Open Access
Issue
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
Article Number 00036
Number of page(s) 16
DOI https://doi.org/10.1051/bioconf/20249700036
Published online 05 April 2024
  • S. Kumar, P. Tiwari, and M. Zymbler, “Internet of Things is a revolutionary approach for future technology enhancement: a review,” J Big Data, vol. 6, no. 1, 2019, DOI: 10.1186/s40537-019-0268-2. [PubMed] [Google Scholar]
  • A. Senthil Kumar and E. Iyer, “An industrial iot in engineering and manufacturing industries - Benefits and challenges,” International Journal of Mechanical and Production Engineering Research and Development, vol. 9, no. 2, 2019, DOI: 10.24247/ijmperdapr201914. [Google Scholar]
  • S. Nižetić, P. Šolić, D. López-de-Ipiña González-de-Artaza, and L. Patrono, “Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future,” J Clean Prod, vol. 274, 2020, DOI: 10.1016/j.jclepro.2020.122877. [Google Scholar]
  • M. C. and M. P. Michael Chui, “IoT value set to accelerate through 2030: Where and how to capture it,” Mckinsey. Accessed: Nov. 21, 2023. [Online]. Available: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/iot-value-set-to-accelerate-through-2030-where-and-how-to-capture-it#/ [Google Scholar]
  • M. K. Patra, A. Kumari, B. Sahoo, and A. K. Turuk, “Challenges and opportunities toward integration of iot with cloud computing,” in Integration of IoT with Cloud Computing for Smart Applications, 2023. DOI: 10.1201/9781003319238-5. [Google Scholar]
  • H. Allioui and Y. Mourdi, “Exploring the Full Potentials of IoT for Better Financial Growth and Stability: A Comprehensive Survey,” Sensors, vol. 23, no. 19. Multidisciplinary Digital Publishing Institute (MDPI), Oct. 01, 2023. DOI: 10.3390/s23198015. [CrossRef] [PubMed] [Google Scholar]
  • R. Das and M. M. Inuwa, “A review on fog computing: Issues, characteristics, challenges, and potential applications,” Telematics and Informatics Reports, vol. 10. 2023. DOI: 10.1016/j.teler.2023.100049. [Google Scholar]
  • M. Al Masarweh, T. Alwada’n, and W. Afandi, “Fog Computing, Cloud Computing and IoT Environment: Advanced Broker Management System,” Journal of Sensor and Actuator Networks, vol. 11, no. 4, Dec. 2022, DOI: 10.3390/jsan11040084. [CrossRef] [Google Scholar]
  • S. Rahul and R. Aron, “Fog Computing Architecture, Application and Resource Allocation: A Review,” 2021. [Google Scholar]
  • A. Yousefpour et al., “All one needs to know about fog computing and related edge computing paradigms: A complete survey,” Journal of Systems Architecture, vol. 98. 2019. DOI: 10.1016/j.sysarc.2019.02.009. [Google Scholar]
  • D. Lima and H. Miranda, “A geographical-aware state deployment service for Fog Computing,” Computer Networks, vol. 216, 2022, DOI: 10.1016/j.comnet.2022.109208. [CrossRef] [Google Scholar]
  • Y. Meng, M. A. Naeem, A. O. Almagrabi, R. Ali, and H. S. Kim, “Advancing the state of the fog computing to enable 5g network technologies,” Sensors (Switzerland), vol. 20, no. 6. 2020. DOI: 10.3390/s20061754. [Google Scholar]
  • A. Sebastian and S. Sivagurunathan, “A Survey on Load Balancing Schemes in RPL based Internet of Things,” Int. J. Sci. Res. in Network Security and Communication, vol. 6, no. 3, 2018. [Google Scholar]
