Citation: | Ruyan WANG, Hongjuan LI, Dapeng WU. Stackelberg Game-based Resource Allocation Strategy in Virtualized Wireless Sensor Network[J]. Journal of Electronics & Information Technology, 2019, 41(2): 377-384. doi: 10.11999/JEIT180277 |
Virtualization is a new technology that can effectively solve the low resource utilization and service inflexibility problem in the current Wireless Sensor Network (WSN). For the resource competition problem in virtualized WSN, a multi-task resource allocation strategy based on Stackelberg game is proposed. According to the different Quality of Service (QoS) requirements of the business carried by Virtual Sensor Network Request (VSNR), the importance of multiple VSNRs is quantified. Then, the optimal price of WSN and the optimal resource requirements of VSNRs are obtained by using distributed iteration method. Finally, the resource corresponding to multiple VSNRs is acquired according to optimal price and optimal resource allocation determined by Nash equilibrium. The simulation results show that the proposed strategy can not only meet the diversified needs of users, but also improve the resource utilization of nodes and links.
EZDIANI S, ACHARYYA I S, SIVAKUMAR S, et al. Wireless sensor network softwarization: Towards WSN adaptive QoS[J]. IEEE Internet of Things Journal, 2017, 4(5): 1517–1527. doi: 10.1109/JIOT.2017.2740423
|
LIAO Yizheng, MOLLINEAUX M, HSU R, et al. SnowFort: An open source wireless sensor network for data analytics in infrastructure and environmental monitoring[J]. IEEE Sensors Journal, 2014, 14(12): 4253–4263. doi: 10.1109/JSEN.2014.2358253
|
HU Xiaoya, YANG Liuqing, and XIONG Wei. A novel wireless sensor network frame for urban transportation[J]. IEEE Internet of Things Journal, 2015, 2(6): 586–595. doi: 10.1109/JIOT.2015.2475639
|
ALAIAD A and ZHOU Lina. Patients’ adoption of WSN-Based smart home healthcare systems: an integrated model of facilitators and barriers[J]. IEEE Transactions on Professional Communication, 2017, 60(1): 4–23. doi: 10.1109/TPC.2016.2632822
|
PARK P, MARCO P D, and JOHANSSON K H. Cross-layer optimization for industrial control applications using wireless sensor and actuator mesh networks[J]. IEEE Transactions on Industrial Electronics, 2017, 64(4): 3250–3259. doi: 10.1109/TIE.2016.2631530
|
KHAN I, BELQASMI F, GLITHO R, et al. Wireless sensor network virtualization: Early architecture research perspectives[J]. IEEE Network, 2015, 29(3): 104–112. doi: 10.1109/MNET.2015.7113233
|
KHAN I, BELQASMI F, GLITHO R, et al. Wireless sensor network virtualization: A survey[J]. IEEE Communications Surveys & Tutorials, 2016, 18(1): 553–576. doi: 10.1109/COMST.2015.2412971
|
GUO Lei, NING Zhaolong, SONG Qingyang, et al. A QoS-oriented high-efficiency resource allocation scheme in wireless multimedia sensor networks[J]. IEEE Sensors Journal, 2017, 17(5): 1538–1548. doi: 10.1109/JSEN.2016.2645709
|
DELGADO C, BOUSNINA S, CESANA M, et al. On optimal resource allocation in virtual sensor networks[J]. Ad Hoc Networks, 2016, 50(C): 23–40. doi: 10.1016/j.adhoc.2016.04.004
|
DELGADO C, CANALES M, ORTIN J, et al. Joint application admission control and network slicing in virtual sensor networks[J]. IEEE Internet of Things Journal, 2017, 5(1): 28–43. doi: 10.1109/JIOT.2017.2769446
|
OBELE B O, IFTIKHAR M, MANIPORNSUT S, et al. Analysis of the behavior of self-similar traffic in a QoS-aware architecture for integrating WiMAX and GEPON[J]. Journal of Optical Communication and Network, 2009, 1(4): 259–273. doi: 10.1364/JOCN.1.000259
|
MILAN G, JUAN E S, and JAMETT M. A simple estimator of the Hurst exponent for self-similar traffic flows[J]. IEEE Latin America Transactions, 2015, 12(8): 1349–1354. doi: 10.1109/TLA.2014.7014500
|
TRAN T D and LE L B. Stackelberg game approach for wireless virtualization design in wireless networks[C]. 2017 IEEE International Conference on Communications (ICC), Paris, France, 2017: 1–6.
|
WANG Cong, WANG Cuirong, and YUAN Ying. Game based dynamical bandwidth allocation model for virtual networks[C]. 2009 First International Conference on Information Science and Engineering, Nanjing, China, 2009: 1745–1747.
|
LUONG N C, HOANG D T, WANG Ping, et al. Data collection and wireless communication in Internet of Things (IoT) using economic analysis and pricing models: A survey[J]. IEEE Communications Surveys & Tutorials, 2016, 18(4): 2546–2590. doi: 10.1109/COMST.2016.2582841
|
AL-ZAHRANI A Y and YU F R. An energy-efficient resource allocation and interference management scheme in green heterogeneous networks using game theory[J]. IEEE Transactions on Vehicular Technology, 2016, 65(7): 5384–5396. doi: 10.1109/TVT.2015.2464322
|
XU Qichao, SU Zhou, and GUO Song. A game theoretical incentive scheme for relay selection services in mobile social networks[J]. IEEE Transactions on Vehicular Technology, 2016, 65(8): 6692–6702. doi: 10.1109/TVT.2015.2472289
|
GHOSH A, COTTATELLUCCI L, and ALTMAN E. Normalized Nash equilibrium for power allocation in cognitive radio Networks[J]. IEEE Transactions on Cognitive Communications and Networking, 2015, 1(1): 86–99. doi: 10.1109/TCCN.2015.2496578
|
RAO M S S and SOMAN S A. Marginal pricing of transmission services using min-max fairness policy[J]. IEEE Transactions on Power Systems, 2015, 30(2): 573–584. doi: 10.1109/TPWRS.2014.2331424
|
ZHANG Yueyue, ZHU Yaping, YAN Feng, et al. Energy-efficient radio resource allocation in software-defined wireless sensor networks[J]. IET Communications, 2018, 12(3): 349–358. doi: 10.1049/iet-com.2017.0937
|