ABSTRACT
Crowdsensing allows citizens to contribute to the monitoring of their living environment using the sensors embedded in their mobile devices, e.g., smartphones. However, crowdsensing at scale involves significant communication, computation, and financial costs due to the dependence on cloud infrastructures for the analysis (e.g., interpolation and aggregation) of spatio-temporal data. This limits the adoption of crowdsensing by activists although sorely needed to inform our knowledge of the environment. As an alternative to the centralized analysis of crowdsensed observations, this paper introduces a fully distributed interpolation-mediated aggregation approach running on smartphones. To achieve so efficiently, we model the interpolation as a distributed tensor completion problem, and we introduce a lightweight aggregation strategy that anticipates the likelihood of future encounters according to the quality of the interpolation. Our approach thus shifts the centralized post-processing of crowdsensed data to distributed pre-processing on the move, based on opportunistic encounters of crowdsensors through state-of-the-art D2D networking. The evaluation using a dataset of quantitative environmental measurements collected from 550 crowdsensors over 1 year shows that our solution significantly reduces –and may even eliminate– the dependence on the cloud infrastructure, while it incurs a limited resource cost on end devices. Meanwhile, the overall data accuracy remains comparable to that of the centralized approach.
- Yanshuai Cao and David J Fleet. 2014. Generalized product of experts for automatic and principled fusion of Gaussian process predictions. In Modern Nonparametrics 3: Automating the Learning Pipeline workshop at NIPS.Google Scholar
- Xu Chen, Lingjun Pu, Lin Gao, 2017. Exploiting massive D2D collaboration for energy-efficient mobile edge computing. IEEE Wireless Communications 24, 4 (2017).Google ScholarDigital Library
- Yun Cheng, Xiaoxi He, Zimu Zhou, 2020. MapTransfer: Urban Air Quality Map Generation for Downscaled Sensor Deployments. In ACM International Conference on Internet of Things Design and Implementation.Google Scholar
- Marc Peter Deisenroth and Jun Wei Ng. 2015. Distributed Gaussian processes. In International Conference on Machine Learning.Google Scholar
- Ngoc Do, Ye Zhao, Cheng-Hsin Hsu, 2016. Crowdsourced mobile data transfer with delay bound. ACM Transactions on Internet Technology 16, 4 (2016).Google ScholarDigital Library
- Yifan Du, Francoise Sailhan, and Valerie Issarny. 2020. Let opportunistic crowdsensors work together for resource-efficient, quality-aware observations. In IEEE International Conference on Pervasive Computing and Communications.Google ScholarCross Ref
- Khalid Eldrandaly and Ahmed Abdelmouty. 2017. Spatio-temporal Interpolation: Current Practices and Future Prospects. International Journal of Digital Content Technology and its Applications 11 (06 2017).Google Scholar
- Michele Girolami, Stefano Chessa, Gaia Adami, 2017. Sensing interpolation strategies for a mobile crowdsensing platform. In IEEE International Conference on Mobile Cloud Computing, Services, and Engineering.Google ScholarCross Ref
- Paul Harris, AS Fotheringham, R Crespo, 2010. The use of geographically weighted regression for spatial prediction: an evaluation of models using simulated data sets. Mathematical Geosciences 42, 6 (2010).Google Scholar
- Takamasa Higuchi, Hirozumi Yamaguchi, Teruo Higashino, 2014. A neighbor collaboration mechanism for mobile crowd sensing in opportunistic networks. In IEEE International Conference on Communications.Google ScholarCross Ref
- Valerie Issarny, Vivien Mallet, Kinh Nguyen, 2016. Dos and don’ts in mobile phone sensing middleware: Learning from a large-scale experiment. In ACM/IFIP International Middleware Conference.Google ScholarDigital Library
- Paulo Jesus, Carlos Baquero, and Paulo Sérgio Almeida. 2014. A survey of distributed data aggregation algorithms. IEEE Communications Surveys & Tutorials 17, 1 (2014).Google Scholar
- Haiming Jin, Lu Su, Houping Xiao, 2018. Incentive mechanism for privacy-aware data aggregation in mobile crowd sensing systems. IEEE Transactions on Networking 26, 5 (2018).Google ScholarDigital Library
- Oskar Juhlin and Mattias Östergren. 2006. Time to meet face-to-face and device-to-device. In ACM Conference on Human-Computer Interaction with Mobile Devices and Services.Google ScholarDigital Library
- Goran Kalic, Iva Bojic, and Mario Kusek. 2012. Energy consumption in android phones when using wireless communication technologies. In IEEE International Convention MIPRO.Google Scholar
- Xu Kang, Liang Liu, and Huadong Ma. 2016. Data correlation based crowdsensing enhancement for environment monitoring. In IEEE International Conference on Communications.Google ScholarCross Ref
