DCTC: Dynamic convoy tree-based collaboration for target tracking in sensor networks

Wensheng Zhang, Guohong Cao

Research output: Contribution to journalArticlepeer-review

416 Scopus citations


Most existing work on sensor networks concentrates on finding efficient ways to forward data from the information source to the data centers, and not much work has been done on collecting local data and generating the data report. This paper studies this issue by proposing techniques to detect and track a mobile target. We introduce the concept of dynamic convoy tree-based collaboration, and formalize it as a multiple objective optimization problem which needs to find a convoy tree sequence with high tree coverage and low energy consumption. We propose an optimal solution which achieves 100% coverage and minimizes the energy consumption under certain ideal situations. Considering the real constraints of a sensor network, we propose several practical implementations: the conservative scheme and the prediction-based scheme for tree expansion and pruning; the sequential and the localized reconfiguration schemes for tree reconfiguration. Extensive experiments are conducted to compare the practical implementations and the optimal solution. The results show that the prediction-based scheme outperforms the conservative scheme and it can achieve similar coverage and energy consumption to the optimal solution. The experiments also show that the localized reconfiguration scheme outperforms the sequential reconfiguration scheme when the node density is high, and the trend is reversed when the node density is low.

Original languageEnglish (US)
Pages (from-to)1689-1701
Number of pages13
JournalIEEE Transactions on Wireless Communications
Issue number5
StatePublished - Sep 2004

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


Dive into the research topics of 'DCTC: Dynamic convoy tree-based collaboration for target tracking in sensor networks'. Together they form a unique fingerprint.

Cite this