Collaborative Data Delivery for Smart City-Oriented Mobile Crowdsensing Systems

The huge increase of population living in cities calls for a sustainable urban development. Mobile crowdsensing (MCS) leverages participation of active citizens to improve performance of existing sensing infrastructures. In typical MCS systems, sensing tasks are allocated and reported on individual-basis. In this paper, we investigate on collaboration among users for data delivery as it brings a number of benefits for both users and sensing campaign organizers and leads to better coordination and use of resources. By taking advantage from proximity, users can employ device-to-device (D2D) communications like Wi-Fi Direct that are more energy efficient than 3G/4G technology. In such scenario, once a group is set, one of its member is elected to be the owner and perform data forwarding to the collector. The efficiency of forming groups and electing suitable owners defines the efficiency of the whole collaborative-based system. This paper proposes three policies optimized for MCS that are compliant with current Android implementation of Wi-Fi Direct. The evaluation results, obtained using CrowdSenSim simulator, demonstrate that collaborative-based approaches outperform significantly individual-based approaches.

[1]  Paolo Bellavista,et al.  Proximity discovery and data dissemination for mobile crowd sensing using LTE direct , 2017, Comput. Networks.

[2]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

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

[4]  W. Marsden.  I and J , 2012 .

[5]  Sanaa Sharafeddine,et al.  Optimized group owner selection in WiFi direct networks , 2016, 2016 24th International Conference on Software, Telecommunications and Computer Networks (SoftCOM).

[6]  Daniel Camps-Mur,et al.  Device-to-device communications with Wi-Fi Direct: overview and experimentation , 2013, IEEE Wireless Communications.

[8]  Fabrizio Granelli,et al.  Towards Energy Efficient Multi-Hop D2D Networks Using WiFi Direct , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[9]  Vincenzo Mancuso,et al.  WiFi Direct and LTE D2D in action , 2013, 2013 IFIP Wireless Days (WD).

[10]  Ahmed Abukmail,et al.  Automatic Android-based Wireless Mesh Networks , 2014, Informatica.

[11]  Cyrus Shahabi,et al.  A Real-Time Framework for Task Assignment in Hyperlocal Spatial Crowdsourcing , 2017, ACM Trans. Intell. Syst. Technol..

[12]  Franca Delmastro,et al.  Context-Aware Configuration and Management of WiFi Direct Groups for Real Opportunistic Networks , 2017, 2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS).

[13]  Vincenzo Mancuso,et al.  A Survey on Opportunistic Scheduling in Wireless Communications , 2013, IEEE Communications Surveys & Tutorials.

[14]  Paolo Giaccone,et al.  Data Connectivity and Smart Group Formation in Wi-Fi Direct Multi-Group Networks , 2018, IEEE Transactions on Network and Service Management.

[15]  Jianwei Huang,et al.  Delay-Sensitive Mobile Crowdsensing: Algorithm Design and Economics , 2018, IEEE Transactions on Mobile Computing.

[16]  Cheng Li,et al.  Task Assignment in Mobile Crowdsensing: Present and Future Directions , 2018, IEEE Network.

[17]  Vijay Sivaraman,et al.  HazeWatch: A participatory sensor system for monitoring air pollution in Sydney , 2013, 38th Annual IEEE Conference on Local Computer Networks - Workshops.

[18]  Marco Conti,et al.  Experimenting opportunistic networks with WiFi Direct , 2013, 2013 IFIP Wireless Days (WD).

[19]  Dzmitry Kliazovich,et al.  Cost analysis of smart lighting solutions for smart cities , 2017, 2017 IEEE International Conference on Communications (ICC).

[20]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[21]  Mao Ye,et al.  Exploiting geographical influence for collaborative point-of-interest recommendation , 2011, SIGIR.

[22]  Stefano Giordano,et al.  CrowdSenSim: a Simulation Platform for Mobile Crowdsensing in Realistic Urban Environments , 2017, IEEE Access.

[23]  Prem Prakash Jayaraman,et al.  City Data Fusion: Sensor Data Fusion in the Internet of Things , 2015, Int. J. Distributed Syst. Technol..

[24]  Stefania Bandini,et al.  Group Dynamics in Pedestrian Crowds , 2014 .

[25]  Paolo Giaccone,et al.  High-Precision Design of Pedestrian Mobility for Smart City Simulators , 2018, 2018 IEEE International Conference on Communications (ICC).

[26]  Antonio Corradi,et al.  Crowdsensing in Urban Areas for City-Scale Mass Gathering Management: Geofencing and Activity Recognition , 2014, IEEE Sensors Journal.

[27]  Stefano Giordano,et al.  A Cost-Effective Distributed Framework for Data Collection in Cloud-Based Mobile Crowd Sensing Architectures , 2017, IEEE Transactions on Sustainable Computing.

[28]  Dzmitry Kliazovich,et al.  Sociability-Driven User Recruitment in Mobile Crowdsensing Internet of Things Platforms , 2016, 2016 IEEE Global Communications Conference (GLOBECOM).

[29]  Dzmitry Kliazovich,et al.  Profiling Energy Efficiency of Mobile Crowdsensing Data Collection Frameworks for Smart City Applications , 2018, 2018 6th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud).