Gradient-driven target acquisition in mobile wireless sensor networks

Qingquan Zhang, Gerald E Sobelman, Tian He

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations


Navigation of mobile wireless sensor networks and fast target acquisition without a map are two challenging problems in search and rescue applications. In this paper, we propose and evaluate a novel Gradient Driven method, called GraDrive. Our approach integrates per-node prediction with global collaborative prediction to estimate the position of a stationary target and to direct mobile nodes towards the target along the shortest path. We demonstrate that a high accuracy in localization can be achieved much faster than other random work models without any assistance from stationary sensor networks. We evaluate our model through a light-intensity matching experiment in MicaZ motes, which indicates that our model works well in a wireless sensor network environment. Through simulation, we demonstrate almost a 40% reduction in the target acquisition time, compared to a random walk model, while obtaining less than 2 unit error in target position estimation.

Original languageEnglish (US)
Title of host publicationMobile Ad-Hoc and Sensor Networks - 2nd International Conference, MSN 2006, Proceedings
Number of pages12
StatePublished - 2006
Event2nd International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2006 - Hong Kong, China
Duration: Dec 13 2006Dec 15 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4325 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other2nd International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2006
CityHong Kong


  • Localization
  • Navigation
  • Probabilistic model
  • Rescue
  • Wireless sensor network


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