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ContextAiDe: End-to-End Architecture for Mobile Crowd-sensing Applications

Published:03 April 2019Publication History
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Abstract

Mobile crowd-sensing (MCS) enables development of context-aware applications by mining relevant information from a large set of devices selected in an ad hoc manner. For example, MCS has been used for real-time monitoring such as Vehicle ad hoc Networks-based traffic updates as well as offline data mining and tagging for future use in applications with location-based services. However, MCS could be potentially used for much more demanding applications such as real-time perpetrator tracking by online mining of images from nearby mobile users. A recent example is tracking the miscreant responsible for the Boston bombing. We present a new design approach for tracking using MCS for such complex processing in real time. Since MCS applications assume an unreliable underlying computational platform, most typically sample size for recruited devices is guided by concerns such as fault tolerance and reliability of information. As the real-time requirements get stricter coupled with increasing complexity of data-mining approaches, the communication and computation overheads can impose a very tight constraint on the sample size of devices needed for realizing real-time operation. This results in trade-off in acquiring context-relevant data and resource usage incurred while the real-time operation requirements get updated dynamically. Such effects have not been properly studied and optimized to enable real-time MCS applications such as perpetrator tracking. In this article, we propose ContextAiDe architecture, a combination of API, middleware, and optimization engine. The key innovation in ContextAiDe is context-optimized recruitment for execution of computation- and communication-heavy MCS applications in edge environment.

ContextAiDe uses a notion of two types of contexts, exact (hard constraints), which have to be satisfied, and preferred (soft constraints), which may be satisfied to a certain degree. By adjusting the preferred contexts, ContextAiDe can optimize the operational overheads to enable real-time operation. ContextAiDe provides an API to specify contexts requirements and the code of MCS app, offload execution environment, a middleware that enables context-optimized and a fault-tolerant distributed execution. ContextAiDe evaluation using a real-time perpetrator tracking application shows reduced energy consumption of 37.8%, decrease in data transfer of 24.8%, and 43% less time compared to existing strategy. In spite of a small increase in the minimum distance from the perpetrator, iterations of optimization tracks the perpetrator successfully. Pro-actively learning the context and using stochastic optimization strategy minimizes the performance degradation caused due to uncertainty (<20%) in usage-dependent contexts.

