Fog Computing Algorithms: A Survey and Research Opportunities

Malukani, Shaifali P.; Bhensdadia, C. K.

doi:10.2478/acss-2021-0017

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.

References

[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[7] R. K. Naha, S. Garg, and A. Chan, “Fog computing architecture: Survey and challenges,” arXiv, no.1811.09047, 2018.
Search in Google Scholar Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[67] J. Wang, “When data cleaning meets crowdsourcing,” AMPlab, UC, Berkeley, 2015.
Search in Google Scholar Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article
[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 Back to article

Fog Computing Algorithms: A Survey and Research Opportunities

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