Abstract
The number of connected devices contributing to the Internet of Things (IoT) has grown exponentially due to recent developments in wireless technology. The advent of IoT adds an entirely new category of applications to current services. Since the services are regulated by contact among objects, advantages are now being enhanced by utilizing these services. Many sensors and things are installed to track one or more activities. Hence, the load balancing protocol is essential in wireless IoT device architecture. To meet the QoS needs of IoT applications, it is crucial to measure, balance, analyze, and optimize these devices. Moreover, the IoT's vast amount of data and its processing can result in network outages. Studies on load balancing have primarily been conducted on cloud-based systems, and this challenge is an NP-hard problem. Consequently, this paper suggests a new energy-aware method for balancing the load on wireless IoT devices using a biogeography-inspired algorithm named Biogeography-Based Optimization (BBO) based on chaos theory. The BBO algorithm can become trapped in local optima. Chaos theory is one of the most effective techniques for improving the performance of evolutionary algorithms in terms of both the avoidance of local optimums and the rate of convergence. Therefore, the combination of these algorithms is suggested in this paper to improve the efficiency of the balancing method. The effectiveness of the method is simulated using MATLAB. Related current methods are compared to the proposed method, and the findings showed substantial improvements in delay time and load balancing using the proposed technique. The proposed method has decreased the delay time and energy consumption by 7.58% and 15% compared to other methods.
Similar content being viewed by others
Data Availability Statement
All data are reported in the paper.
References
Gardas, B. B., et al. (2022). A fuzzy-based method for objects selection in blockchain-enabled edge-IoT platforms using a hybrid multi-criteria decision-making model. Applied Sciences, 12(17), 8906.
Vahdat, S. (2021). The role of IT-based technologies on the management of human resources in the COVID-19 era. Kybernetes, 17, 265.
Li, X., et al. (2020). Cooperative wireless-powered NOMA relaying for B5G IoT networks with hardware impairments and channel estimation errors. IEEE Internet of Things Journal, 8(7), 5453–5467.
Lee, I., & Lee, K. (2015). The internet of things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), 431–440.
Karahoca, A., Karahoca, D., & Aksöz, M. (2018). Examining intention to adopt to internet of things in healthcare technology products. Kybernetes, 47, 742.
Baccelli, E., et al. (2018). RIOT: An open source operating system for low-end embedded devices in the IoT. IEEE Internet of Things Journal, 5(6), 4428–4440.
Sharma, G., & Kalra, S. (2019). A lightweight user authentication scheme for cloud-IoT based healthcare services. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 43(1), 619–636.
Al-Janabi, T.A., Al-Raweshidy, H.S. (2017). Optimised clustering algorithm-based centralised architecture for load balancing in IoT network. in 2017 International symposium on wireless communication systems (ISWCS). IEEE.
Gong, J., & Navimipour, N. J. (2022). An in-depth and systematic literature review on the blockchain-based approaches for cloud computing. Cluster Computing, 25(1), 383–400.
Heidari, A., & Navimipour, N. J. (2021). Service discovery mechanisms in cloud computing: a comprehensive and systematic literature review. Kybernetes, 51, 952.
Ghiasi, M., et al. (2022). Evolution of smart grids towards the Internet of energy: Concept and essential components for deep decarbonisation. IET Smart Grid. https://doi.org/10.1049/stg2.12095
Liu, Y., et al (2017). Wireless mesh networks in IoT networks. in 2017 International workshop on electromagnetics: applications and student innovation competition. IEEE.
Adhikari, M., & Amgoth, T. (2018). Heuristic-based load-balancing algorithm for IaaS cloud. Future Generation Computer Systems, 81, 156–165.
Rui, X., et al. (2020). Load balancing in the internet of things using fuzzy logic and shark smell optimization algorithm. Circuit World, 47(4), 335.
Ghomi, E. J., Rahmani, A. M., & Qader, N. N. (2017). Load-balancing algorithms in cloud computing: A survey. Journal of Network and Computer Applications, 88, 50–71.
Zhang, T.T., et al (2018). Research on image encryption based on dna sequence and chaos theory. in Journal of physics: conference series. . IOP Publishing.
Anter, A. M., & Ali, M. (2020). Feature selection strategy based on hybrid crow search optimization algorithm integrated with chaos theory and fuzzy c-means algorithm for medical diagnosis problems. Soft Computing, 24(3), 1565–1584.
Sangaiah, A. K., et al. (2019). A novel quality-of-service-aware web services composition using biogeography-based optimization algorithm. Soft Computing, 24, 1–13.
Kadhim, A. S., & Manaa, M. E. (2022). Hybrid load-balancing algorithm for distributed fog computing in internet of things environment. Bulletin of Electrical Engineering and Informatics, 11(6), 3462–3470.
Abdulhammed, O. Y. (2022). Load balancing of IoT tasks in the cloud computing by using sparrow search algorithm. The Journal of Supercomputing, 78(3), 3266–3287.
Mayilsamy, J., & Rangasamy, D. P. (2021). Load Balancing in Software-Defined Networks Using Spider Monkey Optimization Algorithm for the Internet of Things. Wireless Personal Communications, 116(1), 23–43.
Rui, X., et al. (2020). Load balancing in the internet of things using fuzzy logic and shark smell optimization algorithm. Circuit World, 47, 335.