References

  1. Ageitgey. 2017. Face Recognition. Retrieved from https://github.com/ageitgey/face_recognition.Google ScholarGoogle Scholar
  2. Oscar Alvear, Carlos T. Calafate, Juan-Carlos Cano, and Pietro Manzoni. 2018. Crowd-sensing in smart cities: Overview, platforms, and environment sensing issues. Sensors 18, 2 (2018).Google ScholarGoogle Scholar
  3. Ayan Banerjee and Sandeep K. S. Gupta. 2015. Analysis of smart mobile applications for health care under dynamic context changes. IEEE Trans. Mobile Comput. 14, 5 (2015), 904--919.Google ScholarGoogle ScholarCross RefCross Ref
  4. Xuan Bao and Romit Roy Choudhury. 2010. Movi: Mobile phone-based video highlights via collaborative sensing. In Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services. ACM, 357--370. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. L. Baresi, S. Guinea, and D. F. Mendonca. 2016. A3Droid: A framework for developing distributed crowd-sensing. In Proceedings of the IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom’16). 1--6.Google ScholarGoogle Scholar
  6. Szymon Bobek and Grzegorz J. Nalepa. 2017. Uncertain context data management in dynamic mobile environments. Future Gen. Comput. Syst. 66 (2017), 110--124.Google ScholarGoogle ScholarCross RefCross Ref
  7. S. Bradai, S. Khemakhem, and M. Jmaiel. 2016. Re-OPSEC: Real-time opportunistic scheduler framework for energy aware mobile crowd-sensing. In Proceedings of the 24th International Conference on Software, Telecommunications and Computer Networks (SoftCOM’16). 1--5.Google ScholarGoogle Scholar
  8. Niels Brouwers and Koen Langendoen. 2012. Pogo, a middleware for mobile phone sensing. In Proceedings of the 13th International Middleware Conference. Springer-Verlag New York, Inc., 21--40. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Andrea Capponi, Claudio Fiandrino, Dzmitry Kliazovich, Pascal Bouvry, and Stefano Giordano. 2017. A cost-effective distributed framework for data collection in cloud-based mobile crowd-sensing architectures. IEEE Trans. Sustain. Comput. 2, 1 (2017), 3--16.Google ScholarGoogle ScholarCross RefCross Ref
  10. Licia Capra, Wolfgang Emmerich, and Cecilia Mascolo. 2003. Carisma: Context-aware reflective middleware system for mobile applications. IEEE Trans. Softw. Eng. 29, 10 (2003), 929--945. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. G. Cardone, L. Foschini, P. Bellavista, A. Corradi, C. Borcea, M. Talasila, and R. Curtmola. 2013. Fostering participaction in smart cities: A geo-social crowd-sensing platform. IEEE Commun. Mag. 51, 6 (2013), 112--119.Google ScholarGoogle ScholarCross RefCross Ref
  12. Alvin TS Chan and Siu-Nam Chuang. 2003. MobiPADS: A reflective middleware for context-aware mobile computing. IEEE Trans. Softw. Eng. 29, 12 (2003), 1072--1085. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. H. Chen, B. Guo, Z. Yu, and Q. Han. 2016. Toward real-time and cooperative mobile visual sensing and sharing. In Proceedings of the 35th Annual IEEE International Conference on Computer Communications (INFOCOM’16). 1--9.Google ScholarGoogle Scholar
  14. Yuh-Shyan Chen and Yi-Ting Tsai. 2018. A mobility management using follow-me cloud-cloudlet in fog-computing-based RANs for smart cities. In Proceedings of the Sensors Conference.Google ScholarGoogle Scholar
  15. Yohan Chon, Elmurod Talipov, Hyojeong Shin, and Hojung Cha. 2012. CRAWDAD dataset yonsei/lifemap (v. 2012-01-03). Retrieved from https://crawdad.org/yonsei/lifemap/20120103.Google ScholarGoogle Scholar
  16. J. Cortellazzi, L. Foschini, C. R. De Rolt, A. Corradi, C. A. A. Neto, and G. D. Alperstedt. 2016. Crowd-sensing and proximity services for impaired mobility. In Proceedings of the IEEE Symposium on Computers and Communication (ISCC’16). 44--49.Google ScholarGoogle Scholar
  17. JoÃčo H. da Rosa, Jorge L.V. Barbosa, and Giovane D. Ribeiro. 2016. ORACON: An adaptive model for context prediction. Expert Syst. Appl. 45, Supplement C (2016), 56--70. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. A. V. Dastjerdi and R. Buyya. 2016. Fog computing: Helping the internet of things realize its potential. Computer 49, 8 (Aug. 2016), 112--116. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Carlos Roberto De Rolt, Rebecca Montanari, Marcelo L Brocardo, Luca Foschini, and Julio da Silva Dias. 2016. COLLEGA middleware for the management of participatory mobile health communities. In Proceedings of the IEEE Symposium on Computers and Communication (ISCC’16). IEEE, 999--1005.Google ScholarGoogle ScholarCross RefCross Ref
  20. Niroshinie Fernando, Seng Wai Loke, and J. Wenny Rahayu. 2012. Honeybee: A programming framework for mobile crowd computing. In Proceedings of the Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous’12).Google ScholarGoogle Scholar
