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Intelligent Contextual Information Collection in Internet of Things

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Abstract

As we are moving towards the internet of things (IoT), a significant growth of stationary and mobile sensing and computing IoT devices continuously generate enormous amounts of contextual information, e.g., environmental data. Contextual information collection, reasoning, and inference plays critical role in IoT. In this paper, we consider the contextual information collection and harvesting problem in which stationary sensing and computing devices (sources), which are incapable to communicate with each other either due to their long distance, or for energy efficiency, or spatially dispersed network, rely on mobile IoT devices (collectors) to ‘drain’ their acquired contextual information. (e.g., generating from IoT applications: smart cities, smart metering, and smart agriculture). At the contact instances with the collectors, sources have to decide whether to deliver the contextual information obtained so far or postpone their delivery for later hitting epochs in an effort to sense fresher (or more critical) contextual information. We rest on the principles of Optimal Stopping Theory and propose an intelligent context collection scheme in IoT environments. We show through simulations with synthetic and real mobility data the effectiveness of our scheme compared to other approaches.

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References

  1. H. Sundmaeker, P. Guillemin, P. Friess and S. Woelffle ‘Vision and challenges for realising the internet of things’, Tech. Rep, March 2010.

  2. Paolo Bellavista, Antonio Corradi, Mario Fanelli, Luca Foschini. 2012. ‘A survey of context data distribution for mobile ubiquitous systems’. ACM Comput. Surv. 44, 4, Article 24 (September 2012), 45 pages.

  3. A. Ramakrishnana, et al. ‘Enabling self-learning in dynamic and open IoT environments’, Procedia Computer Science, Elsevier, Vol. 38, pp. 207–214, May 2014.

    Article  Google Scholar 

  4. A. Asin and D. Gascon, ‘50 sensor applications for a smarter world’, Libelium Comunicaciones Distribuidas, Tech. Rep., 2012, http://www.libelium.com/top 50 iot sensor applications ranking/pdf [Accessed on: 2015-05-01].

  5. Perera, C.; Zaslavsky, A.; Christen, P.; Georgakopoulos, D., ‘Context Aware Computing for The Internet of Things: A Survey,’ in Communications Surveys & Tutorials, IEEE, vol. 16, no.1, pp. 414–454, First Quarter 2014

  6. O. Bello, et al., ‘Intelligent Device-to-Device Communication in the Internet of Things’, IEEE Systems Journal, pp. 1–11, 2014.

  7. C. S. Lee, et al., ‘Smart Ubiquitous Networks for future telecommunication environments,’ Elsevier, Computer Standards & Interfaces, vol. 36, pp. 412–422, 2014.

    Article  Google Scholar 

  8. U. Moenks, et al., ‘Assisting the design of sensor and information fusion systems’, Elsevier, Procedia Technology, vol. 15, pp. 35–45, 2014.

    Article  Google Scholar 

  9. D.G. Zhang, et al., ‘A new constructing approach for a weighted topology of wireless sensor networks based on local- world theory for the Internet of Things’, (IOT), Elsevier, Computers & Mathematics with Applications, Volume 64, Issue 5, September 2012, Pages 1044–1055.

    Article  Google Scholar 

  10. F. Razzak, D. Bonino, F. Corno, ‘Mobile interaction with smart environments through linked data’, Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on, vol., no., pp. 2922–2929, 10–13 Oct. 2010.

  11. D. Guinard, V. Trifa, F. Mattern, E. Wilde, ‘From the Internet of Things to the Web of Things: Resource Oriented Architecture and Best Practices’. In: Dieter Uckelmann, Mark Harrison, Florian Michahelles (Eds.): Architecting the Internet of Things. Springer, pp. 97–129, New York Dordrecht Heidelberg London, 2011.

  12. T. Anagnostopoulos, A. Zaslavsky, A. Medvedev, S. Khoruzhnikov, 2015b. ‘Top–k Query based Dynamic Scheduling for IoT-enabled Smart City Waste Collection’. In Proc. of the 16th IEEE International Conference on Mobile Data Management (MDM 2015), Pittsburgh, US.

  13. T. Anagnostopoulos, K. Kolomvatsos, C. Anagnostopoulos, A. Zaslavsky, S. Hadjiefthymiades, S. (2015) ‘Assessing dynamic models for high priority waste collection in smart cities’. Journal of Systems and Software, Elsevier, (Accepted for Publication); June 2015.

  14. G. Baldassarre, V. Trianni, M. Bonani, F. Mondada, M. Dorigo, S. Nolfi, ‘Self–Organized Coordinated Motion in Groups of Physically Connected Robots’, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 37(1):224–239, Feb. 2007.

