1. bookVolume 26 (2021): Issue 2 (December 2021)
Journal Details
License
Format
Journal
eISSN
2255-8691
First Published
08 Nov 2012
Publication timeframe
2 times per year
Languages
English
access type Open Access

Fog Computing Algorithms: A Survey and Research Opportunities

Published Online: 30 Dec 2021
Volume & Issue: Volume 26 (2021) - Issue 2 (December 2021)
Page range: 139 - 149
Journal Details
License
Format
Journal
eISSN
2255-8691
First Published
08 Nov 2012
Publication timeframe
2 times per year
Languages
English
Abstract

The classic Internet of Things-Cloud Computing model faces challenges like high response latency, high bandwidth consumption, and high storage requirement with increasing velocity and volume of generated data. Fog computing offers better services to end users by bringing processing, storage, and networking closer to them. Recently, there has been significant research addressing architectural and algorithmic aspects of fog computing. In the existing literature, a systematic study of architectural designs is widely conducted for various applications. Algorithms are seldom examined. Algorithms play a crucial role in fog computing. This survey aims to performing a comparative study of existing algorithms. The study also presents a systematic classification of the current fog computing algorithms and highlights the key challenges and research issues associated with them.

Keywords

[1] C. Puliafito, E. Mingozzi, F. Longo, A. Puliafito, and O. Rana, “Fogcomputing for the internet of things: A survey,” ACM Transactions on Internet Technology (TOIT), vol. 19, no. 2, pp. 1–41, Apr. 2019. https://doi.org/10.1145/330144310.1145/3301443 Search in Google Scholar

[2] F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, “Fog computing and its role in the internet of things,” in Proceedings of the first edition of the MCC workshop on Mobile cloud computing, Aug. 2012, pp. 13–16. https://doi.org/10.1145/2342509.234251310.1145/2342509.2342513 Search in Google Scholar

[3] R. Mahmud, R. Kotagiri, R.Buyya. “Fog computing: A taxonomy, survey and future directions”. in Internet of everything, Springer; 2018. p. 103–130.10.1007/978-981-10-5861-5_5 Search in Google Scholar

[4] M. Aazam, M. St-Hilaire, CH. Lung, I. Lambadaris, EN Huh.” IoT resource estimation challenges and modeling in fog”, in Fog Computing in the Internet of Things. Springer; 2018. p. 17–31.10.1007/978-3-319-57639-8_2 Search in Google Scholar

[5] C. Mouradian, D. Naboulsi, S. Yangui, R. H. Glitho, M. J. Morrow, and P. A. Polakos, “A comprehensive survey on fog computing: Stateof-theart and research challenges,” IEEE communications surveys & tutorials, vol. 20, no. 1, pp. 416–464, 2017. https://doi.org/10.1109/COMST.2017.277115310.1109/COMST.2017.2771153 Search in Google Scholar

[6] C. C. Byers, “Architectural imperatives for fog computing: Use cases, requirements, and architectural techniques for fog-enabled IoT networks,” IEEE Communications Magazine, vol. 55, no. 8, pp. 14–20, Aug. 2017. https://doi.org/10.1109/MCOM.2017.160088510.1109/MCOM.2017.1600885 Search in Google Scholar

[7] R. K. Naha, S. Garg, and A. Chan, “Fog computing architecture: Survey and challenges,” arXiv, no.1811.09047, 2018. Search in Google Scholar

[8] M. Aazam and E.-N. Huh, “Fog computing micro datacenter based dynamic resource estimation and pricing model for IoT,” in 2015 IEEE 29th International Conference on Advanced Information Networking and Applications. Gwangju, Korea (South), Mar. 2015, pp. 687–694. https://doi.org/10.1109/AINA.2015.25410.1109/AINA.2015.254 Search in Google Scholar

[9] H. Wang, T. Liu, B. Kim, C.-W. Lin, S. Shiraishi, J. Xie, and Z. Han, “Architectural design alternatives based on cloud/edge/fog computing for connected vehicles,” IEEE Communications Surveys & Tutorials, vol. 22, no. 4, pp. 2349–2377, Sep. 2020. https://doi.org/10.1109/COMST.2020.302085410.1109/COMST.2020.3020854 Search in Google Scholar

[10] A. A. Alli and M. M. Alam, “The fog cloud of things: A survey on concepts, architecture, standards, tools, and applications,” Internet of Things, vol. 9, Art no. 100177, Mar. 2020. https://doi.org/10.1016/j.iot.2020.10017710.1016/j.iot.2020.100177 Search in Google Scholar

