research-article

Game Theory Framework for MAC Parameter Optimization in Energy-Delay Constrained Sensor Networks

Authors:

Messaoud Doudou,

Jose M. Barcelo-Ordinas,

Djamel Djenouri,

Jorge Garcia-Vidal,

Abdelmadjid Bouabdallah,

Nadjib BadacheAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 12, Issue 2

Article No.: 10, Pages 1 - 35

https://doi.org/10.1145/2883615

Published: 03 May 2016 Publication History

Abstract

Optimizing energy consumption and end-to-end (e2e) packet delay in energy-constrained, delay-sensitive wireless sensor networks is a conflicting multiobjective optimization problem. We investigate the problem from a game theory perspective, where the two optimization objectives are considered as game players. The cost model of each player is mapped through a generalized optimization framework onto protocol-specific MAC parameters. From the optimization framework, a game is first defined by the Nash bargaining solution (NBS) to assure energy consumption and e2e delay balancing. Secondy, the Kalai-Smorodinsky bargaining solution (KSBS) is used to find an equal proportion of gain between players. Both methods offer a bargaining solution to the duty-cycle MAC protocol under different axioms. As a result, given the two performance requirements (i.e., the maximum latency tolerated by the application and the initial energy budget of nodes), the proposed framework allows to set tunable system parameters to reach a fair equilibrium point that dually minimizes the system latency and energy consumption. For illustration, this formulation is applied to six state-of-the-art wireless sensor network (WSN) MAC protocols: B-MAC, X-MAC, RI-MAC, SMAC, DMAC, and LMAC. The article shows the effectiveness and scalability of such a framework in optimizing protocol parameters that achieve a fair energy-delay performance trade-off under the application requirements.

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References

[1]

Andrea Abrardo, Lapo Balucanti, and Alessandro Mecocci. 2013. A game theory distributed approach for energy optimization in WSNs. ACM Transactions on Sensor Networks 9, 4, Article No. 44.

Digital Library

[2]

F. Afghah, M. Costa, A. Razi, A. Abedi, and A. Ephremides. 2013. A reputation-based Stackelberg game approach for spectrum sharing with cognitive cooperation. In Proceedings of the 2013 IEEE 52nd Annual Conference on Decision and Control (CDC’13). 3287--3292.

[3]

Tarek AlSkaif, Manel Guerrero Zapata, and Boris Bellalta. 2015. Game theory for energy efficiency in wireless sensor networks: Latest trends. Journal of Network and Computer Applications 54, 33--61.

Digital Library

[4]

G. Bacci, L. Sanguinetti, M. Luise, and H. V. Poor. 2013. A game-theoretic approach for energy-efficient contention-based synchronization in OFDMA systems. IEEE Transactions on Signal Processing 61, 1258--1271.

Digital Library

[5]

P. V. (Sundar) Balakrishnan, Juan Camilo Gomez, and Rakesh V. Vohra. 2011. The tempered aspirations solution for bargaining problems with a reference point. Mathematical Social Sciences 62, 3, 144--150.

[6]

Saeed Bastani, Bjorn Landfeldt, Christian Rohner, and Per Gunningberg. 2012. A social node model for realising information dissemination strategies in delay tolerant networks. In Proceedings of the 15th ACM International Conference on Modeling, Analysis, and Simulation of Wireless and Mobile Systems (MSWiM’12). ACM, New York, NY, 79--88.

Digital Library

[7]

H. P. Benson. 2004. On the global optimization of sums of linear fractional functions over a convex set. Journal of Optimization Theory and Applications 121, 1, 19--39.

Digital Library

[8]

S. Boyd and L. Vandenberghe. 2004. Convex Optimization. Cambridge University Press.

Digital Library

[9]

Michael Buettner, Gary V. Yee, Eric Anderson, and Richard Han. 2006. X-MAC: A short preamble MAC protocol for duty-cycled wireless sensor networks. In Proceedings of the 4th ACM Conference on Embedded Networked Sensor Systems (SenSys’06). 307--320.

