Quality-aware incentive mechanism based on payoff maximization for mobile crowdsensing

Abstract Recent years, we have witnessed the explosion of smart devices. These smart devices are more and more powerful with a set of built in sensor devices, such as GPS, accelerometer, gyroscope, camera, etc. The large scale and powerful smart devices make the mobile crowdsensing applications which leverage public crowd equipped with various mobile devices for large scale sensing tasks be possible. In this paper, we study a critical problem of payoff maximization in mobile crowdsensing system with incentive mechanism. Due to the influence of various factors (e.g. sensor quality, noise, etc.), the quality of the sensed data contributed by individual users varies significantly. Obtaining the high quality sensed data with less expense is the ideal of sensing platforms. Therefore, we take the quality of individuals which is determined by the sensing platforms into incentive mechanism design. We propose to maximize the social welfare of the whole system, due to that the private parameters of the mobile users are unknown to the sensing platforms. It is impossible to solve the problem in a central manner. Then a dual decomposition method is employed to divide the social welfare maximization problem into sensing platforms’ local optimization problems and mobile users’ local optimization problems. Finally, distributed algorithms based on an iterative gradient descent method are designed to achieve the close-to-optimal solution. Extensive simulations demonstrate the effectiveness of the proposed incentive mechanism.

[1]  Minyi Guo,et al.  MELODY: A Long-Term Dynamic Quality-Aware Incentive Mechanism for Crowdsourcing , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).

[2]  Xue Liu,et al.  Generalized Decision Aggregation in Distributed Sensing Systems , 2014, 2014 IEEE Real-Time Systems Symposium.

[3]  Baik Hoh,et al.  Dynamic pricing incentive for participatory sensing , 2010, Pervasive Mob. Comput..

[4]  Jiming Chen,et al.  An Exchange Market Approach to Mobile Crowdsensing: Pricing, Task Allocation, and Walrasian Equilibrium , 2017, IEEE Journal on Selected Areas in Communications.

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

[6]  Vincent W. S. Wong,et al.  Optimal Real-Time Pricing Algorithm Based on Utility Maximization for Smart Grid , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[7]  Deepak Ganesan,et al.  mCrowd: a platform for mobile crowdsourcing , 2009, SenSys '09.

[8]  Yanmin Zhu,et al.  Social welfare maximization in participatory smartphone sensing , 2014, Comput. Networks.

[9]  Deborah Estrin,et al.  Image browsing, processing, and clustering for participatory sensing: lessons from a DietSense prototype , 2007, EmNets '07.

[10]  Jing Wang,et al.  Quality-Aware and Fine-Grained Incentive Mechanisms for Mobile Crowdsensing , 2016, 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS).

[11]  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.

[12]  Bo Zhao,et al.  Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation , 2014, SIGMOD Conference.

[13]  Ramachandran Ramjee,et al.  Nericell: rich monitoring of road and traffic conditions using mobile smartphones , 2008, SenSys '08.

[14]  Kin K. Leung,et al.  A Survey of Incentive Mechanisms for Participatory Sensing , 2015, IEEE Communications Surveys & Tutorials.

[15]  Maria E. Niessen,et al.  NoiseTube: Measuring and mapping noise pollution with mobile phones , 2009, ITEE.

[16]  Bo Zhao,et al.  A Confidence-Aware Approach for Truth Discovery on Long-Tail Data , 2014, Proc. VLDB Endow..

[17]  Deborah Estrin,et al.  Recruitment Framework for Participatory Sensing Data Collections , 2010, Pervasive.

[18]  Baik Hoh,et al.  Sell your experiences: a market mechanism based incentive for participatory sensing , 2010, 2010 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[19]  Sivan Toledo,et al.  VTrack: accurate, energy-aware road traffic delay estimation using mobile phones , 2009, SenSys '09.

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

[21]  Kai Ma,et al.  Hierarchical-Game-Based Uplink Power Control in Femtocell Networks , 2014, IEEE Transactions on Vehicular Technology.

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

[23]  Bo Zhang,et al.  An Event-Driven QoI-Aware Participatory Sensing Framework with Energy and Budget Constraints , 2015, ACM Trans. Intell. Syst. Technol..

[24]  Sung-Kwan Joo,et al.  Social Welfare Maximization in Transmission Enhancement Considering Network Congestion , 2008, IEEE Transactions on Power Systems.

