Enhancement of Execution Time and Management in A Smart City Environment
Keywords:
Smart city, Execution time, Computing offloading, Edge computing, Cloud computingAbstract
The research focuses on improving task execution and aid management in clever towns, where interconnected gadgets and systems collaborate to improve citizens' excellent of existence. The research proposes an efficient computation offloading mechanism for clever cities, leveraging network facet infrastructure. This mechanism offloads computationally extensive tasks from aid-constrained devices to greater effective aspect servers or cloud assets, lowering the load on local devices. The mechanism makes use of dynamic choice-making algorithms considering factors like tool abilties, network situations, project requirements, and person preferences. The research suggests huge enhancements in execution time, power efficiency, and system performance in smart town contexts, supplying sensible insights for designing and enforcing smart city infrastructures, optimizing resource usage, and improving consumer revel in.
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Ketu, S. and Mishra, P. K., "A contemporary survey on IoT based smart cities: architecture, applications, and open issues," Wireless Personal Communications, vol. 125, no. 3, pp. 2319-2367, 2022.
Pourghebleh, B., Hayyolalam, V., and Aghaei Anvigh, A., "Service discovery in the Internet of Things: review of current trends and research challenges," Wireless Networks, vol. 26, no. 7, pp. 5371-5391, 2020.
Mell, P. and Grance, T., "The NIST definition of cloud computing," 2011.
Chen, Y., Zhang, N., Zhang, Y., Chen, X., Wu, W., and Shen, X. S., "TOFFEE: Task offloading and frequency scaling for energy efficiency of mobile devices in mobile edge computing," IEEE Transactions on Cloud Computing, vol. 9, no. 4, pp. 1634-1644, 2019.
Xiao, K., Gao, Z., Shi, W., Qiu, X., Yang, Y., and Rui, L., "EdgeABC: An architecture for task offloading and resource allocation in the Internet of Things," Future Generation Computer Systems, vol. 107, pp. 498-508, 2020.
He, Q. et al., "A game-theoretical approach for user allocation in edge computing environment," IEEE Transactions on Parallel and Distributed Systems, vol. 31, no. 3, pp. 515-529, 2019.
Pabla, C. S., "COMPLETELY FAIR SCHEDULER-Linux's latest scheduler makeover," Linux journal, no. 184, p. 68, 2009.
Chisnall, D., The definitive guide to the xen hypervisor. Pearson Education, 2008.
Xavier, M. G., Neves, M. V., Rossi, F. D., Ferreto, T. C., Lange, T., and De Rose, C. A., "Performance evaluation of container-based virtualization for high performance computing environments," in 2013 21st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, 2013, pp. 233-240: IEEE.
Dakshayini, D. M. and Guruprasad, D. H., "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.
Sun, Y., Guo, X., Zhou, S., Jiang, Z., Liu, X., and Niu, Z., "Learning-based task offloading for vehicular cloud computing systems," in 2018 IEEE International Conference on Communications (ICC), 2018, pp. 1-7: IEEE.
Chen, M. and Hao, Y., "Task offloading for mobile edge computing in software-defined ultra-dense network," IEEE Journal on Selected Areas in Communications, vol. 36, no. 3, pp. 587-597, 2018.
Hasan, R., Hossain, M., and Khan, R., "Aura: An incentive-driven ad-hoc IoT cloud framework for proximal mobile computation offloading," Future Generation Computer Systems, vol. 86, pp. 821-835, 2018.
Langmead, B. and Nellore, A., "Cloud computing for genomic data analysis and collaboration," Nature Reviews Genetics, vol. 19, no. 4, pp. 208-219, 2018.
Chen, J., Hu, K., Wang, Q., Sun, Y., Shi, Z., and He, S., "Narrowband internet of things: Implementations and applications," IEEE Internet of Things Journal, vol. 4, no. 6, pp. 2309-2314, 2017.
Firdhous, M., Ghazali, O., and Hassan, S., "Fog computing: Will it be the future of cloud computing?," 2014.
Li, J. (2020). Resource optimization scheduling and allocation for hierarchical distributed cloud service system in smart city. Future Generation Computer Systems, 107, 247-256.
Chen, M., Wang, L., Chen, J., Wei, X., & Lei, L. (2019). A computing and content delivery network in the smart city: Scenario, framework, and analysis. IEEE Network, 33(2), 89-95.
Wu, H., Zhang, Z., Guan, C., Wolter, K., & Xu, M. (2020). Collaborate edge and cloud computing with distributed deep learning for smart city internet of things. IEEE Internet of Things Journal, 7(9), 8099-8110.
