nach oben

Cluster Computing

Erschienen in:

05.06.2023

Task and resource allocation in the internet of things based on an improved version of the moth-flame optimization algorithm

verfasst von: Masoud Nematollahi, Ali Ghaffari, A. Mirzaei

Erschienen in: Cluster Computing | Ausgabe 2/2024

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The Internet of Things (IoT) technology is used to develop a wide range of applications and services, including intelligent healthcare systems and virtual reality applications. Low processing power limits IoT devices’ capabilities. It’s common practice to use cloud services to do operations that would otherwise require a user’s device to be overloaded with data. High latency, high traffic, and high energy consumption remain, though. Given the above concerns, Fog Computing (FC) should be applied in the IoT to speed up time-sensitive data processing and management. In this study, a novel architecture for offloading jobs and allocating resources in the IoT is presented. Sensors, controllers, and FC servers are all part of the upgraded system. The second layer uses the subtask pool approach to offload work and the Moth-Flame Optimization (MFO) algorithm combined with Opposition-based Learning (OBL) to distribute resources. This combination is known as OBLMFO. A stack cache approach is used to complete resource allocation in the second layer to avoid system load imbalance. In addition, the second layer relies on the blockchain to guarantee the accuracy of transaction data. Another way to put it is that the proposed architecture utilizes blockchain advantages to optimize resource distribution in the IoT. The evaluation of the OBLMFO model was done through the Python 3.9 environment, which contains a large variety of distinct jobs. The results show that the OBLMFO model reduced the delay factor by 12.18% and the energy consumed by 6.22%.

Vorheriger Artikel A novel sparrow search algorithm with integrates spawning strategy

Nächster Artikel An efficient multi-objective scheduling algorithm based on spider monkey and ant colony optimization in cloud computing

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Sobin, C.C.: A Survey on Architecture, Protocols and Challenges in IoT. Wirel. Pers. Commun. 112(3), 1383–1429 (2020)

Sun, J., Wang, H., Feng, G., Lv, H., Liu, J., Gao, Z.: TOS-LRPLM: a task value-aware offloading scheme in IoT edge computing system. Clust. Comput. 26(1), 319–335 (2023)

Khanna, A., Kaur, S.: Internet of Things (IoT), applications and Challenges: a Comprehensive Review. Wirel. Pers. Commun. 114(2), 1687–1762 (2020)

Marietta, J., Chandra Mohan, B.: A Review on routing in internet of things. Wirel. Pers. Commun. 111(1), 209–233 (2020)

Pratap, A., Gupta, R., Nadendla, V.S.S., Das, S.K.: Bandwidth-constrained task throughput maximization in IoT-enabled 5G networks. Pervasive Mob. Comput. 69(1), 101281 (2020)

Jazebi, S.J., Ghaffari, A.: RISA: routing scheme for internet of things using shuffled frog leaping optimization algorithm. J. Ambient. Intell. Humaniz. Comput. 11(10), 4273–4283 (2020)

Karthick, T., Chandrasekaran, K.: Design of IoT based smart compact energy meter for monitoring and controlling the usage of energy and power quality issues with demand side management for a commercial building. Sustain. Energy, Grids Netw. 26(3), 100454 (2021)

Sharma, S., Saini, H.: A novel four-tier architecture for delay aware scheduling and load balancing in fog environment. Sustain. Comput.: Inf. Syst. 24(1), 100355 (2019)

Bellavista, P., Berrocal, J., Corradi, A., Das, S.K., Foschini, L., Zanni, A.: A survey on fog computing for the Internet of Things. Pervasive Mob. Comput. 52(5), 71–99 (2019)

10.

Singh, R., Gill, S.S.: Edge AI: a survey. Int. Things Cyber-Phys. Syst. 3, 71–92 (2023)

11.

Shi, Z., Wei, H., Zhu, J.: Edge computing-empowered task offloading in PLC-wireless integrated network based on matching with quota. Comput. Commun. 182(10), 110–116 (2022)

12.

