An autonomous compensation game to facilitate peer data exchange in crowdsensing

The rapid penetration of mobile devices has provided ample opportunities for mobile devices to exchange sensing data on a peer basis without any centralized backend. In this paper, we design a peer based data exchanging model, where relay nodes move to certain locations to connect data providers and consumers to facilitate data delivery. Both relays and data providers can gain rewards from consumers who are willing to pay for the data. We first prove the problem of relay node assignment is NP-hard, and provide a centralized optimal method to decide which relay nodes goes to which location with an approximation ratio. Then we define an autonomous compensation game to allow relays make individual decisions without any central authority. We derive a sufficient and necessary condition for the existence of Nash equilibrium. We analyze and compare this distributed game to the centralized social optimal solution, and show that the game incurs small bounded social costs, and efficient under various network sizes, numbers of providers, consumers, and device mobility.

[1]  David B. Smith,et al.  A game theoretic approach to sensor data communications in an opportunistic network , 2015, 2015 IEEE International Conference on Communications (ICC).

[2]  Lusheng Wang,et al.  The Euclidean Bottleneck Steiner Tree and Steiner Tree with Minimum Number of Steiner Points , 2001, COCOON.

[3]  J. Munkres ALGORITHMS FOR THE ASSIGNMENT AND TRANSIORTATION tROBLEMS* , 1957 .

[4]  Hwee Pink Tan,et al.  Profit-maximizing incentive for participatory sensing , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[5]  Eitan Altman,et al.  Analysis and design of message ferry routes in sensor networks using polling models , 2010, 8th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks.

[6]  Ellen W. Zegura,et al.  Controlling the mobility of multiple data transport ferries in a delay-tolerant network , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[7]  Jian Tang,et al.  Truthful incentive mechanisms for crowdsourcing , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[8]  Kannan Ramchandran,et al.  Efficient Algorithms for the Data Exchange Problem , 2015, IEEE Transactions on Information Theory.

[9]  Lin Gao,et al.  Providing long-term participation incentive in participatory sensing , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[10]  David Kotz,et al.  Extracting a Mobility Model from Real User Traces , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[11]  M. Anwar Hossain,et al.  Privacy preserving secure data exchange in mobile P2P cloud healthcare environment , 2016, Peer-to-Peer Netw. Appl..

[12]  David Malone,et al.  Greener Data Exchange in the Cloud: A Coding-Based Optimization for Big Data Processing , 2016, IEEE Journal on Selected Areas in Communications.

[13]  Antonio J. Caamano,et al.  2015 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC) , 2015 .