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A Platform for Building Context-Aware Mobile Crowdsensing Applications In A Platform for Building Context-aware Mobile Crowdsensing Applications in Vehicular Social Networks by Xiping Hu A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Electrical and Computer Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) November 2015 © Xiping Hu, 2015 Abstract In the past few years, many research works have demonstrated that mobile crowdsensing could be effectively applied in vehicular social networks (VSNs) to serve many purposes and bring huge economic benefits in transportation. In this thesis, we provide a crowdsensing platform which addresses the research challenges in the overall workflow of crowdsensing in VSNs in terms of task allocation and task execution. This platform supports the creation of different context-aware mobile crowdsensing applications and facilitates their real-world deployments in VSNs. First, because of the inherent nature of crowdsensing, usually a crowdsensing task needs a group of different participants to finish it collaboratively. Thus, for task allocation in crowdsensing, we propose an application-oriented service collaboration model (ASCM). This ASCM automatically allocates multiple participants with multiple crowdsensing tasks across different mobile devices and cloud platform in an efficient and effective manner in VSNs. Second, for task exaction of mobile crowdsensing applications in VSNs, the dynamic network connectivity of the underlying vehicular ad-hoc networks (VANETs) may cause failures of such applications during their executions. We design S-Aframe, an agent-based multi-layer framework, which provides a programming model to support creation and deployment of robust and reliable crowdsensing applications that self-adapt to the dynamic nature of VANETs. Furthermore, due to the dynamism of VANETs and the opportunism of user connections in VSNs, the changing environments of the users involved in the VSNs may also result in users’ dynamic contexts. We propose a context-aware semantic service (CSS), and incorporate this service with S-Aframe to improve the self-adaptiveness of ii mobile crowdsensing applications to users’ dynamic contexts of VSNs. Finally, we design and develop SAfeDJ, a crowdsensing-based situation-aware music recommendation application for drivers. The development of SAfeDJ has further demonstrated how our platform supports the creation of a context-aware mobile crowdsensing application, and facilitates the realization of such an application in real-world deployment in VSNs. iii Preface The following publications describe the work completed in this thesis. In some cases, the conference papers contain materials overlapping with the journal papers. All the chapters are based on these papers co-authored with Dr. Victor C.M. Leung, who has also supervised this research. Journal Papers Published: 1. X. Hu, T.H.S. Chu, V.C.M. Leung, E. C.-H. Ngai, P. Kruchten, and H.C.B. Chan, “A Survey on Mobile Social Networks: Applications, Platforms, System Architectures, and Future Research Directions,” IEEE Communications Surveys & Tutorials, vol. 17, no. 3, pp. 1557 – 1581, 2015. The material is partly incorporated in Chapter 2. 2. X. Hu, J. Deng, J. Zhao, W. Hu, E.C.-H. Ngai, R. Wang, J. Shen, M. Liang, X. Li, V.C.M. Leung, and Y. Kwok, “SAfeDJ: A Crowd-Cloud Co-design Approach to Situation-aware Music Delivery for Drivers,” ACM Transactions on Multimedia Computing, Communications and Applications, vol. 12, no. 1s, Article: 21, 2015. The material is incorporated in Chapter 5. 3. X. Hu, J. Zhao, B.C. Seet, V.C.M. Leung, T.H.S. Chu, and H.C.B. Chan, “S-Aframe: Agent-based Multi-layer Framework with Context-aware Semantic Service for Vehicular Social Networks,” IEEE Transactions on Emerging Topics in Computing, vol. 3, no. 1, pp. 44-63, March, 2015. The material is incorporated in Chapter 4. 4. X. Hu, X. Li, E. C.-H. Ngai, V.C.M Leung, and P. Kruchten, “Multi-dimensional context-aware social network architecture for mobile crowdsensing,” IEEE Communications Magazine, vol. 52, no. 6, pp. 78-87, June, 2014. The material is partly iv incorporated in Chapter 3 and Chapter 4, respectively. 5. X. Hu, T.H.S. Chu, H.C.B. Chan, and V.C.M. Leung, “Vita: A Crowdsensing-oriented Mobile Cyber Physical System”, IEEE Transactions on Emerging Topics in Computing, vol. 1, no. 1, pp. 148-165, June, 2013. The material is incorporated in Chapter 3. Conference Papers Published: 1. X. Hu, and V.C.M. Leung, “Towards Context-aware Mobile Crowdsensing in Vehicular Social Networks,” in Proc. 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), Shenzhen, China, 2015. The material is incorporated in Chapter 1. 