  • Y. Abuseta, “A Fog Computing Based Architecture for IoT Services and Applications Development,” International Journal of Computer Trends and Technology, vol. 67, 2019, [Online]. Available: http://www.ijcttjournal.org [Google Scholar]
  • A. Adel, “Utilizing technologies of fog computing in educational IoT systems: privacy, security, and agility perspective,” J Big Data, vol. 7, no. 1, 2020, DOI: 10.1186/s40537-020-00372-z. [CrossRef] [Google Scholar]
  • S. T. Siddiqui, M. R. Khan, Z. Khan, N. Rana, H. Khan, and M. I. Alam, “Significance of Internet-of-Things Edge and Fog Computing in Education Sector,” in International Conference on Smart Computing and Application, ICSCA 2023, 2023. DOI: 10.1109/ICSCA57840.2023.10087582. [Google Scholar]
  • S. R. Waheed et al., “Design a Crime Detection System based Fog Computing and IoT,” Malaysian Journal of Fundamental and Applied Sciences, vol. 19, no. 3, 2023, DOI: 10.11113/mjfas.v19n3.2906. [Google Scholar]
  • M. Haghi Kashani, A. M. Rahmani, and N. Jafari Navimipour, “Quality of service-aware approaches in fog computing,” International Journal of Communication Systems, vol. 33, no. 8, 2020, DOI: 10.1002/dac.4340. [CrossRef] [Google Scholar]
  • Z. Qu, Y. Wang, L. Sun, D. Peng, and Z. Li, “Study QoS optimization and energy saving techniques in cloud, Fog, EDge, and IoT,” Complexity, vol. 2020, 2020, DOI: 10.1155/2020/8964165. [Google Scholar]
  • M. H. Kashani and E. Mahdipour, “Load Balancing Algorithms in Fog Computing,” IEEE Trans Serv Comput, vol. 16, no. 2, pp. 1505–1521, Mar. 2023, DOI: 10.1109/TSC.2022.3174475. [CrossRef] [Google Scholar]
  • D. Puthal, M. S. Obaidat, P. Nanda, M. Prasad, S. P. Mohanty, and A. Y. Zomaya, “Secure and Sustainable Load Balancing of Edge Data Centers in Fog Computing,” IEEE Communications Magazine, vol. 56, no. 5, pp. 60–65, May 2018, DOI: 10.1109/MCOM.2018.1700795. [CrossRef] [Google Scholar]
  • A. Jangra and N. Mangla, “An efficient load balancing framework for deploying resource schedulingin cloud based communication in healthcare,” Measurement: Sensors, vol. 25, 2023, DOI: 10.1016/j.measen.2022.100584. [CrossRef] [Google Scholar]
  • V. Sethi and S. Pal, “FedDOVe: A Federated Deep Q-learning-based Offloading for Vehicular fog computing,” Future Generation Computer Systems, vol. 141, 2023, DOI: 10.1016/j.future.2022.11.012. [Google Scholar]
  • N. Tahmasebi Pouya and M. Agha Sarram, “Blind Load-Balancing Algorithm using Double-Q- learning in the Fog Environment,” in ICCKE 2021-11th International Conference on Computer Engineering and Knowledge, 2021. DOI: 10.1109/ICCKE54056.2021.9721449. [Google Scholar]
  • M. Kaur and R. Aron, “A systematic study of load balancing approaches in the fog computing environment,” Journal of Supercomputing, vol. 77, no. 8, 2021, DOI: 10.1007/s11227-020-03600-8. [Google Scholar]
  • O. A. Khashan, “Parallel Proxy Re-Encryption Workload Distribution for Efficient Big Data Sharing in Cloud Computing,” in 2021 IEEE 11th Annual Computing and Communication Workshop and Conference, CCWC 2021, 2021. DOI: 10.1109/CCWC51732.2021.9375967. [Google Scholar]
  • L. Ducongé, C. Lac, B. Vié, T. Bergot, and J. D. Price, “Fog in heterogeneous environments: the relative importance of local and non-local processes on radiative-advective fog formation,” Quarterly Journal of the Royal Meteorological Society, vol. 146, no. 731, 2020, DOI: 10.1002/qj.3783. [Google Scholar]
