Abstract
Mobile sensor networks are the most promising solution to cover an Area of Interest (AoI) in safety critical scenarios. Mobile devices can coordinate with each other according to a distributed deployment algorithm, without resorting to human supervision for device positioning and network configuration. In this paper, we focus on the vulnerabilities of the deployment algorithms based on Voronoi diagrams to coordinate mobile sensors and guide their movements. We give a geometric characterization of possible attack configurations, proving that a simple attack consisting of a barrier of few compromised sensors can severely reduce network coverage. On the basis of the above characterization, we propose two new secure deployment algorithms, named SecureVor and Secure Swap Deployment (SSD). These algorithms allow a sensor to detect compromised nodes by analyzing their movements, under different and complementary operative settings. We show that the proposed algorithms are effective in defeating a barrier attack, and both have guaranteed termination. We perform extensive simulations to study the performance of the two algorithms and compare them with the original approach. Results show that SecureVor and SSD have better robustness and flexibility and excellent coverage capabilities and deployment time, even in the presence of an attack.
Recommended Citation
N. Bartolini et al., "On the Vulnerabilities of Voronoi-Based Approaches to Mobile Sensor Deployment," IEEE Transactions on Mobile Computing, vol. 15, no. 12, pp. 3114 - 3128, article no. 7401077, Institute of Electrical and Electronics Engineers, Dec 2016.
The definitive version is available at https://doi.org/10.1109/TMC.2016.2524630
Department(s)
Computer Science
Keywords and Phrases
Mobile sensors; Self-deployment; Voronoi approach
International Standard Serial Number (ISSN)
1536-1233
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Dec 2016
Comments
National Science Foundation, Grant 1355406