  21. Claudio Fiandrino, Fazel Anjomshoa, Burak Kantarci, Dzmitry Kliazovich, Pascal Bouvry, and Jeanna Neefe Matthews. 2017. Sociability-driven framework for data acquisition in mobile crowd-sensing over fog computing platforms for smart cities. IEEE Trans. Sustain. Comput. 2, 4 (Oct. 2017), 345--358.Google ScholarGoogle ScholarCross RefCross Ref
  22. 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. ACM, 3. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Pedro Garcia Lopez, Alberto Montresor, Dick Epema, Anwitaman Datta, Teruo Higashino, Adriana Iamnitchi, Marinho Barcellos, Pascal Felber, and Etienne Riviere. 2015. Edge-centric computing: Vision and challenges. SIGCOMM Comput. Commun. Rev. 45, 5 (Sept. 2015), 37--42. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. C. Y. Goh, J. Dauwels, N. Mitrovic, M. T. Asif, A. Oran, and P. Jaillet. 2012. Online map-matching based on hidden Markov model for real-time traffic sensing applications. In Proceedings of the 15th International IEEE Conference on Intelligent Transportation Systems. 776--781.Google ScholarGoogle Scholar
  25. 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). Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. H. Habibzadeh, Z. Qin, T. Soyata, and B. Kantarci. 2017. Large-scale distributed dedicated- and non-dedicated smart city sensing systems. IEEE Sensors J. 17, 23 (Dec. 2017), 7649--7658.Google ScholarGoogle ScholarCross RefCross Ref
  27. K. Han, C. Zhang, and J. Luo. 2016. Taming the uncertainty: Budget limited robust crowd-sensing through online learning. IEEE/ACM Trans. Netw. 24, 3 (June 2016), 1462--1475. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Waze Mobile. 2019. Waze (March 2019). Retrieved from https://www.waze.com.Google ScholarGoogle Scholar
  29. A. Hassani, P. D. Haghighi, and P. P. Jayaraman. 2015. Context-aware recruitment scheme for opportunistic mobile crowd-sensing. In Proceedings of the IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS’15). 266--273. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. He Wang Saurabh Bagchi Rajesh K. Panta Heng Zhang, Nawanol Theera-Ampornpunt. 2017. Sense-aid: A framework for enabling network as a service for participatory sensing. In Proceedings of the International Middleware Conference. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. T. Higashino, H. Yamaguchi, A. Hiromori, A. Uchiyama, and K. Yasumoto. 2017. Edge computing and IoT-based research for building safe smart cities resistant to disasters. In Proceedings of the IEEE 37th International Conference on Distributed Computer Systems (ICDCS’17). 1729--1737.Google ScholarGoogle Scholar
  32. W. Hou, Z. Ning, and L. Guo. 2018. Green survivable collaborative edge computing in smart cities. IEEE Trans. Industrial Info. 14, 4 (2018), 1594--1605.Google ScholarGoogle ScholarCross RefCross Ref
  33. Shaohan Hu, Lu Su, Hengchang Liu, Hongyan Wang, and Tarek F. Abdelzaher. 2015. SmartRoad: Smartphone-based crowd-sensing for traffic regulator detection and identification. ACM Trans. Sens. Netw. 11, 4, Article 55 (July 2015). Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Bret Hull, Vladimir Bychkovsky, Yang Zhang, Kevin Chen, Michel Goraczko, Allen Miu, Eugene Shih, Hari Balakrishnan, and Samuel Madden. 2006. CarTel: A distributed mobile sensor computing system. In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys’06). ACM, 125--138. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Prem Prakash Jayaraman, João Bártolo Gomes, Hai-Long Nguyen, Zahraa Said Abdallah, Shonali Krishnaswamy, and Arkady Zaslavsky. 2015. Scalable energy-efficient distributed data analytics for crowd-sensing applications in mobile environments. IEEE Trans. Comput. Soc. Syst. 2, 3 (2015), 109--123.Google ScholarGoogle ScholarCross RefCross Ref
  36. Prem Prakash Jayaraman, Charith Perera, Dimitrios Georgakopoulos, and Arkady Zaslavsky. 2013. Efficient opportunistic sensing using mobile collaborative platform mosden. In Proceedings of the International Conference on Collaborative Computing: Networking, Applications and Worksharing (Collaboratecom’13). IEEE, 77--86.Google ScholarGoogle ScholarCross RefCross Ref
  37. Merkourios Karaliopoulos, Iordanis Koutsopoulos, and Michalis Titsias. 2016. First learn then earn: Optimizing mobile crowd-sensing campaigns through data-driven user profiling. In Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc’16). ACM, 271--280. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Mohammad A. Khan, Hillol Debnath, Nafize R. Paiker, Narain Gehani, Xiaoning Ding, Reza Curtmola, and Cristian Borcea. 2016. Moitree: A middleware for cloud-assisted mobile distributed apps. In Proceedings of the 4th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud’16). IEEE, 21--30.Google ScholarGoogle ScholarCross RefCross Ref
