Papers
Topics
Authors
Recent
Search
2000 character limit reached

Fog Architectures and Sensor Location Certification in Distributed Event-Based Systems

Published 15 Dec 2023 in cs.NI | (2312.09921v1)

Abstract: Since smart cities aim at becoming self-monitoring and self-response systems, their deployment relies on close resource monitoring through large-scale urban sensing. The subsequent gathering of massive amounts of data makes essential the development of event-filtering mechanisms that enable the selection of what is relevant and trustworthy. Due to the rise of mobile event producers, location information has become a valuable filtering criterion, as it not only offers extra information on the described event, but also enhances trust in the producer. Implementing mechanisms that validate the quality of location information becomes then imperative. The lack of such strategies in cloud architectures compels the adoption of new communication schemes for Internet of Things (IoT)-based urban services. To serve the demand for location verification in urban event-based systems (DEBS), we have designed three different fog architectures that combine proximity and cloud communication. We have used network simulations with realistic urban traces to prove that the three of them can correctly identify between 73% and 100% of false location claims.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (40)
  1. Mone, G. The new smart cities. Commun. ACM 2015, 58, 20–21.
  2. A mobile crowdsensing ecosystem enabled by a cloud-based publish/subscribe middleware. In Proceedings of the 2014 International Conference on Future Internet of Things and Cloud, Barcelona, Spain, 27–29 August 2014; IEEE: Piscataway, NJ, USA, 2014, pp. 107–114.
  3. Processing flows of information: From data stream to complex event processing. ACM Comput. Surv. (CSUR) 2012, 44, 15.
  4. A survey of context data distribution for mobile ubiquitous systems. ACM Comput. Surv. (CSUR) 2012, 44, 24.
  5. Participatory Sensing; Center for Embedded Network Sensing: Los Angeles, CA, USA, 2006.
  6. From participatory sensing to mobile crowd sensing. In Proceedings of the 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS), Budapest, Hungary, 24–28 March 2014; IEEE: Piscataway, NJ, USA, 2014; pp. 593–598.
  7. Collaboratively assessing urban alerts in ad hoc participatory sensing. Comput. Netw. 2018, 131, 129–143.
  8. Fog computing and its role in the internet of things. In Proceedings of the First Edition of the MCC Workshop on Mobile cloud Computing, Helsinki, Finland, 17 August 2012; ACM: New York, NY, USA, 2012; pp. 13–16, doi:10.1145/2342509.2342513.
  9. Fog computing for sustainable smart cities: A survey. ACM Comput. Surv. (CSUR) 2017, 50, 32.
  10. Enhancing Context Data Distribution for the Internet of Things using QoC-awareness and Attribute-based Access Control. Ann. Telecommun. 2016, 71, 121–132, doi:10.1007/s12243-015-0480-9.
  11. Combining Fog Architectures and Distributed Event-Based Systems for Mobile Sensor Location Certification. In Proceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence, Philadelphia, PA, USA, 7–10 November 2017; Springer: Berlin, Germany, 2017; pp. 27–33.
  12. Mobile crowdsensing: current state and future challenges. IEEE Commun. Mag. 2011, 49, 32–39.
  13. A mobile crowd sensing ecosystem enabled by CUPUS: Cloud-based publish/subscribe middleware for the Internet of Things. Future Gener. Comput. Syst. 2016, 56, 607–622.
  14. A data protection model for fog computing. In Proceedings of the Second International Conference on Fog and Mobile Edge Computing (FMEC), Valencia, Spain, 8–11 May 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 32–38.
  15. Key ingredients in an IoT recipe: Fog Computing, Cloud computing, and more Fog Computing. In Proceedings of the 2014 IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Athens, Greece, 1–3 December 2014; IEEE: Piscataway, NJ, USA, 2014; pp. 325–329.
  16. Mobile Fog: A Programming Model for Large-scale Applications on the Internet of Things. In Proceedings of the Second ACM SIGCOMM Workshop on Mobile Cloud Computing, Hong Kong, China, 16 August 2013; pp. 15–20, doi:10.1145/2491266.2491270.
  17. Security and trust issues in Fog computing: A survey. Future Gener. Comput. Syst. 2018, 88, 16–27.
  18. Participatory noise mapping works! An evaluation of participatory sensing as an alternative to standard techniques for environmental monitoring. Pervasive Mobile Comput. 2013, 9, 681–694.
  19. TwitterSensing: An Event-Based Approach for Wireless Sensor Networks Optimization Exploiting Social Media in Smart City Applications. Sensors 2018, 18, 1080.