  • D. Kanellopoulos and V. K. Sharma, “Dynamic Load Balancing Techniques in the IoT: A Review,” Symmetry, vol. 14, no. 12. 2022. DOI: 10.3390/sym14122554. [CrossRef] [Google Scholar]
  • D. Baburao, T. Pavankumar, and C. S. R. Prabhu, “Survey on Service Migration, load optimization and Load Balancing in Fog Computing Environment,” in 2019 IEEE 5th International Conference for Convergence in Technology, I2CT 2019, 2019. DOI: 10.1109/I2CT45611.2019.9033579. [Google Scholar]
  • M. Ghobaei-Arani, A. Souri, and A. A. Rahmanian, “Resource Management Approaches in Fog Computing: a Comprehensive Review,” Journal of Grid Computing, vol. 18, no. 1. 2020. DOI: 10.1007/s10723-019-09491-1. [CrossRef] [Google Scholar]
  • E. Batista, G. Figueiredo, and C. Prazeres, “Load balancing between fog and cloud in fog of things based platforms through software-defined networking,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 9, 2022, DOI: 10.1016/j.jksuci.2021.10.003. [Google Scholar]
  • A. J. Kadhim and J. I. Naser, “Proactive load balancing mechanism for fog computing supported by parked vehicles in IoV-SDN,” China Communications, vol. 18, no. 2, 2021, DOI: 10.23919/JCC.2021.02.019. [Google Scholar]
  • J. Singh, P. Singh, E. M. Amhoud, and M. Hedabou, “Energy-Efficient and Secure Load Balancing Technique for SDN-Enabled Fog Computing,” Sustainability (Switzerland), vol. 14, no. 19, Oct. 2022, DOI: 10.3390/su141912951. [Google Scholar]
  • K. Cui, W. Sun, B. Lin, and W. Sun, “Load balancing mechanisms of unmanned surface vehicle cluster based on marine vehicular fog computing,” in Proceedings - 2020 16th International Conference on Mobility, Sensing and Networking, MSN 2020, Institute of Electrical and Electronics Engineers Inc., Dec. 2020, pp. 797–802. DOI: 10.1109/MSN50589.2020.00136. [CrossRef] [Google Scholar]
  • N. Muslim, S. Islam, and J. C. Grégoire, “Offloading framework for computation service in the edge cloud and core cloud: A case study for face recognition,” International Journal of Network Management, vol. 31, no. 4, 2021, DOI: 10.1002/nem.2146. [CrossRef] [Google Scholar]
  • N. Mohamed, J. Al-Jaroodi, S. Lazarova-Molnar, and I. Jawhar, “Applications of integrated iot-fog-cloud systems to smart cities: A survey,” Electronics (Switzerland), vol. 10, no. 23, 2021, DOI: 10.3390/electronics10232918. [Google Scholar]
  • M. Vijarania, S. Gupta, A. Agrawal, M. O. Adigun, S. A. Ajagbe, and J. B. Awotunde, “Energy Efficient LoadBalancing Mechanism in Integrated IoT-Fog-Cloud Environment,” Electronics (Switzerland), vol. 12, no. 11, 2023, DOI: 10.3390/electronics12112543. [Google Scholar]
  • S. S. Hajam and S. A. Sofi, “IoT-Fog architectures in smart city applications: A survey,” China Communications, vol. 18, no. 11. 2021. DOI: 10.23919/JCC.2021.11.009. [Google Scholar]
  • A. A. Mutlag et al., “A new fog computing resource management (FRM) model based on hybrid load balancing and scheduling for critical healthcare applications,” Physical Communication, vol. 59, 2023, DOI: 10.1016/j.phycom.2023.102109. [CrossRef] [Google Scholar]
  • A. U. Rehman et al., “Dynamic energy efficient resource allocation strategy for load balancing in fog environment,” IEEE Access, vol. 8, 2020, DOI: 10.1109/ACCESS.2020.3035181. [Google Scholar]
