Article

Impact of radio irregularity on wireless sensor networks

Authors:

Sudha Krishnamurthy,

John A. StankovicAuthors Info & Claims

MobiSys '04: Proceedings of the 2nd international conference on Mobile systems, applications, and services

Pages 125 - 138

https://doi.org/10.1145/990064.990081

Published: 06 June 2004 Publication History

Abstract

In this paper, we investigate the impact of radio irregularity on the communication performance in wireless sensor networks. Radio irregularity is a common phenomenon which arises from multiple factors, such as variance in RF sending power and different path losses depending on the direction of propagation. From our experiments, we discover that the variance in received signal strength is largely random; however, it exhibits a continuous change with incremental changes in direction. With empirical data obtained from the MICA2 platform, we establish a radio model for simulation, called the Radio Irregularity Model (RIM). This model is the first to bridge the discrepancy between spherical radio models used by simulators and the physical reality of radio signals. With this model, we are able to analyze the impact of radio irregularity on some of the well-known MAC and routing protocols. Our results show that radio irregularity has a significant impact on routing protocols, but a relatively small impact on MAC protocols. Finally, we propose six solutions to deal with radio irregularity. We evaluate two of them in detail. The results obtained from both the simulation and a running testbed demonstrate that our solutions greatly improve communication performance in the presence of radio irregularity.

References

[1]

V. Bharghavan, A. Demers, S. Shenker and L. Zhang, MACAW: A Media Access Protocol for Wireless LANs, In Proc. of ACM SIGCOMM, pp. 212--225, 1994.

Digital Library

[2]

N. Bulusu, J. Heidemann and D. Estrin, GPS-less Low Cost Outdoor Localization for Very Small Devices, In IEEE Personal Communications Magazine, pp. 28--34, October 2000.

[3]

A. Cerpa and D. Estrin, ASCENT: Adaptive Self-Configuring Sensor Networks Topologies, In Proc. of the IEEE Infocom, 2002.

[4]

A. Cerpa, N. Busek and D. Estrin, SCALE: A Tool for Simple Connectivity Assessment in Lossy Environments http://lecs.cs.ucla.edu/Publications/papers/scale-tech-report.pdf, In CENS Technical Report 0021, September 2003.

[5]

B. Chen, K. Jamieson, H. Balakrishnan and R. Morris, Span: An Energy-Efficient Coordination Algorithm for Topology Maintenance in Ad-hoc Wireless Networks, In ACM MobiCom, July 2001.

Digital Library

[6]

Chipcon CC1000 Low Power Radio Transceiver, http:// www.chipcon.com/files/cc1000_data_sheet_2_1.pdf

[7]

J. L. Devore, Probability and Statistics for Engineering and the Sciences, Brooks/Cole Publishing, 1982.

[8]

IEEE 802.11, Part II: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specification, ANSI/IEEE Std. 802.11, 1999.

[9]

D. Ganesan, B. Krishnamachari, A. Woo, D. Culler, D. Estrin and S. Wicker, Complex Behavior at Scale: An Experimental Study of Low-Power Wireless Sensor Networks, In Technical Report UCLA/CSD-TR 02-0013, 2002.

[10]

T. He, C. Huang, B. M. Blum, J. A. Stankovic and T. F. Abdelzaher, Range-Free Localization Schemes in Large Scale Sensor Networks, In Proc. MOBICOM, 2003.

Digital Library

[11]

T. He, S. Krishnamurthy, J. A. Stankovic, T. F. Abdelzaher, L. Luo, R. Stoleru, T. Yan, L. Gu, J. Hui, B. Krogh, Energy-Efficient Surveillance System Using Wireless Sensor Networks, In ACM MobiSys 2004, Boston, MA, June 2004.

Digital Library

[12]

T. He, J. A. Stankovic, C. Lu and T. F. Abdelzaher, SPEED: A Stateless Protocol for Real-Time Communication in Sensor Networks, In IEEE ICDCS 2003, Providence, RI, May 2003.

Digital Library

[13]

C. Intanagonwiwat, R. Govindan and D. Estrin, Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks, In Proc. MOBICOM, pp. 56--67, March 2000.

Digital Library

[14]

D. B. Johnson and D. A. Maltz, Dynamic Source Routing in Ad Hoc Wireless Networks, Mobile Computing, edited by Tomas Imielinski and Hank Korth, Kluwer Academic Publishers, ISBN: 0792396979, 1996.

[15]

P. Karn, MACA - A New Channel Access Method for Packet Radio, In ARRL/CRRL Amateur Radio 9th Computer Networking Conference, pp. 134--140, 1990.

[16]

B. Karp and H. T. Kung, GPSR: Greedy Perimeter Stateless Routing for Wireless Networks, In Proc. MOBICOM, pp. 243--254, August 2000.

Digital Library

[17]

B. Karp, Geographic Routing for Wireless Networks, Ph.D. Dissertation, Harvard University, Cambridge, MA, October 2000.

Digital Library

[18]

L. Kleinrock and F. A. Tobagi, Packet Switching in Radio Channels: Part I - Carrier Sense Multiple-access Modes and Their Throughput-delay Characteristics, In IEEE Trans. Commun., vol. COM-23, pp. 1400--1416, Dec. 1975.