Digital Library

[10]

Matteo Ceriotti, Michele Corrà, Leandro D’Orazio, Roberto Doriguzzi, Daniele Facchin, Stefan Guna, Gian Paolo Jesi, Renato Lo Cigno, Luca Mottola, Amy L. Murphy, Massimo Pescalli, Gian Pietro Picco, Denis Pregnolato, and Carloalberto Torghele. 2011. Is there light at the ends of the tunnel? Wireless sensor networks for adaptive lighting in road tunnels. In Proceedings of the International Conference on Information Processing in Sensor Networks (ISPN’11). 187--198.

[11]

J. Chen and A. L. Swindlehurst. 2012. Applying bargaining solutions to resource allocation in multiuser MIMO-OFDMA broadcast systems. IEEE Journal of Selected Topics in Signal Processing 6, 2, 127--139.

[12]

Xiaoyu Chu and Harish Sethu. 2015. Cooperative topology control with adaptation for improved lifetime in wireless sensor networks. Ad Hoc Networks 30, 99--114.

Digital Library

[13]

Messaoud Doudou, Djamel Djenouri, and Nadjib Badache. 2013. Survey on latency issues of asynchronous MAC protocols in delay-sensitive wireless sensor networks. IEEE Communications Surveys and Tutorials 15, 2, 528--550.

[14]

Adam Dunkels. 2011. The ContikiMAC Radio Duty Cycling Protocol. Technical Report T2011:13. Swedish Institute of Computer Science. http://dunkels.com/adam/dunkels11contikimac.pdf.

[15]

Prabal Dutta, Stephen Dawson-Haggerty, Yin Chen, Chieh-Jan Mike Liang, and Andreas Terzis. 2012. A-MAC: A versatile and efficient receiver-initiated link layer for low-power wireless. ACM Transactions on Sensor Networks 8, 4, 30:1--30:29.

Digital Library

[16]

C. A. Floudas and V. Visweswaran. 1993. Primal-relaxed dual global optimization approach. Journal of Optimization Theory and Applications 78, 2, 187--225.

Digital Library

[17]

A. El Gamal, James Mammen, Bharat Prabhakar, and Devavrat Shah. 2004. Throughput-delay trade-off in wireless networks. In Proceedings of the 23rd Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM’04), Vol. 1. IEEE, Los Alamitos, CA.

[18]

A. Ghasemi and K. Faez. 2008. A Nash power-aware MAC game for ad hoc wireless networks. In Proceedings of the IEEE 19th International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC’08). 1--5.

[19]

Matthias Grossglauser and David Tse. 2001. Mobility increases the capacity of ad-hoc wireless networks. In Proceedings of the 20th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM’01), Vol. 3. IEEE, Los Alamitos, CA, 1360--1369.

[20]

Piyush Gupta and Panganmala R. Kumar. 2000. The capacity of wireless networks. IEEE Transactions on Information Theory 46, 2, 388--404.

Digital Library

[21]

Zhu Han, Dusit Niyato, Walid Saad, Tamer Baar, and Are Hjrungnes. 2011. Game Theory in Wireless and Communication Networks. Cambridge University Press.

Digital Library

[22]

Y. Hayel, S. Trajanovski, E. Altman, H. Wang, and P. Van Mieghem. 2014. Complete game-theoretic characterization of SIS epidemics protection strategies. In Proceedings of the IEEE 53rd Annual Conference on Decision and Control (CDC’14). 1179--1184.

[23]

Jason L. Hill and David E. Culler. 2002. Mica: A wireless platform for deeply embedded networks. IEEE Micro 22, 6, 12--24.

Digital Library

[24]

Ehud Kalai and Meir Smorodinsky. 1975. Other solutions to Nash’s bargaining problem. Econometrica 43, 3, 513--518. http://www.jstor.org/stable/1914280.