  • F. M. Talaat, M. S. Saraya, A. I. Saleh, H. A. Ali, and S. H. Ali, “A load balancing and optimization strategy (LBOS) using reinforcement learning in fog computing environment,” J Ambient Intell Humaniz Comput, vol. 11, no. 11, 2020, DOI: 10.1007/s12652-020-01768-8. [Google Scholar]
  • M. M. Shahriar Maswood, M. R. Rahman, A. G. Alharbi, and D. Medhi, “A Novel Strategy to Achieve Bandwidth Cost Reduction and Load Balancing in a Cooperative Three-Layer Fog-Cloud Computing Environment,” IEEE Access, vol. 8, 2020, DOI: 10.1109/ACCESS.2020.3003263. [Google Scholar]
  • D. Baburao, T. Pavankumar, and C. S. R. Prabhu, “Load balancing in the fog nodes using particle swarm optimization-based enhanced dynamic resource allocation method,” Applied Nanoscience (Switzerland), vol. 13, no. 2. 2023. DOI: 10.1007/s13204-021-01970-w. [Google Scholar]
  • A. R. Hameed, S. ul Islam, I. Ahmad, and K. Munir, “Energy- and performance-aware load-balancing in vehicular fog computing,” Sustainable Computing: Informatics and Systems, vol. 30, 2021, DOI: 10.1016/j.suscom.2020.100454. [CrossRef] [Google Scholar]
  • P. Singh et al., “A Fog-Cluster Based Load-Balancing Technique,” Sustainability (Switzerland), vol. 14, no. 13, 2022, DOI: 10.3390/su14137961. [Google Scholar]
  • A. B. Kanbar and K. Faraj, “Region aware dynamic task scheduling and resource virtualization for load balancing in loT-fog multi-cloud environment,” Future Generation Computer Systems, vol. 137. 2022. DOI: 10.1016/j.future.2022.06.005. [Google Scholar]
  • F. M. Talaat, H. A. Ali, M. S. Saraya, and A. I. Saleh, “Effective scheduling algorithm for load balancing in fog environment using CNN and MPSO,” Knowl Inf Syst, vol. 64, no. 3, 2022, DOI: 10.1007/s10115-021-01649-2. [Google Scholar]
  • S. P. Singh, “Effective load balancing strategy using fuzzy golden eagle optimization in fog computing environment,” Sustainable Computing: Informatics and Systems, vol. 35, 2022, DOI: 10.1016/j.suscom.2022.100766. [Google Scholar]
  • D. Yu, Z. Ma, and R. Wang, “Efficient Smart Grid Load Balancing via Fog and Cloud Computing,” Math Probl Eng, vol. 2022, 2022, DOI: 10.1155/2022/3151249. [Google Scholar]
  • M. Ebrahim and A. Hafid, “Resilience and load balancing in Fog networks: A Multi-Criteria Decision Analysis approach,” Microprocess Microsyst, vol. 101, 2023, DOI: 10.1016/j.micpro.2023.104893. [CrossRef] [Google Scholar]
  • F. Ramezani Shahidani, A. Ghasemi, A. Toroghi Haghighat, and A. Keshavarzi, “Task scheduling in edge-fog- cloud architecture: a multi-objective load balancing approach using reinforcement learning algorithm,” Computing, vol. 105, no. 6, 2023, DOI: 10.1007/s00607-022-01147-5. [Google Scholar]
  • G. Shruthi, M. R. Mundada, S. Supreeth, and B. Gardiner, “Deep Learning-based Resource Prediction and Mutated Leader Algorithm Enabled Load Balancing in Fog Computing,” International Journal of Computer Network and Information Security, vol. 15, no. 4, 2023, DOI: 10.5815/ijcnis.2023.04.08. [Google Scholar]
  • I. Z. Yakubu and M. Murali, “An efficient meta-heuristic resource allocation with load balancing in IoT-Fog-cloud computing environment,” J Ambient Intell Humaniz Comput, vol. 14, no. 3, 2023, DOI: 10.1007/s12652-023-04544-6. [Google Scholar]
  • H. F. Atlam, R. J. Walters, and G. B. Wills, “Fog computing and the internet of things: A review,” Big Data and Cognitive Computing, vol. 2, no. 2. 2018. DOI: 10.3390/bdcc2020010. [CrossRef] [Google Scholar]
