Abstract
IoT is one of the most significant technological breakthroughs and promises a higher level of connection and control in the future. The IoT network continues to expand rapidly, and the IoT ecosystem comprises millions of interconnected ad hoc devices across the network. Effective resource utilization guarantees the improvement of service quality. Everything is connected to the Internet through the distribution system known as the Internet of Things (IoT). Plenty of gateways and resources are in IoT infrastructure. Resource allocation (RA) is challenging due to network heterogeneity and the diversity of IoT devices; numerous practical approaches, strategies, and implementations are being presented and employed to resolve the RA problem (RAP). IoT resource allocation and scheduling (RAS) performance is essential in such a system since RAS allocates resources to open gateways and handles mapping resources and gateways. A gateway is needed to connect to hundreds of resources in the IoT environment. The proposed work is based on the RAS problem and aims to achieve optimal RA in the IoT by using the Elephant Herding Optimization (EHO) algorithm to lower the total Communication Cost between gateways and resources. The proposed EHO algorithm has been contrasted with others already in use, and the results show that the suggested algorithm performs as expected. The proposed solution is superior to others regarding TCC and Convergence rate than Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Gray Wolf Optimization (GWO).
Similar content being viewed by others
Data availibility statement
Yes, data availability: GoCJ: Google Cloud Jobs Dataset https://data.mendeley.com/datasets/b7bp6xhrcd/1 (accessed on 11-7-2022) Hussain, Altaf; Aleem, Muhammad (2018), GoCJ: Google Cloud Jobs Dataset, Mendeley Data, V1, https://doi.org/10.17632/b7bp6xhrcd.1. 2. EEE Access Dataset from ieee-dataport https://ieeedataport.org.
References
Smys S (2020) A survey on internet of things (IoT) based smart systems. J ISMAC 2(04):181–9
Prabhakara Rao T, Satyanarayana Murthy B (2023) Extended group-based verification approach for secure M2M communications. Int J Inf Technol 1–10
Luckshmi AI, Visalakshi P, Karthikeyan N (2011) Intelligent schemes for bandwidth allocation in cellular mobile networks. In: 2011 international conference on process automation, control and computing. IEEE, pp. 1–6
Kim KS, Uno S, Kim MW (2010) Adaptive QoS mechanism for wireless mobile network. J Comput Sci Eng 4(2):153–72
Sen AAA, Yamin M (2021) Advantages of using fog in IoT applications. Int J Inf Technol 13:829–37
Alli AA, Alam MM (2020) The fog cloud of things: a survey on concepts, architecture, standards, tools, and applications. Internet Things 9:100177
Xu Y, Gui G, Gacanin H, Adachi F (2021) A survey on resource allocation for 5G heterogeneous networks: current research, future trends, and challenges. IEEE Commun Surv Tutor 23(2):668–95
Ramegowda A, Agarkhed J, Patil SR (2020) Adaptive task scheduling method in multi-tenant cloud computing. Int J Inf Technol 12:1093–102
Keller T (2011) Mining the internet of things: detection of false-positive RFID tag reads using low-level reader data. na
Aggarwal CC (2013) Managing and mining sensor data. Springer, New York
Kim M, Ko IY (2015) An efficient resource allocation approach based on a genetic algorithm for composite services in IoT environments. In: (2015) IEEE international conference on web services. IEEE 2015, pp 543–550
Avval DB, Heris PO, Navimipour NJ, Mohammadi B, Yalcin S (2022) A new QoS-aware method for production scheduling in the industrial internet of things using elephant herding optimization algorithm. Clust Comput 1–16
Ajmera K, Tewari TK (2023) Energy-efficient virtual machine scheduling in IaaS cloud environment using energy-aware green-particle swarm optimization. Int J Inf Technol 15(4):1927–35
Sumathi M, Vijayaraj N, Raja SP, Rajkamal M (2023) HHO-ACO hybridized load balancing technique in cloud computing. Int J Inf Technol 15(3):1357–65
Neelakantan P, Yadav NS (2023) Proficient job scheduling in cloud computation using an optimized machine learning strategy. Int J Inf Technol 1–13
Kanagaraj G, Subashini G (2023) Uniform distribution elephant herding optimization (UDEHO) based virtual machine consolidation for energy-efficient cloud data centres. Automatika 64(3):530–40
Trojovskỳ P, Dehghani M (2023) A new bio-inspired metaheuristic algorithm for solving optimization problems based on walruses behavior. Sci Rep 13(1):8775
Sangaiah AK, Hosseinabadi AAR, Shareh MB, Bozorgi Rad SY, Zolfagharian A, Chilamkurti N (2020) IoT resource allocation and optimization based on heuristic algorithm. Sensors 20(2):539
Sangaiah AK, Hosseinabadi AAR, Shareh MB, Bozorgi Rad SY, Zolfagharian A, Chilamkurti N (2020) IoT resource allocation and optimization based on heuristic algorithm. Sensors 20(2):539
Ali SM, Kumaran N, Balaji G (2023) Hybrid elephant herding optimization and Flamingo search algorithm for effective load balancing in cloud computing. Int J Intell Syst Appl Eng 11(3):872–82
An B, Lesser V, Irwin D, Zink M (2010) Automated negotiation with decommitment for dynamic resource allocation in cloud computing. In: Proceedings of the 9th international conference on autonomous agents and multiagent systems: volume 1–volume 1, pp 981–988
Aerts JC, Heuvelink GB (2002) Using simulated annealing for resource allocation. Int J Geogr Inf Sci 16(6):571–87
Bouleimen K, Lecocq H (2003) A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version. Eur J Oper Res 149(2):268–81
Belfares L, Klibi W, Lo N, Guitouni A (2007) Multi-objectives Tabu Search based algorithm for progressive resource allocation. Eur J Oper Res 177(3):1779–99
Kim M, Ko IY (2015) An efficient resource allocation approach based on a genetic algorithm for composite services in IoT environments. In: (2015) IEEE international conference on web services. IEEE 2015, pp 543–550
Chaharsooghi SK, Kermani AHM (2008) An effective ant colony optimization algorithm (ACO) for multi-objective resource allocation problem (MORAP). Appl Math Comput 200(1):167–77
Yin PY, Wang JY (2006) A particle swarm optimization approach to the nonlinear resource allocation problem. Appl Math Comput 183(1):232–42
Lee ZJ, Lee CY (2005) A hybrid search algorithm with heuristics for resource allocation problem. Inf Sci 173(1–3):155–67
Dai YS, Wang XL (2006) Optimal resource allocation on grid systems for maximizing service reliability using a genetic algorithm. Reliab Eng Syst Saf 91(9):1071–82
Tsai CW, Lai CF, Chiang MC, Yang LT (2013) Data mining for internet of things: a survey. IEEE Commun Surv Tutor 16(1):77–97
Krishnapriya S, Joby P (2015) QoS aware resource scheduling in internet of things-cloud environment. Int J Sci Eng Res 6(4)
Jain RK, Chiu DMW, Hawe WR et al (1984) A quantitative measure of fairness and discrimination. Digital Equipment Corporation, Hudson, MA, Eastern Research Laboratory, p 21
Balasubramanian A, Levine B, Venkataramani A (2007) DTN routing as a resource allocation problem. In: Proceedings of the 2007 conference on applications, technologies, architectures, and protocols for computer communications, pp 373–384
Lin WY, Lin GY, Wei HY (2010) Dynamic auction mechanism for cloud resource allocation. In: 2010 10th IEEE/ACM international conference on cluster, cloud and grid computing. IEEE, pp 591–592
Funding
This paper does not get funds from any funding agency, institute, or company.
Author information
Authors and Affiliations
Contributions
All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version.
Corresponding author
Ethics declarations
Conflict of interest
This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. Authors have no conflict of interest.
Ethical approval
Followed the ethics of research. This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Mageswari, U., Deepak, G., Santhanavijayan, A. et al. The IoT resource allocation and scheduling using Elephant Herding Optimization (EHO-RAS) in IoT environment. Int. j. inf. tecnol. (2024). https://doi.org/10.1007/s41870-024-01800-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s41870-024-01800-6