Xu, J., Li, D., Gu, W., Chen, Y.: UAV-assisted task offloading for IoT in smart buildings and environment via deep reinforcement learning. Build. Environ. 222(2), 109218 (2022)

13.

Shahryari, O.-K., Pedram, H., Khajehvand, V., TakhtFooladi, M.D.: Energy and task completion time trade-off for task offloading in fog-enabled IoT networks. Pervasive Mob. Comput. 10(1), 101395 (2021)

14.

Gao, J., Chang, R., Yang, Z., Huang, Q., Zhao, Y., Wu, Y.: A task offloading algorithm for cloud-edge collaborative system based on Lyapunov optimization. Clust. Comput. 26(1), 337–348 (2023)

15.

Emami Khansari, M., Sharifian, S.: A modified water cycle evolutionary game theory algorithm to utilize QoS for IoT services in cloud-assisted fog computing environments. J. Supercomput. 76(7), 5578–5608 (2020)

16.

Aazam, M., Zeadally, S., Harras, K.A.: Offloading in fog computing for IoT: review, enabling technologies, and research opportunities. Futur. Gener. Comput. Syst. 87(6), 278–289 (2018)

17.

Xavier, T.C.S., Santos, I.L., Delicato, F.C., Pires, P.F., Alves, M.P., Calmon, T.S., Oliveira, A.C., Amorim, C.L.: Collaborative resource allocation for Cloud of Things systems. J. Netw. Comput. Appl. 159(1), 102592 (2020)

18.

Wang, K.: Energy-efficient resource allocation optimization algorithm in industrial IoTs scenarios based on energy harvesting. Sustain. Energy Technol. Assess. 45(1), 101201 (2021)

19.

Yang, S.: A joint optimization scheme for task offloading and resource allocation based on edge computing in 5G communication networks. Comput. Commun. 160(1), 759–768 (2020)

20.

Kamalinia, A., Ghaffari, A.: Hybrid Task scheduling method for cloud computing by genetic and DE algorithms. Wirel. Pers. Commun. 97(4), 6301–6323 (2017)

21.

Wang, Z., Lv, T., Chang, Z.: Computation offloading and resource allocation based on distributed deep learning and software defined mobile edge computing. Comput. Netw. 205(2), 108732 (2022)

22.

Zhang, K., Gui, X., Ren, D., Du, T., He, X.: Optimal pricing-based computation offloading and resource allocation for blockchain-enabled beyond 5G networks. Comput. Netw. 203(1), 108674 (2022)

23.

Hossain, M.S., Nwakanma, C.I., Lee, J.M., Kim, D.-S.: Edge computational task offloading scheme using reinforcement learning for IIoT scenario. ICT Express. 6(4), 291–299 (2020)

24.

Mirjalili, S.: Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl.-Based Syst. 89(3), 228–249 (2015)

25.

Tizhoosh, H. R.: Opposition-Based Learning: A New Scheme for Machine Intelligence. in International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06). (2005).

26.

Xiao, K., Gao, Z., Shi, W., Qiu, X., Yang, Y., Rui, L.: EdgeABC: an architecture for task offloading and resource allocation in the internet of things. Futur. Gener. Comput. Syst. 107(2), 498–508 (2020)

27.

Seyfollahi, A., Moodi, M., Ghaffari, A.: MFO-RPL: a secure RPL-based routing protocol utilizing moth-flame optimizer for the IoT applications. Comput. Stand. Interfaces. 82, 103622 (2022)

28.

Hussein, M.K., Mousa, M.H.: Efficient task offloading for IoT-based applications in fog computing using ant colony optimization. IEEE Access. 8, 37191–37201 (2020)

29.

Alqarni, M., Cherif, A., Alkayyal, E.: ODM-BCSA: an offloading decision-making framework based on binary cuckoo search algorithm for mobile edge computing. Comput. Netw. 226, 109647 (2023)

30.

Liao, L., Lai, Y., Yang, F., Zeng, W.: Online computation offloading with double reinforcement learning algorithm in mobile edge computing. J. Parallel Distrib. Comput. 171, 28–39 (2023)

31.