  • B. Wu, X. Lv, W. Deyah Shamsi, and E. Gholami Dizicheh, “Optimal deploying IoT services on the fog computing: A metaheuristic-based multi-objective approach,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 10, pp. 10010–10027, Nov. 2022, DOI: 10.1016/j.jksuci.2022.10.002. [CrossRef] [Google Scholar]
  • S. Nayak, R. Patgiri, L. Waikhom, and A. Ahmed, “A review on edge analytics: Issues, challenges, opportunities, promises, future directions, and applications,” Digital Communications and Networks, 2022, DOI: 10.1016/j.dcan.2022.10.016. [Google Scholar]
  • N. A. Perifanis and F. Kitsios, “Edge and Fog Computing Business Value Streams through IoT Solutions: A Literature Review for Strategic Implementation,” Information (Switzerland), vol. 13, no. 9, 2022, DOI: 10.3390/info13090427. [Google Scholar]
  • Y. Zhang, R. H. Deng, G. Han, and D. Zheng, “Secure smart health with privacy-aware aggregate authentication and access control in Internet of Things,” Journal of Network and Computer Applications, vol. 123, 2018, DOI: 10.1016/j.jnca.2018.09.005. [Google Scholar]
  • O. Akrivopoulos, N. Zhu, D. Amaxilatis, C. Tselios, A. Anagnostopoulos, and I. Chatzigiannakis, “A fog computing-oriented, highly scalable iot framework for monitoring public educational buildings,” in IEEE International Conference on Communications, 2018. DOI: 10.1109/ICC.2018.8422489. [Google Scholar]
  • M. Zahid, N. Javaid, K. Ansar, K. Hassan, M. Kaleem Ullah Khan, and M. Waqas, “Hill climbing load balancing algorithm on fog computing,” in Lecture Notes on Data Engineering and Communications Technologies, vol. 24, 2019. DOI: 10.1007/978-3-030-02607-3_22. [Google Scholar]
  • M. B. Kamal, N. Javaid, S. A. A. Naqvi, H. Butt, T. Saif, and M. D. Kamal, “Heuristic Min-conflicts Optimizing Technique for Load Balancing on Fog Computing,” in Lecture Notes on Data Engineering and Communications Technologies, vol. 23, 2019. DOI: 10.1007/978-3-319-98557-2_19. [Google Scholar]
  • L. Q. Y. Y. W. D. A. X. L. Xiaolong Xu Qingxiang Liu, “A Heuristic Virtual Machine Scheduling Method for Load Balancing in Fog-Cloud Computing,” in 2018 4th IEEE International Conference on Big Data Security on Cloud, Oman: IEEE, Oct. 2018, pp. 355–365. [Google Scholar]
  • F. Banaie, M. H. Yaghmaee, S. A. Hosseini, and F. Tashtarian, “Load-Balancing Algorithm for Multiple Gateways in Fog-Based Internet of Things,” IEEE Internet Things J., vol. 7, no. 8, 2020, DOI: 10.1109/JIOT.2020.2982305. [Google Scholar]
  • J. Wan, B. Chen, S. Wang, M. Xia, D. Li, and C. Liu, “Fog Computing for Energy-Aware Load Balancing and Scheduling in Smart Factory,” IEEE Trans Industr Inform, vol. 14, no. 10, 2018, DOI: 10.1109/TII.2018.2818932. [Google Scholar]
  • J. Yang, “Low-latency cloud-fog network architecture and its load balancing strategy for medical big data,” J Ambient Intell Humaniz Comput, 2020, DOI: 10.1007/s12652-020-02245-y. [Google Scholar]
  • S. S. Karthik and A. Kavithamani, “Fog computing-based deep learning model for optimization of microgrid- connected WSN with load balancing,” Wireless Networks, vol. 27, no. 4, 2021, DOI: 10.1007/s11276-021-02613-2. [Google Scholar]
  • C. Li, H. Zhuang, Q. Wang, and X. Zhou, “SSLB: Self-Similarity-Based Load Balancing for Large-Scale Fog Computing,” Arab J Sci Eng, vol. 43, no. 12, 2018, DOI: 10.1007/s13369-018-3169-3. [Google Scholar]
