Feedback-Based Resource Utilization for Smart Home Automation in Fog Assistance IoT-Based Cloud
Pages 41 - 63
Abstract
In this article, the proposed feedback-based resource management approach provides data processing, huge computation, large storage, and networking services between Internet of Things (IoT)-based Cloud data centers and the end-users. The real-time applications of IoT, such as smart city, smart home, health care management systems, traffic management systems, and transportation management systems, require less response time and latency to process the huge amount of data. The proposed feedback-based resource management plan provides a novel resource management technique, consisting of an integrated architecture and maintains the service-level agreement (SLA). It can optimize energy consumption, response time, network bandwidth, security, and reduce latency. The experimental results are tested with the IFogSim tool kit and have proved that the proposed approach is effective and suitable for smart communication in IoT-based cloud.
References
[1]
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R. H., Konwinski, A., & Zaharia, M. et al. (2010). A view of Cloud Computing . Communications of the ACM, 53(4), 5058.
[2]
Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15), 2787–2805.
[3]
Barik, R. K., Dubey, H., Misra, C., Borthakur, D., Constant, N., Sasane, S. A., & Mankodiya, K. (2018). Fog assisted cloud computing in era of big data and internet-of-things: systems, architectures, and applications. In Cloud computing for optimization: foundations, applications, and challenges (pp. 367–394). Cham: Springer.
[4]
BonomiF.MilitoR.ZhuJ.AddepalliS. (2012, August). Fog computing and its role in the internet of things. Proceedings of the first edition of the MCC workshop on Mobile cloud computing (pp. 13-16). ACM. 10.1145/2342509.2342513
[5]
Bu, Y., Howe, B., Balazinska, M., & Ernst, M. D. (2010). HaLoop: Efficient iterative data processing on large clusters. Proceedings of the VLDB Endowment International Conference on Very Large Data Bases, 3(1-2), 285–296.
[6]
Buyya, R., Yeo, C. S., & Venugopal, S. (2008, September). Market-oriented cloud computing: Vision, hype, and reality for delivering it services as computing utilities. Proceedings of the 2008 10th IEEE International Conference on High Performance Computing and Communications (pp. 5-13). IEEE.
[7]
Chen, Y. K. (2012, January). Challenges and opportunities of internet of things. Proceedings of the 17th Asia and South Pacific design automation conference (pp. 383-388). IEEE. 10.1109/ASPDAC.2012.6164978
[8]
Dastjerdi, A. V., & Buyya, R. (2016). Fog computing: Helping the Internet of Things realize its potential. Computer, 49(8), 112–116.
[9]
Gill, S. S., & Buyya, R. (2018) Fog-assisted Cloud based Resource Management for IoT and Big Data Analytics: A Case Study with Smart Home Application.
[10]
Gupta, H., Vahid Dastjerdi, A., Ghosh, S. K., & Buyya, R. (2017). iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments. Software, Practice & Experience, 47(9), 1275–1296.
[11]
Kortuem, G., Kawsar, F., Sundramoorthy, V., & Fitton, D. (2009). Smart objects as building blocks for the internet of things. IEEE Internet Computing, 14(1), 44–51.
[12]
Krishna, P. V., Misra, S., Joshi, D., Gupta, A., & Obaidat, M. S. (2014). Secure socket layer certificate verification: A learning automata approach. Security and Communication Networks, 7(11), 1712–1718.
[13]
KrishnaP. V.MisraS.NagarajuD.SarithaV.ObaidatM. S. (2016, July). Learning automata based decision making algorithm for task offloading in mobile cloud. Proceedings of the 2016 International Conference on Computer, Information and Telecommunication Systems (CITS) (pp. 1-6). IEEE. 10.1109/CITS.2016.7546451
[14]
Krishna, P. V., Misra, S., Obaidat, M. S., & Saritha, V. (2009). An efficient approach for distributed dynamic channel allocation with queues for real-time and non-real-time traffic in cellular networks. Journal of Systems and Software, 82(7), 1112–1124.
[15]
KrishnaP. V.MisraS.ObaidatM. S.SarithaV. (2009, March). A new scheme for distributed channel allocation in cellular networks. Proceedings of the 2009 Spring Simulation Multiconference (p. 75). Society for Computer Simulation International.
[16]
Kumar, T. P., & Krishna, P. V. (2018). Power modelling of sensors for IoT using reinforcement learning. International Journal of Advanced Intelligence Paradigms, 10(1-2), 3–22.
[17]
Lee, W., Nam, K., Roh, H. G., & Kim, S. H. (2016, January). A gateway based fog computing architecture for wireless sensors and actuator networks. Proceedings of the 2016 18th International Conference on Advanced Communication Technology (ICACT) (pp. 210-213). IEEE.