[25]

F. Kelly. 1997. Charging and rate control for elastic traffic. European Transactions on Telecommunications 8, 1, 33--37.

[26]

E. Kim, H. Park, and P. Frossard. 2012. Low complexity iterative multimedia resource allocation based on game theoretic approach. In Proceedings of the 2012 IEEE International Symposium on Circuits and Systems (ISCAS’12). 1099--1102.

[27]

K. Langendoen and A. Meier. 2010. Analyzing MAC protocols for low data-rate applications. ACM Transactions on Sensor Networks 7, 1, Article No. 10.

Digital Library

[28]

W. L. Leow and H. Pishro-Nik. 2007. Delay and energy tradeoff in multi-state wireless sensor networks. In Proceedings of the IEEE Telecommunications Conference (GLOBECOM’07). 1028--1032.

[29]

Philip Levis, Nelson Lee, Matt Welsh, and David Culler. 2003. TOSSIM: Accurate and scalable simulation of entire tinyOS applications. In Proceedings of the 1st International Conference on Embedded Networked Sensor Systems (SenSys’03). ACM, New York, NY, 126--137.

Digital Library

[30]

Philip Levis, Sam Madden, Joseph Polastre, Robert Szewczyk, Alec Woo, David Gay, Jason Hill, Matt Welsh, Eric Brewer, and David Culler. 2004. TinyOS: An operating system for sensor networks. In Ambient Intelligence. Springer.

[31]

Gang Lu, Bhaskar Krishnamachari, and Cauligi S. Raghavendra. 2007. An adaptive energy-efficient and low-latency MAC for tree-based data gathering in sensor networks. Wireless Communications and Mobile Computing 7, 7, 863--875.

Digital Library

[32]

Kai Ma, Xinping Guan, Bin Zhao, and Juan Wang. 2011. A cooperation strategy based on bargaining solution in wireless sensor networks. Journal of Control Theory and Applications 9, 1, 121--126.

[33]

Renita Machado and Sirin Tekinay. 2008. A survey of game-theoretic approaches in wireless sensor networks. Computer Networks 52, 16, 3047--3061.

Digital Library

[34]

MACsoup. 2011. Taxonomy: The MAC Alphabet Soup Served in Wireless Sensor Networks. Retrieved March 21, 2016, from http://www.st.ewi.tudelft.nl/&sim;koen/MACsoup/taxonomy.

[35]

R. Mazumdar, L. G. Mason, and C. Douligeris. 1991. Fairness in network optimal flow control: Optimality of product forms. Communications, IEEE Transactions on 39, 5 (May 1991), 775--782.

[36]

F. Meshkati, H. V. Poor, and S. C. Schwartz. 2009a. Energy efficiency-delay tradeoffs in CDMA networks: A game-theoretic approach. IEEE Transactions on Information Theory 55, 7, 3220--3228.

Digital Library

[37]

F. Meshkati, H. V. Poor, S. C. Schwartz, and R. V. Balan. 2009b. Energy-efficient resource allocation in wireless networks with quality-of-service constraints. IEEE Transactions on Communications 57, 11, 3406--3414.

Digital Library

[38]

Mihail Mihaylov, Yann-Aël Le Borgne, Karl Tuyls, and Ann Nowé. 2011. Distributed cooperation in wireless sensor networks. In Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS’11), Vol. 1. 249--256.

Digital Library

[39]

David Moss and Philip Levis. 2008. BoX-MACs: Exploiting Physical and Link Layer Boundaries in Low-Power Networking. Technical Report. Technical Report SING-08-00, Stanford University.

[40]

A. Nahir and A. Orda. 2007. The Energy-Delay Tradeoff in Wireless Networks—System-Wide Optimization and Game-Theoretic Perspectives. Technical Report. Technion University, Haifa, Israel.

