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On the Effectiveness of Movement Prediction to Reduce Energy Consumption in Wireless Communication
February 2006 (vol. 5 no. 2)
pp. 157-169
ASCII Text
x 
 
Srijan Chakraborty, Yu Dong, David K.Y. Yau, John C.S. Lui, "On the Effectiveness of Movement Prediction to Reduce Energy Consumption in Wireless Communication," IEEE Transactions on Mobile Computing, vol. 5, no. 2, pp. 157-169, February, 2006.
 
 
BibTex
x
 
@article{ 10.1109/TMC.2006.24,
author = {Srijan Chakraborty and Yu Dong and David K.Y. Yau and John C.S. Lui},
title = {On the Effectiveness of Movement Prediction to Reduce Energy Consumption in Wireless Communication},
journal ={IEEE Transactions on Mobile Computing},
volume = {5},
number = {2},
issn = {1536-1233},
year = {2006},
pages = {157-169},
doi = {http://doi.ieeecomputersociety.org/10.1109/TMC.2006.24},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Mobile Computing
TI - On the Effectiveness of Movement Prediction to Reduce Energy Consumption in Wireless Communication
IS - 2
SN - 1536-1233
SP157
EP169
EPD - 157-169
A1 - Srijan Chakraborty,
A1 - Yu Dong,
A1 - David K.Y. Yau,
A1 - John C.S. Lui,
PY - 2006
KW - Index Terms- Mobile computing
KW - wireless networking
KW - energy management
KW - movement prediction.
VL - 5
JA - IEEE Transactions on Mobile Computing
ER -
 
Node movement can be exploited to reduce the energy consumption of wireless network communication. The strategy consists in delaying communication until a mobile node moves close to its target peer node within an application-imposed deadline. We evaluate the performance of various heuristics that, based on the movement history of the mobile node, estimate an optimal time (in the sense of least energy use) of communication subject to the delay constraint. We evaluate the impact of the node movement model, length of movement history maintained, allowable delay, single hop versus multiple hop communication, and size of data transfer on the energy consumption. We also present measurement results on an iPAQ pocket PC that quantify energy consumption in executing the prediction algorithms. Our results show that, with relatively simple and, hence, efficient prediction heuristics, energy savings in communication can significantly outweigh the energy expenses in executing the prediction algorithms. Moreover, it is possible to achieve robust system performance across diverse node movement models.

