Mobile data offloading based on minority game theoretic framework
Mobile data offloading is a current-day networking paradigm to channelize certain fraction of the cellular data traffic over unlicensed spectrum of WiFi. In this paper, we present a novel data offloading scheme built upon the exponential learning-based minority game (MG) theory. Performance comparis...
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| Veröffentlicht in: | Wireless networks Jg. 28; H. 7; S. 2967 - 2982 |
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| Abstract | Mobile data offloading is a current-day networking paradigm to channelize certain fraction of the cellular data traffic over unlicensed spectrum of WiFi. In this paper, we present a novel data offloading scheme built upon the exponential learning-based minority game (MG) theory. Performance comparison between cellular and WiFi services with respect to the offered load, has been used to derive an appropriate offloading condition for our proposed MG-based distributed data offloading algorithm. Effectiveness of the MG algorithm is tested by studying its performance through extensive simulation by varying several important parameters, like, pricing parameter
β
, cellular offered throughput (
S
c
), and temperature coefficient of the algorithm (
γ
). An effective model for tuning pricing parameter
β
with respect to the offered load (named as,
Target Pricing scheme
) is also presented using reverse engineering approach by considering the dynamic traffic condition. We have provisioned the application of our algorithm in multi-access point environment. We have studied the behaviour of different classes of nodes in heterogeneous population, while applying a MG-based networking algorithm. Through extensive NS3 based simulation we have evaluated the performance of our proposed algorithm in an IEEE 802.11ax environment and studied the effect of MIMO, QoS, transport layer and unsaturated traffic condition. |
|---|---|
| AbstractList | Mobile data offloading is a current-day networking paradigm to channelize certain fraction of the cellular data traffic over unlicensed spectrum of WiFi. In this paper, we present a novel data offloading scheme built upon the exponential learning-based minority game (MG) theory. Performance comparison between cellular and WiFi services with respect to the offered load, has been used to derive an appropriate offloading condition for our proposed MG-based distributed data offloading algorithm. Effectiveness of the MG algorithm is tested by studying its performance through extensive simulation by varying several important parameters, like, pricing parameter β, cellular offered throughput (Sc), and temperature coefficient of the algorithm (γ). An effective model for tuning pricing parameter β with respect to the offered load (named as, Target Pricing scheme) is also presented using reverse engineering approach by considering the dynamic traffic condition. We have provisioned the application of our algorithm in multi-access point environment. We have studied the behaviour of different classes of nodes in heterogeneous population, while applying a MG-based networking algorithm. Through extensive NS3 based simulation we have evaluated the performance of our proposed algorithm in an IEEE 802.11ax environment and studied the effect of MIMO, QoS, transport layer and unsaturated traffic condition. Mobile data offloading is a current-day networking paradigm to channelize certain fraction of the cellular data traffic over unlicensed spectrum of WiFi. In this paper, we present a novel data offloading scheme built upon the exponential learning-based minority game (MG) theory. Performance comparison between cellular and WiFi services with respect to the offered load, has been used to derive an appropriate offloading condition for our proposed MG-based distributed data offloading algorithm. Effectiveness of the MG algorithm is tested by studying its performance through extensive simulation by varying several important