journal article
Open Access
Aug 17, 2023
A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm
Abstract
The Internet of Things (IoT) represents a cutting-edge technical domain, encompassing billions of intelligent objects capable of bridging the physical and virtual worlds across various locations. IoT services are responsible for delivering essential functionalities. In this dynamic and interconnected IoT landscape, providing high-quality services is paramount to enhancing user experiences and optimizing system efficiency. Service composition techniques come into play to address user requests in IoT applications, allowing various IoT services to collaborate seamlessly. Considering the resource limitations of IoT devices, they often leverage cloud infrastructures to overcome technological constraints, benefiting from unlimited resources and capabilities. Moreover, the emergence of fog computing has gained prominence, facilitating IoT application processing in edge networks closer to IoT sensors and effectively reducing delays inherent in cloud data centers. In this context, our study proposes a cloud-/fog-based service composition for IoT, introducing a novel fuzzy-based hybrid algorithm. This algorithm ingeniously combines Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) optimization algorithms, taking into account energy consumption and Quality of Service (QoS) factors during the service selection process. By leveraging this fuzzy-based hybrid algorithm, our approach aims to revolutionize service composition in IoT environments by empowering intelligent decision-making capabilities and ensuring optimal user satisfaction. Our experimental results demonstrate the effectiveness of the proposed strategy in successfully fulfilling service composition requests by identifying suitable services. When compared to recently introduced methods, our hybrid approach yields significant benefits. On average, it reduces energy consumption by 17.11%, enhances availability and reliability by 8.27% and 4.52%, respectively, and improves the average cost by 21.56%.
Topics
No keywords indexed for this article. Browse by subject →
References
100
[1]
Goumagias "Making sense of the internet of things: A critical review of internet of things definitions between 2005 and 2019" Internet Res. (2021) 10.1108/intr-01-2020-0013
[2]
Chen "Effectively detecting operational anomalies in large-scale iot data infrastructures by using a gan-based predictive model" Comput. J. (2022) 10.1093/comjnl/bxac085
[3]
Cao "RFID reader anticollision based on distributed parallel particle swarm optimization" IEEE Internet Things J. (2020) 10.1109/jiot.2020.3033473
[4]
Min "A fault diagnosis framework for autonomous vehicles with sensor self-diagnosis" Expert Syst. Appl. (2023) 10.1016/j.eswa.2023.120002
[5]
Kumar "A blockchain-orchestrated deep learning approach for secure data transmission in IoT-enabled healthcare system" J. Parallel Distrib. Comput. (2023) 10.1016/j.jpdc.2022.10.002
[6]
Ma "Real-time assessment of asphalt pavement moduli and traffic loads using monitoring data from Built-in Sensors: Optimal sensor placement and identification algorithm" Mech. Syst. Signal Process. (2023) 10.1016/j.ymssp.2022.109930
[7]
Pan "A low-profile programmable beam scanning holographic array antenna without phase shifters" IEEE Internet Things J. (2021) 10.1109/jiot.2021.3116158
[8]
Liu "A Survey on Blockchain-based Trust Management for Internet of Things" IEEE Internet Things J. (2023) 10.1109/jiot.2023.3237893
[9]
Cao "Enhancing physical-layer security for IoT with nonorthogonal multiple access assisted semi-grant-free transmission" IEEE Internet Things J. (2022) 10.1109/jiot.2022.3193189
[10]
Jiang "An energy-efficient framework for internet of things underlaying heterogeneous small cell networks" IEEE Trans. Mob. Comput. (2020) 10.1109/tmc.2020.3005908
[11]
Perception Task Offloading With Collaborative Computation for Autonomous Driving
Zhu Xiao, Jinmei Shu, Hongbo Jiang et al.
