Abstract
Although mobile crowdsensing (MCS) has become a green paradigm of collecting, analyzing, and exploiting massive amounts of sensory data, existing incentive mechanisms are not effective to stimulate users’s active participation and service contribution in multi-service exchange in MCS due to its specific features: a large number of heterogeneous users have asymmetric service requirements, workers have the freedom to choose sensing tasks as well as participation levels, and multiple sensing tasks have heterogeneous values which may be untruthful declared by the corresponding requesters. To address this issue, this article develops a green Stackelberg-game incentive mechanism to achieve selective fairness, truthfulness, and bounded efficiency while reducing the burden on the platform. First, we model the multi-service exchange problem as a Stackelberg multi-service exchange game consisting of multi-leader and multi-follower, in which each requester as a leader first chooses the reward declaration strategy and thus the payment for each sensing task, each worker as a follower then chooses the sensing plan strategy to maximize her own utility. We next introduce the concept of virtual currency to maintain the selective fairness to balance service request and service provision between users, in which a user earns/consumes virtual currency for providing/receiving services, and thus no one can always get services without providing services. Then, we present two novel algorithms to compute the unique Nash equilibrium for the sensing plan determination game and the reward declaration determination game, respectively, which together forms a unique Stackelberg equilibrium for the proposed game. Afterwards, we theoretically prove that the proposed green Stackelberg-game incentive mechanism achieves the desirable properties of selective fairness, truthfulness, bounded efficiency. Finally, extensive evaluation results are provided to support the validity and effectiveness of our mechanism compared with both baseline and theoretical optimal approaches.
- [1] . 2014. Mechanism design for crowdsourcing: An optimal 1-1/e competitive budget-feasible mechanism for large markets. In Proceedings of the 55th IEEE Annual Symposium on Foundations of Computer Science (FOCS’14). 266–275. Google Scholar
Digital Library
- [2] . 2020. QnQ: Quality and quantity-based unified approach for secure and trustworthy mobile crowdsensing. IEEE Trans. Mob. Comput. 19, 1 (2020), 200–216.Google Scholar
Digital Library
- [3] . 2019. A survey on mobile crowdsensing systems: Challenges, solutions, and opportunities. IEEE Commun. Surv. Tutor. 21, 3 (2019), 2419–2465.Google Scholar
Cross Ref
- [4] . 2011. Conflicts and incentives in wireless cooperative relaying: A distributed market pricing framework. IEEE Trans. Parallel Distrib. Syst. 22, 5 (2011), 758–772. Google Scholar
Digital Library
- [5] . 2020. Android HIV: A study of repackaging malware for evading machine-learning detection. IEEE Trans. Info. Forens. Secur. 15 (2020), 987–1001.Google Scholar
Digital Library
- [6] . 2019. RACOON++: A semi-automatic framework for the selfishness-aware design of cooperative systems. IEEE Trans. Depend. Sec. Comput. 16, 4 (2019), 635–650. Google Scholar
Digital Library
- [7] . 2019. Survey on computational trust and reputation models. ACM Comput. Surv. 51, 5 (2019), 101:1–101:40.Google Scholar
Digital Library
- [8] . 2009. Crowdsourcing and all-pay auctions. In Proceedings of the 10th ACM Conference on Electronic Commerce (EC’09). ACM, 119–128. Google Scholar
Digital Library
