Cited By
View all- Wang TLiang YShen XZheng XMahmood ASheng Q(2023)Edge Computing and Sensor-Cloud: Overview, Solutions, and DirectionsACM Computing Surveys10.1145/358227055:13s(1-37)Online publication date: 13-Jul-2023
Predicting Spatio-Temporal Phenomena of Mobile Resources in Sensor Cloud Infrastructure
Article No.: 11, Pages 1 - 38
Abstract
Integration of sensor and cloud technologies enable distributed sensing and data collection. We consider a scenario when sensing requests are originated from sensor aware applications that are hosted inside sensor-cloud infrastructures. These requests need to be satisfied using geographically distributed sensors. However, if the sensing resources are mobile, then sensing territory is not limited to a fixed region, rather spatially diverse. In this work, we present a generic scheme for integrating spatio-temporal information of mobile sensors for Internet of Things– (IoT) based environment monitoring system. A set of algorithms are proposed in this work to model spatial and temporal features of mobile resources and exploit resource mobility. We also propose probabilistic models to measure feasibility of a resource to sense a specific spatio-temporal phenomenon. We rank the resources based on their feasibility of satisfying the sensing requests and later use the information for efficient resource allocation and scheduling.
References
[1]
G. Abdelbacet, Z. Ghada, S. Mounir, and K. Abdennaceur. 2015. A real time environmental monitoring for smart city surveillance based GUI on Android platform. In Proceedings of the 2015 IEEE 12th International Multi-Conference on Systems, Signals Devices (SSD’15). 1–6.
[2]
Ã. Alvear, W. Zamora, C. T. Calafate, J. Cano, and P. Manzoni. 2016. EcoSensor: Monitoring environmental pollution using mobile sensors. In Proceedings of the 2016 IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM’16). 1–6.
[3]
S. Bose and N. Mukherjee. 2020. SenSchedule: Scheduling heterogeneous periodic sensing resources with non uniform performance in IoT. IEEE Trans. Serv. Comput. (2020), 1–1.
[4]
Sunanda Bose, Debangana Sarkar, and Nandini Mukherjee. 2019. A framework for heterogeneous resource allocation in sensor-cloud environment. Wireless Personal Communications 108, 1 (2019), 19–36.
[5]
S. Chang, W. Ren, and K. Nahrstedt. 2015. Dynamic incentive mechanism for traffic surveillance. In Proceedings of the 2015 IEEE Globecom Workshops (GC Wkshps’15). 1–6.
[6]
Ling Chen, Mingqi Lv, Qian Ye, Gencai Chen, and John Woodward. 2011. A personal route prediction system based on trajectory data mining. Inf. Sci. 181, 7 (2011), 1264–1284.
[7]
David H. Douglas and Thomas K. Peucker. 2011. Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or its Caricature. John Wiley & Sons, 15–28.
[8]
Jon Froehlich and John Krumm. 2008. Route prediction from trip observations. In Proceedings of the World Congress of the Society of Automotive Engineers (SAE’08).
[9]
Sébastien Gambs, Marc-Olivier Killijian, and Miguel Núñez del Prado Cortez. 2012. Next place prediction using mobility Markov chains. In Proceedings of the 1st Workshop on Measurement, Privacy, and Mobility (MPM’12). Association for Computing Machinery, New York, NY.
[10]
B. Guo, C. Chen, D. Zhang, Z. Yu, and A. Chin. 2016. Mobile crowd sensing and computing: when participatory sensing meets participatory social media. IEEE Commun. Mag. 54, 2 (2016), 131–137.
[11]
Bin Guo, Zhu Wang, Zhiwen Yu, Yu Wang, Neil Y. Yen, Runhe Huang, and Xingshe Zhou. 2015. Mobile crowd sensing and computing: The review of an emerging human-powered sensing paradigm. ACM Comput. Surv. 48, 1, Article 7 (Aug. 2015), 31 pages.
[12]
David Hasenfratz, Olga Saukh, Christoph Walser, Christoph Hueglin, Martin Fierz, Tabita Arn, Jan Beutel, and Lothar Thiele. 2015. Deriving high-resolution urban air pollution maps using mobile sensor nodes. Perv. Mobile Comput. 16 (2015), 268–285.
[13]
R. K. Kodali, S. Mandal, and S. S. Haider. 2017. Flow based environmental monitoring for smart cities. In Proceedings of the 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI’17). 455–460.
[14]
A. Krause, E. Horvitz, A. Kansal, and F. Zhao. 2008. Toward community sensing. In Proceedings of the 2008 International Conference on Information Processing in Sensor Networks (IPSN’08). 481–492.