IEEE Journal on Selected Areas in Communications
2022
10.1109/jsac.2022.3227027
[12]
Cao "Diversified personalized recommendation optimization based on mobile data" IEEE Trans. Intell. Transp. Syst. (2020) 10.1109/tits.2020.3040909
[13]
Kour "Recent developments of the internet of things in agriculture: A survey" IEEE Access (2020) 10.1109/access.2020.3009298
[14]
Jamshed "Challenges, applications, and future of wireless sensors in Internet of Things: A review" IEEE Sensors J. (2022) 10.1109/jsen.2022.3148128
[15]
Cao "A many-objective optimization model of industrial internet of things based on private blockchain" IEEE Netw. (2020) 10.1109/mnet.011.1900536
[16]
A Q-Learning-based distributed routing protocol for frequency-switchable magnetic induction-based wireless underground sensor networks
Guanghua Liu
Future Generation Computer Systems
2023
10.1016/j.future.2022.10.004
[17]
Liu "Data collection in mi-assisted wireless powered underground sensor networks: Directions, recent advances, and challenges" IEEE Commun. Mag. (2021) 10.1109/mcom.001.2000921
[18]
Shao "Replica selection and placement techniques on the IoT and edge computing: A deep study" Wirel. Networks (2021) 10.1007/s11276-021-02793-x
[19]
Hamzei "Toward efficient service composition techniques in the internet of things" IEEE Internet Things J. (2018) 10.1109/jiot.2018.2861742
[20]
Sadhu, P.K., Yanambaka, V.P., and Abdelgawad, A. (2022). Internet of Things: Security and Solutions Survey. Sensors, 22.
10.3390/s22197433
[21]
Kumar "Toward design of an intelligent cyber attack detection system using hybrid feature reduced approach for iot networks" Arab. J. Sci. Eng. (2021) 10.1007/s13369-020-05181-3
[22]
Dai "Task offloading for cloud-assisted fog computing with dynamic service caching in enterprise management systems" IEEE Trans. Ind. Inform. (2022) 10.1109/tii.2022.3186641
[23]
Liu "Reduced reference perceptual quality model with application to rate control for video-based point cloud compression" IEEE Trans. Image Process. (2021) 10.1109/tip.2021.3096060
[24]
Darbandi "Proposing new intelligent system for suggesting better service providers in cloud computing based on Kalman filtering" HCTL Int. J. Technol. Innov. Res. (2017)
[25]
Zhang "Physical unclonable function-based key sharing via machine learning for IoT security" IEEE Trans. Ind. Electron. (2019) 10.1109/tie.2019.2938462
[26]
Shen "A cloud-aided privacy-preserving multi-dimensional data comparison protocol" Inf. Sci. (2020) 10.1016/j.ins.2020.09.052
[27]
Liu "Pufa-gan: A frequency-aware generative adversarial network for 3d point cloud upsampling" IEEE Trans. Image Process. (2022) 10.1109/tip.2022.3222918
[28]
Darbandi "Kalman filtering for estimation and prediction servers with lower traffic loads for transferring high-level processes in cloud computing" HCTL Int. J. Technol. Innov. Res. (2017)
[29]
Wang "A privacy-enhanced retrieval technology for the cloud-assisted internet of things" IEEE Trans. Ind. Inform. (2021) 10.1109/tii.2021.3103547
[30]
Ramzanpoor "Energy-aware and Reliable Service Placement of IoT applications on Fog Computing Platforms by Utilizing Whale Optimization Algorithm" J. Adv. Comput. Eng. Technol. (2021)
[31]
Cao "Resource allocation in 5G IoV architecture based on SDN and fog-cloud computing" IEEE Trans. Intell. Transp. Syst. (2021) 10.1109/tits.2020.3048844
[32]
Dai "Task co-offloading for d2d-assisted mobile edge computing in industrial internet of things" IEEE Trans. Ind. Inform. (2022) 10.1109/tii.2022.3158974
[33]
Cao "Large-scale many-objective deployment optimization of edge servers" IEEE Trans. Intell. Transp. Syst. (2021) 10.1109/tits.2021.3059455
[34]
Xiao, Z., Shu, J., Jiang, H., Lui, J.C.S., Min, G., Liu, J., and Dustdar, S. (2022). Multi-objective parallel task offloading and content caching in D2D-aided MEC networks. IEEE Trans. Mob. Comput., 1–16.
