ACM Home Page
Please provide us with feedback. Feedback
Optimally balancing energy consumption versus latency in sensor network routing
Full text PdfPdf (257 KB)
Source
ACM Transactions on Sensor Networks (TOSN) archive
Volume 4 ,  Issue 4  (August 2008) table of contents
Article No. 21  
Year of Publication: 2008
ISSN:1550-4859
Authors
Wei Lai  Boston University
Ioannis C. Paschalidis  Boston University
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 21,   Downloads (12 Months): 257,   Citation Count: 0
Additional Information:

abstract   references   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1387663.1387667
What is a DOI?

ABSTRACT

We consider wireless sensor networks with nodes switching ON (awake) and OFF (sleeping) to preserve energy, and transmitting data over channels with varying quality. The objective is to determine the best path from each node to a single gateway. The performance metrics we are interested in are: the expected energy consumption, and the probability that the latency exceeds a certain threshold. Under Markovian assumptions on the sleeping schedules and the channel conditions, we obtain the expected energy consumption of transmitting a packet on any path to the gateway. We also provide an upper (Chernoff) bound and a tight large deviations asymptotic for the latency probability on each path. To capture the trade-off between energy consumption and latency probability, we formulate the problem of choosing a path to minimize a weighted sum of the expected energy consumption and the exponent of the latency probability. We provide two algorithms to solve this problem: a centralized stochastic global optimization algorithm, and a distributed algorithm based on simulated annealing. The proposed methodology can also optimize over the fraction of time that sensor nodes remain ON (duty cycle).


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
 
2
Bertsekas, D. 1999. Nonlinear Programming, 2nd ed. Athena Scientific, Belmont, MA.
 
3
 
4
Bhattacharya, R. N. and Waymire, E. C. 1990. Stochastic Processes with Applications. Wiley Series in Probability and Mathematical Statistics.
 
5
 
6
Elliot, E. O. 1963. Estimates of error rates for codes on burst-noise channels. Bell Systems Tech. J. 42, 1977--1997.
7
 
8
 
9
Fuemmeler, J. and Veeravalli, V. V. 2007. Smart sleeping policies for energy efficient tracking in sensor networks. IEEE Trans. Signal Proc. 56, 5, 2091--2101.
 
10
 
11
Gilbert, E. N. 1960. Capacity of a burst-noise channel. Bell Systems Tech. J. 39, 1253--1266.
 
12
 
13
14
 
15
Lu, G., Sadagopan, N., Krishnamachari, B., and Goel, A. 2005. Delay efficient sleep scheduling in wireless sensor networks. In Proceedings of the Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM'05). Vol. 14. 2470--2481.
 
16
Magnani, A., Lall, S., and Boyd, S. 2005. Tractable fitting with convex polynomials via sum-of-squares. In Proceedings of the 43rd Conference on Decision and Control, 1672--1677.
17
 
18
Miao, L. and Cassandras, C. G. 2006. Optimal transmission scheduling for energy-efficient wireless networks. In Proceedings of the Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM'06).
 
19
Paschalidis, I. C., Shen, Y., Vakili, P., and Vajda, S. 2007. SDU: a semi-definite programming-based underestimation method for stochastic global optimization in protein docking. IEEE Trans. Automat. Contr. 52, 4, 664--676.
 
20
 
21
Wang, H. S. and Moayeri, N. 1995. Finite-state Markov channel-a useful model for radio communication channels. IEEE Trans. Veh. Tech. 44, 163--171.
 
22
 
23
 
24
Yu, Y., Krishnamachari, B., and Prasanna, V. K. 2004. Energy-latency tradeoff for data gathering in wireless sensor networks. In Proceedings of the Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM'04). Vol. 1. p. 255.
 
25
Zhang, Q. and Kassam, K. 1999. Finite-state Markov model for Rayleigh fading channels. IEEE Trans. on Comm. 47, 11, 1688--1692.

Collaborative Colleagues:
Wei Lai: colleagues
Ioannis C. Paschalidis: colleagues