Proposing a New Framework to Decreasing Delay in the Internet of Things by Using Computing Power of Fog
Subject Areas :Mohammad Taghi Shaykhan 1 , kianoosh azadi 2
1 - Senior research and development expert
2 - University student
Keywords: Internet of Things, Fog Computing, Quality of Service, Security, Privacy,
Abstract :
As the Internet of Things (IoT) expands and becomes more widespread, we will soon see the dependence of human life on its services. At that time, it would be difficult to imagine the survival without the IoT, and disruption of its services would cause great loss of life and property. Disruption of IoT services can occur for two reasons: network errors due to congestion, collision, interruption and noise, and disruption due to the malicious activities of infiltrator. Also, the destructive activities of infiltrators can lead to various cyber attacks and violation of the privacy of individuals. Therefore, before the interdependence between human life and IoT, it is necessary to consider measures to ensure the quality and security of service and privacy. In this study, a solution to reduce service delay (improve quality) and ensure security and privacy of things by relying on the computing power of nodes available in the Fog Layer has been proposed. The proposed solution simultaneously improves service quality and maintains security and privacy. Other features of presented algorithm in proposed solution of fairness between objects are in terms of the quality of service received and minimizing overhead processing and transfer of expired packages (packages that will certainly experience a consumedly threshold delay). Adherence to fairness ensures that the quality of service of any of the things does not be a subject of the reduction of the delay of the service of the entire network; These aforementioned objects may be subjects of critical applications, such as health.
D. Raggett, “The web of things: Challenges and opportunities,” Computer, vol. 48, no. 5, pp. 26-32, 2015.
[2] A. Whitmore, A. Agarwal, and L. Da Xu, “The Internet of Things—A survey of topics and trends,” Information Systems Frontiers, vol. 17, no. 2, pp. 261-274, 2015.
[3] K. L. Lueth. "The 10 most popular Internet of Things applications right now," iot-analytics.com/10-internet-of-things-applications/, 2017.
[4] A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, “Internet of things: A survey on enabling technologies, protocols, and applications,” IEEE communications surveys & tutorials, vol. 17, no. 4, pp. 2347-2376, 2015.
[5] K. Kumar, and Y.-H. Lu, “Cloud computing for mobile users: Can offloading computation save energy?,” Computer, vol. 43, no. 4, pp. 51-56, 2010.
[6] B.-G. Chun, S. Ihm, P. Maniatis, M. Naik, and A. Patti, "Clonecloud: elastic execution between mobile device and cloud." pp. 301-314, 2011.
[7] A. Rudenko, P. Reiher, G. J. Popek, and G. H. Kuenning, “Saving portable computer battery power through remote process execution,” ACM SIGMOBILE Mobile Computing and Communications Review, vol. 2, no. 1, pp. 19-26, 1998.
[8] G. C. Hunt, and M. L. Scott, "A guided tour of the Coign automatic distributed partitioning system." pp. 2.262-52, 1998
[9] S. Kosta, A. Aucinas, P. Hui, R. Mortier, and X. Zhang, "Thinkair: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading." pp. 945-953, 2012.
[10] A. Yousefpour, G. Ishigaki, and J. P. Jue, "Fog Computing: Towards Minimizing Delay in the Internet of Things." pp. 17-24, 2017.
[11] A. Demers, S. Keshav, and S. Shenker, “Analysis and simulation of a fair queueing algorithm,” ACM SIGCOMM Computer Communication Review, vol. 19, no. 4, pp. 1-12, 1989.
[12] H. Zhang, and J. C. Bennett, "WF2Q: worst-case fair weighted fair queueing." pp. 120-128, 1996.
[13] D. M. Dakshayini, and D. H. Guruprasad, “An optimal model for priority based service scheduling policy for cloud computing environment,” International journal of computer applications, vol. 32, no. 9, pp. 23-29, 2011.
[14] J. Jang, J. Jung, and J. Hong, “K-LZF: An efficient and fair scheduling for Edge Computing servers,” Future Generation Computer Systems, vol. 98, pp. 44-53, 2019.