[1] S. Basagni, I. Chlamtac, V.R. Syrotiuk, and B.A. Woodward, “A Distance Routing Effect Algorithm for Mobility (DREAM),” Proc. Fourth Ann. ACM/IEEE Int'l Conf. Mobile Computing and Networking (MobiCom), pp. 76-84, Oct. 1998.
[2] V. Bharghavan and M. Jayanth, “Profile-Based Next-Cell Prediction in Indoor Wireless LAN,” Proc. IEEE SICON '97, Apr. 1997.
[3] A. Bhattacharya and S.K. Das, “LeZi-Update: An Information-Theoretic Approach to Track Mobile Users in PCS Networks,” Proc. ACM/IEEE MobiCom, pp. 1-12, Aug. 1999.
[4] S. Biaz, G.D. Holland, Y.B. Ko, and N.H. Vaidya, “Evaluation of Protocols for Wireless Networks,” Parallel and Distributed Processing Techniques and Applications, July 1999.
[5] S. Chakraborty, D.K.Y. Yau, and J.C.S. Lui, “On the Effectiveness of Movement Prediction to Reduce Energy Consumption in Wireless Communication (Extended Abstract),” Proc. ACM SIGMETRICS, June 2003.
[6] J. Chan and A. Seneviratne, “A Practical User Mobility Prediction Algorithm for Supporting Adaptive QoS in Wireless Networks,” Proc. IEEE Int'l Conf. Networks, Sept. 1999.
[7] P.R. Freeman, “The Secretary Problem and Its Extensions: A Review,” Int'l Statistical Rev. 51, pp. 189-206, 1983.
[8] Z.J. Haas, “A New Routing Protocol for Reconfigurable Wireless Networks,” Proc. IEEE Sixth Int'l Conf. Universal Personal Comm., pp. 562-566, 1997.
[9] X. Hong, M. Gerla, G. Pei, and C.C. Chiang, “A Group Mobility Model for Ad Hoc Wireless Networks,” Proc. ACM/IEEE MSWiM '99, pp. 53-60, Aug. 1999.
[10] D.B. Johnson, D.A. Maltz, and J. Broch, “DSR: The Dynamic Source Routing Protocol for Multi-Hop Wireless Ad Hoc Networks,” Ad Hoc Networking, C.E. Perkins, ed., chapter 5, pp. 139-172, Addison-Wesley, 2001.
[11] Y. Ko and N.H. Vaidya, “Location-Aided Routing (LAR) Mobile Ad Hoc Networks,” Proc. ACM/IEEE Mobicom, Oct. 1998.
[12] D. Levine, I. Akyildiz, and M. Naghshineh, “A Resource Estimation and Call Admission Algorithm for Wireless Multimedia Networks Using the Shadow Cluster Concept,” IEEE/ACM Trans. Networking, vol. 5, no. 1, pp. 1-12, Feb. 1997.
[13] G. Liu and G. Maguire Jr., “A Class of Mobile Motion Prediction Algorithms for Wireless Mobile Computing and Communications,” ACM/Baltzer MONET, vol. 1, no. 2, pp. 113-121, 1996.
[14] T. Liu, P. Bahl, and I. Chlamtac, “Mobility Modeling, Location Tracking and Trajectory Perdition in Wireless ATM Networks,” IEEE J. Selected Areas in Comm., vol. 16, no. 6, pp. 922-936, Aug. 1998.
[15] V.D. Park and M.S. Corson, “A Highly Adaptive Distributed Routing Algorithm for Mobile Wireless Networks,” Proc. IEEE INFOCOM '97, Apr. 1997.
[16] C.E. Perkins and E.M. Royer, “Ad Hoc On-Demand Distance Vector Routing,” Proc. Second IEEE Workshop Mobile Computing Systems and Applications, pp. 90-100, Feb. 1999.
[17] W. Su, S.J. Lee, and M. Gerla, “Mobility Prediction and Routing in Ad Hoc Wireless Networks,” Int'l J. Network Management, 2000.
[18] D.S. Tan, S. Zhou, J. Ho, J.S. Mehta, and H. Tanabe, “Design and Evaluation of an Individually Simulated Mobility Model in Wireless Ad Hoc Networks,” Proc. Comm. Networks and Distributed Systems Modeling and Simulation, 2002.
[19] Y. Xu, J. Heidemann, and D. Estrin, “Geography-Informed Energy Conservation for Ad-Hoc Routing,” Proc. Seventh Ann. ACM/IEEE Int'l Conf. Mobile Computing and Networking (MobiCom), July 2001.
[20] http://may.cs.ucla.edu/projects/maisiemodels.html , 2005.
[21] Carnegie Mellon Univ. Monarch Project, CMU Monarch Project Home Page, http:/www.monarch.cs.cmu.edu, 2003.
[22] T.S. Ferguson, “Who Solved the Secretary Problem?” Statistical Science, vol. 4, pp. 282-296, 1989.
[23] Y.H. Chun, H. Moskowitz, and R. Plante, “Single Threshold Selection Strategy in the Group Interview Problem,” INFOR: Information Systems and Operational Research, vol 35., no. 3, pp. 157-169, Aug. 1997.
[24] J.D. Hey, “Search for Rules for Search,” J. Economic Behaviour and Organization, vol. 3, pp. 65-81, 1982.
[25] L. Breslau et al., “Advances in Network Simulation,” IEEE Computer, vol. 33, no. 5, pp. 59-67, May 2000, expanded version available as USC TR 99-702b at http://www.isi.edu/johnh/PAPERSBajaj99a.html .
[26] D. Johnson and D.A. Maltz, “Dynamic Source Routing in Ad Hoc Wireless Networks,” Mobile Computing, T. Imielinski and H. Korth, eds., Kluwer Academic, 1994.
[27] M.P. Quine and J.S. Law, “Exact Results for Secretary Problem,” J. Applied Probability, vol. 33, pp. 630-339, 1996.
[28] M. Grossglauser and D. Tse, “Mobility Increases the Capacity of Ad-Hoc Wireless Networks,” IEEE/ACM Trans. Networking, vol. 10, no. 4, pp. 477-486, Aug. 2002.
[29] J. Yoon, M. Liu, and B. Noble, “Random Waypoint Considered Harmful,” Proc. IEEE INFOCOM, 2003.

Index Terms:
Index Terms- Mobile computing, wireless networking, energy management, movement prediction.
Citation:
Srijan Chakraborty, Yu Dong, David K.Y. Yau, John C.S. Lui, "On the Effectiveness of Movement Prediction to Reduce Energy Consumption in Wireless Communication," IEEE Transactions on Mobile Computing, vol. 5, no. 2, pp. 157-169, Feb. 2006, doi:10.1109/TMC.2006.24
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