  • S. F. Abedin, A. K. Bairagi, M. S. Munir, N. H. Tran, and C. S. Hong, “Fog Load Balancing for Massive Machine Type Communications: A Game and Transport Theoretic Approach,” IEEE Access, vol. 7, 2019, DOI: 10.1109/ACCESS.2018.2888869. [Google Scholar]
  • S. P. Singh, A. Sharma, and R. Kumar, “Design and exploration of load balancers for fog computing using fuzzy logic,” Simul Model Pract Theory, vol. 101, 2020, DOI: 10.1016/j.simpat.2019.102017. [CrossRef] [Google Scholar]
  • R. Beraldi, C. Canali, R. Lancellotti, and G. P. Mattia, “Randomized Load Balancing under Loosely Correlated State Information in Fog Computing,” in MSWiM 2020 - Proceedings of the 23rd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, 2020. DOI: 10.1145/3416010.3423244. [Google Scholar]
  • E. Barros, M. Peixoto, D. Leite, B. Batista, and B. Kuehne, “A Fog Model for Dynamic Load Flow Analysis in Smart Grids,” in Proceedings - IEEE Symposium on Computers and Communications, 2018. DOI: 10.1109/ISCC.2018.8538738. [Google Scholar]
  • R. Beraldi and H. Alnuweiri, “Sequential Randomization load balancing for Fog Computing,” in 2018 26th International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2018, 2018. DOI: 10.23919/SOFTCOM.2018.8555797. [Google Scholar]
  • M. Mukherjee, Y. Liu, J. Lloret, L. Guo, R. Matam, and M. Aazam, “Transmission and Latency-Aware Load Balancing for Fog Radio Access Networks,” in 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings, 2018. DOI: 10.1109/GLOCOM.2018.8647580. [Google Scholar]
  • S. Sthapit, J. Thompson, N. M. Robertson, and J. R. Hopgood, “Computational Load Balancing on the Edge in Absence of Cloud and Fog,” IEEE Trans Mob Comput, vol. 18, no. 7, 2019, DOI: 10.1109/TMC.2018.2863301. [Google Scholar]
  • Q. Fan and N. Ansari, “Towards Workload Balancing in Fog Computing Empowered IoT,” IEEE Trans Netw Sci Eng, vol. 7, no. 1, 2020, DOI: 10.1109/TNSE.2018.2852762. [Google Scholar]
  • T. Nazar, N. Javaid, M. Waheed, A. Fatima, H. Bano, and N. Ahmed, “Modified Shortest Job First for Load Balancing in Cloud-Fog Computing,” in Lecture Notes on Data Engineering and Communications Technologies, vol. 25, 2019. DOI: 10.1007/978-3-030-02613-4_6. [Google Scholar]
  • N. Ahmad, N. Javaid, M. Mehmood, M. Hayat, A. Ullah, and H. A. Khan, “Fog-Cloud Based Platform for Utilization of Resources Using Load Balancing Technique,” in Lecture Notes on Data Engineering and Communications Technologies, vol. 22, 2019. DOI: 10.1007/978-3-319-98530-5_48. [Google Scholar]
  • D. A. Chekired, L. Khoukhi, and H. T. Mouftah, “Queuing Model for EVs Energy Management: Load Balancing Algorithms Based on Decentralized Fog Architecture,” in IEEE International Conference on Communications, 2018. DOI: 10.1109/ICC.2018.8422605. [Google Scholar]
  • E. Batista, G. Figueiredo, M. Peixoto, M. Serrano, and C. Prazeres, “Load Balancing in the Fog of Things Platforms Through Software-Defined Networking,” in 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), IEEE, Jul. 2018, pp. 1785–1791. DOI: 10.1109/Cybermatics_2018.2018.00297. [CrossRef] [Google Scholar]
  • S. Tariq, N. Javaid, M. Majeed, F. Ahmed, and S. Nazir, “Priority Based Load Balancing in Cloud and Fog Based Systems,” in Lecture Notes on Data Engineering and Communications Technologies, vol. 25, 2019. DOI: 10.1007/978-3-030-02613-4_65. [Google Scholar]
