A learning automata based edge resource allocation approach for IoT-enabled smart cities

Abstract

The development of the Internet of Things (IoT) technology is leading to a new era of smart applications such as smart transportation, buildings, and smart homes. Moreover, these applications act as the building blocks of IoT-enabled smart cities. The high volume and high velocity of data generated by various smart city applications are sent to flexible and efficient cloud computing resources for processing. However, there is a high computation latency due to the presence of a remote cloud server. Edge computing, which brings the computation close to the data source is introduced to overcome this problem. In an IoT-enabled smart city environment, one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay constraint. An efficient resource allocation at the edge is helpful to address this issue. In this paper, an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation problem. First, we presented a three-layer network architecture for IoT-enabled smart cities. Then, we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization problem. Learning Automata (LA) is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource mapping. An extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.

Keywords

Edge computing / IoT / Learning automata / Resource allocation / Smart city

References

[1] S. Sahoo, K.S. Sahoo, B. Sahoo, A.H. Gandomi, An auction based edge resource allocation mechanism for iot-enabled smart cities, in: 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2020, pp. 1280-1286.

[2] X. Liu, J. Yu, J. Wang, Y. Gao, Resource allocation with edge computing in iot networks via machine learning, IEEE Int. Things J. 7(4) (2020) 3415-3426.

[3] H. Sun, H. Yu, G. Fan, L. Chen, Energy and time efficient task offloading and re-source allocation on the generic iot-fog-cloud architecture, Peer-to-Peer Netw. Appl. 13 (2) (2020) 548-563.

[4] Q. Wang, S. Chen, Latency-minimum offloading decision and resource allocation for fog-enabled Internet of things networks, Trans. Emerg. Telecommun. Technol.(2020) e3880.

[5] Q. Fan, N. Ansari, Application aware workload allocation for edge computing-based iot, IEEE Int. Things J. 5(3) (2018) 2146-2153.

[6] J. Xu, B. Palanisamy, H. Ludwig, Q. Wang, Zenith: utility-aware resource allocation for edge computing, in: 2017 IEEE International Conference on Edge Computing, EDGE, 2017, pp. 47-54.

[7] K.S. Narendra, M.A. Thathachar, Learning automata-a survey, IEEE Trans. Syst. Man Cybern. 4 (1974) 323-334.

[8] A. Rezvanian, M.R. Meybodi, Finding minimum vertex covering in stochastic graphs: a learning automata approach, Cybern. Syst. 46 (8) (2015) 698-727.

[9] M. Ranjbari, J.A. Torkestani, A learning automata-based algorithm for energy and sla efficient consolidation of virtual machines in cloud data centers, J. Parallel Dis-trib. Comput. 113 (2018) 55-62.

[10] K. Xiao, Z. Gao, W. Shi, X. Qiu, Y. Yang, L. Rui, Edgeabc: an architecture for task offloading and resource allocation in the Internet of things, Future Gener. Comput. Syst. 107 (2020) 498-508.

[11] S. Xia, Z. Yao, Y. Li, S. Mao, Online distributed offloading and computing resource management with energy harvesting for heterogeneous mec-enabled iot, IEEE Trans. Wirel. Commun. 20 (10) (2021) 6743-6757.

[12] L. Zhao, J. Wang, J. Liu, N. Kato, Optimal edge resource allocation in iot-based smart cities, IEEE Netw. 33 (2) (2019) 30-35.

[13] B. Mohebali, A. Tahmassebi, A.H. Gandomi, A. Meyer-Baese, A Big Data Inspired Preprocessing Scheme for Bandwidth Use Optimization in Smart Cities Applications Using Raspberry Pi, Big Data: Learning, Analytics, and Applications, vol. 10989, International Society for Optics and Photonics, 2019, p. 1098902.

[14] K.S. Sahoo, P. Mishra, M. Tiwary, S. Ramasubbareddy, B. Balusamy, A.H. Gandomi, Improving end-users utility in software-defined wide area network systems, IEEE Trans. Netw. Serv. Manag. 17 (2) (2019) 696-707.

[15] Y. Li, H. Ma, L. Wang, S. Mao, G. Wang, Optimized content caching and user associ-ation for edge computing in densely deployed heterogeneous networks, IEEE Trans. Mob. Comput. (2020).

[16] Y. Li, J. Liu, B. Cao, C. Wang, Joint optimization of radio and virtual machine resources with uncertain user demands in mobile cloud computing, IEEE Trans. Mul-timed. 20 (9) (2018) 2427-2438.

[17] S. Mao, J. Wu, L. Liu, D. Lan, A. Taherkordi, Energy-efficient cooperative communi-cation and computation for wireless powered mobile-edge computing, IEEE Syst. J.(2020).

[18] F. Zeng, Q. Chen, L. Meng, J. Wu, Volunteer assisted collaborative offloading and resource allocation in vehicular edge computing, IEEE Trans. Intell. Transp. Syst. 22 (6) (2020) 3247-3257.

[19] H. Song, J. Bai, Y. Yi, J. Wu, L. Liu, Artificial intelligence enabled Internet of things: network architecture and spectrum access, IEEE Comput. Intell. Mag. 15 (1) (2020) 44-51.

[20] J. Wu, S. Guo, H. Huang, W. Liu, Y. Xiang, Information and communications tech-nologies for sustainable development goals: state-of-the-art, needs and perspectives, IEEE Commun. Surv. Tutor. 20 (3) (2018) 2389-2406.

[21] S. Sahoo, B. Sahoo, A.K. Turuk, A learning automata-based scheduling for deadline sensitive task in the cloud, IEEE Trans. Serv. Comput. 14 (6) (2019) 1662-1674.

[22] M. Thathachar, B.R. Harita, Learning automata with changing number of actions, IEEE Trans. Syst. Man Cybern. 17 (6) (1987) 1095-1100.

[23] A. Mukhopadhyay, M. Ruffini,Learning automata for multi-access edge computing server allocation with minimal service migration, in: ICC 2020-2020 IEEE Interna-tional Conference on Communications (ICC), IEEE, 2020, pp. 1-6.

[24] X. Deng, Y. Jiang, L.T. Yang, L. Yi, J. Chen, Y. Liu, X. Li, Learning-automata-based confident information coverage barriers for smart ocean Internet of things, IEEE Int. Things J. 7 (10) (2020) 9919-9929.

[25] S. Gheisari, E. Tahavori, Cccla: a cognitive approach for congestion control in In-ternet of things using a game of learning automata, Comput. Commun. 147 (2019) 40-49.

[26] W. Li, E. Özcan, R. John, A learning automata-based multiobjective hyper-heuristic, IEEE Trans. Evol. Comput. 23 (1) (2017) 59-73.

[27] A. Rezvanian, B. Moradabadi, M. Ghavipour, M.M.D. Khomami, M.R. Meybodi, In-troduction to learning automata models, in: Learning Automata Approach for Social Networks, Springer, 2019, pp. 1-49.

[28] G. Velusamy, R. Lent, Dynamic cost-aware routing of web requests, Future Internet 10 (7) (2018) 57.

[29] M.S. Aslanpour, M. Ghobaei-Arani, M. Heydari, N. Mahmoudi, Larpa: a learning automata-based resource provisioning approach for massively multiplayer online games in cloud environments, Int. J. Commun. Syst. 32 (14) (2019) e4090.

[30] S. Sahoo, B. Sahoo, A.K. Turuk, An energy-efficient scheduling framework for cloud using learning automata, in: 2018 9th International Conference on Computing, Com-munication and Networking Technologies (ICCCNT), 2018, pp. 1-5.