    Article  Google Scholar 

  15. C. Ampatzis, E. Tuci, V. Trianni, A. Lyhne Christensen, M. Dorigo, ‘Evolving Self–Assembly in Autonomous Homogeneous Robots: Experiments with Two Physical Robots’, Artificial Life, 15(4):465–484, July 2009.

    Article  Google Scholar 

  16. J. Cortes, S. Martinez, T. Karatas, F. Bullo, ‘Coverage control for mobile sensing networks’, IEEE Transactions on Robotics and Automation, 20(2):243–255, April 2004.

    Article  Google Scholar 

  17. M. Di Francesco, S. K. Das, G. Anastasi. 2011. ‘Data Collection in Wireless Sensor Networks with Mobile Elements: A Survey’. ACM Trans. Sen. Netw. 8, 1, Article 7 (August 2011), 31 pages.

  18. R. C. Shah, S. Roy, S. Jain, W. Brunette, ‘Data MULEs: modeling a three-tier architecture for sparse sensor networks’, in Proceedings of the First IEEE International Workshop on Sensor Network Protocols and Applications, 2003. , pp.30–41, 11 May 2003.

  19. R. Sugihara, R. K. Gupta. 2011. ‘Path Planning of Data Mules in Sensor Networks’, ACM Trans. Sen. Netw. 8, 1, Article 1 (August 2011), 27 pages.

  20. Z. M. Wang, S. Basagni, E. Melachrinoudis, C. Petrioli. 2005. ‘Exploiting sink mobility for maximizing sensor networks lifetime’. In Proceedings of the 38th Hawaii International Conference on System Sciences (HICSS 2005).

  21. J. Rao, S. Biswas. 2008. ‘Joint routing and navigation protocols for data harvesting in sensor networks’. In Proceedings of the 5th IEEE International Conference on Mobile Ad Hoc and Sensor Systems (MASS 2008). 143–152.

  22. G. Anastasi, E. Borgia, M. Conti, E. Gregori, ‘A Hybrid Adaptive Protocol for Reliable Data Delivery in WSNs with Multiple Mobile Sinks’, The Computer Journal (2011) 54 (2): 213–229.

    Article  Google Scholar 

  23. Zhao, W., Ammar, M., and Zegura, E. 2004. ‘A message ferrying approach for data delivery in sparse mobile ad hoc networks’. In Proceedings of the 5th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc 2004). 187–198.

  24. S. B. Eisenman, G.-S. Ahn, N. D. Lane, E. Miluzzo, R. A. Peterson, A. T. Campbell, ‘MetroSense project: People-centric sensing at scale’, in Proc. ACM WSW, Boulder, CO, Oct./Nov. 2006.

  25. O. Riva and C. Borcea, ‘The urbanet revolution: Sensor power to the people!’, IEEE Pervasive Comput., vol. 6, no. 2, pp. 41–49, Apr.– Jan. 2007.

  26. M. D. Dikaiakos, S. Iqbal, T. Nadeem, and L. Iftode, ‘VITP: An information transfer protocol for vehicular computing’, in Proc. ACM VANET, Cologne, Germany, Sep. 2005, pp. 30–39.

  27. J. Burke, D. Estrin, M. Hansen, A. Parker, N. Ramanathan, S. Reddy, and M. B. Srivastava, ‘Participatory sensing’, in Proc. ACM WSW, Boulder, CO, Oct./Nov. 2006, pp. 1–5.

  28. P. Juang, H. Oki, Y. Wang, M. Martonosi, L.-S. Peh, and D. Rubenstein, ‘Energy-efficient computing for wildlife tracking: Design tradeoffs and early experiences with ZebraNet’, in Proc. ACM ASPLOS–X, San Jose, CA, Oct. 2002, pp. 96-107.

  29. T. Small and Z. J. Haas, ‘The shared wireless infostation model: A new ad hoc networking paradigm (or where there is a whale, there is a way)’, in Proc. ACM MOBIHOC, Annapolis, MD, Jun. 2003, pp. 233–244.

  30. S. Yang, U. Adeel, J. McCann, ‘Backpressure meets taxes: Faithful data collection in stochastic mobile phone sensing systems’, 2015 IEEE Conference on Computer Communications (INFOCOM), pp.1490–1498, April 26 2015–May 1 2015.