Gulec, O., Sahin, E.: Red deer algorithm based nano-sensor node clustering for IoNT. J. Netw. Comput. Appl. 213, 103591 (2023)

32.

Jagadeesh, S., Muthulakshmi, I.: A novel oppositional artificial fish swarm based clustering with improved moth flame optimization based routing protocol for wireless sensor networks. Energy Syst. (2022). https://doi.org/10.1007/s12667-022-00534-3CrossRef

33.

Sayed, G.I., Darwish, A., Hassanien, A.E.: Binary whale optimization algorithm and binary moth flame optimization with clustering algorithms for clinical breast cancer diagnoses. J. Classif. 37(1), 66–96 (2020)MathSciNet

34.

Gupta, D., Ahlawat, A.K., Sharma, A., Rodrigues, J.J.P.C.: Feature selection and evaluation for software usability model using modified moth-flame optimization. Computing 102(6), 1503–1520 (2020)MathSciNet

35.

Barham, R., Sharieh, A., Sleit, A.: Multi-moth flame optimization for solving the link prediction problem in complex networks. Evol. Intel. 12(4), 563–591 (2019)

36.

Sayed, G.I., Hassanien, A.E.: Moth-flame swarm optimization with neutrosophic sets for automatic mitosis detection in breast cancer histology images. Appl. Intell. 47(2), 397–408 (2017)

37.

Shukla, P., Pandey, S., Hatwar, P., Pant, A.: FAT-ETO: fuzzy-AHP-TOPSIS-based efficient task offloading algorithm for scientific workflows in heterogeneous fog-cloud environment. Proc. Natl. Acad. Sci., India, Sect. A 93(2), 339–353 (2023)MathSciNet

38.

Senthil Kumar, A.M., Padmanaban, K., Velmurugan, A.K., Asha Shiny, X.S., Anguraj, D.K.: A novel resource management framework in a cloud computing environment using hybrid cat swarm BAT (HCSBAT) algorithm. Distrib. Parallel Databases 41(1), 53–63 (2023)

39.

Chen, Y., Chen, S., Li, K.-C., Liang, W., Li, Z.: DRJOA: intelligent resource management optimization through deep reinforcement learning approach in edge computing. Cluster Comput. (2022). https://doi.org/10.1007/s10586-022-03768-zCrossRef

40.

Gupta, S., Singh, N.: Fog-GMFA-DRL: enhanced deep reinforcement learning with hybrid grey wolf and modified moth flame optimization to enhance the load balancing in the fog-IoT environment. Adv. Eng. Softw. 174, 103295 (2022)

41.

Wu, S., Xue, H., Zhang, L.: Q-learning-aided offloading strategy in edge-assisted federated learning over industrial IoT. Electronics (2023). https://doi.org/10.3390/electronics12071706CrossRef

42.

Wei, Z., Pan, J., Lyu, Z., Xu, J., Shi, L., Xu, J.: An offloading strategy with soft time windows in mobile edge computing. Comput. Commun. 164(1), 42–49 (2020)

43.

Cui, Y.-Y., Zhang, D.-G., Zhang, T., Zhang, J., Piao, M.: A novel offloading scheduling method for mobile application in mobile edge computing. Wirel. Netw. 28(6), 2345–2363 (2022)

44.

Elgendy, I.A., Zhang, W., Tian, Y.-C., Li, K.: Resource allocation and computation offloading with data security for mobile edge computing. Futur. Gener. Comput. Syst. 100(4), 531–541 (2019)

45.

Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for IoT-enabled cloud-edge computing. Futur. Gener. Comput. Syst. 95(2), 522–533 (2019)

46.

Bi, J., Yuan, H., Duanmu, S., Zhou, M., Abusorrah, A.: Energy-optimized partial computation offloading in mobile-edge computing with genetic simulated-annealing-based particle swarm optimization. IEEE Internet Things J. 8(5), 3774–3785 (2021)

47.

Lu, F., Gu, L., Yang, L.T., Shao, L., Jin, H.: Mildip: an energy efficient code offloading framework in mobile cloudlets. Inf. Sci. 513(10), 84–97 (2020)

48.