[25]  Mirco Musolesi,et al.  Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application , 2008, SenSys '08.

[26]  Lei Chen,et al.  Free Market of Crowdsourcing: Incentive Mechanism Design for Mobile Sensing , 2014, IEEE Transactions on Parallel and Distributed Systems.

[27]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[28]  Romit Roy Choudhury,et al.  Micro-Blog: sharing and querying content through mobile phones and social participation , 2008, MobiSys '08.

[29]  Klara Nahrstedt,et al.  Quality of Information Aware Incentive Mechanisms for Mobile Crowd Sensing Systems , 2015, MobiHoc.

[30]  Deepak Ganesan,et al.  TruCentive: A game-theoretic incentive platform for trustworthy mobile crowdsourcing parking services , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[31]  Jean C. Walrand,et al.  Motivating Smartphone Collaboration in Data Acquisition and Distributed Computing , 2014, IEEE Transactions on Mobile Computing.

[32]  Salil S. Kanhere,et al.  IncogniSense: An anonymity-preserving reputation framework for participatory sensing applications , 2012, 2012 IEEE International Conference on Pervasive Computing and Communications.

[33]  Deborah Estrin,et al.  PEIR, the personal environmental impact report, as a platform for participatory sensing systems research , 2009, MobiSys '09.

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

[35]  Miguel A. Labrador,et al.  A location-based incentive mechanism for participatory sensing systems with budget constraints , 2012, 2012 IEEE International Conference on Pervasive Computing and Communications.

[36]  Wen Hu,et al.  Ear-phone: an end-to-end participatory urban noise mapping system , 2010, IPSN '10.

[37]  Roy D. Yates,et al.  A Framework for Uplink Power Control in Cellular Radio Systems , 1995, IEEE J. Sel. Areas Commun..

[38]  Minho Shin,et al.  Anonysense: privacy-aware people-centric sensing , 2008, MobiSys '08.

[39]  Kai Ma,et al.  Robust power allocation based on hierarchical game with consideration of different user requirements in two-tier femtocell networks , 2017, Comput. Networks.

[40]  Iordanis Koutsopoulos,et al.  Optimal incentive-driven design of participatory sensing systems , 2013, 2013 Proceedings IEEE INFOCOM.

[41]  Shiguang Wang,et al.  Towards Cyber-Physical Systems in Social Spaces: The Data Reliability Challenge , 2014, 2014 IEEE Real-Time Systems Symposium.

[42]  Margaret Martonosi,et al.  SignalGuru: leveraging mobile phones for collaborative traffic signal schedule advisory , 2011, MobiSys '11.

[43]  Jie Wu,et al.  Privacy-Preserving Social Tie Discovery Based on Cloaked Human Trajectories , 2017, IEEE Trans. Veh. Technol..

[44]  Yunhao Liu,et al.  Smartphones Based Crowdsourcing for Indoor Localization , 2015, IEEE Transactions on Mobile Computing.

[45]  Bo Zhao,et al.  The wisdom of minority: discovering and targeting the right group of workers for crowdsourcing , 2014, WWW.

[46]  Wazir Zada Khan,et al.  Mobile Phone Sensing Systems: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[47]  Yanmin Zhu,et al.  Distributed social welfare maximization in vehicular participatory sensing systems , 2014, 2014 IEEE 22nd International Symposium of Quality of Service (IWQoS).

[48]  Mikael Skoglund,et al.  Distributed Transceiver Design and Power Control for Wireless MIMO Interference Networks , 2015, IEEE Transactions on Wireless Communications.

[49]  Kin K. Leung,et al.  Credible and energy-aware participant selection with limited task budget for mobile crowd sensing , 2016, Ad Hoc Networks.

[50]  Xi Fang,et al.  Incentive Mechanisms for Crowdsensing: Crowdsourcing With Smartphones , 2016, IEEE/ACM Transactions on Networking.

[51]  Junshan Zhang,et al.  Distributed Algorithms to Compute Walrasian Equilibrium in Mobile Crowdsensing , 2017, IEEE Transactions on Industrial Electronics.

[52]  Zhijun Li,et al.  AirCloud: a cloud-based air-quality monitoring system for everyone , 2014, SenSys.

[53]  Yunhao Liu,et al.  Robust Trajectory Estimation for Crowdsourcing-Based Mobile Applications , 2014, IEEE Transactions on Parallel and Distributed Systems.