Exploring spatial correlation for link quality estimation in wireless sensor networks

The irregularity in quality of wireless communication links poses significant research challenges in wireless sensor network design. Dynamic network conditions and environmental factors make an online, self-adapted link quality estimation mechanism within sensor nodes a necessity for making routing decisions and improving network performance. In this paper, we present a weighted regression algorithm for efficient and accurate estimation of link quality in wireless sensor networks. This algorithm captures the spatial correlation in quality of links between a sensor node and its neighbor nodes, such that the quality of a link to a neighbor node can be estimated based on the quality of links to other nodes geographically close. We evaluate the proposed algorithm using a trace-based simulator which takes into account the variances of link quality over time and spatial locations. The experimental results show that the weighted regression algorithm is able to achieve more accurate estimates than WMEWMA, a state-of-the-art link quality estimator, at a much lower communication cost

[1]  Ramesh Govindan,et al.  Understanding packet delivery performance in dense wireless sensor networks , 2003, SenSys '03.

[2]  Wang-Chien Lee,et al.  Dual prediction-based reporting for object tracking sensor networks , 2004, The First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, 2004. MOBIQUITOUS 2004..

[3]  David Maier,et al.  No pane, no gain: efficient evaluation of sliding-window aggregates over data streams , 2005, SGMD.

[4]  Brad Karp,et al.  GPSR: greedy perimeter stateless routing for wireless networks , 2000, MobiCom '00.

[5]  Deborah Estrin,et al.  Computing aggregates for monitoring wireless sensor networks , 2003, Proceedings of the First IEEE International Workshop on Sensor Network Protocols and Applications, 2003..

[6]  David Evans,et al.  Localization for mobile sensor networks , 2004, MobiCom '04.

[7]  Mani Srivastava,et al.  Energy-aware wireless microsensor networks , 2002, IEEE Signal Process. Mag..

[8]  David E. Culler,et al.  Evaluation of Efficient Link Reliability Estimators for Low-Power Wireless Networks , 2004 .

[9]  Giuseppe Anastasi,et al.  Understanding the real behavior of Mote and 802.11 ad hoc networks: an experimental approach , 2005, Pervasive Mob. Comput..

[10]  David A. Maltz,et al.  Quantitative lessons from a full-scale multi-hop wireless ad hoc network testbed , 2000, 2000 IEEE Wireless Communications and Networking Conference. Conference Record (Cat. No.00TH8540).

[11]  Deborah Estrin,et al.  Geographical and Energy Aware Routing: a recursive data dissemination protocol for wireless sensor networks , 2002 .

[12]  Deborah Estrin,et al.  SCALE: A tool for Simple Connectivity Assessment in Lossy Environments , 2003 .

[13]  Tian He,et al.  Differentiated surveillance for sensor networks , 2003, SenSys '03.

[14]  David E. Culler,et al.  Taming the underlying challenges of reliable multihop routing in sensor networks , 2003, SenSys '03.

[15]  Tomasz Imielinski,et al.  Prediction-based monitoring in sensor networks: taking lessons from MPEG , 2001, CCRV.

[16]  Deborah Estrin,et al.  GPS-less low-cost outdoor localization for very small devices , 2000, IEEE Wirel. Commun..

[17]  Ahmed Helmy,et al.  Energy-efficient forwarding strategies for geographic routing in lossy wireless sensor networks , 2004, SenSys '04.

[18]  Deborah Estrin,et al.  Complex Behavior at Scale: An Experimental Study of Low-Power Wireless Sensor Networks , 2002 .

[19]  David Maier,et al.  Semantics and evaluation techniques for window aggregates in data streams , 2005, SIGMOD '05.

[20]  C. Guestrin,et al.  Distributed regression: an efficient framework for modeling sensor network data , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[21]  Jianliang Xu,et al.  PSGR: priority-based stateless geo-routing in wireless sensor networks , 2005, IEEE International Conference on Mobile Adhoc and Sensor Systems Conference, 2005..

[22]  Deborah Estrin,et al.  Temporal Properties of Low Power Wireless Links: Modeling and Implications on Multi-Hop Routing , 2005 .

[23]  Roger Wattenhofer,et al.  Worst-Case optimal and average-case efficient geometric ad-hoc routing , 2003, MobiHoc '03.

[24]  Karsten P. Ulland,et al.  Vii. References , 2022 .

[25]  Robert Tappan Morris,et al.  Performance of multihop wireless networks: shortest path is not enough , 2003, CCRV.

[26]  Marco Zuniga,et al.  Analyzing the transitional region in low power wireless links , 2004, 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004..

[27]  Gang Zhou,et al.  Impact of radio irregularity on wireless sensor networks , 2004, MobiSys '04.