Collaborative vehicle sensing in bus networks: A Stackelberg game approach

Collecting urban data has become a crucial issue for smart city. Unlike mobile devices and other vehicles, buses have unique characteristics: fixed mobility trajectories, strong periodicity, uniform sensor interfaces and low influence of exposure privacy. We leverage the characteristics and consider a collaborative vehicle sensing paradigm in bus networks. Data center and buses are two sides of trading sense service in the paradigm. The buses are coordinated to collect data while they are competitors for the reward from the data center. We design a primary-secondary coordination strategy using a Stackelberg game approach, where the data center is the leader while the buses are the followers. With the proposed heuristic algorithm, the Stackelberg game can reach a unique Stackelberg equilibrium (SE), where the data center and each bus respectively obtain maximum utility. The simulations validate the effectiveness of our coordination strategy.

[1]  Xiong Li,et al.  Heterogeneous Participant Recruitment for Comprehensive Vehicle Sensing , 2015, PloS one.

[2]  Tom H. Luan,et al.  Fog Computing: Focusing on Mobile Users at the Edge , 2015, ArXiv.

[3]  Osamu Masutani,et al.  A sensing coverage analysis of a route control method for vehicular crowd sensing , 2015, 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops).

[4]  Yan Zhang,et al.  Modeling Prioritized Broadcasting in Multichannel Vehicular Networks , 2012, IEEE Transactions on Vehicular Technology.

[5]  Sangjin Kim,et al.  Using public buses as mobile gateways in vehicular clouds , 2014, 2014 IEEE International Conference on Consumer Electronics (ICCE).

[6]  Quanyan Zhu,et al.  Dependable Demand Response Management in the Smart Grid: A Stackelberg Game Approach , 2013, IEEE Transactions on Smart Grid.

[7]  Yan Zhang,et al.  QoS-Aware Channel Selection in Cognitive Radio Networks: A Game-Theoretic Approach , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

[8]  Yan Zhang,et al.  Optimal Resource Sharing in 5G-Enabled Vehicular Networks: A Matrix Game Approach , 2016, IEEE Transactions on Vehicular Technology.

[9]  Yunhao Liu,et al.  Incentives for Mobile Crowd Sensing: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[10]  Honggang Wang,et al.  Stackelberg Game for Bandwidth Allocation in Cloud-Based Wireless Live-Streaming Social Networks , 2014, IEEE Systems Journal.

[11]  Jian Chen,et al.  Multi-objective optimization for coverage control in wireless sensor network with adjustable sensing radius , 2009, Comput. Math. Appl..

[12]  Huirong Fu,et al.  An IEEE 802.11p-Based Multichannel MAC Scheme With Channel Coordination for Vehicular Ad Hoc Networks , 2012, IEEE Transactions on Intelligent Transportation Systems.

[13]  Holger Ziekow,et al.  Towards a Big Data Analytics Framework for IoT and Smart City Applications , 2015 .

[14]  Victor O. K. Li,et al.  Granger-Causality-based air quality estimation with spatio-temporal (S-T) heterogeneous big data , 2015, 2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[15]  Jiannong Cao,et al.  High quality participant recruitment in vehicle-based crowdsourcing using predictable mobility , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[16]  Yan Zhang,et al.  Coalitional games for the management of anonymous access in online social networks , 2013, 2013 Eleventh Annual Conference on Privacy, Security and Trust.

[17]  Paul J. M. Havinga,et al.  Unified routing for data dissemination in smart city networks , 2012, 2012 3rd IEEE International Conference on the Internet of Things.

[18]  Yan Zhang,et al.  Demand Response Management With Multiple Utility Companies: A Two-Level Game Approach , 2014, IEEE Transactions on Smart Grid.

[19]  Hsiao-Hwa Chen,et al.  Sensing-Energy Tradeoff in Cognitive Radio Networks With Relays , 2013, IEEE Systems Journal.

[20]  Thierry Delot,et al.  Collaborative Sensing for Urban Transportation , 2014, IEEE Data Eng. Bull..

[21]  H. Vincent Poor,et al.  Three-Party Energy Management With Distributed Energy Resources in Smart Grid , 2014, IEEE Transactions on Industrial Electronics.

[22]  Yih-Chun Hu,et al.  CRAWDAD dataset rice/ad_hoc_city (v.2003-09-11) , 2003 .

[23]  Xi Fang,et al.  Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing , 2012, Mobicom '12.