[11] 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, p. 10, 2018. https://doi.org/10.3390/bdcc202001010.3390/bdcc2020010 Search in Google Scholar

[12] H. Wu, “Multi-objective decision-making for mobile cloud offloading: A survey,” IEEE Access, vol. 6, pp. 3962–3976, Jan. 2018. https://doi.org/10.1109/ACCESS.2018.279150410.1109/ACCESS.2018.2791504 Search in Google Scholar

[13] X. Meng, W. Wang, and Z. Zhang, “Delay -constrained hybrid computation offloading with cloud and fog computing,” IEEE Access, vol. 5, pp. 21355–21367, Sep. 2017. https://doi.org/10.1109/ACCESS.2017.274814010.1109/ACCESS.2017.2748140 Search in Google Scholar

[14] D. Rahbari and M. Nickray, “Task offloading in mobile fog computing by classification and regression tree,” Peer-to-Peer Networking and Applications, vol. 13, pp. 1–19, Feb. 2019. https://doi.org/10.1007/s12083-019-00721-710.1007/s12083-019-00721-7 Search in Google Scholar

[15] C. Fricker, F. Guillemin, P. Robert, and G. Thompson, “Analysis of an offloading scheme for data centers in the framework of fog computing,” ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS), vol. 1, no. 4, Art no. 16, pp. 1–18, Sep. 2016. https://doi.org/10.1145/295004710.1145/2950047 Search in Google Scholar

[16] F. Chiti, R. Fantacci, and B. Picano, “A matching game for tasks offloading in integrated edge-fog computing systems,” Transactions on Emerging Telecommunications Technologies, vol. 31, no. 2, p. Art no. e3718, Aug. 2020. https://doi.org/10.1002/ett.371810.1002/ett.3718 Search in Google Scholar

[17] L. Liu, Z. Chang, X. Guo, S. Mao, and T. Ristaniemi, “Multiobjective optimization for computation offloading in fog computing,” IEEE Internet of Things Journal, vol. 5, no. 1, pp. 283–294, Dec. 2017. https://doi.org/10.1109/JIOT.2017.278023610.1109/JIOT.2017.2780236 Search in Google Scholar

[18] M. A. Hassan, M. Xiao, Q. Wei, and S. Chen, “Help your mobile applications with fog computing,” in 2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking – Workshop (SECON Workshops). Seattle, WA, USA, June 2015, pp. 1–6. https://doi.org/10.1109/SECONW.2015.732814610.1109/SECONW.2015.7328146 Search in Google Scholar

[19] H. Shah-Mansouri and V. W. Wong, “Hierarchical fog-cloud computing for IoT systems: A computation offloading game,” IEEE Internet of Things Journal, vol. 5, no. 4, pp. 3246–3257, Aug. 2018. https://doi.org/10.1109/JIOT.2018.283802210.1109/JIOT.2018.2838022 Search in Google Scholar

[20] J. Du, L. Zhao, J. Feng, and X. Chu, “Computation offloading and resource allocation in mixed fog/cloud computing systems with min-max fairness guarantee,” IEEE Transactions on Communications, vol. 66, no. 4, pp. 1594–1608, 2018. https://doi.org/10.1109/TCOMM.2017.278770010.1109/TCOMM.2017.2787700 Search in Google Scholar

[21] F. Jazayeri, A. Shahidinejad, and M. Ghobaei-Arani, “A latency-aware and energy-efficient computation offloading in mobile fog computing: A hidden Markov model-based approach,” The Journal of Supercomputing, vol. 77, no. 5, pp. 4887–4916, 2021. https://doi.org/10.1007/s11227-020-03476-810.1007/s11227-020-03476-8 Search in Google Scholar

[22] F. Jazayeri, A. Shahidinejad, and M. Ghobaei-Arani, “Autonomous computation offloading and auto-scaling the in the mobile fog computing: a deep reinforcement learning-based approach,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 8265–8284, 2021. https://doi.org/10.1007/s12652-020-02561-310.1007/s12652-020-02561-3 Search in Google Scholar

[23] G. Baranwal and D. P. Vidyarthi, “Computation offloading model for smart factory,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 8305–8318, 2021. https://doi.org/10.1007/s12652-020-02564-010.1007/s12652-020-02564-0 Search in Google Scholar

[24] X. Li, Z. Zang, F. Shen, and Y. Sun, “Task offloading scheme based on improved contract net protocol and beetle antennae search algorithm in fog computing networks,” Mobile Networks and Applications, vol. 25, no. 6, pp. 2517–2526, 2020. https://doi.org/10.1007/s11036-020-01593-510.1007/s11036-020-01593-5 Search in Google Scholar

