Network. Preprints 2024, 2024110437. https://doi.org/10.20944/preprints202411.0437.v1
Kósa, B.; Bukovinszki, M.; Michaletzky, T. V.; Tihanyi, V. Combined Barrier-Target Coverage for Directional Sensor Network. Preprints2024, 2024110437. https://doi.org/10.20944/preprints202411.0437.v1
APA Style
Kósa, B., Bukovinszki, M., Michaletzky, T. V., & Tihanyi, V. (2024). Combined Barrier-Target Coverage for Directional Sensor Network. Preprints. https://doi.org/10.20944/preprints202411.0437.v1
Chicago/Turabian Style
Kósa, B., Tamás Vilmos Michaletzky and Viktor Tihanyi. 2024 "Combined Barrier-Target Coverage for Directional Sensor Network" Preprints. https://doi.org/10.20944/preprints202411.0437.v1
Abstract
Over the past twenty years, camera networks, where individual cameras can function both independently and together, have become increasingly popular. Various application fields may impose distinct requirements on camera networks. In response to these demands, several coverage models have been developed in the scientific literature, such as area, trap, barrier, and target coverage. In this paper, a new type of coverage task, the Maximum Target Coverage with k-Barrier Coverage (MTCBC-k) problem is defined. Here, the goal is to cover as many moving targets as possible from time step to time step, while continuously maintaining k-barrier coverage over the region of interest (ROI). This approach is different from independently solving the two tasks and then merging the results. Generally, multiple camera configurations can ensure k-barrier coverage. The challenge is to find, at each time step, the optimal configuration where the cameras providing barrier coverage can also assist in covering targets, while the rest of the cameras efficiently cover the remaining targets. An ILP formulation for the MTCBC-k problem is presented. Additionally, two types of camera clustering methods have been developed. This approach allows for solving smaller ILPs within clusters, and combining their solutions. Furthermore, a polynomial-time greedy algorithm has been introduced as an alternative to solve the MTCBC-k problem. The conducted simulations convincingly supported the usefulness of both the clustering and the greedy methods.
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