2. X. Hu, X. Li, E. C. -H. Ngai, J. Zhao, and V.C.M. Leung, and P. Nasiopoulos, “Health Drive: Mobile Healthcare Onboard Vehicles to Promote Safe Driving,” in Proc. 48th Hawaii International Conference on System Sciences (HICSS), Kauai, HI, USA, 2015. The material is incorporated in Chapter 3. 3. X. Hu, and V.C.M. Leung, “Application-oriented Approaches to Context-aware Mobile Crowdsensing in Vehicular Social Networks,” ACM Conference on Embedded Networked Sensor Systems (SenSys), Doctoral Colloquium, Memphis, TN, USA, 2014. The material is incorporated in Chapter 1. 4. X. Hu, J. Deng, W. Hu, G. Fotopoulos, E.C.-H. Ngai, Z. Sheng, M. Liang, X. Li, V.C.M. Leung, and S. Fels, “SAfeDJ Community: Situation-Aware In-Car Music Delivery for Safe Driving,” in Proc. 20th ACM Conference on Mobile Computing and Networking (MobiCom), Maui, HI, USA, 2014. The material is incorporated in Chapter 5. 5. X. Hu, J. Deng, W. Hu, G. Fotopoulos, E.C.-H. Ngai, Z. Sheng, M. Liang, X. Li, K. Shafiee, V. C. M. Leung, S. Fels, C. Lau, Y. Kwok, “SAfeDJ: Situation-aware Music v Delivery for Drivers”, ACM MobiCom 2014 Mobile App Competition final. (acceptance rate = 14.0%) 6. X. Hu, V.C.M. Leung, K. Li, E. Kong, H. Zhang, N. Surendrakumar, and P. TalebiFard, “Social Drive: A Crowdsourcing-based Vehicular Social Networking System for Green Transportation,” in Proc. ACM MSWiM-DIVANet symp., Barcelona, Spain, 2013. The material is incorporated in Chapter 5. (acceptance rate = 14.5%) 7. X. Hu, V.C.M. Leung, C. Zhu, T. H. S. Chu, X. Li, and M. Liang, “Social Drive”, ACM MobiCom 2013 Mobile App Competition final. (acceptance rate = 19.2%) 8. X. Hu, V.C.M. Leung and W. Wang, “VSSA: A Service-oriented Vehicular Social- Networking Platform for Transportation Efficiency,” in Proc. ACM MSWiM-DIVANet symp., Paphos, Cyprus, 2012. The material is incorporated in Chapter 3. (acceptance rate = 25.0%) 9. X. Hu, J. Zhao, D. Zhou and V.C.M. Leung, “A Semantics-based Multi-agent Framework for Vehicular Social Network Development,” in Proc. ACM MSWiM- DIVANet symp., Miami, FL, USA, 2011. The material is incorporated in Chapter 4. (acceptance rate = 23.7%) The explanations about the co-authorship of all the papers are as follows: Journal paper 1 was co-authored with Mr. Terry H. S. Chu, Dr. Edith. C.-H. Ngai, Dr. Philippe Kruchten, and Dr. Henry. C.B. Chan. I proposed the topic and initial idea, studied and summarized the overall system architecture of MSNs and VSNs, and outlined the future research directions of MSNs. Mr. Terry H. S. Chu and Dr. Henry C. B. Chan helped to summarize the existing works about data forwarding and dissemination, and resource management and user behavior of MSNs (this part vi is not presented in this thesis). Dr. Edith C.-H. Ngai helped to summarize the existing work about mobile internet of things (this part is not presented in this thesis). Dr. Philippe Kruchten provided the initial ideas about how to describe the system architecture of MSNs through three views. Journal paper 2 was co-authored with Mr. Junqi Deng, Dr. Jidi Zhao, Ms. Wenyan Hu, Dr. Edith C.-H. Ngai, Mr. Renfei Wang, Mr. Johnny Shen, Mr. Min Liang, and Dr. Xitong Li. I proposed the topic and initial idea of SAfeDJ, designed the overall architecture, all the key components and flows of SAfeDJ, and the experimental settings. Mr. Junqi Deng helped in the design of music feature extraction method, and set up the websites and recruited volunteers to do the surveys about music-mood mapping and driving context with music. Dr. Jidi Zhao helped to implement the ontology-based similarity computing method, and conducted the case study which involved human-being tests of SAfeDJ. Ms. Wenyan Hu and Dr. Edith C. -H. Ngai helped to implement the mood-fatigue detector on Android devices. Mr. Renfei Wang helped to implement the music mapping algorithm on cloud platform and Android devices. Mr. Johnny Shen helped to implement the music satisfaction calculation algorithm on Android devices. Mr. Min Liang helped to maintain the cloud platform and database during the periods of our experiments. Dr. Xitong Li provided suggestions about using the Stata 13 to conduct data statistics about the impact of context to music choice in driving situations. Journal paper 3 was co-author with Dr. Jidi Zhao, Dr. Boon-Chong Seet, Mr. Terry H. S. Chu, and Dr. Henry C. B. Chan. I proposed the topic and initial idea, and finished the designs of the S-Aframe framework and the CSS, and conducted the experiments. Dr. Jidi Zhao and her team helped to implement the CSS. Dr. Boon- vii Chong Seet provided the initial idea of the ridesharing application example to demonstrate the idea and usefulness of S-Aframe and CSS, and provided some suggestions about the experimental setting. Mr. Terry H. S. Chu and Dr. Henry C. B. Chan helped to develop the prototype application example on Android devices. Journal paper 4 was co-authored with Dr. Xitong Li, Dr. Edith. C.-H. Ngai, and Dr.
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