- Xu Kang, Liang Liu, and Huadong Ma. 2017. Enhance the quality of crowdsensing for fine-grained urban environment monitoring via data correlation. MDPI Sensors 17, 1 (2017).Google Scholar
- Linghe Kong, Mingyuan Xia, Xiao-Yang Liu, 2013. Data loss and reconstruction in sensor networks. In IEEE International Conference on Computer Communications.Google ScholarCross Ref
- Ioannis Koukoutsidis. 2017. Estimating spatial averages of environmental parameters based on mobile crowdsensing. ACM Transactions on Sensor Networks 14, 1 (2017).Google Scholar
- Youngki Lee, Younghyun Ju, Chulhong Min, 2012. Comon: Cooperative ambience monitoring platform with continuity and benefit awareness. In ACM International Conference on Mobile Systems, Applications, and Services.Google ScholarDigital Library
- Chenguang Liu, Jie Hua, and Christine Julien. 2019. Scents: Collaborative sensing in proximity iot networks. In IEEE International Conference on Pervasive Computing and Communications Workshops.Google ScholarCross Ref
- Haitao Liu, Yew-Soon Ong, Xiaobo Shen, 2020. When Gaussian process meets big data: A review of scalable GPs. IEEE Transactions on Neural Networks and Learning Systems (2020).Google Scholar
- Diego Mendez, Miguel Labrador, and Kandethody Ramachandran. 2013. Data interpolation for participatory sensing systems. Pervasive and Mobile Computing 9, 1 (2013).Google Scholar
- Budiman Minasny and Alex B McBratney. 2005. The Matérn function as a general model for soil variograms. Geoderma 128, 3-4 (2005).Google ScholarCross Ref
- Orlando Ohashi and Luis Torgo. 2012. Spatial interpolation using multiple regression. In IEEE International Conference on Data Mining.Google ScholarDigital Library
- Zhaokun Qin and Yanmin Zhu. 2016. NoiseSense: A crowd sensing system for urban noise mapping service. In IEEE International Conference on Parallel and Distributed Systems.Google ScholarCross Ref
- Carl Edward Rasmussen. 2003. Gaussian processes in machine learning. In Springer Summer School on Machine Learning.Google Scholar
- Francoise Sailhan, Valérie Issarny, and Otto Tavares-Nascimiento. 2017. Opportunistic multiparty calibration for robust participatory sensing. In IEEE International Conference on Mobile Ad Hoc and Sensor Systems.Google ScholarCross Ref
- Jing Shi, Rui Zhang, Yunzhong Liu, 2010. Prisense: privacy-preserving data aggregation in people-centric urban sensing systems. In IEEE International Conference on Computer Communications.Google ScholarCross Ref
- Muhammad Umer, Lars Kulik, and Egemen Tanin. 2010. Spatial interpolation in wireless sensor networks: localized algorithms for variogram modeling and Kriging. Geoinformatica 14, 1 (2010).Google Scholar
- Raphaël Ventura, Vivien Mallet, and Valérie Issarny. 2018. Assimilation of mobile phone measurements for noise mapping of a neighborhood. The journal of the acoustical society of America 144, 3 (2018).Google Scholar
- Jiangtao Wang, Yasha Wang, Daqing Zhang, 2018. Learning-assisted optimization in mobile crowd sensing: A survey. IEEE Transactions on Industrial Informatics 15, 1 (2018).Google Scholar
- Leye Wang, Daqing Zhang, Yasha Wang, 2016. Sparse mobile crowdsensing: challenges and opportunities. IEEE Communications Magazine 54, 7 (2016).Google ScholarDigital Library
- Leye Wang, Daqing Zhang, Haoyi Xiong, 2016. ecoSense: Minimize participants’ total 3G data cost in mobile crowdsensing using opportunistic relays. IEEE Transactions on Systems, Man, and Cybernetics: Systems 47, 6(2016).Google Scholar
- Yu Xiao, Pieter Simoens, Padmanabhan Pillai, 2013. Lowering the barriers to large-scale mobile crowdsensing. In ACM International Workshop on Mobile Computing Systems and Applications.Google ScholarDigital Library
- Liwen Xu, Xiaohong Hao, Nicholas D Lane, 2015. More with less: Lowering user burden in mobile crowdsourcing through compressive sensing. In ACM International Joint Conference on Pervasive and Ubiquitous Computing.Google ScholarDigital Library
- Xi Xu, Rashid Ansari, Ashfaq Khokhar, 2015. Hierarchical data aggregation using compressive sensing (HDACS) in WSNs. ACM Transactions on Sensor Networks 11, 3 (2015).Google ScholarDigital Library
- Yanan Xu, Yanmin Zhu, and Zhaokun Qin. 2019. Urban noise mapping with a crowd sensing system. Wireless Networks 25, 5 (2019).Google Scholar
- Sijia Yang, Jiang Bian, Licheng Wang, 2018. EdgeSense: Edge-mediated spatial-temporal crowdsensing. IEEE Access 7(2018).Google Scholar
- Yu Zheng, Tong Liu, Yilun Wang, 2014. Diagnosing New York city’s noises with ubiquitous data. In ACM International Joint Conference on Pervasive and Ubiquitous Computing.Google ScholarDigital Library
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