Yu, R., et al (2019). Load balancing for interdependent IoT microservices. in IEEE INFOCOM 2019-IEEE conference on computer communications. IEEE.
Gohil, B.N. and D.R. Patel (2018). A hybrid GWO-PSO algorithm for load balancing in cloud computing environment. in Proceedings of the second international conference on green computing and internet of things (ICGCIoT), Bangalore, India.
Shi, C., et al (2018). Ultra-low latency cloud-fog computing for industrial internet of things. in 2018 IEEE wireless communications and networking conference (WCNC). IEEE.
LD, D. B., & Krishna, P. V. (2013). Honey bee behavior inspired load balancing of tasks in cloud computing environments. Applied soft computing, 13(5), 2292–2303.
Cai, Z., Bourgeois, A., & Tong, W. (2017). Guest editorial: Special issue on Internet of Things. Tsinghua Science and Technology, 22(4), 343–344.
Kishor, A., C. Chakraborty, and W. Jeberson (2021). A novel fog computing approach for minimization of latency in healthcare using machine learning.
Sabireen, H., & Neelanarayanan, V. (2021). A review on fog computing: Architecture, fog with IoT, algorithms and research challenges. Ict Express, 7(2), 162–176.
Sun, X., & Ansari, N. (2016). EdgeIoT: Mobile edge computing for the Internet of Things. IEEE Communications Magazine, 54(12), 22–29.
Fan, Q., & Ansari, N. (2018). Towards workload balancing in fog computing empowered IoT. IEEE Transactions on Network Science and Engineering, 7(1), 253–262.
Laili, Y., et al. (2013). A ranking chaos algorithm for dual scheduling of cloud service and computing resource in private cloud. Computers in Industry, 64(4), 448–463.
Doungmo Goufo, E. F. (2019). Strange attractor existence for non-local operators applied to four-dimensional chaotic systems with two equilibrium points. Chaos An Interdisciplinary Journal of Nonlinear Science, 29(2), 023117.
Jiménez, M., et al. (2018). A portable embedded system for point-to-point secure signals transmission. Microprocessors and Microsystems, 61, 126–134.
Asadollahi, M., Ghiasi, A. R., & Badamchizadeh, M. A. (2020). Adaptive synchronization of chaotic systems with hysteresis quantizer input. ISA transactions, 98, 137–148.
Noori, B., P. Salehpoor, and H.S. Aghdasi (2022). RGB image encryption and decryption with neural Chaotic functions. in 2022 27th International computer conference, computer society of Iran (CSICC). IEEE.
Zhang, M., et al. (2020). Image compression and encryption scheme based on compressive sensing and Fourier transform. IEEE Access, 8, 40838–40849.
Procopiou, A., Komninos, N., & Douligeris, C. (2019). ForChaos: Real time application DDoS detection using forecasting and chaos theory in smart home IoT network. Wireless Communications and Mobile Computing. https://doi.org/10.1155/2019/8469410
Shaukat, S., et al. (2020). Chaos theory and its application: An essential framework for image encryption. Chaos Theory and Applications, 2(1), 17–22.
Takens, F. (1981). Detecting strange attractors in turbulence. Dynamical systems and turbulence, Warwick 1980 (pp. 366–381). Springer.
Simon, D. (2008). Biogeography-based optimization. IEEE transactions on evolutionary computation, 12(6), 702–713.
Ma, H., & Simon, D. (2011). Blended biogeography-based optimization for constrained optimization. Engineering Applications of Artificial Intelligence, 24(3), 517–525.
Ghasemi, A., Toroghi Haghighat, A., & Keshavarzi, A. (2022). Enhanced multi-objective virtual machine replacement in cloud data centers: combinations of fuzzy logic with reinforcement learning and biogeography-based optimization algorithms. Cluster Computing. https://doi.org/10.1007/s10586-022-03794-x
Zhang, X., et al. (2018). A novel hybrid algorithm based on biogeography-based optimization and grey wolf optimizer. Applied Soft Computing, 67, 197–214.
Wang, X., & Xu, Z. (2014). Multi-objective optimization algorithm based on biogeography with chaos. Int. J. Hybrid. Inf. Technol, 7, 225–234.
Jovic, S., et al. (2018). Evolutionary algorithm for reference evapotranspiration analysis. Computers and electronics in agriculture, 150, 1–4.
Saber, W., et al. (2021). Hybrid load balance based on genetic algorithm in cloud environment. International Journal of Electrical and Computer Engineering, 11(3), 2477–2489.
Taghizadeh, S., Bobarshad, H., & Elbiaze, H. (2018). CLRPL: Context-aware and load balancing RPL for IoT networks under heavy and highly dynamic load. IEEE Access, 6, 23277–23291.
Li, G., et al (2017). A new weighted connection-least load balancing algorithm based on delay optimization strategy. in International Conference on Geo-Spatial Knowledge and Intelligence Springer.
Ezhilarasi, M., & Krishnaveni, V. (2019). An evolutionary multipath energy-efficient routing protocol (EMEER) for network lifetime enhancement in wireless sensor networks. Soft Computing, 23(18), 8367–8377.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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
Siyi, L. A New Energy-Aware Method for Balancing the Load on Wireless IoT Devices Using an Optimization Algorithm Based on Chaos Theory. Wireless Pers Commun 130, 1677–1697 (2023). https://doi.org/10.1007/s11277-023-10350-2
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-023-10350-2