- [9] . 2019. A reputation-based contract for repeated crowdsensing with costly verification. IEEE Trans. Signal Process. 67, 23 (2019), 6092–6104.Google Scholar
Digital Library
- [10] . 2014. Motivating smartphone collaboration in data acquisition and distributed computing. IEEE Trans. Mob. Comput. 13, 10 (2014), 2320–2333.Google Scholar
Cross Ref
- [11] . 2006. Free-riding and whitewashing in peer-to-peer systems. IEEE J. Select. Areas Commun. 24, 5 (2006), 1010–1019. Google Scholar
Digital Library
- [12] . 2017. Incentivize multi-class crowd labeling under budget constraint. IEEE J. Sel. Areas Commun. 35, 4 (2017), 893–905.Google Scholar
Digital Library
- [13] . 2017. Identifying propagation sources in networks: State-of-the-art and comparative studies. IEEE Commun. Surv. Tutor. 19, 1 (2017), 465–481.Google Scholar
Digital Library
- [14] . 2018. Incentive mechanism for privacy-aware data aggregation in mobile crowd sensing systems. IEEE/ACM Trans. Netw. 26, 5 (2018), 2019–2032. Google Scholar
Digital Library
- [15] . 2018. Context-aware hierarchical online learning for performance maximization in mobile crowdsourcing. IEEE/ACM Trans. Netw. 26, 3 (2018), 1334–1347. Google Scholar
Digital Library
- [16] . 2018. Service exchange problem. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI’18), (Ed.). ijcai.org, 354–360. Google Scholar
Digital Library
- [17] . 2020. A blockchain-enabled decentralized settlement model for IoT data exchange services. Wireless Netw. 12 (2020), 1–15. https://doi.org/10.1007/s11276-020-02345-9Google Scholar
Cross Ref
- [18] . 2018. A survey of mobile crowdsensing techniques: A critical component for the internet of things. ACM Trans. Cyber-Phys. Syst. 2, 3 (2018), 18:1–18:26. Google Scholar
Digital Library
- [19] . 2019. DeepBalance: Deep-learning and fuzzy oversampling for vulnerability detection. IEEE Trans. Fuzzy Syst. 28, 7 (2019), 1329–1343. https://doi.org/10.1109/TFUZZ.2019.2958558Google Scholar
Digital Library
- [20] . 2019. Data-oriented mobile crowdsensing: A comprehensive survey. IEEE Commun. Surv. Tutor. 21, 3 (2019), 2849–2885.Google Scholar
Cross Ref
- [21] . 2017. Designing socially optimal rating protocols for crowdsourcing contest dilemma. IEEE Trans. Info. Forens. Secur. 12, 6 (2017), 1330–1344. Google Scholar
Digital Library
- [22] . 2018. Game-theoretic design of optimal two-sided rating protocols for service exchange dilemma in crowdsourcing. IEEE Trans. Info. Forens. Secur. 13, 11 (2018), 2801–2815.Google Scholar
Cross Ref
- [23] . 2016. Incentive mechanism design for heterogeneous crowdsourcing using all-pay contests. IEEE Trans. Mob. Comput. 15, 9 (2016), 2234–2246.Google Scholar
Digital Library
- [24] . 2017. Sustainable incentives for mobile crowdsensing: Auctions, lotteries, and trust and reputation systems. IEEE Commun. Mag. 55, 3 (2017), 68–74. Google Scholar
Digital Library
- [25] . 2018. Evolutionary learning model of social networking services with diminishing marginal utility. In Proceedings of the Web Conference (WWW’18), , , , and (Eds.). ACM, 1323–1329. Google Scholar
Digital Library
- [26] . 2019. FaRM: Fair reward mechanism for information aggregation in spontaneous localized settings. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI’19), (Ed.). ijcai.org, 506–512. Google Scholar
Digital Library
- [27] . 2015. Workload factoring: A game-theoretic perspective. IEEE/ACM Trans. Netw. 23, 6 (2015), 1998–2009. Google Scholar
Digital Library
- [28] . 2020. Enabling strong privacy preservation and accurate task allocation for mobile crowdsensing. IEEE Trans. Mob. Comput. 19, 6 (2020), 1317–1331.Google Scholar
Cross Ref
