research-article

A reliable and data aggregation aware routing protocol for wireless sensor networks

Authors:

Leandro A. Villas,

Azzedine Boukerche,

Regina B. Araujo,

Antonio A.F. LoureiroAuthors Info & Claims

MSWiM '09: Proceedings of the 12th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems

Pages 245 - 252

https://doi.org/10.1145/1641804.1641846

Published: 26 October 2009 Publication History

Abstract

This paper presents the Data-Aggregation Aware Routing Protocol, DAARP, for wireless sensor networks. This novel protocol reduces the number of messages necessary to set up a routing tree, maximizes the number of overlapping routes, selects routes with the highest aggregation rate, and performs reliable data aggregation transmission. DAARP was compared to three existing solutions reported in the literature regarding communication costs, delivery efficiency, aggregation rate and aggregated data delivery rate. The results show that DAARP outperforms these solutions for different scenarios in all evaluations performed.

References

[1]

I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cyirci. Wireless sensor networks: A survey. Computer Networks, 38(4):393--422, March 2002.

Digital Library

[2]

A. Baddeley. Spatial point processes and their application. In W. Weil, editor, Stochastic Geometry, volume 1892 of Lecture Notes in Mathematics, pages 1--75. Springer, Berlin, 2006.

[3]

F. Bauer and A. Varma. Distributed algorithms for multicast path setup in data networks. IEEE/ACM Transaction on Networking, 4(2):181--191, 1996.

Digital Library

[4]

A. Boukerche, R. B. Araujo, and L. Villas. Optimal route selection for highly dynamic wireless sensor and actor networks environment. In MSWiM '07: Proceedings of the 10th ACM Symposium on Modeling, analysis, and simulation of wireless and mobile systems, pages 21--27, New York, NY, USA, 2007. ACM.

Digital Library

[5]

A. C. Frery, H. Ramos, J. Alencar-Neto, and E. Nakamura. Error estimation in wireless sensor networks. In SAC'08: Proceedings of ACM Symposium on Applied Computing, volume 3, pages 1927--1932, 2008.

Digital Library

[6]

J. Hill, R. Szewczyk, A. Woo, S. Hollar, D. Culler, and K. Pister. System architecture directions for networked sensors. SIGPLAN Not., 35(11):93--104, 2000.

Digital Library

[7]

S. Hougardy and H. J. Prömel. A 1.598 approximation algorithm for the steiner problem in graphs. In SODA '99: Proceedings of the 10th annual ACM-SIAM symposium on Discrete algorithms, pages 448--453, Philadelphia, PA, USA, 1999. Society for Industrial and Applied Mathematics.

Digital Library

[8]

C. Intanagonwiwat, R. Govindan, and D. Estrin. Directed diffusion: a scalable and robust communication paradigm for sensor networks. In MobiCom '00: Proceedings of the 6th annual international conference on Mobile computing and networking, pages 56--67, New York, NY, USA, 2000. ACM.

Digital Library

[9]

B. Krishnamachari, D. Estrin, and S. B. Wicker. The impact of data aggregation in wireless sensor networks. In ICDCSW '02: Proceedings of the 22nd International Conference on Distributed Computing Systems, pages 575--578, Washington, DC, USA, 2002. IEEE Computer Society.

Digital Library

[10]

E. F. Nakamura, H. A. B. F. de Oliveira, L. F. Pontello, and A. A. F. Loureiro. On demand role assignment for event-detection in sensor networks. In ISCC '06: Proceedings of the 11th IEEE Symposium on Computers and Communications, pages 941--947, Washington, DC, USA, 2006. IEEE Computer Society.

Digital Library

[11]

E. F. Nakamura, A. A. F. Loureiro, and A. C. Frery. Information fusion for wireless sensor networks: Methods, models, and classifications. ACM Computing Surveys, 39(3):9-1/9-55, 2007.

Digital Library

[12]

G. Robins and A. Zelikovsky. Improved steiner tree approximation in graphs. In SODA '00: Proceedings of the 11th annual ACM-SIAM symposium on Discrete algorithms, pages 770--779, Philadelphia, PA, USA, 2000. Society for Industrial and Applied Mathematics.

Digital Library

[13]

K. Romer and F. Mattern. The design space of wireless sensor networks. IEEE Wireless Communications, 11(6):54--61, December 2004.

Digital Library

[14]

Sinalgo. Simulator for network algorithms, 2008. Distributed Computing Group - ETH-Zurich, last visited in October, 2008.

[15]

O. Younis, M. Krunz, and S. Ramasubramanina. Node clustering in wireless sensor networks: Recent developments and deployment challenges. IEEE Network, 20(3):20--25, December 2006.

