Efficient Location Training Protocols for Localization in Heterogeneous Sensor and Actor Networks

Abstract--In this work we consider a large-scale geographic area populated by tiny sensors and some more powerful devices called actors, authorized to organize the sensors in their vicinity into short-lived, actor-centric sensor networks. The tiny sensors run on miniature non-rechargeable batteries,...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on mobile computing Vol. 9; no. 7; pp. 1187 - 1200
Main Authors Barsi, F., Bertossi, A.A., Lavault, Christian, Navarra, A., Olariu, Stephan, Pinotti, Cristina, Ravelomanana, Vlady
Format Magazine Article
LanguageEnglish
Published Institute of Electrical and Electronics Engineers 2010
Subjects
Computer Science
Discrete Mathematics
Distributed, Parallel, and Cluster Computing
Sensor and actor networks
heterogeneous sensors
location training protocols
localization protocols
coarse-grain localization
Online AccessGet full text

Cover

More Information
Summary:Abstract--In this work we consider a large-scale geographic area populated by tiny sensors and some more powerful devices called actors, authorized to organize the sensors in their vicinity into short-lived, actor-centric sensor networks. The tiny sensors run on miniature non-rechargeable batteries, are anonymous and are unaware of their location. The sensors differ in their ability to dynamically alter their sleep times. Indeed, the periodic sensors have sleep periods of predefined lengths, established at fabrication time; by contrast, the free sensors can dynamically alter their sleep periods, under program control. The main contribution of this work is to propose an energy-efficient location training protocol for heterogeneous actor-centric sensor networks where the sensors acquire coarse-grain location awareness with respect to the actor in their vicinity. Our analytical analysis, confirmed by experimental evaluation, show that the proposed protocol outperforms the best previously-known location training protocols in terms of the number of sleep/awake transitions, overall sensor awake time, and energy consumption.
ISSN:1536-1233