Al-Mahruqi, A.A.H., Morison, G., Stewart, B.G., Athinarayanan, V.: Hybrid heuristic algorithm for better energy optimization and resource utilization in cloud computing. Wirel. Pers. Commun. 11, 1 (2021)

49.

Vhatkar, K.N., Bhole, G.P.: Optimal container resource allocation in cloud architecture: a new hybrid model. J. King Saud Univ.—Comput. Inf. Sci. 10(1), 1–15 (2019)

50.

N. Mansouri, B. Mohammad, M. M. Javidi, (2019) Hybrid task scheduling strategy for cloud computing by modified particle swarm optimization and fuzzy theory," Computers & Industrial Engineering. Vol. 130, No. 6, pp. 597-633, 2019.

51.

Hussain, A., Manikanthan, S.V., Padmapriya, T., Nagalingam, M.: Genetic algorithm based adaptive offloading for improving IoT device communication efficiency. Wirel. Netw. 26(4), 2329–2338 (2020)

52.

Nanjappan, M., Natesan, G., Krishnadoss, P.: An adaptive neuro-fuzzy inference system and black widow optimization approach for optimal resource utilization and task scheduling in a cloud environment. Wirel. Pers. Commun. 121(3), 1891–1916 (2021)

53.

Hmimz, Y., Chanyour, T., El Ghmary, M., Cherkaoui Malki, M.O.: Joint radio and local resources optimization for tasks offloading with priority in a mobile edge computing network. Pervasive Mob. Comput. 73(1), 101368 (2021)

54.

Kuang, L., Gong, T., OuYang, S., Gao, H., Deng, S.: Offloading decision methods for multiple users with structured tasks in edge computing for smart cities. Futur. Gener. Comput. Syst. 105(10), 717–729 (2020)

55.

Abbasi, M., Mohammadi Pasand, E., Khosravi, M.R.: Workload allocation in IoT-fog-cloud architecture using a multi-objective genetic algorithm. J. Grid Comput. 18(1), 43–56 (2020)

56.

Abdel-Basset, M., Mohamed, R., Elhoseny, M., Bashir, A.K., Jolfaei, A., Kumar, N.: Energy-aware marine predators algorithm for task scheduling in IoT-based fog computing applications. IEEE Trans. Industr. Inf. 17(7), 5068–5076 (2021)

57.

Adhikari, M., Srirama, S.N., Amgoth, T.: Application offloading strategy for hierarchical fog environment through swarm optimization. IEEE Internet Things J. 7(5), 4317–4328 (2020)

58.

Molinet Berenguer, J. A., Coello Coello, C. A.: Evolutionary Many-Objective Optimization Based on Kuhn-Munkres’ Algorithm. in Evolutionary Multi-Criterion Optimization. Cham, Springer International Publishing (2015)

59.

Ahandani, M.A.: Opposition-based learning in the shuffled bidirectional differential evolution algorithm. Swarm Evol. Comput. 26(1), 64–85 (2016)

60.

You, C., Huang, K., Chae, H., Kim, B.H.: Energy-efficient resource allocation for mobile-edge computation offloading. IEEE Trans. Wirel. Commun. 16(3), 1397–1411 (2017)

61.

Shannon, C.E.: A mathematical theory of communication. The Bell Syst. Tech. J. 27(3), 379–423 (1948)MathSciNet

Titel: Task and resource allocation in the internet of things based on an improved version of the moth-flame optimization algorithm
verfasst von: Masoud Nematollahi
Ali Ghaffari
A. Mirzaei
Publikationsdatum: 05.06.2023
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe 2/2024
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-023-04041-7

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 2/2024

Hybrid cuckoo finch optimisation based machine learning classifier for seizure prediction using EEG signals in IoT network

Prototypical contrastive learning for image classification

CUDA-aware MPI implementation of Gibbs sampling for an IRT model

Improved Harris Hawks optimization for global optimization and engineering design

An efficient cloud resource exchange model based on the double auction and evolutionary game theory

Emotion and sentiment analysis for intelligent customer service conversation using a multi-task ensemble framework

Premium Partner