[19]

Y. B. Ko and N. H. Vaidya, Location-Aided Routing (LAR) in Mobile Ad Hoc Networks, In Proc. MOBICOM, pp. 66--75, 1998.

Digital Library

[20]

D. Niculescu and B. Nath, DV Based Positioning in Ad hoc Networks, In Journal of Telecommunication Systems, 2003.

[21]

C. E. Perkins, and E. M. Royer, Ad-hoc On-Demand Distance Vector Routing, In Proc. of the 2nd IEEE Workshop on Mobile Computing Systems and Applications, pp. 90--100, February 1999.

Digital Library

[22]

P. M. Shankar, Introduction to Wireless Systems, John Wiley & Sons, Inc, ISBN 0-471-32167-2, 2001.

Digital Library

[23]

A. Woo, T. Tong and D. Culler, Taming the Underlying Challenges of Reliable Multihop Routing in Sensor Networks, In ACM SenSys 2003, Los Angeles, CA, November 2003.

Digital Library

[24]

XBOW MICA2 Mote Specifications: http://www.xbow.com/

[25]

Y. Xu, J. Heidemann and D. Estrin, Geography-informed Energy Conservation for Ad Hoc Routing, In ACM MOBICOM 2001, Rome, Italy, July 2001.

Digital Library

[26]

T. Yan, T. He, J. A. Stankovic, Differentiated Surveillance for Sensor Networks, In ACM SenSys 2003, Los Angeles, CA, November 2003.

Digital Library

[27]

X. Zeng, R. Bagrodia and M. Gerla, GloMoSim: a Library for Parallel Simulation of Large-scale Wireless Networks, In Proc. of the 12th Workshop on Parallel and Distributed Simulations, May 1998.

Digital Library

[28]

Y. J. Zhao and R. Govindan, Understanding Packet Delivery Performance in Dense Wireless Sensor Network, In ACM SenSys 2003, Los Angeles, CA, November 2003.

Digital Library

Cited By

Ma CLiang WZheng MXia XChen L(2023)A Voronoi Diagram and Q-Learning based Relay Node Placement Method Subject to Radio IrregularityACM Transactions on Sensor Networks10.1145/361712420:1(1-27)Online publication date: 20-Oct-2023
https://dl.acm.org/doi/10.1145/3617124
Kam YBayraktar MUlusar U(2023)Swarm Optimization-Based Hyperparameter Selection for Machine Learning Algorithms in Indoor Localization2023 8th International Conference on Computer Science and Engineering (UBMK)10.1109/UBMK59864.2023.10286800(358-363)Online publication date: 13-Sep-2023
https://doi.org/10.1109/UBMK59864.2023.10286800
Sadhukhan PDahal KDas P(2023)A Novel Weighted Fusion Based Efficient Clustering for Improved Wi-Fi Fingerprint Indoor PositioningIEEE Transactions on Wireless Communications10.1109/TWC.2022.322579622:7(4461-4474)Online publication date: Jul-2023
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Index Terms

Impact of radio irregularity on wireless sensor networks
1. Computing methodologies
  1. Modeling and simulation
2. Networks
  1. Network architectures

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Published In

cover image ACM Conferences

MobiSys '04: Proceedings of the 2nd international conference on Mobile systems, applications, and services

June 2004

294 pages

ISBN:1581137931

DOI:10.1145/990064

General Chairs:
Guruduth S. Banavar
IBM Research
,
Willy Zwaenepoel
EPFL
,
Program Chairs:
Doug Terry
Microsoft Research
,
Roy Want
Intel Research

Copyright © 2004 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
ACM: Association for Computing Machinery
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New York, NY, United States

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Published: 06 June 2004

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MobiSys04: The Second International Conference on Mobile Systems, Applications and Services 2004

June 6 - 9, 2004

MA, Boston, USA

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MobiSys '04 Paper Acceptance Rate 22 of 162 submissions, 14%;

Overall Acceptance Rate 274 of 1,679 submissions, 16%

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https://dl.acm.org/doi/10.1145/3617124
Kam YBayraktar MUlusar U(2023)Swarm Optimization-Based Hyperparameter Selection for Machine Learning Algorithms in Indoor Localization2023 8th International Conference on Computer Science and Engineering (UBMK)10.1109/UBMK59864.2023.10286800(358-363)Online publication date: 13-Sep-2023
https://doi.org/10.1109/UBMK59864.2023.10286800
Sadhukhan PDahal KDas P(2023)A Novel Weighted Fusion Based Efficient Clustering for Improved Wi-Fi Fingerprint Indoor PositioningIEEE Transactions on Wireless Communications10.1109/TWC.2022.322579622:7(4461-4474)Online publication date: Jul-2023
https://doi.org/10.1109/TWC.2022.3225796
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Chen QCai ZCheng LGao HLi J(2021)Energy-collision-aware Minimum Latency Aggregation Scheduling for Energy-harvesting Sensor NetworksACM Transactions on Sensor Networks10.1145/346101317:4(1-34)Online publication date: 16-Jul-2021
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Wen JDargie W(2021)Characterization of Link Quality Fluctuation in Mobile Wireless Sensor NetworksACM Transactions on Cyber-Physical Systems10.1145/34487375:3(1-24)Online publication date: 15-Apr-2021
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