Abstract
In this article, we address the problem of autonomously deploying mobile sensors in an unknown complex environment. In such a scenario, mobile sensors may encounter obstacles or environmental sources of noise, so that movement and sensing capabilities can be significantly altered and become anisotropic. Any reduction of device capabilities cannot be known prior to their actual deployment, nor can it be predicted. We propose a new algorithm for autonomous sensor movements and positioning, called DOMINO (DeplOyment of Mobile Networks with Obstacles). Unlike traditional approaches, DOMINO explicitly addresses these issues by realizing a grid-based deployment throughout the Area of Interest (AoI) and subsequently refining it to cover the target area more precisely in the regions where devices experience reduced sensing. We demonstrate the capability of DOMINO to entirely cover the AoI in a finite time. We also give bounds on the number of sensors necessary to cover an AoI with asperities. Simulations show that DOMINO provides a fast deployment with precise movements and no oscillations, with moderate energy consumption. Furthermore, DOMINO provides better performance than previous solutions in all the operative settings.
Recommended Citation
N. Bartolini et al., "Autonomous Mobile Sensor Placement in Complex Environments," ACM Transactions on Autonomous and Adaptive Systems, vol. 12, no. 2, article no. a7, Association for Computing Machinery (ACM), May 2017.
The definitive version is available at https://doi.org/10.1145/3050439
Department(s)
Computer Science
Keywords and Phrases
Autonomous coordination; Communication and movement obstacles; Obstacle sensing; Path planning; Wireless mobile sensor networks
International Standard Serial Number (ISSN)
1556-4703; 1556-4665
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Association for Computing Machinery (ACM), All rights reserved.
Publication Date
01 May 2017
Comments
North Atlantic Treaty Organization, Grant G4936