[25] O. Skarlat, M. Nardelli, S. Schulte, M. Borkowski, and P. Leitner, “Optimized IoT service placement in the fog,” Service Oriented Computing and Applications, vol. 11, no. 4, pp. 427–443, Oct. 2017. https://doi.org/10.1007/s11761-017-0219-810.1007/s11761-017-0219-8 Search in Google Scholar

[26] R. Mahmud, S. N. Srirama, K. Ramamohanarao, and R. Buyya, “Quality of experience (QoE)-aware placement of applications in fog computing environments,” Journal of Parallel and Distributed Computing, vol. 132, pp. 190–203, Oct. 2019. https://doi.org/10.1016/j.jpdc.2018.03.00410.1016/j.jpdc.2018.03.004 Search in Google Scholar

[27] Y. Xia, X. Etchevers, L. Letondeur, T. Coupaye, and F. Desprez, “Combining hardware nodes and software components ordering-based heuristics for optimizing the placement of distributed IoT applications in the fog,” in Proceedings of the 33rd Annual ACM Symposium on Applied Computing, Apr. 2018, pp. 751–760. https://doi.org/10.1145/3167132.316721510.1145/3167132.3167215 Search in Google Scholar

[28] R. Mahmud, K. Ramamohanarao, and R. Buyya, “Latency-aware application module management for fog computing environments,” ACM Transactions on Internet Technology (TOIT), vol. 19, no. 1, Art no. 9, pp. 1–21, Mar. 2018. https://doi.org/10.1145/318659210.1145/3186592 Search in Google Scholar

[29] C. Guerrero, I. Lera, and C. Juiz, “A lightweight decentralized service placement policy for performance optimization in fog computing,” Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 6, pp. 2435–2452, 2019. https://doi.org/10.1007/s12652-018-0914-010.1007/s12652-018-0914-0 Search in Google Scholar

[30] M. Taneja and A. Davy, “Resource aware placement of IoT application modules in fog-cloud computing paradigm,” in 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), Lisbon, Portugal, Vfy 2017, pp. 1222–1228. https://doi.org/10.23919/INM.2017.798746410.23919/INM.2017.7987464 Search in Google Scholar

[31] C. Mouradian, S. Kianpisheh, M. Abu-Lebdeh, F. Ebrahimnezhad, N. T. Jahromi, and R. H. Glitho, “Application component placement in NFV- based hybrid cloud/fog systems with mobile fog nodes,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 5, pp. 1130–1143, May 2019. https://doi.org/10.1109/JSAC.2019.290679010.1109/JSAC.2019.2906790 Search in Google Scholar

[32] S. Venticinque and A. Amato, “A methodology for deployment of IoT application in fog,” Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 5, pp. 1955–1976, 2019. https://doi.org/10.1007/s12652-018-0785-410.1007/s12652-018-0785-4 Search in Google Scholar

[33] M. A. Al-Tarawneh, “Bi-objective optimization of application placement in fog computing environments,” Journal of Ambient Intelligence and Humanized Computing, pp. 1–24, Feb. 2021. https://doi.org/10.1007/s12652-021-02910-w10.1007/s12652-021-02910-w Search in Google Scholar

[34] H. Nashaat, E. Ahmed, and R. Rizk, “IoT application placement algorithm based on multi-dimensional QoE prioritization model in fog computing environment,” IEEE Access, vol. 8, pp. 111 253–111 264, June 2020. https://doi.org/10.1109/ACCESS.2020.300324910.1109/ACCESS.2020.3003249 Search in Google Scholar

[35] F. Faticanti, F. De Pellegrini, D. Siracusa, D. Santoro, and S. Cretti, “Throughput-aware partitioning and placement of applications in fog computing,” IEEE Transactions on Network and Service Management, vol. 17, no. 4, pp. 2436–2450, Dec. 2020. https://doi.org/10.1109/TNSM.2020.302301110.1109/TNSM.2020.3023011 Search in Google Scholar

[36] T. Djemai, P. Stolf, T. Monteil, and J.-M. Pierson, “Mobility support for energy and QoS aware IoT services placement in the fog,” in 2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Split, Croatia, Sep. 2020, pp. 1–7. https://doi.org/10.23919/SoftCOM50211.2020.923823610.23919/SoftCOM50211.2020.9238236 Search in Google Scholar

[37] G. Baranwal, R. Yadav, and D. P. Vidyarthi, “QoE aware IoT application placement in fog computing using modified-TOPSIS,” Mobile Networks and Applications, vol. 25, no. 5, pp. 1816–1832, Oct. 2020. https://doi.org/10.1007/s11036-020-01563-x10.1007/s11036-020-01563-x Search in Google Scholar