Digital Library

Cited By

Zhang LZhou YWang J(2023)An Energy Saving Strategy of WSNs Based on Data Sensing and SimilarityWireless Personal Communications10.1007/s11277-023-10540-y131:3(2241-2268)Online publication date: 17-Jun-2023
https://doi.org/10.1007/s11277-023-10540-y
Licui ZPengcheng WChunxia Z(2020)SC-EEDC: Similarity Based Clustering for Energy Efficient Data Collection in WSNWireless Personal Communications10.1007/s11277-020-07892-0Online publication date: 26-Nov-2020
https://doi.org/10.1007/s11277-020-07892-0
Umadevi MDevapriya M(2020)Neighbor-Aware Coverage-Based Probabilistic Data Aggregation for Reducing Transmission Overhead in Wireless Sensor NetworksAdvances in Electrical and Computer Technologies10.1007/978-981-15-5558-9_6(55-66)Online publication date: 8-Sep-2020
https://doi.org/10.1007/978-981-15-5558-9_6
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Index Terms

A reliable and data aggregation aware routing protocol for wireless sensor networks
1. Networks
  1. Network protocols
    1. Network layer protocols
      1. Routing protocols

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Information & Contributors

Information

Published In

cover image ACM Conferences

MSWiM '09: Proceedings of the 12th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems

October 2009

438 pages

ISBN:9781605586168

DOI:10.1145/1641804

General Chairs:
Nael Abu-Ghazaleh
State University of New York at Binghamton, USA
,
Luciano Bononi
University of Bologna, Italy
,
Program Chairs:
Brahim Bensaou
The Hong Kong University of Science and Technology, Hong Kong
,
Violet R. Syrotiuk
Arizona State University, USA

Copyright © 2009 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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SIGSIM: ACM Special Interest Group on Simulation and Modeling

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 October 2009

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MSWiM '09

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SIGSIM

MSWiM '09: The 12th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

October 26 - 29, 2009

Tenerife, Canary Islands, Spain

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Overall Acceptance Rate 398 of 1,577 submissions, 25%

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Cited By

Zhang LZhou YWang J(2023)An Energy Saving Strategy of WSNs Based on Data Sensing and SimilarityWireless Personal Communications10.1007/s11277-023-10540-y131:3(2241-2268)Online publication date: 17-Jun-2023
https://doi.org/10.1007/s11277-023-10540-y
Licui ZPengcheng WChunxia Z(2020)SC-EEDC: Similarity Based Clustering for Energy Efficient Data Collection in WSNWireless Personal Communications10.1007/s11277-020-07892-0Online publication date: 26-Nov-2020
https://doi.org/10.1007/s11277-020-07892-0
Umadevi MDevapriya M(2020)Neighbor-Aware Coverage-Based Probabilistic Data Aggregation for Reducing Transmission Overhead in Wireless Sensor NetworksAdvances in Electrical and Computer Technologies10.1007/978-981-15-5558-9_6(55-66)Online publication date: 8-Sep-2020
https://doi.org/10.1007/978-981-15-5558-9_6
Sun PBoukerche A(2018)Modeling and Analysis of Coverage Degree and Target Detection for Autonomous Underwater Vehicle-Based SystemIEEE Transactions on Vehicular Technology10.1109/TVT.2018.286414167:10(9959-9971)Online publication date: Oct-2018
https://doi.org/10.1109/TVT.2018.2864141
Das AChakraborty S(2018)SDCF: Sensory Data Collection Framework for Smart Building Application2018 IEEE SENSORS10.1109/ICSENS.2018.8589950(1-4)Online publication date: Oct-2018
https://doi.org/10.1109/ICSENS.2018.8589950
Raja KKarlmarx L(2016)Information Driven Data Gathering for Energy Efficient Wireless Sensor NetworkCircuits and Systems10.4236/cs.2016.71132407:11(3886-3895)Online publication date: 2016
https://doi.org/10.4236/cs.2016.711324
Zegarra ESchouery RMiyazawa FVillas L(2016)A Continuous Enhancement Routing Solution aware of data aggregation for Wireless Sensor Networks2016 IEEE 15th International Symposium on Network Computing and Applications (NCA)10.1109/NCA.2016.7778600(93-100)Online publication date: Oct-2016
https://doi.org/10.1109/NCA.2016.7778600
Shih CYang CCheng Y(2015)Data Alignment for Multiple Temporal Data Streams without Synchronized Clocks on IoT Fusion GatewayProceedings of the 2015 IEEE International Conference on Data Science and Data Intensive Systems (DSDIS)10.1109/DSDIS.2015.117(667-674)Online publication date: 11-Dec-2015
https://dl.acm.org/doi/10.1109/DSDIS.2015.117
Ramos HFrery ABoukerche AOliveira ELoureiro A(2014)Topology-Related Metrics and Applications for the Design and Operation of Wireless Sensor NetworksACM Transactions on Sensor Networks10.1145/251232810:3(1-35)Online publication date: 6-May-2014
https://dl.acm.org/doi/10.1145/2512328
Awasthi LChauhan SEmary IRamakrishnan S(2013)Data Aggregation and Data GatheringWireless Sensor Networks10.1201/b15425-4(41-58)Online publication date: 19-Aug-2013
https://doi.org/10.1201/b15425-4
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