[15]
Prashant Kumar, Lidia Morawska, Claudio Martani, George Biskos, Marina Neophytou, Silvana [Di Sabatino], Margaret Bell, Leslie Norford, and Rex Britter. 2015. The rise of low-cost sensing for managing air pollution in cities. Environ. Int. 75 (2015), 199–205.
[16]
Y. Lassoued, J. Monteil, Y. Gu, G. Russo, R. Shorten, and M. Mevissen. 2017. A hidden Markov model for route and destination prediction. In Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC’17). 1–6.
[17]
A. Mandal, S. Bose, and N. Mukherjee. 2017. Impact of mobility on community sensing with environment sensors. In Proceedings of the 2017 13th International Conference on Signal-Image Technology Internet-Based Systems (SITIS’17). 396–401.
[18]
Radu Mariescu-Istodor and Pasi Fränti. 2017. Grid-based method for GPS route analysis for retrieval. ACM Trans. Spatial Algor. Syst. 3, 3, Article 8 (Sep. 2017), 28 pages.
[19]
Radu Mariescu-Istodor and Pasi Fränti. 2018. CellNet: Inferring road networks from GPS trajectories. ACM Trans. Spatial Algor. Syst. 4, 3, Article 8 (Sep. 2018), 22 pages.
[20]
A. Marjovi, A. Arfire, and A. Martinoli. 2015. High resolution air pollution maps in urban environments using mobile sensor networks. In Proceedings of the 2015 International Conference on Distributed Computing in Sensor Systems. 11–20.
[21]
J. Ren, Y. Zhang, K. Zhang, and X. Shen. 2015. Exploiting mobile crowdsourcing for pervasive cloud services: challenges and solutions. IEEE Commun. Mag. 53, 3 (2015), 98–105.
[22]
Shuo Shang, Lisi Chen, Zhewei Wei, Christian S. Jensen, Kai Zheng, and Panos Kalnis. 2018. Parallel trajectory similarity joins in spatial networks. VLDB J. 27, 3 (2018), 395–420.
[23]
M. M. R. Siddiquee, R. Karim, M. Z. Khan, R. Monzur, L. Iftekhar, and N. Ahmed. 2015. Map matching for error prone GPS data on a sparse road network and predicting travel time of a route. In Proceedings of the 2015 18th International Conference on Computer and Information Technology (ICCIT’15). 17–22.
[24]
S. Taguchi, S. Koide, and T. Yoshimura. 2019. Online map matching with route prediction. IEEE Trans. Intell. Transport. Syst. 20, 1 (2019), 338–347.
[25]
Vishnu Shankar Tiwari, Arti Arya, and Sudha Chaturvedi. 2018. Scalable prediction by partial match (PPM) and its application to route prediction. Appl. Inf. 5, 1 (2018).
[26]
Gaurav Tripathi, Kuldeep Singh, and Dinesh Kumar Vishwakarma. 2019. Convolutional neural networks for crowd behaviour analysis: A survey. Vis. Comput. 35, 5 (May 2019), 753–776.
[27]
Justin Van Dijk and Tom De Jong. 2017. Post-processing GPS-tracks in reconstructing travelled routes in a GIS-environment: network subset selection and attribute adjustment. Ann. GIS 23, 3 (2017), 203–217.
[28]
Daqing Zhang, Nan Li, Zhi-Hua Zhou, Chao Chen, Lin Sun, and Shijian Li. 2011. IBAT: Detecting anomalous taxi trajectories from GPS traces. In Proceedings of the 13th International Conference on Ubiquitous Computing (UbiComp’11). Association for Computing Machinery, New York, NY, 99–108.
[29]
Junbo Zhang, Yu Zheng, and Dekang Qi. 2016. Deep spatio-temporal residual networks for citywide crowd flows prediction. In Proceeding of the 31st AAAI Conference on Artificial Intelligence (AAAI-17).
[30]
Junbo Zhang, Yu Zheng, Dekang Qi, Ruiyuan Li, and Xiuwen Yi. 2016. DNN-based prediction model for spatio-temporal data. In Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPACIAL’16). Association for Computing Machinery, New York, NY.
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- Predicting Spatio-Temporal Phenomena of Mobile Resources in Sensor Cloud Infrastructure
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September 2021
185 pages
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Publication History
Published: 08 June 2021
Accepted: 01 January 2021
Revised: 01 November 2020
Received: 01 June 2019
Published in TSAS Volume 7, Issue 3
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View all- Wang TLiang YShen XZheng XMahmood ASheng Q(2023)Edge Computing and Sensor-Cloud: Overview, Solutions, and DirectionsACM Computing Surveys10.1145/358227055:13s(1-37)Online publication date: 13-Jul-2023
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