  31. C. Anagnostopoulos, S. Hadjiefthymiades, E. Zervas, ‘Information Dissemination between Mobile Nodes for Collaborative Context Awareness’, IEEE Transactions on Mobile Computing, 10(12):1710–1725, Dec., 2011

  32. U. Lee, E. Magistretti, M. Gerla, P. Bellavista, A. Corradi, ‘Dissemination and Harvesting of Urban Data Using Vehicular Sensing Platforms’, IEEE Transactions on Vehicular Technology, 58(2):882–901, Feb. 2009.

    Article  Google Scholar 

  33. B. Zeng, J. Wei, HaiQing Liu, A data collection protocol for local mobile sensor network, in: Proceedings of the IEEE International Conference on Communications and Mobile, Computing, vol. 01, (WRI-CMC ’09), 2009, pp. 523-527

  34. C. Anagnostopoulos, S. Hadjiefthymiades, ‘Context Discovery in Mobile Environments: A Particle Swarm Optimization Approach’, 3rd International ICST Conference on Autonomic Computing and Communication Systems (Autonomics 2009), Cyprus, Sept., 2009.

  35. C. Anagnostopoulos, S. Hadjiefthymiades, ‘Delay-tolerant delivery of quality information in ad hoc networks’, Journal of Parallel and Distributed Computing, 71(7):974–987, July 2011.

    Article  MATH  Google Scholar 

  36. C. Anagnostopoulos, S. Hadjiefthymiades, ‘Multivariate context collection in mobile sensor networks’, Computer Networks, 57(6), 22 April 2013, Pages 1394–1407,

  37. C. Anagnostopoulos, S. Hadjiefthymiades, E. Zervas, ‘Optimal Stopping of the Context Collection Process in Mobile Sensor Networks’, IEEE 24th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), London, UK, Sept., 2013

  38. G. Peskir, A. Shiryaev, ‘Optimal Stopping and Free Boundary Problems’, (ETH Zuerich), Birkhauser, 2006.

  39. A. Somasundara, A. Kansal, D. Jea, E. Estrin, M. Srivastava. 2006. Controllably mobile infrastructure for low energy embedded networks. IEEE Transactions on Mobile Computing 5, 8, 1536–1233.

    Article  Google Scholar 

  40. S. Poduri, G. Sukhatme. 2007. Achieving connectivity through coalescence in mobile robot networks. In Proceedings of the 1st International Conference on Robot Communication and Coordination (RoboComm 2007). 1–6.

  41. Nain, P., Towsley, D., Benyuan Liu, Zhen Liu, ‘Properties of random direction models’, in INFOCOM 2005. 24th Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings IEEE , vol.3, no., pp. 1897–1907 vol. 3, 13–17 March 2005

  42. Christian Bettstetter, Hannes Hartenstein, and Xavier Perez-Costa. 2004. ‘Stochastic properties of the random waypoint mobility model’. Wirel. Netw. 10, 5 (September 2004), 555–567.

  43. R. Groenevelt, P. Nain, G. Koole, ‘Message Delay in MANET’, Report No 5372, INRIA, Nov. 2004

  44. F. Bai, N. Sadagopan, A. Helmy, ‘The IMPORTANT framework for analyzing the Impact of Mobility on Performance Of RouTing protocols for Ad-hoc NeTworks’, Ad Hoc Networks, 1, (2003), 383–403, Elsevier, 2003.

  45. M. Piorkowski, N. Sarafijanovoc-Djukic, M. Grossglauser, ‘A Parsimonious Model of Mobile Partitioned Networks with Clustering’, 1st IEEE International Conference on COMmunication Systems and NETworkS (COMSNETS), Bangalore, India, pp:1–10, 2009.

  46. G. Peskir, A.Shiryanev, ’Optimal Stopping and Free Boundary Problems’, Lectures in Mathematics, ETH Zuerich, XXII, 500p., Birkhauser, 2006.

  47. S. Buruhanudeen, M. Othman, B.M. Ali, ‘Mobility models, broadcasting methods and factors contributing towards the efficiency of the MANET routing protocols: Overview’, Proc. IEEE ICT–MICC’07, pp. 226–230, 2007

  48. BONNMOTION: ‘A mobility scenario generation and analysis tool’, University of Bonn, [Online]. Available: http://net.cs.uni-bonn.de/wg/cs/applications/bonnmotion/

  49. ns–2 (The Network Simulator). [Online]. Available: http://www.isi.edu/nsnam/ns/

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  1. School of Computing Science, University of Glasgow, Glasgow, G12 8QQ, UK

    Christos Anagnostopoulos

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Anagnostopoulos, C. Intelligent Contextual Information Collection in Internet of Things. Int J Wireless Inf Networks 23, 28–39 (2016). https://doi.org/10.1007/s10776-015-0293-9

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