parameters, like, pricing parameter β , cellular offered throughput ( S c ), and temperature coefficient of the algorithm ( γ ). An effective model for tuning pricing parameter β with respect to the offered load (named as, Target Pricing scheme ) is also presented using reverse engineering approach by considering the dynamic traffic condition. We have provisioned the application of our algorithm in multi-access point environment. We have studied the behaviour of different classes of nodes in heterogeneous population, while applying a MG-based networking algorithm. Through extensive NS3 based simulation we have evaluated the performance of our proposed algorithm in an IEEE 802.11ax environment and studied the effect of MIMO, QoS, transport layer and unsaturated traffic condition. |
| Author | Venkatesh, T. G. Majumder, Bhaswar |
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| CitedBy_id | crossref_primary_10_1007_s11467_023_1378_z crossref_primary_10_1016_j_chaos_2024_114905 crossref_primary_10_3390_fi15050173 crossref_primary_10_3390_su141610163 crossref_primary_10_3390_app13105867 |
| Cites_doi | 10.1093/oso/9780198566403.001.0001 10.1109/WIOPT.2014.6850355 10.1016/j.comcom.2020.07.041 10.1109/TWC.2016.2574862 10.1109/49.840210 10.1109/ACCESS.2018.2799546 10.1016/S0378-4371(97)00419-6 10.1109/ACCESS.2017.2780253 10.1109/PIMRC.2010.5671703 10.1007/978-3-642-12331-3_2 10.1109/LCOMM.2017.2663431 10.1016/j.phycom.2008.03.001 10.1016/j.comcom.2016.02.008 10.1109/ICACCE49060.2020.9154981 10.1364/JOCN.9.000601 10.1109/TWC.2020.2981328 10.1016/j.comnet.2010.06.020 10.1109/MCOM.2017.1600568CM 10.1016/j.jnca.2018.09.003 |
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| Keywords | Pricing model Mobile data offloading Wireless networks Distributed algorithms Game theory Minority game |
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| References | WangYYuanJYuGChenQYinRMinority game for distributed user association in unlicensed heterogenous networksIEEE Transactions on Wireless Communications20201964220423310.1109/TWC.2020.2981328 Riley, G. F., & Henderson, T. R. (2010). The ns-3 network simulator. In Modeling and tools for network simulation (pp. 15–34). Springer. CharilasDEPanagopoulosADA survey on game theory applications in wireless networksComputer Networks201054183421343010.1016/j.comnet.2010.06.020 ChalletDMarsiliMZhangYCMinority games: Interacting agents in financial markets2004Oxford University Press1053.91001 BianchiGPerformance analysis of the ieee 802.11 distributed coordination functionIEEE Journal on Selected Areas in Communications200018353554710.1109/49.840210 AjithAVenkateshTGQoe enhanced mobile data offloading with balkingIEEE Communications Letters20172151143114610.1109/LCOMM.2017.2663431 ChalletDZhangYCEmergence of cooperation and organization in an evolutionary gamePhysica A: Statistical Mechanics and its Applications1997246340741810.1016/S0378-4371(97)00419-6 Rohoden, K., Estrada, R., Otrok, H., & Dziong, Z. (2020). Evolutionary game theoretical model for stable femtocells’ clusters formation in hetnets. Computer Communications,161, 266–278. Błaszczyszyn, B., Jovanovicy, M., & Karray, M. K. (2014). How user throughput depends on the traffic demand in large cellular networks. In 2014 12th international symposium on modeling and optimization in mobile, ad hoc, and wireless networks (WiOpt) (pp. 611–619). https://doi.org/10.1109/WIOPT.2014.6850355 Saito, Y., Yamamoto, K., Murata, H., & Yoshida, S. (2010). Robust interference management to satisfy allowable outage probability using minority game. In IEEE PMIRC (pp. 2314–2319). https://doi.org/10.1109/PIMRC.2010.5671703 Cisco annual internet report (2018–2023) white paper (2020). https://www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.html. Accessed 20 September, 2020 ArthurWBInductive reasoning and bounded rationalityThe American Economic Review1994842406411 Majumder, B., Venkatesh, T. G., & Vuppalapati, N. (2020). Wireless mac protocol based on crowd-anticrowd theory. In 2020 