[38] H. Zhang, Y. Xiao, S. Bu, D. Niyato, F. R. Yu, and Z. Han, “Computing resource allocation in three-tier IoT fog networks: A joint optimization approach combining stackelberg game and matching,” IEEE Internet of Things Journal, vol. 4, no. 5, pp. 1204–1215, Oct. 2017. https://doi.org/10.1109/JIOT.2017.268892510.1109/JIOT.2017.2688925 Search in Google Scholar

[39] Y. Jiao, P. Wang, D. Niyato, and K. Suankaewmanee, “Auction mechanisms in cloud/fog computing resource allocation for public blockchain networks,” IEEE Transactions on Parallel and Distributed Systems, vol. 30, no. 9, pp. 1975–1989, Sep. 2019. https://doi.org/10.1109/TPDS.2019.290023810.1109/TPDS.2019.2900238 Search in Google Scholar

[40] Y. Gu, Z. Chang, M. Pan, L. Song, and Z. Han, “Joint radio and computational resource allocation in IoT fog computing,” IEEE Transactions on Vehicular Technology, vol. 67, no. 8, pp. 7475–7484, Aug. 2018. https://doi.org/10.1109/TVT.2018.282083810.1109/TVT.2018.2820838 Search in Google Scholar

[41] B. Jia, H. Hu, Y. Zeng, T. Xu, and Y. Yang, “Double-matching resource allocation strategy in fog computing networks based on cost efficiency,” Journal of Communications and Networks, vol. 20, no. 3, pp. 237–246, June 2018. https://doi.org/10.1109/JCN.2018.00003610.1109/JCN.2018.000036 Search in Google Scholar

[42] S. F. Abedin, M. G. R. Alam, S. A. Kazmi, N. H. Tran, D. Niyato, and C. S. Hong, “Resource allocation for ultra-reliable and enhanced mobile broadband IoT applications in fog network,” IEEE Transactions on Communications, vol. 67, no. 1, pp. 489–502, Jan. 2018. https://doi.org/10.1109/TCOMM.2018.287088810.1109/TCOMM.2018.2870888 Search in Google Scholar

[43] L. Yin, J. Luo, and H. Luo, “Tasks scheduling and resource allocation in fog computing based on containers for smart manufacturing,” IEEE Transactions on Industrial Informatics, vol. 14, no. 10, pp. 4712–4721, Oct. 2018. https://doi.org/10.1109/TII.2018.285124110.1109/TII.2018.2851241 Search in Google Scholar

[44] C. T. Do, N. H. Tran, C. Pham, M. G. R. Alam, J. H. Son, and C. S. Hong, “A proximal algorithm for joint resource allocation and minimizing carbon footprint in geo-distributed fog computing,” in 2015International Conference on Information Networking (ICOIN), Cambodia, Mar. 2015, pp. 324–329. https://doi.org/10.1109/ICOIN.2015.705790510.1109/ICOIN.2015.7057905 Search in Google Scholar

[45] L. Ni, J. Zhang, C. Jiang, C. Yan, and K. Yu, “Resource allocation strategy in fog computing based on priced timed petri nets,” IEEE Internet of Things Journal, vol. 4, no. 5, pp. 1216–1228, Oct. 2017. https://doi.org/10.1109/JIOT.2017.270981410.1109/JIOT.2017.2709814 Search in Google Scholar

[46] N. C. Luong, Y. Jiao, P. Wang, D. Niyato, D. I. Kim, and Z. Han, “A machine-learning-based auction for resource trading in fog computing,” IEEE Communications Magazine, vol. 58, no. 3, pp. 82–88, Mar. 2020. https://doi.org/10.1109/MCOM.001.190013610.1109/MCOM.001.1900136 Search in Google Scholar

[47] X. Peng, K. Ota, and M. Dong, “Multiattribute-based double auction toward resource allocation in vehicular fog computing,” IEEE Internet of Things Journal, vol. 7, no. 4, pp. 3094–3103, Apr. 2020. https://doi.org/10.1109/JIOT.2020.296500910.1109/JIOT.2020.2965009 Search in Google Scholar

[48] B. Cao, Z. Sun, J. Zhang, and Y. Gu, “Resource allocation in 5G IoV architecture based on SDN and fog-cloud computing,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 6, pp. 3832-3840, June 2021. https://doi.org/10.1109/TITS.2020.304884410.1109/TITS.2020.3048844 Search in Google Scholar

