Accurate, dynamic, and distributed localization of phenomena for mobile sensor networks

Anagnostopoulos, C. , Hadjiefthymiades, S. and Kolomvatsos, K. (2016) Accurate, dynamic, and distributed localization of phenomena for mobile sensor networks. ACM Transactions on Sensor Networks, 12(2), 9. (doi: 10.1145/2882966)

Abstract

We present a robust, dynamic scheme for the automatic self-deployment and relocation of mobile sensor nodes (e.g., unmanned ground vehicles, robots) around areas where phenomena take place. Our scheme aims (i) to sense environmental contextual parameters and accurately capture the spatio-temporal evolution of a certain phenomenon (e.g., fire, air contamination) and (ii) to fully automate the deployment process by letting nodes relocate, self-organize (and self-reorganize) and optimally cover the focus area. Our intention is to ‘opportunistically’ modify the previous placement of nodes to attain high quality phenomena monitoring. The required intelligence is fully distributed within the mobile sensor network so that the deployment algorithm is executed incrementally by different nodes. The presented algorithm adopts the Particle Swarm Optimization technique, which yields very promising results as reported in the paper (performance assessment). Our findings show that the proposed algorithm captures a certain phenomenon with very high accuracy while maintaining the network-wide energy expenditure at low levels. Random occurrences of similar phenomena put stress upon the algorithm which manages to react promptly and efficiently manage the available sensing resources in the broader setting.

• Item Type: Articles
• Additional Information: This work has been co-financed by the European Union (European Social Fund–ESF) and Greek national funds through the Operational Program ‘Education and Lifelong Learning’ of the National Strategic Reference Framework (NSRF) in the scope of the Research Funding Program: THALES–UOA–Sensor Web Fire Shield (SWeFS).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Anagnostopoulos, Dr Christos and Kolomvatsos, Dr Kostas
• Authors: Anagnostopoulos, C., Hadjiefthymiades, S., and Kolomvatsos, K.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: ACM Transactions on Sensor Networks
• Publisher: Association for Computing Machinery, Inc.
• ISSN: 1550-4859
• ISSN (Online): 1550-4867