  20. A fuzzy-based approach for sensing, coding and transmission configuration of visual sensors in smart city applications. Sensors 2017, 17, 93.
  21. A survey of trust and reputation systems for online service provision. Decis. Support Syst. 2007, 43, 618–644.
  22. Event Processing in Action; Manning Greenwich: Shelter Island, NY, USA, 2011.
  23. Supporting mobility in content-based publish/subscribe middleware. In Proceedings of the ACM/IFIP/USENIX International Conference on Distributed Systems Platforms and Open Distributed Processing, Rio de Janeiro, Brazil, 16–20 June 2003; Springer: Berlin, Germany, 2003; pp. 103–122.
  24. Practical Location Validation in Participatory Sensing Through Mobile WiFi Hotspots. In Proceedings of the 17th International Conference On Trust, Security And Privacy in Computing And Communications/12th International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), New York, NY, USA, 1–3 August 2018; pp. 596–607.
  25. BonnMotion: A mobility scenario generation and analysis tool. In Proceedings of the 3rd International ICST Conference on Simulation Tools and Techniques, Malaga, Spain, 16–18 March 2010; p. 51.
  26. OpenStreetMap. Available online: http://wiki.openstreetmap.org/ (accessed on 28 December 2018).
  27. Introducing geographic restrictions to the slaw human mobility model. In Proceedings of the 21st International Symposium on Modeling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS), San Francisco, CA, USA, 14–16 August 2013; IEEE: Piscataway, NJ, USA, 2013; pp. 264–272.
  28. NS - 3 network simulator. Available online: https://www.nsnam.org/ (accessed on 28 December 2018).
  29. SHIELD: A data verification framework for participatory sensing systems. In Proceedings of the 8th ACM Conference on Security & Privacy in Wireless and Mobile Networks, New York, NY, USA, 22–26 June 2015; ACM: New York, NY, USA, 2015; p. 16.
  30. Reshaping Mobile Crowd Sensing using Cross Validation to Improve Data Credibility. In Proceedings of the Global Communications Conference (GLOBECOM), Singapore, 4–8 December 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 1–7.
  31. Collaborative Location Certification for Ad-Hoc Sensor Networks. In Proceedings of the IEEE Sarnoff Symposium, Princeton, NJ, USA, 28–30 April 2008; pp. 1–6.
  32. Improving location reliability in crowd sensed data with minimal efforts. In Proceedings of the 6th Joint IFIP Wireless and Mobile Networking Conference (WMNC), Dubai, UAE, 23–25 April 2013; IEEE: Piscataway, NJ, USA, 2013; pp. 1–8.
  33. Link: Location verification through immediate neighbors knowledge. In Proceedings of the International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services, Sydney, NSW, Australia, 6–9 December 2010; Springer: Berlin, Germany, 2010; pp. 210–223.
  34. Toward privacy preserving and collusion resistance in a location proof updating system. IEEE Trans. Mobile Comput. 2013, 12, 51–64.
  35. STAMP: Enabling privacy-preserving location proofs for mobile users. IEEE/ACM Trans. Netw. 2016, 24, 3276–3289.
  36. PROPS: A privacy-preserving location proof system. In Proceedings of the 33rd International Symposium on Reliable Distributed Systems (SRDS), Nara, Japan, 6–9 October 2014; IEEE: Piscataway, NJ, USA, 2014; pp. 1–10.
  37. SPARSE: Privacy–Aware and Collusion Resistant Location Proof Generation and Verification. In Proceedings of the IEEE Global Communications Conference, Abu Dhabi, UAE, 9–13 December 2018,
  38. A Multiscale Approach for a Distributed Event-Based Internet of Things. In Proceedings of the Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence & Computing, Orlando, FL, USA, 6–10 November 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 844–852.
  39. Distance-bounding protocols. In Proceedings of the Workshop on the Theory and Application of of Cryptographic Techniques, Lofthus, Norway, 23–27 May 1993; Springer: Berlin, Germany, 1993; pp. 344–359.
  40. Spatial group mobility model scenarios formation in mobile ad hoc networks. In Proceedings of the International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 5–7 January 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 1–5.
Citations (2)
View on Semantic Scholar

Summary

No one has generated a summary of this paper yet.

Sign Up to Summarize

Paper to Video (Beta)

No one has generated a video about this paper yet.

Sign Up to Generate All Videos Subscribe on YouTube

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Generate Now

Collections

Sign up for free to add this paper to one or more collections.

Sign Up

Tweets

Sign up for free to view the 1 tweet with 0 likes about this paper.

Sign Up for Free