[49] 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,” Journal of Ambient Intelligence and Humanized Computing, vol. 11, pp. 4951–4966, Nov. 2020. https://doi.org/10.1007/s12652-020-01768-810.1007/s12652-020-01768-8 Search in Google Scholar

[50] R. K. Naha, S. Garg, A. Chan, and S. K. Battula, “Deadline-based dynamic resource allocation and provisioning algorithms in Fog-Cloud environment,” Future Generation Computer Systems, vol. 104, pp. 131–141, Mar. 2020. https://doi.org/10.1016/j.future.2019.10.01810.1016/j.future.2019.10.018 Search in Google Scholar

[51] D. Tychalas and H. Karatza, “A scheduling algorithm for a fog computing system with bag-of-tasks jobs: Simulation and performance evaluation,” Simulation Modelling Practice and Theory, vol. 98, Art no. 101982, Jan. 2020. https://doi.org/10.1016/j.simpat.2019.10198210.1016/j.simpat.2019.101982 Search in Google Scholar

[52] T. Aladwani, “Scheduling IoT healthcare tasks in fog computing based on their importance,” Procedia Computer Science, vol. 163, pp. 560–569, 2019. https://doi.org/10.1016/j.procs.2019.12.13810.1016/j.procs.2019.12.138 Search in Google Scholar

[53] D. Zeng, L. Gu, S. Guo, Z. Cheng, and S. Yu, “Joint optimization of task scheduling and image placement in fog computing supported software defined embedded system,” IEEE Transactions on Computers, vol. 65, no. 12, pp. 3702–3712, Dec. 2016. https://doi.org/10.1109/TC.2016.253601910.1109/TC.2016.2536019 Search in Google Scholar

[54] S. Bitam, S. Zeadally, and A. Mellouk, “Fog computing job scheduling optimization based on bees swarm,” Enterprise Information Systems, vol. 12, no. 4, pp. 373–397, 2018. https://doi.org/10.1080/17517575.2017.130457910.1080/17517575.2017.1304579 Search in Google Scholar

[55] Z. Liu, X. Yang, Y. Yang, K. Wang, and G. Mao, “DATS: Dispersive stable task scheduling in heterogeneous fog networks,” IEEE Internet of Things Journal, vol. 6, no. 2, pp. 3423–3436, Apr. 2018. https://doi.org/10.1109/JIOT.2018.288472010.1109/JIOT.2018.2884720 Search in Google Scholar

[56] X.-Q. Pham and E.-N. Huh, “Towards task scheduling in a cloud-fog computing system,” in 2016 18th Asia-Pacific network operations and management symposium (APNOMS), Kanazawa, Japan, Nov. 2016, pp. 1–4. https://doi.org/10.1109/APNOMS.2016.773724010.1109/APNOMS.2016.7737240 Search in Google Scholar

[57] T. Choudhari, M. Moh, and T.-S. Moh, “Prioritized task scheduling in fog computing,” in Proceedings of the ACMSE’18 conference, Art no. 22, Mar. 2018, pp. 1–8. https://doi.org/10.1145/3190645.319069910.1145/3190645.3190699 Search in Google Scholar

[58] S. Zhao, Y. Yang, Z. Shao, X. Yang, H. Qian, and C.-X. Wang, “FEMOS: Fog-enabled multitier operations scheduling in dynamic wireless networks,” IEEE Internet of Things Journal, vol. 5, no. 2, pp. 1169–1183, Apr. 2018. https://doi.org/10.1109/JIOT.2018.280828010.1109/JIOT.2018.2808280 Search in Google Scholar

[59] B. Jamil, M. Shojafar, I. Ahmed, A. Ullah, K. Munir, and H. Ijaz, “A job scheduling algorithm for delay and performance optimization in fog computing,” Concurrency and Computation: Practice and Experience, vol. 32, no. 7, Art no. e5581, Apr. 2020. https://doi.org/10.1002/cpe.558110.1002/cpe.5581 Search in Google Scholar

[60] S. Wang, T. Zhao, and S. Pang, “Task scheduling algorithm based on improved firework algorithm in fog computing,” IEEE Access, vol. 8, pp. 32 385–32 394, Feb. 2020. https://doi.org/10.1109/ACCESS.2020.297375810.1109/ACCESS.2020.2973758 Search in Google Scholar

[61] P. Hosseinioun, M. Kheirabadi, S. R. K. Tabbakh, and R. Ghaemi, “A new energy-aware tasks scheduling approach in fog computing using hybrid meta-heuristic algorithm,” Journal of Parallel and Distributed Computing, vol. 143, pp. 88–96, Sep. 2020. https://doi.org/10.1016/j.jpdc.2020.04.00810.1016/j.jpdc.2020.04.008 Search in Google Scholar