  39. Vassilis Kostakos, Denzil Ferreira, Jorge Goncalves, and Simo Hosio. 2016. Modelling smartphone usage: A Markov state transition model. In Proceedings of the International Conference on Pervasive and Ubiquitous Computing (UbiComp’16). 486--497. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Kilho Lee and Insik Shin. 2013. User mobility-aware decision making for mobile computation offloading. In Proceedings of the IEEE 1st International Conference on Cyber-Physical Systems, Networks, and Applications (CPSNA’13). IEEE, 116--119.Google ScholarGoogle ScholarCross RefCross Ref
  41. Youngki Lee, Younghyun Ju, Chulhong Min, Jihyun Yu, and Junehwa Song. 2012. MobiCon: Mobile context monitoring platform: Incorporating context-awareness to smartphone-centric personal sensor networks. In Proceedings of the 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON’12). IEEE, 109--111.Google ScholarGoogle ScholarCross RefCross Ref
  42. C. H. Liu, B. Zhang, X. Su, J. Ma, W. Wang, and K. K. Leung. 2017. Energy-aware participant selection for smartphone-enabled mobile crowd-sensing. IEEE Syst. J. 11, 3 (Sept. 2017), 1435--1446.Google ScholarGoogle ScholarCross RefCross Ref
  43. Antonella Longo, Marco Zappatore, Mario Bochicchio, and Shamkant B. Navathe. 2017. Crowd-sourced data collection for urban monitoring via mobile sensors. ACM Trans. Internet Technol. 18, 1, Article 5 (Oct. 2017). Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Q. Lv, Y. Qiao, N. Ansari, J. Liu, and J. Yang. 2017. Big data-driven hidden Markov model-based individual mobility prediction at points of interest. IEEE Trans. Vehicular Technol. 66, 6 (2017), 5204--5216.Google ScholarGoogle ScholarCross RefCross Ref
  45. Christian Meurisch, Karsten Planz, Daniel Schäfer, and Immanuel Schweizer. 2013. Noisemap: Discussing scalability in participatory sensing. In Proceedings of the 1st International Workshop on Sensing and Big Data Mining (SENSEMINE’13). Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Emiliano Miluzzo, Nicholas D. Lane, Shane B. Eisenman, and Andrew T. Campbell. 2007. CenceMe: Injecting sensing presence into social networking applications. In Proceedings of the 2nd European Conference on Smart Sensing and Context. 1--28. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Osnat Mokryn, Dror Karmi, Akiva Elkayam, and Tomer Teller. 2012. Help me: Opportunistic smart rescue application and system. In Proceedings of the 11th Annual Mediterranean Ad Hoc Networking Workshop (MedHocNet’12). IEEE, 98--105.Google ScholarGoogle ScholarCross RefCross Ref
  48. Koosha Sadeghi Oskooyee, Ayan Banerjee, and Sandeep KS Gupta. 2015. Neuro movie theatre: A real-time internet-of-people-based mobile application. In Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications.Google ScholarGoogle Scholar
  49. Koosha Sadeghi Oskooyee, Ayan Banerjee, Javad Sohankar, and Sandeep K. S. Gupta. 2016. SafeDrive: An autonomous driver safety application in aware cities. In Proceedings of the IEEE Pervasive Computing and Communications Workshops (PerCom’16). 1--6.Google ScholarGoogle Scholar
  50. S. Panichpapiboon and P. Leakkaw. 2017. Traffic density estimation: A mobile sensing approach. IEEE Commun. Mag. 55, 12 (Dec. 2017), 126--131. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Madhurima Pore, Koosha Sadeghi, Vinaya Chakati, Ayan Banerjee, and Sandeep K. S. Gupta. 2015. Enabling real-time collaborative brain-mobile interactive applications on volunteer mobile devices. In Proceedings of the 2nd International Workshop on Hot Topics in Wireless. ACM, 46--50. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Moo-Ryong Ra, Bin Liu, Tom F. La Porta, and Ramesh Govindan. 2012. Medusa: A programming framework for crowd-sensing applications. In Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services (MobiSys’12). 337--350. Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Sasank Reddy, Deborah Estrin, and Mani Srivastava. 2010. Recruitment framework for participatory sensing data collections. In Proceedings of the International Conference on Pervasive Computing. Springer, 138--155. Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Jose Santos, Tim Wauters, Bruno Volckaert, and Filip De Turck. 2018. Fog computing: Enabling the management and orchestration of smart city applications in 5G networks. Entropy 20, 1 (2018).Google ScholarGoogle Scholar
  55. M. Sapienza, E. Guardo, M. Cavallo, G. La Torre, G. Leombruno, and O. Tomarchio. 2016. Solving critical events through mobile edge computing: An approach for smart cities. In Proceedings of the IEEE International Conference on Smart Computing (SmartComp’16). 1--5.Google ScholarGoogle Scholar