International conference on advances in computing and communication engineering (ICACCE) (pp. 1–6). LasaulceSTembineHGame theory and learning for wireless networks: Fundamentals and applications20111Academic Press Inc Unlicensed spectrum may be critical to 5g. IEEE Spectrum. https://spectrum.ieee.org/computing/it/unlicensed-spectrum-may-be-critical-to-5g. Accessed 20 September, 2020 SemasinghePMaghsudiSHossainEGame theoretic mechanisms for resource management in massive wireless iot systemsIEEE Communications Magazine201755212112710.1109/MCOM.2017.1600568CM MähönenPPetrovaMMinority game for cognitive radios: Cooperating without cooperationPhysical Communication2008129410210.1016/j.phycom.2008.03.001 SikeridisDTsiropoulouEEDevetsikiotisMPapavassiliouSWireless powered public safety iot: A uav-assisted adaptive-learning approach towards energy efficiencyJournal of Network and Computer Applications2018123697910.1016/j.jnca.2018.09.003 SahaNVesiloRAn evolutionary game theory approach for joint offloading and interference management in a two-tier hetnetIEEE Access201861807182110.1109/ACCESS.2017.2780253 ApostolarasAIosifidisGChounosKKorakisTTassiulasLA mechanism for mobile data offloading to wireless mesh networksIEEE Transactions on Wireless Communications20161595984599710.1109/TWC.2016.2574862 ZhouHWangHLiXLeungVCMA survey on mobile data offloading technologiesIEEE Access201865101511110.1109/ACCESS.2018.2799546 RimalBPMaierMMobile data offloading in fiwi enhanced lte-a heterogeneous networksIEEE/OSA Journal of Optical Communications and Networking20179760161510.1364/JOCN.9.000601 KouyoumdjievaSTKarlssonGEnergy-aware opportunistic mobile data offloading under full and limited cooperationComputer Communications201684849510.1016/j.comcom.2016.02.008 S Lasaulce (2993_CR4) 2011 D Challet (2993_CR6) 1997; 246 G Bianchi (2993_CR21) 2000; 18 N Saha (2993_CR11) 2018; 6 2993_CR22 Y Wang (2993_CR19) 2020; 19 H Zhou (2993_CR9) 2018; 6 D Challet (2993_CR8) 2004 D Sikeridis (2993_CR20) 2018; 123 2993_CR23 A Ajith (2993_CR14) 2017; 21 2993_CR18 P Mähönen (2993_CR17) 2008; 1 2993_CR15 ST Kouyoumdjieva (2993_CR13) 2016; 84 2993_CR16 2993_CR2 2993_CR1 DE Charilas (2993_CR3) 2010; 54 BP Rimal (2993_CR10) 2017; 9 A Apostolaras (2993_CR12) 2016; 15 P Semasinghe (2993_CR5) 2017; 55 WB Arthur (2993_CR7) 1994; 84 |
| References_xml | – reference: LasaulceSTembineHGame theory and learning for wireless networks: Fundamentals and applications20111Academic Press Inc – reference: CharilasDEPanagopoulosADA survey on game theory applications in wireless networksComputer Networks201054183421343010.1016/j.comnet.2010.06.020 – reference: ZhouHWangHLiXLeungVCMA survey on mobile data offloading technologiesIEEE Access201865101511110.1109/ACCESS.2018.2799546 – reference: SahaNVesiloRAn evolutionary game theory approach for joint offloading and interference management in a two-tier hetnetIEEE Access201861807182110.1109/ACCESS.2017.2780253 – reference: MähönenPPetrovaMMinority game for cognitive radios: Cooperating without cooperationPhysical Communication2008129410210.1016/j.phycom.2008.03.001 – reference: Majumder, B., Venkatesh, T. G., & Vuppalapati, N. (2020). Wireless mac protocol based on crowd-anticrowd theory. In 2020 International conference on advances in computing and communication engineering (ICACCE) (pp. 1–6). – reference: Riley, G. F., & Henderson, T. R. (2010). The ns-3 network simulator. In Modeling and tools for network simulation (pp. 15–34). Springer. – reference: SemasinghePMaghsudiSHossainEGame theoretic mechanisms for resource management in massive wireless iot systemsIEEE Communications Magazine201755212112710.1109/MCOM.2017.1600568CM – reference: AjithAVenkateshTGQoe enhanced mobile data offloading with balkingIEEE Communications Letters20172151143114610.1109/LCOMM.2017.2663431 – reference: Unlicensed spectrum may be critical to 5g. IEEE Spectrum. https://spectrum.ieee.org/computing/it/unlicensed-spectrum-may-be-critical-to-5g. 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