[41]

John F. Nash Jr. 1950. The bargaining problem. Econometrica: Journal of the Econometric Society 18, 2, 155--162.

[42]

M. J. Neely. 2007. Optimal energy and delay tradeoffs for multiuser wireless downlinks. IEEE Transactions on Information Theory 53, 9, 3095--3113.

Digital Library

[43]

Michael J. Neely and Eytan Modiano. 2005. Capacity and delay tradeoffs for ad hoc mobile networks. IEEE Transactions on Information Theory 51, 6, 1917--1937.

Digital Library

[44]

N. Nissan, T. Roughgarden, E. Tardos, and V. V. Vazirani. 2007. Algorithmic Game Theory. Cambridge University Press.

Digital Library

[45]

Dusit Niyato and Ekram Hossain. 2008. Market-equilibrium, competitive, and cooperative pricing for spectrum sharing in cognitive radio networks: Analysis and comparison. IEEE Transactions on Wireless Communication 7, 11, 4273--4283.

Digital Library

[46]

P. Nuggehalli, M. Sarkar, K. Kulkarni, and R. R. Rao. 2008. A game-theoretic analysis of QoS in wireless MAC. In Proceedings of the 27th Conference on Computer Communications (INFOCOM’08). 1903--1911.

[47]

Jorge Ortn, Jos Ramn Gllego, and Mara Canales. 2013. Joint route selection and resource allocation in multihop wireless networks based on a game theoretic approach. Ad Hoc Networks 11, 8, 2203--2216.

Digital Library

[48]

K. Pandremmenou, L. P. Kondi, and K. E. Parsopoulos. 2013. Geometric bargaining approach for optimizing resource allocation in wireless visual sensor networks. IEEE Transactions on Circuits and Systems for Video Technology 23, 8, 1388--1401.

Digital Library

[49]

Hyunggon Park and M. van der Schaar. 2007. Bargaining strategies for networked multimedia resource management. IEEE Transactions on Signal Processing 55, 7, 3496--3511.

Digital Library

[50]

Pan Gun Park, Carlo Fischione, Alvise Bonivento, Karl Henrik Johansson, and Alberto L. Sangiovanni-Vincentelli. 2011. Breath: An adaptive protocol for industrial control applications using wireless sensor networks. IEEE Transactions on Mobile Computing 10, 6, 821--838.

Digital Library

[51]

Yang Peng, Zi Li, Daji Qiao, and Wensheng Zhang. 2011. Delay-bounded MAC with minimal idle listening for sensor networks. In Proceedings of the 30th IEEE International Conference on Computer Communications (INFOCOM’11). 1314--1322.

[52]

Joseph Polastre, Jason Hill, and David Culler. 2004. Versatile low power media access for wireless sensor networks. In Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems (SenSys’04). ACM, New York, NY, 95--107.

Digital Library

[53]

Yanjun Sun, Omer Gurewitz, and David B. Johnson. 2008. RI-MAC: A receiver-initiated asynchronous duty cycle MAC protocol for dynamic traffic loads in wireless sensor networks. In Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems (SenSys’08). ACM, New York, NY, 1--14.

Digital Library

[54]

Texas Instruments. 2010. CC2420, Single-Chip 2.4 GHz RF Transceiver. Retrieved March 21, 2016, from http://focus.ti.com/lit/ds/symlink/cc2420.pdf.

[55]

C. D. Truong, M. A. Khan, F. Sivrikaya, and S. Albayrak. 2010. Cooperative game theoretic approach to energy-efficient coverage in wireless sensor networks. In Proceedings of the 2010 7th International Conference on Networked Sensing Systems (INSS’10). 73--76.

[56]

L. F. W. van Hoesel and P. J. M. Havinga. 2004. A lightweight medium access protocol (LMAC) for wireless sensor networks: Reducing preamble transmissions and transceiver state switches. In Proceedings of the 1st International Workshop on Networked Sensing Systems (INSS’04). 205--208.