[15] T. Choudhari, M. Moh, and T.-S. Moh, "Prioritized task scheduling in fog computing." pp. 1-8, 2018.
[16] E. S. Gama, R. Immich, and L. F. Bittencourt, "Towards a Multi-Tier Fog/Cloud Architecture for Video Streaming." pp. 13-14, 2018.
[17] C.-F. Lai, D.-Y. Song, R.-H. Hwang, and Y.-X. Lai, "A QoS-aware streaming service over fog computing infrastructures." pp. 94-98, 2016.
[18] B. Oniga, S. H. Farr, A. Munteanu, and V. Dadarlat, "IoT Infrastructure Secured by TLS Level Authentication and PKI Identity System." pp. 78-83, 2018.
[19] J. Won, A. Singla, E. Bertino, and G. Bollella, "Decentralized public key infrastructure for internet-of-things." pp. 907-913, 2018.
[20] I. Stojmenovic, and S. Wen, "The fog computing paradigm: Scenarios and security issues." pp. 1-8, 2014.
[21] K. Christidis, and M. Devetsikiotis, “Blockchains and smart contracts for the internet of things,” Ieee Access, vol. 4, pp. 2292-2303, 2016.
[22] P. K. Sharma, S. Y. Moon, and J. H. Park, “Block-VN: A distributed Blockchain based vehicular network architecture in smart city,” Journal of information processing systems, vol. 13, no. 1, 2017.
[23] D. Shift, "Technology tipping points and societal impact.", 2015.
[24] Z. Cai, Z. He, X. Guan, and Y. Li, “Collective data-sanitization for preventing sensitive information inference attacks in social networks,” IEEE Transactions on Dependable and Secure Computing, vol. 15, no. 4, pp. 577-590, 2018.
[25] X. Ren, X. Yang, J. Lin, Q. Yang, and W. Yu, "On scaling perturbation based privacy-preserving schemes in smart metering systems." pp. 1-7, 2013.
[26] X. Yang, X. Ren, J. Lin, and W. Yu, “On binary decomposition based privacy-preserving aggregation schemes in real-time monitoring systems,” IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 10, pp. 2967-2983, 2016.
[27] L. Zhang, Z. Cai, and X. Wang, “Fakemask: A novel privacy preserving approach for smartphones,” IEEE Transactions on Network and Service Management, vol. 13, no. 2, pp. 335-348, 2016.
[28] M. A. Ferrag, L. Maglaras, and A. Ahmim, “Privacy-preserving schemes for ad hoc social networks: A survey,” IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 3015-3045, 2017.
[29] Y. Hong, W. M. Liu, and L. Wang, “Privacy preserving smart meter streaming against information leakage of appliance status,” IEEE transactions on information forensics and security, vol. 12, no. 9, pp. 2227-2241, 2017.
[30] H. Delfs, H. Knebl, and H. Knebl, Introduction to cryptography: Springer, 2002.
[31] "F-secure," https://campaigns.f-secure.com/total/pm/en_us/.
[32] "Simpy," April 25, 2019; https://simpy.readthedocs.io/en/latest/contents.html.
[33] "NetworkX," April 25, 2019; https://networkx.github.io/documentation/stable/index.html.
[34] Y. Xiao, H.-H. Chen, B. Sun, R. Wang, and S. Sethi, “MAC security and security overhead analysis in the IEEE 802.15. 4 wireless sensor networks,” EURASIP Journal on Wireless Communications and Networking, vol. 2006, no. 2, pp. 81-81, 2006.
[35] O. Barahtian, M. Cuciuc, L. Petcana, C. Leordeanu, and V. Cristea, "Evaluation of Lightweight Block Ciphers for Embedded Systems." pp. 49-58, 2015.
[36] S. Maitra, and K. Yelamarthi, “Rapidly Deployable IoT Architecture with Data Security: Implementation and Experimental Evaluation,” Sensors, vol. 19, no. 11, pp. 2484, 2019.