  • Z. Sharmin, A. W. Malik, A. Ur Rahman, and R. Md Noor, “Toward Sustainable Micro-Level Fog-Federated Load Sharing in Internet of Vehicles,” IEEE Internet Things J., vol. 7, no. 4, 2020, DOI: 10.1109/JIOT.2020.2973420. [Google Scholar]
  • E. P. Pereira, E. L. Padoin, R. D. Medina, and J. F. Mehaut, “Increasing the efficiency of Fog Nodes through of Priority-based Load Balancing,” in Proceedings - IEEE Symposium on Computers and Communications, 2020. DOI: 10.1109/ISCC50000.2020.9219576. [Google Scholar]
  • F. Alqahtani, M. Amoon, and A. A. Nasr, “Reliable scheduling and load balancing for requests in cloud-fog computing,” Peer Peer Netw Appl, vol. 14, no. 4, 2021, DOI: 10.1007/s12083-021-01125-2. [Google Scholar]
  • B. Alamri, M. A. Hossain, and M. S. Jamil Asghar, “Electric power network interconnection: A review on current status, future prospects and research direction,” Electronics (Switzerland), vol. 10, no. 17, pp. 1–29, 2021, DOI: 10.3390/electronics10172179. [Google Scholar]
  • N. Mazumdar, A. Nag, and J. P. Singh, “Trust-based load-offloading protocol to reduce service delays in fogcomputing-empowered IoT,” Computers and Electrical Engineering, vol. 93, 2021, DOI: 10.1016/j.compeleceng.2021.107223. [CrossRef] [Google Scholar]
  • S. A. A. Naqvi, N. Javaid, H. Butt, M. B. Kamal, A. Hamza, and M. Kashif, “Metaheuristic Optimization Technique for Load Balancing in Cloud-Fog Environment Integrated with Smart Grid,” in Lecture Notes on Data Engineering and Communications Technologies, vol. 22, 2019. DOI: 10.1007/978-3-319-98530-5_61. [Google Scholar]
  • S. H. Abbasi, N. Javaid, M. H. Ashraf, M. Mehmood, M. Naeem, and M. Rehman, “Load Stabilizing in Fog Computing Environment Using Load Balancing Algorithm,” in Lecture Notes on Data Engineering and Communications Technologies, vol. 25, 2019. DOI: 10.1007/978-3-030-02613-4_66. [Google Scholar]
  • M. J. Ali, N. Javaid, M. Rehman, M. U. Sharif, M. K. U. Khan, and H. A. Khan, “State Based Load Balancing Algorithm for Smart Grid Energy Management in Fog Computing,” in Lecture Notes on Data Engineering and Communications Technologies, vol. 23, 2019. DOI: 10.1007/978-3-319-98557-2_20. [Google Scholar]
  • M. Zubair, N. Javaid, M. Ismail, M. Zakria, M. Asad Zaheer, and F. Saeed, “Integration of cloud-fog based platform for load balancing using hybrid genetic algorithm using bin packing technique,” in Lecture Notes on Data Engineering and Communications Technologies, vol. 24, 2019. DOI: 10.1007/978-3-030-02607-3_25. [Google Scholar]
  • F. M. Talaat, S. H. Ali, A. I. Saleh, and H. A. Ali, “Effective Load Balancing Strategy (ELBS) for Real-Time Fog Computing Environment Using Fuzzy and Probabilistic Neural Networks,” Journal of Network and Systems Management, vol. 27, no. 4, 2019, DOI: 10.1007/s10922-019-09490-3. [Google Scholar]
  • J. Yan, J. Wu, Y. Wu, L. Chen, and S. Liu, “Task Offloading Algorithms for Novel Load Balancing in Homogeneous Fog Network,” in Proceedings of the 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2021, 2021. DOI: 10.1109/CSCWD49262.2021.9437748. [Google Scholar]
  • A. Khalid, Q. Ul Ain, A. Qasim, and Z. Aziz, “QoS based optimal resource allocation and workload balancing for Fogd enabled IoT,” Open Computer Science, vol. 11, no. 1, 2021, DOI: 10.1515/comp-2020-0162. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.