[62] S. Ghanavati, J. Abawajy, and D. Izadi, “Automata-based dynamic fault tolerant task scheduling approach in fog computing,” IEEE Transactions on Emerging Topics in Computing, Oct. 2020. https://doi.org/10.1109/TETC.2020.303367210.1109/TETC.2020.3033672 Search in Google Scholar

[63] M. I. Naas, P. R. Parvedy, J. Boukhobza, and L. Lemarchand, “iFogStor: an IoT data placement strategy for fog infrastructure,” in 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC), Madrid, Spain, Aug. 2017, pp. 97–104. https://doi.org/10.1109/ICFEC.2017.1510.1109/ICFEC.2017.15 Search in Google Scholar

[64] M. I. Naas, L. Lemarchand, J. Boukhobza, and P. Raipin, “A graph partitioning-based heuristic for runtime IoT data placement strategies in a fog infrastructure,” in Proceedings of the 33rd Annual ACM Symposium on Applied Computing, Apr. 2018, pp. 767–774. https://doi.org/10.1145/3167132.316721710.1145/3167132.3167217 Search in Google Scholar

[65] T. Huang, W. Lin, Y. Li, L. He, and S. Peng, “A latency-aware multiple data replicas placement strategy for fog computing,” Journal of Signal Processing Systems, vol. 91, no. 10, pp. 1191–1204, Feb. 2019. https://doi.org/10.1007/s11265-019-1444-510.1007/s11265-019-1444-5 Search in Google Scholar

[66] N. Wang and J. Wu, “Latency minimization through optimal data placement in fog networks,” Fog Computing: Theory and Practice, pp. 269–291, Apr. 2020. https://doi.org/10.1002/9781119551713.ch1010.1002/9781119551713.ch10 Search in Google Scholar

[67] J. Wang, “When data cleaning meets crowdsourcing,” AMPlab, UC, Berkeley, 2015. Search in Google Scholar

[68] J. Ni, K. Zhang, Y. Yu, X. Lin, and X. S. Shen, “Providing task allocation and secure deduplication for mobile crowdsensing via fog computing,” IEEE Transactions on Dependable and Secure Computing, vol. 17, no. 3, pp. 581–594, 2018. https://doi.org/10.1109/TDSC.2018.279143210.1109/TDSC.2018.2791432 Search in Google Scholar

[69] J. Yan, X. Wang, Q. Gan, S. Li, and D. Huang, “Secure and efficient big data deduplication in fog computing,” Soft Computing, vol. 24, pp. 5671–5682, Jul. 2019. https://doi.org/10.1007/s00500-019-04215-910.1007/s00500-019-04215-9 Search in Google Scholar

[70] P. Shynu, R. Nadesh, V. G. Menon, P. Venu, M. Abbasi, and M. R. Khosravi, “A secure data deduplication system for integrated cloud-edge networks,” Journal of Cloud Computing, vol. 9, Art no. 61, pp. 1–12, Nov. 2020. https://doi.org/10.1186/s13677-020-00214-610.1186/s13677-020-00214-6 Search in Google Scholar

[71] R. Vales, J. Moura, and R. Marinheiro, “Energy-aware and adaptive fog storage mechanism with data replication ruled by spatio-temporal content popularity,” Journal of Network and Computer Applications, vol. 135, pp. 84–96, June 2019. https://doi.org/10.1016/j.jnca.2019.03.00110.1016/j.jnca.2019.03.001 Search in Google Scholar

[72] A. Berkennou, G. Belalem, and S. Limam, “A replication and migration strategy on the hierarchical architecture in the fog computing environment,” Multiagent and Grid Systems, vol. 16, no. 3, pp. 291–307, Oct. 2020. https://doi.org/10.3233/MGS-20033310.3233/MGS-200333 Search in Google Scholar

[73] I. Al Ridhawi, N. Mostafa, Y. Kotb, M. Aloqaily, and I. Abualhaol, “Data caching and selection in 5G networks using F2F communication,” in 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Montreal, QC, Canada, Oct. 2017, pp. 1–6. https://doi.org/10.1109/PIMRC.2017.829268110.1109/PIMRC.2017.8292681 Search in Google Scholar

[74] W. Bai, H. Feng, Y. Wang, and X. Han, “Research on data cache algorithm of fog computing node,” in 2020 IEEE 11th International Conference on Software Engineering and Service Science (ICSESS), Beijing, China, Nov. 2020, pp. 197–200. https://doi.org/10.1109/ICSESS49938.2020.923767010.1109/ICSESS49938.2020.9237670 Search in Google Scholar

