Towards a Stable and Truthful Incentive Mechanism for Task Delegation in Hierarchical Crowdsensing

In order to achieve the desired performance of crowdsensing, the incentive mechanism, which can stimulate the workers to serve the sensing tasks efficiently, is usually indispensable. Different from the existing research efforts of incentive mechanisms, we propose an incentive mechanism to facilitate the delegation of tasks among the workers in hierarchical crowdsensing. Considering the task converging at some skillful workers, which will degrade the system stability and unbalance the workload among the workers, we construct a Stable and Truthful Incentive Mechanism (STIM) to model and restrict the interactions between the requester and the workers. STIM mechanism comprises a queue control algorithm for the workers and an auction scheme with Multi-sEllers for the Divisible tAsks (MEDA), which exploits an optimal winning bids determination strategy and conducts a truthful payment algorithm. The soundness of the modeling and the accuracy of the analysis are verified through extensive simulations.

[1]  E. Maasland,et al.  Auction Theory , 2021, Springer Texts in Business and Economics.

[2]  Zoltán Király,et al.  Efficient implementations of minimum-cost flow algorithms , 2012, ArXiv.

[3]  Jie Wu,et al.  Multi-task assignment for crowdsensing in mobile social networks , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[4]  Ming Xu,et al.  From Uncertain Photos to Certain Coverage: a Novel Photo Selection Approach to Mobile Crowdsensing , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[5]  Athanasios V. Vasilakos,et al.  TRAC: Truthful auction for location-aware collaborative sensing in mobile crowdsourcing , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[6]  Fan Ye,et al.  Mobile crowdsensing: current state and future challenges , 2011, IEEE Communications Magazine.

[7]  Yuanyuan Yang,et al.  CrowdGIS: Updating Digital Maps via Mobile Crowdsensing , 2018, IEEE Transactions on Automation Science and Engineering.

[8]  Xu Chen,et al.  Exploiting Social Trust Assisted Reciprocity (STAR) Toward Utility-Optimal Socially-Aware Crowdsensing , 2015, IEEE Transactions on Signal and Information Processing over Networks.

[9]  Qian Zhang,et al.  Towards Truthful Mechanisms for Mobile Crowdsourcing with Dynamic Smartphones , 2014, 2014 IEEE 34th International Conference on Distributed Computing Systems.

[10]  Chunyan Miao,et al.  Mitigating Herding in Hierarchical Crowdsourcing Networks. , 2016, Scientific reports.

[11]  Chunyan Miao,et al.  Efficient Task Sub-Delegation for Crowdsourcing , 2015, AAAI.

[12]  Sriram Vishwanath,et al.  Efficient and Flexible Crowdsourcing of Specialized Tasks With Precedence Constraints , 2018, IEEE/ACM Transactions on Networking.

[13]  Xiaoming Chen,et al.  Towards truthful auction mechanisms for task assignment in mobile device clouds , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[14]  Ming Xu,et al.  Location Privacy-Preserving Data Recovery for Mobile Crowdsensing , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

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