Heterogeneous Task Allocation in Participatory Sensing

The proliferation of smartphones has enabled a novel paradigm, participatory sensing, which leverages the smartphones to collect and share data about their surrounding environment. Since the sensing tasks are location-dependent and have time features, it is crucial and challenging to find a proper allocation of sensing tasks to ensure the timeliness of tasks and the quality of sensing data. In this paper, we investigate the heterogeneous sensing task allocation problem aiming at minimizing the total penalty caused by the tardiness of tasks. We prove this problem is NP-hard and propose two hybrid algorithms which combine a heuristic algorithm and two meta-heuristic algorithms respectively. The extensive simulation results show that the proposed hybrid algorithms outperform the meta-heuristic algorithms.

[1]  Chichang Jou,et al.  A genetic algorithm with sub-indexed partitioning genes and its application to production scheduling of parallel machines , 2005, Comput. Ind. Eng..

[2]  Chien-Ju Ho,et al.  Online Task Assignment in Crowdsourcing Markets , 2012, AAAI.

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

[4]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

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

[6]  Vana Kalogeraki,et al.  On Task Assignment for Real-Time Reliable Crowdsourcing , 2014, 2014 IEEE 34th International Conference on Distributed Computing Systems.

[7]  Xingshe Zhou,et al.  Recommending travel packages based on mobile crowdsourced data , 2014, IEEE Communications Magazine.

[8]  L. W. Jacobs,et al.  Note: A local-search heuristic for large set-covering problems , 1995 .

[9]  Ramachandran Ramjee,et al.  TrafficSense: Rich Monitoring of Road and Traffic Conditions us ing Mobile Smartphones , 2008 .

[10]  Jiming Chen,et al.  Toward optimal allocation of location dependent tasks in crowdsensing , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

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

[12]  Furkan Kiraç,et al.  A tabu search algorithm for parallel machine total tardiness problem , 2004, Comput. Oper. Res..

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

[14]  Emiliano Miluzzo,et al.  A survey of mobile phone sensing , 2010, IEEE Communications Magazine.

[15]  Bin Guo,et al.  From participatory sensing to Mobile Crowd Sensing , 2014, 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS).

[16]  Bo Li,et al.  Fair energy-efficient sensing task allocation in participatory sensing with smartphones , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

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

[18]  Prasant Mohapatra,et al.  Improving crowd-sourced Wi-Fi localization systems using Bluetooth beacons , 2012, 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON).

[19]  Reza Tavakkoli-Moghaddam,et al.  Design of a genetic algorithm for bi-objective unrelated parallel machines scheduling with sequence-dependent setup times and precedence constraints , 2009, Comput. Oper. Res..

[20]  Chung-Cheng Lu,et al.  Minimization of maximum lateness on parallel machines with sequence-dependent setup times and job release dates , 2011, Comput. Oper. Res..

[21]  Xi Chen,et al.  Mutual privacy-preserving regression modeling in participatory sensing , 2013, 2013 Proceedings IEEE INFOCOM.

[22]  Chunfeng Liu.  A Hybrid Genetic Algorithm to Minimize Total Tardiness for Unrelated Parallel Machine Scheduling with Precedence Constraints , 2013 .

[23]  Dijiang Huang,et al.  QoS-constrained sensing task assignment for mobile crowd sensing , 2014, 2014 IEEE Global Communications Conference.

[24]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

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