  56. Fatemeh Saremi, Omid Fatemieh, Hossein Ahmadi, Hongyan Wang, Tarek Abdelzaher, Raghu Ganti, Hengchang Liu, Shaohan Hu, Shen Li, and Lu Su. 2016. Experiences with GreenGPS—Fuel-efficient navigation using participatory sensing. IEEE Trans. Mobile Comput. 15, 3 (2016), 672--689. Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. Mahadev Satyanarayanan. 2010. Mobile computing: The next decade. In Proceedings of the 1st ACM Workshop on Mobile Cloud Computing and Services: Social Networks and Beyond. ACM, 5. Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. Wanita Sherchan, Prem P. Jayaraman, Shonali Krishnaswamy, Arkady Zaslavsky, Seng Loke, and Abhijat Sinha. 2012. Using on-the-move mining for mobile crowd-sensing. In Proceedings of the 13th International Conference on Mobile Data Management (MDM’12). IEEE, 115--124. Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. Jinyu Shi and Weijia Jia. 2017. Real-time target tracking through mobile crowd-sensing. In Proceedings of the Conference on Web Information Systems Engineering (WISE’17), Athman Bouguettaya, Yunjun Gao, Andrey Klimenko, Lu Chen, Xiangliang Zhang, Fedor Dzerzhinskiy, Weijia Jia, Stanislav V. Klimenko, and Qing Li (Eds.). Springer International Publishing, Cham, 3--18.Google ScholarGoogle Scholar
  60. Pieter Simoens, Yu Xiao, Padmanabhan Pillai, Zhuo Chen, Kiryong Ha, and Mahadev Satyanarayanan. 2013. Scalable crowd-sourcing of video from mobile devices. In Proceedings of the 11th Annual International Conference on ACM International Conference on Mobile Systems, Applications, and Services (MobiSys’13). ACM, 139--152. Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. Fei Song, Zheng-Yang Ai, Jun-Jie Li, Giovanni Pau, Mario Collotta, Ilsun You, and Hongke Zhang. 2017. Smart collaborative caching for information-centric IoT in fog computing. In Proceedings of the Sensors Conference.Google ScholarGoogle ScholarCross RefCross Ref
  62. Jinyuan Sun, Xiaoyan Zhu, Chi Zhang, and Yuguang Fang. 2011. RescueMe: Location-based secure and dependable VANETs for disaster rescue. IEEE J. Select. Areas Commun. 29, 3 (2011), 659--669. Google ScholarGoogle ScholarDigital LibraryDigital Library
  63. Eyal Toledano, Dan Sawada, Andrew Lippman, Henry Holtzman, and Federico Casalegno. 2013. CoCam: A collaborative content sharing framework based on opportunistic P2P networking. In Proceedings of the IEEE Consumer Communications 8 Networking Conference (CCNC’13). IEEE, 158--163.Google ScholarGoogle ScholarCross RefCross Ref
  64. T. X. Tran, A. Hajisami, P. Pandey, and D. Pompili. 2017. Collaborative mobile edge computing in 5G networks: New paradigms, scenarios, and challenges. IEEE Commun. Mag. 55, 4 (2017), 54--61. Google ScholarGoogle ScholarDigital LibraryDigital Library
  65. Yves Vanrompay, Peter Rigole, and Yolande Berbers. 2007. Predicting network connectivity for context-aware pervasive systems with localized network availability. In Proceedings of the European Conference on Computer Systems Workshop on the Internet of Things (WoSSIoT EuroSys’07).Google ScholarGoogle Scholar
  66. Leye Wang, Daqing Zhang, Dingqi Yang, Animesh Pathak, Chao Chen, Xiao Han, Haoyi Xiong, and Yasha Wang. 2017b. SPACE-TA: Cost-effective task allocation exploiting intradata and interdata correlations in sparse crowd-sensing. ACM Trans. Intell. Syst. Technol. 9, 2, Article 20 (Oct. 2017). Google ScholarGoogle ScholarDigital LibraryDigital Library
  67. Shiqiang Wang, Rahul Urgaonkar, Ting He, Kevin Chan, Murtaza Zafer, and Kin K. Leung. 2017a. Dynamic service placement for mobile micro-clouds with predicted future costs. IEEE Trans. Parallel Distrib. Syst. 28, 4 (Apr. 2017), 1002--1016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  68. Y. Wu, Y. Wang, W. Hu, and G. Cao. 2016. SmartPhoto: A resource-aware crowdsourcing approach for image sensing with smartphones. IEEE Trans. Mobile Comput. 15, 5 (2016), 1249--1263. Google ScholarGoogle ScholarDigital LibraryDigital Library
  69. Chaocan Xiang, Panlong Yang, and Shucheng Xiao. 2017. Counter-strike: Accurate and robust identification of low-level radiation sources with crowd-sensing networks. Person. Ubiq. Comput. 21, 1 (Feb. 2017), 75--84. Google ScholarGoogle ScholarDigital LibraryDigital Library

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            cover image ACM Transactions on Internet Technology
            ACM Transactions on Internet Technology  Volume 19, Issue 2
            Special Issue on Fog, Edge, and Cloud Integration
            May 2019
            288 pages
            ISSN:1533-5399
            EISSN:1557-6051
            DOI:10.1145/3322882
            • Editor:
            • Ling Liu
            Issue’s Table of Contents

            Copyright © 2019 ACM

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            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 3 April 2019
            • Accepted: 1 November 2018
            • Revised: 1 October 2018
            • Received: 1 December 2017
            Published in toit Volume 19, Issue 2

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