- [29] . 2019. A stackelberg game approach toward socially-aware incentive mechanisms for mobile crowdsensing. IEEE Trans. Wireless Commun. 18, 1 (2019), 724–738. Google Scholar
Digital Library
- [30] . 2020. Posted pricing for chance constrained robust crowdsensing. IEEE Trans. Mob. Comput. 19, 1 (2020), 188–199.Google Scholar
Digital Library
- [31] . 2016. An overview of Fog computing and its security issues. Concurr. Comput. Pract. Exp. 28, 10 (2016), 2991–3005. Google Scholar
Digital Library
- [32] . 2020. Incentive scheme for cyber physical social systems based on user behaviors. IEEE Trans. Emerg. Top. Comput. 8, 1 (2020), 92–103.Google Scholar
Cross Ref
- [33] . 2019. Trust evaluation mechanism for user recruitment in mobile crowd-sensing in the internet of things. IEEE Trans. Info. Forens. Secur. 14, 10 (2019), 2705–2719.Google Scholar
Digital Library
- [34] . 2018. Twitter spam detection: Survey of new approaches and comparative study. Comput. Secur. 76 (2018), 265–284.Google Scholar
Cross Ref
- [35] . 2017. Incentive mechanism design to meet task criteria in crowdsourcing: How to determine your budget. IEEE J. Select. Areas Commun. 35, 2 (2017), 502–516. Google Scholar
Digital Library
- [36] . 2014. Incentivizing high-quality content from heterogeneous users: On the existence of Nash equilibrium. In Proceedings of the 28th AAAI Conference on Artificial Intelligence. AAAI Press, 819–825. Google Scholar
Digital Library
- [37] . 2018. A secure mobile crowdsensing game with deep reinforcement learning. IEEE Trans. Info. Forensics Secur. 13, 1 (2018), 35–47.Google Scholar
Cross Ref
- [38] . 2014. Sharing in networks of strategic agents. J. Sel. Topics Signal Process. 8, 4 (2014), 717–731.Google Scholar
Cross Ref
- [39] . 2012. Crowdsourcing to smartphones: Incentive mechanism design for mobile phone sensing. In Proceedings of the 18th Annual International Conference on Mobile Computing and Networking (Mobicom’12). ACM, 173–184. Google Scholar
Digital Library
- [40] . 2016. Incentive mechanisms for crowdsensing: Crowdsourcing with smartphones. IEEE/ACM Trans. Netw. 24, 3 (2016), 1732–1744. Google Scholar
Digital Library
- [41] . 2017. Pricing mechanisms for crowd-sensed spatial-statistics-based radio mapping. IEEE Trans. Cogn. Commun. Netw. 3, 2 (2017), 242–254.Google Scholar
Cross Ref
- [42] . 2019. Free market of multi-leader multi-follower mobile crowdsensing: An incentive mechanism design by deep reinforcement learning. IEEE Trans. Mob. Comput. (2019), 1–1.Google Scholar
Cross Ref
- [43] . 2015. Keep your promise: Mechanism design against free-riding and false-reporting in crowdsourcing. IEEE Internet Things J. 2, 6 (2015), 562–572.Google Scholar
Cross Ref
- [44] . 2017. Incentive mechanism for mobile crowdsourcing using an optimized tournament model. IEEE J. Select. Areas Commun. 35, 4 (2017), 880–892.Google Scholar
Digital Library
- [45] . 2014. Rating protocols in online communities. ACM Trans. Econ. Comput. 2, 1 (2014), 4:1–4:34. Google Scholar
Digital Library
- [46] . 2016. Budget-feasible online incentive mechanisms for crowdsourcing tasks truthfully. IEEE/ACM Trans. Netw. 24, 2 (2016), 647–661. Google Scholar
Digital Library
- [47] . 2017. Frugal online incentive mechanisms for mobile crowd sensing. IEEE Trans. Vehic. Technol. 66, 4 (2017), 3319–3330.Google Scholar
Cross Ref
- [48] . 2018. A truthful online mechanism for location-aware tasks in mobile crowd sensing. IEEE Trans. Mob. Comput. 17, 8 (2018), 1737–1749.Google Scholar
Cross Ref
Index Terms
A Green Stackelberg-game Incentive Mechanism for Multi-service Exchange in Mobile Crowdsensing
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