Open Access

MACD-Based Motion Detection Approach in Heterogeneous Networks

  • Yung-Mu Chen1,
  • Tein-Yaw Chung1Email author,
  • Ming-Yen Lai1 and
  • Chih-Hung Hsu1
EURASIP Journal on Wireless Communications and Networking20082008:540873

DOI: 10.1155/2008/540873

Received: 2 January 2008

Accepted: 22 July 2008

Published: 7 August 2008


Optimizing the balance between handoff quality and power consumption is a great challenge for seamless mobile communications in wireless networks. Traditional proactive schemes continuously monitor available access networks and exercise handoff. Although such schemes achieve good handoff quality, they consume much power because all interfaces must remain on all the time. To save power, the reactive schemes use fixed RSS thresholds to determine when to search for a new available access network. However, since they do not consider user motion, these approaches require that all interfaces be turned on even when a user is stationary, and they tend initiate excessive unnecessary handoffs. To address this problem, this research presents a novel motion-aware scheme called network discovery with motion detection (NDMD) to improve handoff quality and minimize power consumption. The NDMD first applies a moving average convergence divergence (MACD) scheme to analyze received signal strength (RSS) samples of the current active interface. These results are then used to estimate user's motion. The proposed NDMD scheme adds very little computing overhead to a mobile terminal (MT) and can be easily incorporated into existing schemes. The simulation results in this study showed that NDMD can quickly track user motion state without a positioning system and perform network discovery rapidly enough to achieve a much lower handoff-dropping rate with less power consumption.

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Authors’ Affiliations

Department of Computer Science and Engineering, Yuan Ze University


© Yung-Mu Chen et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.