[18]
LiuA.NingP. (2008, April). TinyECC: A configurable library for elliptic curve cryptography in wireless sensor networks. Proceedings of the 7th international conference on Information processing in sensor networks (pp. 245-256). IEEE Computer Society. 10.1109/IPSN.2008.47
[19]
Mahmud, R., & Buyya, R. (2019). Modelling and simulation of fog and edge computing environments using iFogSim toolkit. Fog and Edge Computing: Principles and Paradigms, 1-35.
[20]
Mahmud, R., Kotagiri, R., & Buyya, R. (2018). Fog computing: A taxonomy, survey and future directions. In Internet of everything (pp. 103–130). Singapore: Springer.
[21]
Mallikarjuna, B., & Arun Kumar Reddy, D. (2018). Mobile Health care Application Development on Android Operating System in Cloud computing. Proceedings of the 1st International Conference Universal Computing, Communication in Data Engineering (CCODE-2018). Academic Press.
[22]
Mallikarjuna, B., & Krishna, P. V. (2014). A Nature Inspired Approach for Load Balancing of Tasks in Cloud Computing using Equal Time Allocation. International Journal of Innovative Technology and Exploring Engineering.
[23]
Mallikarjuna, B., & Krishna, P. V. (2015). OLB: A Nature Inspired Approach for Load Balancing in Cloud Computing. Cybernetics and Information Technologies, 15(4), 138–148.
[24]
Mallikarjuna, B., Reddy, D. A. K., & Sailaja, G. (2018) Enhancement of Railway Reservation System Using Internet of Things. Proceedings of the 1st International Conference Universal Computing, Communication in Data Engineering (CCODE-2018).
[25]
Mallikarjuna, B., & Shahajad, M. Dohare, A., & Tulika. (2019). Master Slave Scheduling Architecture for Data Processing on Internet of Things. International Journal of Innovative Technology and Exploring Engineering, 8(5), 556–559.
[26]
Mallikarjuna, B., & Shahajad, M. Dohare, A., & Tulika. (2019). Master Slave Scheduling Architecture for Data Processing on Internet of Things. International Journal of Innovative Technology and Exploring Engineering, 8(5), 556–559.
[27]
Mallikarjuna. B, Shahjad, M., Dohare, A., & Tulika. (2019). Feed forward Approach for Data Processing in IoT over Cloud. International Journal of Innovative Technology and Exploring Engineering, 8(5), 899-903.
[28]
Manyika, J. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey. Retrieved from http://www.mckinsey.com/Insights/MGI/Research/Technology_and_Innovation/Big_data_The_next_frontier_for_innovation
[29]
Misra, S., Abraham, K. I., Obaidat, M. S., & Krishna, P. V. (2009). LAID: A learning automata‐based scheme for intrusion detection in wireless sensor networks. Security and Communication Networks, 2(2), 105–115.
[30]
Misra, S., Krishna, P. V., Agarwal, H., Saxena, A., & Obaidat, M. S. (2011, October). A learning automata based solution for preventing distributed denial of service in internet of things. Proceedings of the 2011 International Conference on Internet of Things and 4th International Conference on Cyber, Physical and Social Computing (pp. 114-122). IEEE. 10.1109/iThings/CPSCom.2011.84
[31]
Sahoo, K. C., & Pati, U. C. (2017, May). IoT based intrusion detection system using PIR sensor. Proceedings of the 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 1641-1645). IEEE. 10.1109/RTEICT.2017.8256877
[32]
Sarkar, S., Chatterjee, S., & Misra, S. (2015). Assessment of the Suitability of Fog Computing in the Context of Internet of Things. IEEE Transactions on Cloud Computing, 6(1), 46–59.
[33]
Singh, S., Chana, I., Singh, M., & Buyya, R. (2016). SOCCER: Self-optimization of energy-efficient cloud resources. Cluster Computing, 19(4), 1787–1800.
[34]
Stojkoska, B. R., & Trivodaliev, K. (2017, November). Enabling internet of things for smart homes through fog computing. Proceedings of the 2017 25th Telecommunication Forum (TELFOR) (pp. 1-4). IEEE. 10.1109/TELFOR.2017.8249316
[35]
Yu, L., Jiang, T., & Zou, Y. (2017). Fog-assisted operational cost reduction for cloud data centers. IEEE Access, 5, 13578–13586.
[36]
Zhang, H., Zhang, Y., Gu, Y., Niyato, D., & Han, Z. (2017). A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine, 55(8), 52–57.
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Published: 01 January 2020
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