Huang, H., Peng, K., & Liu, P. (2021, September). A privacy-aware Stackelberg game approach for joint pricing, investment, computation offloading and resource allocation in MEC-enabled smart cities. In 2021 IEEE International Conference on Web Services (ICWS) (pp. 651-656). IEEE.
Zhao, F., Fashola, O. I., Olarewaju, T. I., and Onwumere, I., "Smart city research: A holistic and state-of-the-art literature review," Cities, vol. 119, p. 103406, 2021.
Patrão, C., Moura, P., and Almeida, A. T. d., "Review of smart city assessment tools," Smart Cities, vol. 3, no. 4, pp. 1117-1132, 2020.
Jaddoa, A., Sakellari, G., Panaousis, E., Loukas, G., and Sarigiannidis, P. G., "Dynamic decision support for resource offloading in heterogeneous Internet of Things environments," Simulation Modelling Practice and Theory, vol. 101, p. 102019, 2020.
Wu, H. and Wolter, K., "Software aging in mobile devices: Partial computation offloading as a solution," in 2015 IEEE international symposium on software reliability engineering workshops (ISSREW), 2015, pp. 125-131: IEEE.
Kiani, A. and Ansari, N., "Optimal code partitioning over time and hierarchical cloudlets," IEEE Communications Letters, vol. 22, no. 1, pp. 181-184, 2017.
Mazza, D., Tarchi, D., and Corazza, G. E., "A partial offloading technique for wireless mobile cloud computing in smart cities," in 2014 European Conference on Networks and Communications (EuCNC), 2014, pp. 1-5: IEEE.
Wang, H., Li, X., Ji, H., and Zhang, H., "Dynamic offloading scheduling scheme for MEC-enabled vehicular networks," in 2018 IEEE/CIC International Conference on Communications in China (ICCC Workshops), 2018, pp. 206-210: IEEE.
You, Q. and Tang, B., "Efficient task offloading using particle swarm optimization algorithm in edge computing for industrial internet of things," Journal of Cloud Computing, vol. 10, pp. 1-11, 2021.
Naouri, A., Wu, H., Nouri, N. A., Dhelim, S., and Ning, H., "A novel framework for mobile-edge computing by optimizing task offloading," IEEE Internet of Things Journal, vol. 8, no. 16, pp. 13065-13076, 2021.
Long, C., Cao, Y., Jiang, T., and Zhang, Q., "Edge computing framework for cooperative video processing in multimedia IoT systems," IEEE Transactions on Multimedia, vol. 20, no. 5, pp. 1126-1139, 2017.
Tran, T. X. and Pompili, D., "Joint task offloading and resource allocation for multi-server mobile-edge computing networks," IEEE Transactions on Vehicular Technology, vol. 68, no. 1, pp. 856-868, 2018.
Pourghebleh, B., Hayyolalam, V., and Aghaei Anvigh, A., "Service discovery in the Internet of Things: review of current trends and research challenges," Wireless Networks, vol. 26, no. 7, pp. 5371-5391, 2020.
[35] Hu, S. and Li, G., "Dynamic request scheduling optimization in mobile edge computing for IoT applications," IEEE Internet of Things Journal, vol. 7, no. 2, pp. 1426-1437, 2019.
[36] Zhang, T., Xu, Y., Loo, J., Yang, D., and Xiao, L., "Joint computation and communication design for UAV-assisted mobile edge computing in IoT," IEEE Transactions on Industrial Informatics, vol. 16, no. 8, pp. 5505-5516, 2019.
Chen, B., Wan, J., Celesti, A., Li, D., Abbas, H., and Zhang, Q., "Edge computing in IoT-based manufacturing," IEEE Communications Magazine, vol. 56, no. 9, pp. 103-109, 2018.
Jiang, J., Li, Z., Tian, Y., and Al-Nabhan, N., "A review of techniques and methods for IoT applications in collaborative cloud-fog environment," Security and Communication Networks, vol. 2020, pp. 1-15, 2020.
Zhu, S., Gui, L., Chen, J., Zhang, Q., and Zhang, N., "Cooperative computation offloading for UAVs: A joint radio and computing resource allocation approach," in 2018 IEEE International Conference on Edge Computing (EDGE), 2018, pp. 74-79: IEEE.
Ouyang, T., Zhou, Z., and Chen, X., "Follow me at the edge: Mobility-aware dynamic service placement for mobile edge computing," IEEE Journal on Selected Areas in Communications, vol. 36, no. 10, pp. 2333-2345, 2018.
Junior, W., Oliveira, E., Santos, A., and Dias, K., "A context-sensitive offloading system using machine-learning classification algorithms for mobile cloud environment," Future Generation Computer Systems, vol. 90, pp. 503-520, 2019.
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