10.1109/tmc.2022.3199876
[35]
Darbandi "Proposing New Intelligence Algorithm for Suggesting Better Services to Cloud Users based on Kalman Filtering" J. Comput. Sci. Appl. (2017)
[36]
Zenggang "Social similarity routing algorithm based on socially aware networks in the big data environment" J. Signal Process. Syst. (2022) 10.1007/s11265-022-01790-3
[37]
Guerrero "Genetic-based optimization in fog computing: Current trends and research opportunities" Swarm Evol. Comput. (2022) 10.1016/j.swevo.2022.101094
[38]
Wang, S., Sheng, H., Zhang, Y., Yang, D., Shen, J., and Chen, R. (2023). Blockchain-empowered distributed multi-camera multi-target tracking in edge computing. IEEE Trans. Ind. Inform., 1–10.
10.1109/tii.2023.3261890
[39]
Saini "An Integrated Framework for Smart Earthquake Prediction: IoT, Fog, and Cloud Computing" J. Grid Comput. (2022) 10.1007/s10723-022-09600-7
[40]
Kumar, P., Tripathi, R., and Gupta, G.P. (2021, January 5–8). P2IDF: A privacy-preserving based intrusion detection framework for software defined Internet of Things-fog (SDIoT-Fog). Proceedings of the 2021 International Conference on Distributed Computing and Networking, Nara, Japan.
10.1145/3427477.3429989
[41]
Asghari "Privacy-aware cloud service composition based on QoS optimization in Internet of Things" J. Ambient. Intell. Humaniz. Comput. (2020) 10.1007/s12652-020-01723-7
[42]
Hosseinzadeh "A hybrid service selection and composition model for cloud-edge computing in the internet of things" IEEE Access (2020) 10.1109/access.2020.2992262
[43]
Arellanes "Evaluating IoT service composition mechanisms for the scalability of IoT systems" Futur. Gener. Comput. Syst. (2020) 10.1016/j.future.2020.02.073
[44]
Kashyap "Multi-objective Optimization using NSGA II for service composition in IoT" Procedia Comput. Sci. (2020) 10.1016/j.procs.2020.03.214
[45]
Guzel "Fair and energy-aware IoT service composition under QoS constraints" J. Supercomput. (2022) 10.1007/s11227-022-04398-3
[46]
Chai "A fast energy-centered and QoS-aware service composition approach for Internet of Things" Appl. Soft Comput. (2020) 10.1016/j.asoc.2020.106914
[47]
Razaque "Hybrid energy-efficient algorithm for efficient internet of things deployment" Sustain. Comput. Inform. Syst. (2022)
[48]
Seghir "FDMOABC: Fuzzy Discrete Multi-Objective Artificial Bee Colony approach for solving the non-deterministic QoS-driven web service composition problem" Expert Syst. Appl. (2020) 10.1016/j.eswa.2020.114413
[49]
Safaei "Enterprise service composition models in IoT context: Solutions comparison" J. Supercomput. (2021) 10.1007/s11227-021-03873-7
[50]
Sefati "A QoS-Aware Service Composition Mechanism in the Internet of Things Using a Hidden-Markov-Model-Based Optimization Algorithm" IEEE Internet Things J. (2021) 10.1109/jiot.2021.3074499
Showing 50 of 100 references
Metrics
30
Citations
100
References
Details
- Published
- Aug 17, 2023
- Vol/Issue
- 23(16)
- Pages
- 7233
- License
- View
Authors
Cite This Article
Marzieh Hamzei, Saeed Khandagh, Nima Jafari Navimipour (2023). A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm. Sensors, 23(16), 7233. https://doi.org/10.3390/s23167233
Related
You May Also Like
Metal Oxide Gas Sensors: Sensitivity and Influencing Factors
Chengxiang Wang, Longwei Yin · 2010
2,595 citations
Machine Learning in Agriculture: A Review
Konstantinos Liakos, Patrizia Busato · 2018
2,472 citations
Wearable Electronics and Smart Textiles: A Critical Review
Matteo Stoppa, Alessandro Chiolerio · 2014
1,823 citations
A Review on Biosensors and Recent Development of Nanostructured Materials-Enabled Biosensors
Varnakavi. Naresh, Nohyun Lee · 2021
1,741 citations