[57]

Artemis C. Voulkidis, Markos P. Anastasopoulos, and Panayotis G. Cottis. 2013. Energy efficiency in wireless sensor networks: A game-theoretic approach based on coalition formation. ACM Transactions on Sensor Networks 9, 4, 43:1--43:27.

Digital Library

[58]

O. Yang and W. R. Heinzelman. 2009. Modeling and throughput analysis for S-MAC with a finite queue capacity. In Proceedings of the 5th International Conference on Intelligent Sensors, Sensor Networks, and Information Processing (ISSNIP’09). 409--414.

[59]

Wei Ye, John Heidemann, and Deborah Estrin. 2004. Medium access control with coordinated, adaptive sleeping for wireless sensor networks. ACM/IEEE Transactions on Networking 12, 3, 493--506.

Digital Library

[60]

Wei Ye, Fabio Silva, and John Heidemann. 2006. Ultra-low duty cycle MAC with scheduled channel polling. In Proceedings of the 4th ACM Conference on Embedded Networked Sensor Systems (SenSys’06). 321--334.

Digital Library

[61]

L. Ying, S. Yang, and R. Srikant. 2008. Optimal delay--throughput tradeoffs in mobile ad hoc networks. IEEE Transactions on Information Theory 54, 9, 4119--4143.

Digital Library

[62]

Ruifeng Zhang, Olivier Berder, Jean-Marie Gorce, and Olivier Sentieys. 2012. Energy delay tradeoff in wireless multihop networks with unreliable links. Ad Hoc Networks 10, 7, 1306--1321.

Digital Library

[63]

Xi Zhang and Jia Tang. 2013. Power-delay tradeoff over wireless networks. IEEE Transactions on Communications 61, 9, 3673--3684.

[64]

Liqiang Zhao, Le Guo, Cong Li, and Hailin Zhang. 2009. An energy-efficient MAC protocol for WSNs: Game-theoretic constraint optimization with multiple objectives. Wireless Sensor Network 1, 4, 358--364.

[65]

Yangming Zhao, Sheng Wang, Sizhong Xu, Xiong Wang, Xiujiao Gao, and Chunming Qiao. 2013. Load balance vs energy efficiency in traffic engineering: A game theoretical perspective. In Proceedings of the 32nd IEEE International Conference on Computer Communications (INFOCOM’13). 530--534.

[66]

Tao Zheng, Sridhar Radhakrishnan, and Venkatesh Sarangan. 2011. Modeling and performance analysis of DMAC for wireless sensor networks. In Proceedings of the 14th ACM International Conference on Modeling, Analysis, and Simulation of Wireless and Mobile Systems (MSWiM’11). 119--128.

Digital Library

[67]

Marco Zimmerling, Federico Ferrari, Luca Mottola, Thiemo Voigt, and Lothar Thiele. 2012. PTunes: Runtime parameter adaptation for low-power MAC protocols. In Proceedings of the 11th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN’12). 173--184.

Digital Library

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Game Theory Framework for MAC Parameter Optimization in Energy-Delay Constrained Sensor Networks

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cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 12, Issue 2

May 2016

323 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/2925994

Editor:
Chenyang Lu
Department of Computer Science and Engineering / School of Engineering and Applied Sciences / Washington University / 1 Brookings Drive / St. Louis, MO 63130

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Copyright © 2016 ACM.

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Publication History

Published: 03 May 2016

Accepted: 01 January 2016

Revised: 01 October 2015

Received: 01 April 2015

Published in TOSN Volume 12, Issue 2

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Yu YHsu SChen AChen YTang B(2023)Truthful and Optimal Data Preservation in Base Station-less Sensor Networks: An Integrated Game Theory and Network Flow ApproachACM Transactions on Sensor Networks10.1145/360626320:1(1-40)Online publication date: 27-Jun-2023
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