[75] Y. Fu, X. Qiu, and J. Wang, “F2MC: Enhancing data storage services with fog-toMultiCloud hybrid computing,” in 2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC), London, UK, Oct. 2019, pp. 1–6. https://doi.org/10.1109/IPCCC47392.2019.895874810.1109/IPCCC47392.2019.8958748 Search in Google Scholar

[76] T. Yu, X. Wang, and A. Shami, “A novel fog computing enabled temporal data reduction scheme in IoT systems,” in GLOBECOM 2017-2017 IEEE Global Communications Conference, Singapore, Dec. 2017, pp. 1–5. https://doi.org/10.1109/GLOCOM.2017.825394110.1109/GLOCOM.2017.8253941 Search in Google Scholar

[77] A. Gómez-Cárdenas, X. Masip-Bruin, E. Marín-Tordera, S. Kahvazadeh, and J. Garcia, “A hash-based naming strategy for the fog-to-cloud computing paradigm,” in European Conference on Parallel Processing Workshops. Lecture Notes in Computer Science. vol 10659, Springer, Cham, pp. 316–324, 2017. https://doi.org/10.1007/978-3-319-75178-8_2610.1007/978-3-319-75178-8_26 Search in Google Scholar

[78] D. Guibert, J. Wu, S. He, M. Wang, and J. Li, “CC-fog: Toward content-centric fog networks for E-health,” in 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), Dalian, China, Oct. 2017, pp. 1–5. https://doi.org/10.1109/HealthCom.2017.821083010.1109/HealthCom.2017.8210830 Search in Google Scholar

[79] A. J. Kadhim and S. A. H. Seno, “Energy-efficient multicast routing protocol based on SDN and fog computing for vehicular networks,” Ad Hoc Networks, vol. 84, pp. 68–81, Mar. 2019. https://doi.org/10.1016/j.adhoc.2018.09.01810.1016/j.adhoc.2018.09.018 Search in Google Scholar

[80] A. P. Abidoye and B. Kabaso, “Energy-efficient hierarchical routing in wireless sensor networks based on fog computing,” EURASIP Journal on Wireless Communications and Networking, Art no. 8(2021), pp. 1–26, Jan. 2021. https://doi.org/10.1186/s13638-020-01835-w10.1186/s13638-020-01835-w Search in Google Scholar

[81] N. Noorani and S. A. H. Seno, “SDN and fog computing-based switchable routing using path stability estimation for vehicular ad hoc networks,” Peer-to-Peer Networking and Applications, vol. 13, pp. 948–964, 2020. https://doi.org/10.1007/s12083-019-00859-410.1007/s12083-019-00859-4 Search in Google Scholar

[82] T. Saito, S. Nakamura, T. Enokido, and M. Takizawa, “Epidemic and topic-based data transmission protocol in a mobile fog computing model,” in International Conference on Broadband and Wireless Computing, Communication and Applications. Springer, Oct. 2020, pp. 34–43. https://doi.org/10.1007/978-3-030-61108-8_410.1007/978-3-030-61108-8_4 Search in Google Scholar

[83] P. Hu, H. Ning, T. Qiu, H. Song, Y. Wang, and X. Yao, “Security and privacy preservation scheme of face identification and resolution framework using fog computing in internet of things,” IEEE Internet of Things Journal, vol. 4, no. 5, pp. 1143–1155, Oct. 2017. https://doi.org/10.1109/JIOT.2017.265978310.1109/JIOT.2017.2659783 Search in Google Scholar

[84] M. Wazid, A. K. Das, N. Kumar, and A. V. Vasilakos, “Design of secure key management and user authentication scheme for fog computing services,” Future Generation Computer Systems, vol. 91, pp. 475–492, Feb. 2019. https://doi.org/10.1016/j.future.2018.09.01710.1016/j.future.2018.09.017 Search in Google Scholar

[85] Z. Ali, S. A. Chaudhry, K. Mahmood, S. Garg, Z. Lv, and Y. B. Zikria, “A clogging resistant secure authentication scheme for fog computing services,” Computer Networks, vol. 185, Art no. 107731, Feb. 2021. https://doi.org/10.1016/j.comnet.2020.10773110.1016/j.comnet.2020.107731 Search in Google Scholar

[86] R. Lu, K. Heung, A. Habibi Lashkari, and A. Ghorbani, “A lightweight privacy-preserving data aggregation scheme for fog computing-enhanced IoT,” IEEE Access, vol. 5, pp. 3302–3312, Mar. 2017. https://doi.org/10.1109/ACCESS.2017.267752010.1109/ACCESS.2017.2677520 Search in Google Scholar

[87] C. Zuo, J. Shao, G. Wei, M. Xie, and M. Ji, “CCA-secure ABE with outsourced decryption for fog computing,” Future Generation Computer Systems, vol. 78, no. 2, pp. 730–738, Jan. 2018. https://doi.org/10.1016/j.future.2016.10.02810.1016/j.future.2016.10.028 Search in Google Scholar

[88] Z. Guan, Y. Zhang, L. Wu, J. Wu, J. Li, Y. Ma, and J. Hu, “APPA: An anonymous and privacy preserving data aggregation scheme for fogenhanced IoT,” Journal of Network and Computer Applications, vol. 125, pp. 82–92, Jan. 2019. https://doi.org/10.1016/j.jnca.2018.09.01910.1016/j.jnca.2018.09.019 Search in Google Scholar

[89] F. Wang, J. Wang, and W. Yang, “Efficient incremental authentication for the updated data in fog computing,” Future Generation Computer Systems, vol. 114, pp. 130–137, Jan. 2021. https://doi.org/10.1016/j.future.2020.07.03910.1016/j.future.2020.07.039 Search in Google Scholar

[90] H. Noura, O. Salman, A. Chehab, and R. Couturier, “Preserving data security in distributed fog computing,” Ad Hoc Networks, vol. 94, Art no. 101937, Nov. 2019. https://doi.org/10.1016/j.adhoc.2019.10193710.1016/j.adhoc.2019.101937 Search in Google Scholar

[91] M. Al-Khafajiy, T. Baker, M. Asim, Z. Guo, R. Ranjan, A. Longo, D. Puthal, and M. Taylor, “COMITMENT: A fog computing trust management approach,” Journal of Parallel and Distributed Computing, vol. 137, pp. 1–16, Mar. 2020. https://doi.org/10.1016/j.jpdc.2019.10.00610.1016/j.jpdc.2019.10.006 Search in Google Scholar

[92] J. Xu, H. Liu, W. Shao, and K. Deng, “Quantitative 3-D shape features based tumor identification in the fog computing architecture,” Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 8, pp. 2987–2997, Feb. 2019. https://doi.org/10.1007/s12652-018-0695-510.1007/s12652-018-0695-5 Search in Google Scholar

[93] 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 Transactions on Industrial Informatics, vol. 14, no. 10, pp. 4548–4556, Oct. 2018. https://doi.org/10.1109/TII.2018.281893210.1109/TII.2018.2818932 Search in Google Scholar

[94] V. Vijayakumar, D. Malathi, V. Subramaniyaswamy, P. Saravanan, and R. Logesh, “Fog computing-based intelligent healthcare system for the detection and prevention of mosquito-borne diseases,” Computers in Human Behavior, vol. 100, pp. 275–285, Nov. 2019. https://doi.org/10.1016/j.chb.2018.12.00910.1016/j.chb.2018.12.009 Search in Google Scholar

[95] R. Siddharth and G. Aghila, “A light weight background subtraction algorithm for motion detection in fog computing,” IEEE Letters of the Computer Society, vol. 3, no. 1, pp. 17–20, 2020. https://doi.org/10.1109/LOCS.2020.297470310.1109/LOCS.2020.2974703 Search in Google Scholar

[96] J. Xu, K. Ota, and M. Dong, “Fast deployment of emergency fog service for disaster response,” IEEE Network, vol. 34, no. 6, pp. 100–105, 2020. https://doi.org/10.1109/MNET.001.190067110.1109/MNET.001.1900671 Search in Google Scholar

[97] A. Ali, Y. Zhu, and M. Zakarya, “A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing,” Multimedia Tools and Applications, vol. 80, pp. 31401–31433, Jan. 2021. https://doi.org/10.1007/s11042-020-10486-410.1007/s11042-020-10486-4 Search in Google Scholar

[98] F. A. Salaht, F. Desprez, and A. Lebre, “An overview of service placement problem in fog and edge computing,” ACM Computing Surveys (CSUR), vol. 53, no. 3, Art no. 65, pp. 1–35, June 2020. https://doi.org/10.1145/339119610.1145/3391196 Search in Google Scholar

[99] S. Yi, Z. Hao, Z. Qin, and Q. Li, “Fog computing: Platform and applications,” in 2015 Third IEEE workshop on hot topics in web systems and technologies (HotWeb), Washington, DC, USA, Nov. 2015, pp. 73–78. https://doi.org/10.1109/HotWeb.2015.2210.1109/HotWeb.2015.22 Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo