Edge computing: current trends, research challenges and future directions

Abstract

The edge computing (EC) paradigm brings computation and storage to the edge of the network where data is both consumed and produced. This variation is necessary to cope with the increasing amount of network-connected devices and data transmitted, that the launch of the new 5G networks will expand. The aim is to avoid the high latency and traffic bottlenecks associated with the use of Cloud Computing in networks where several devices both access and generate high volumes of data. EC also improves network support for mobility, security, and privacy. This paper provides a discussion around EC and summarized the definition and fundamental properties of the EC architectures proposed in the literature (Multi-access Edge Computing, Fog Computing, Cloudlet Computing, and Mobile Cloud Computing). Subsequently, this paper examines significant use cases for each EC architecture and debates some promising future research directions.

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References

  1. 1.

    Abbas N, Zhang Y, Taherkordi A, Skeie T (2017) Mobile edge computing: a survey. IEEE Internet Things J 5(1):450–465

    Article  Google Scholar 

  2. 2.

    Ahmed A, Ahmed E (2016) A survey on mobile edge computing. In: 10th international conference on intelligent systems and control (ISCO’16). pp 1–8

  3. 3.

    Aldmour R, Yousef S, Yaghi M, Tapaswi S, Pattanaik KK, Cole M (2017) New cloud offloading algorithm for better energy consumption and process time. Int J Syst Assur Eng Manag 8(s2):730–733

    Article  Google Scholar 

  4. 4.

    Ayad M, Taher M, Salem A (2014) Real-time mobile cloud computing: a case study in face recognition. In: 28th International conference on advanced information networking and applications workshops. pp 73–78

  5. 5.

    Badidi E (2020) Qos-aware placement of tasks on a fog cluster in an edge computing environment. J Ubiquitous Syst Pervasive Netw 13(1):11–19

    Article  Google Scholar 

  6. 6.

    Bagchi S, Siddiqui MB, Wood P, Zhang H (2020) Dependability in edge computing. Commun ACM 63(1):58–66

    Article  Google Scholar 

  7. 7.

    Baktayan A, AlGabri M, Alhomdy S (2018) Fog computing for network slicing in 5G networks: an overview. J Telecom Syst Manag 07(02):1–18

    Google Scholar 

  8. 8.

    Baktir AC, Ozgovde A, Ersoy C (2017) How can edge computing benefit from software-defined networking: a survey, use cases, and future directions. IEEE Commun Surv Tutor 19(4):2359–2391

    Article  Google Scholar 

  9. 9.

    Barbarossa S, Sardellitti S, Di Lorenzo P (2014) Communicating while computing: distributed mobile cloud computing over 5G heterogeneous networks. IEEE Signal Process Mag 31(6):45–55

    Article  Google Scholar 

  10. 10.

    Beck MT, Werner M, Feld S, Schimper T (2014) Mobile edge computing: a taxonomy. In: 6th International conference on advances in future internet, (AFIN). pp 48–54

  11. 11.

    Bilal K, Khalid O, Erbad A, Khan SU (2018) Potentials, trends, and prospects in edge technologies: fog, cloudlet, mobile edge, and micro data centers. Comput Netw 130:94–120

    Article  Google Scholar 

  12. 12.

    Billah F, Adnan M (2019) Smartlet: a dynamic architecture for real time face recognition in smartphone using cloudlets and cloud. Big Data Res 17:45–55

    Article  Google Scholar 

  13. 13.

    Bodkhe U, Tanwar S, Parekh K, Khanpara P, Tyagi S, Kumar N, Alazab M (2020) Blockchain for industry 4.0: a comprehensive review. IEEE Access 8:79764–79800

    Article  Google Scholar 

  14. 14.

    Bonomi F, Milito R, Natarajan P, Zhu J (2014) Fog computing: a platform for internet of things and analytics. Big Data Internet Things Roadmap Smart Environ 546:169–186

    Article  Google Scholar 

  15. 15.

    Bonomi F, Milito R, Zhu J, Addepalli S (2012) Fog computing and its role in the internet of things. In: Proceedings of the First Edition of the MCC workshop on mobile cloud computing. Association for Computing Machinery, Helsinki, Finland, pp 13–16. https://doi.org/10.1145/2342509.2342513

  16. 16.

    Bou Abdo J, Demerjian J (2017) Evaluation of mobile cloud architectures. Pervasive Mobile Comput 39(December):284–303

    Article  Google Scholar 

  17. 17.

    Cao Z, Zhou P, Li R, Huang S, Wu D (2020) Multiagent deep reinforcement learning for joint multichannel access and task offloading of mobile-edge computing in industry 4.0. IEEE Internet Things J 7(7):6201–6213

    Article  Google Scholar 

  18. 18.

    Carvalho G, Cabral B, Pereira V, Bernardino J (2019) A case for machine learning in edge-oriented computing to enhance mobility as a service. In: 15th International conference on distributed computing in sensor systems, (DCOSS’19). pp 530–537

  19. 19.

    Chanakya B, Kiran PS (2017) A comprehensive survey of fog computing with internet of everything (IoE). Int J Control Theory Appl 10(29):99–106

    Google Scholar 

  20. 20.

    Chandavale A, Gade A, Dixit A (2019) Medical knowledge extraction scheme for cloudlet-based healthcare system to avoid malicious attacks. Int J Cloud Comput 8(4):319–331

    Article  Google Scholar 

  21. 21.

    Chen L, Wu J, Zhou G, Ma L (2018) QUICK: qos-guaranteed efficient cloudlet placement in wireless metropolitan area networks. J Supercomput 74(8):4037–4059

    Article  Google Scholar 

  22. 22.

    Chen N, Chen Y, You Y, Ling H, Liang P, Zimmermann R (2016) Dynamic urban surveillance video stream processing using fog computing. In: Proceedings—016 IEEE 2nd international conference on multimedia big data, BigMM 2016. pp 105–112

  23. 23.

    Chiang M, Ha S, I, CL, Risso, F, Zhang T, (2017) Clarifying fog computing and networking: 10 questions and answers. IEEE Commun Mag 55:18–20

  24. 24.

    Chiang M, Zhang T (2016) Fog and IoT: an overview of research opportunities. IEEE Internet Things J 3(6):854–864

    Article  Google Scholar 

  25. 25.

    Consortium O (2017) OpenFog reference architecture for fog computing. Technical report

  26. 26.

    Dastjerdi A, Gupta H, Calheiros R, Ghosh S, Buyya R (2016) Chapter-4 fog computing: principles, architectures, and applications. In: Internet of things. pp 61–75

  27. 27.

    Datla D, Chen X, Tsou T, Raghunandan S, Hasan SM, Reed JH, Dietrich CB, Bose T, Fette B, Kim JH (2012) Wireless distributed computing: a survey of research challenges. IEEE Commun Mag 50(1):144–152

    Article  Google Scholar 

  28. 28.

    Davis A, Parikh J, Weihl WE (2004) Edgecomputing: extending enterprise applications to the edge of the internet. In: Proceedings of the 13th international World Wide Web conference on Alternate track papers and posters. pp 180–187

  29. 29.

    De D, Mukherjee A, Roy DG (2020) Power and delay efficient multilevel offloading strategies for mobile cloud computing. Wirel Pers Commun 112(4):2159–2186

    Article  Google Scholar 

  30. 30.

    Dinh HT, Lee C, Niyato D, Wang P (2013) A survey of MCC: architecture, applications, and approaches. Wirel Commun Mobile Comput 13:1587–1611

    Article  Google Scholar 

  31. 31.

    Dolezal J, Becvar Z, Zeman T (2016) Performance evaluation of computation offloading from mobile device to the edge of mobile network. In: 2016 IEEE conference on standards for communications and networking, CSCN 2016. pp 1–7

  32. 32.

    Duan Q, Wang S, Ansari N (2020) Convergence of networking and cloud/edge computing: status, challenges, and opportunities. IEEE Netw 34:1–8

    Article  Google Scholar 

  33. 33.

    Dubey H, Yang J, Constant N, Amiri AM, Yang Q, Makodiya K (2015) Fog data: enhancing telehealth big data through fog computing. In: ASE BigData and socialInformatics (ASE BD&SI). pp 1–6

  34. 34.

    El-Sayed H, Sankar S, Prasad M, Puthal D (2018) Edge of things: the big picture on the integration of edge. IoT and the Cloud. IEEE Access 6:1706–1717

    Article  Google Scholar 

  35. 35.

    ETSI: MEC 003 - V2.1.1-Multi-access edge computing (MEC); framework and reference architecture. Technical report (2019)

  36. 36.

    Fernández-CaramésTM Fraga-Lamas P, Suárez-Albela M, Vilar-Montesinos M (2018) A fog computing and cloudlet based augmented reality system for the industry 4.0 shipyard. Sensors 18(6):1798

    Article  Google Scholar 

  37. 37.

    Fernando N, Loke SW, Rahayu W (2013) Mobile cloud computing: a survey. Future Gener Comput Syst 29(1):84–106

    Article  Google Scholar 

  38. 38.

    Fernando N, Loke SW, Rahayu W (2016) Computing with nearby mobile devices: a work sharing algorithm for mobile edge-clouds. IEEE Trans CC 7161:1–14

    Google Scholar 

  39. 39.

    Ferrer AJ, Marquès JM, Jorba J (2019) Towards the decentralised cloud: survey on approaches and challenges for mobile, ad-hoc and edge computing. ACM Comput Surv 51(6):1–39

    Article  Google Scholar 

  40. 40.

    Firdhous M, Ghazali O, Hassan S (2014) Fog computing: will it be the future of cloud computing? In: 3rd International conference on informatics and applications. pp 8–15

  41. 41.

    Gao Z, Hao W, Zhang R, Yang S (2020) Markov decision process-based computation offloading algorithm and resource allocation in time constraint for mobile cloud computing. IET Commun 14(13):2068–2078

    Article  Google Scholar 

  42. 42.

    Garcia Lopez P, Montresor A, Epema D, Datta A, Higashino T, Iamnitchi A, Barcellos M, Felber P, Riviere E (2015) Edge-centric computing. ACM SIGCOMM Comput Commun Rev 45(5):37–42

    Article  Google Scholar 

  43. 43.

    Gedeon J, Brandherm F, Egert R, Grube T, Mühlhäuser M (2019) What the fog? edge computing revisited: promises. Applications and future challenges. IEEE Access 7:152847–152878

    Article  Google Scholar 

  44. 44.

    Gedeon J, Krisztinkovics J, Meurisch C, Stein M, Wang L, Mühlhäuser M (2018) A multi-cloudlet infrastructure for future smart cities: an empirical study. In: 1st International workshop on edge systems, analytics and networking. pp 19–24

  45. 45.

    Giordano A, Spezzano G, Vinci A (2016) Smart agents and fog computing for smart city applications. In: International conference smart cities. pp 137–146

  46. 46.

    Gonzalez NM, Goya WA, Silva EA, Cristina T, Brito MD (2016) Fog computing: data analytics and cloud distributed processing on the network edges. In: 35th International conference of the Chilean computer science society, (SCCC). pp 1–9

  47. 47.

    Grewe D, Wagner M, Arumaithurai M, Psaras I, Kutscher D (2017) Information-centric mobile edge computing for connected vehicle environments. In: Workshop on mobile edge communications, (MECOMM). pp 7–12

  48. 48.

    Gu Z, Takahashi R, Fukazawa Y (2019) Real-time resources allocation framework for multi-task offloading in mobile cloud computing. In: International conference on computer, information and telecomm, systems, CITS’19. pp 1–5

  49. 49.

    Guan T, Zaluska E, De Roure D (2005) A grid service infrastructure for mobile devices. In: 1st international conference on semantics, knowledge and grid. pp 2–5

  50. 50.

    Gupta H, Chakraborty S, Ghosh SK, Buyya R (2016) Fog computing in 5G networks: an application perspective. Fog 5G:1–36

    Google Scholar 

  51. 51.

    Hall P, Miller H (2018) Fog computing architecture, evaluation, and future research directions. IEEE Commun Mag 56:46–52

    Google Scholar 

  52. 52.

    Han D, Chen W, Bai B, Fang Y (2019) Offloading optimization and bottleneck analysis for mobile cloud computing. IEEE Trans Commun 67(9):6153–6167

    Article  Google Scholar 

  53. 53.

    Hassan N, Yau KLA, Wu C (2019) Edge computing in 5G: a review. IEEE Access Special Section on MEC and MCC 7:127276–127289

    Google Scholar 

  54. 54.

    Hong CH, Varghese B (2019) Resource management in fog/edge computing: a survey on architectures, infrastructure, and algorithms. ACM Comput Surv 52(5):1–37

    Article  Google Scholar 

  55. 55.

    Hu P, Dhelim S, Ning H, Qiu T (2017) Survey on fog computing: architecture, key technologies, applications and open issues. J Netw Comput Appl 98:27–42. https://doi.org/10.1016/j.jnca.2017.09.002

  56. 56.

    Huang J, Liang J, Ali S (2020) A simulation-based optimization approach for reliability-aware service composition in edge computing. IEEE Access 8:50355–50366

    Article  Google Scholar 

  57. 57.

    Issarny V, Georgantas N, Hachem S, Zarras A, Vassiliadist P, Autili M, Gerosa MA, Hamida AB (2011) Service-oriented middleware for the Future Internet: state of the art and research directions. J Internet Serv Appl 2(1):23–45

    Article  Google Scholar 

  58. 58.

    Jararweh Y, Doulat A, Alqudah O, Ahmed E, Al-Ayyoub M, Benkhelifa E (2016) The future of mobile cloud computing: integrating cloudlets and mobile edge computing. In: 23rd International conference on telecommunications, (ICT). pp 1–5

  59. 59.

    Javadzadeh G, Rahmani AM (2020) Fog computing applications in smart cities: a systematic survey. Wireless Netw 26(2):1433–1457

    Article  Google Scholar 

  60. 60.

    Jha D, Alwasel K, Alshoshan A, Huang X, Naha R, Battula S, Garg S, Puthal D, James P, Zomaya A, Dustdar S, Ranjan R (2020) IoTSim-Edge: a simulation framework for modeling the behavior of IoT and EC environments. Softw Pract Exp 50:1–19

    Article  Google Scholar 

  61. 61.

    Jia G, Han G, Li A, Du J (2018) SSL: smart street lamp based on fog computing for smarter cities. IEEE Trans Ind Inf 14(11):4995–5004

    Article  Google Scholar 

  62. 62.

    Jia M, Liang W, Xu Z (2017) Qos-aware task offloading in distributed cloudlets with virtual network function services. In: Proceedings of the 20th ACM international conference on modelling, analysis and simulation of wireless and mobile systems, pp 106–119

  63. 63.

    Jiang C, Cheng X, Gao H, Zhou X, Wan J (2019) Toward computation offloading in edge computing: a survey. IEEE Access 7:131543–131558

    Article  Google Scholar 

  64. 64.

    Kang S, Lee J, Jeon J, Chun I (2019) Multi-access edge computing based simulation offloading for 5g mobile application. In: 17th annual international conference on mobile systems, applications, and services. pp 590–591

  65. 65.

    Khan WZ, Ahmed E, Hakak S, Yaqoob I, Ahmed A (2019) Edge computing: a survey. Future Gener Comput Syst 97:219–235

    Article  Google Scholar 

  66. 66.

    Kiss P, Reale A, Ferrari CJ, Istenes Z (2018) Deployment of IoT applications on 5G edge. In: IEEE international conference on future IoT technologies. pp 1–9

  67. 67.

    Kitanov S, Monteiro E, Janevski T (2016) 5G and the fog-survey of related technologies and research directions. In: 18th Mediterranean Electrotechnical conference: intelligent and efficient technologies and services for the citizen. pp 18–20

  68. 68.

    Lee J, Kang S, Jeon J, Chun I (2020) Multiaccess edge computing-based simulation as a service for 5G mobile applications: a case study of tollgate selection for autonomous vehicles. Wirel Commun Mobile Comput. https://doi.org/10.1155/2020/9869434

    Article  Google Scholar 

  69. 69.

    Li C, Xue Y, Wang J, Zhang W, Li T (2018) Edge-oriented computing paradigms: a survey on architecture design and system management. ACM Comput Surv 51(2):A34–A39

    Article  Google Scholar 

  70. 70.

    Liu F, Tang G, Li Y, Cai Z, Zhang X, Zhou T (2019) A survey on edge computing systems and tools. Proc IEEE 107(8):1537–1562

    Article  Google Scholar 

  71. 71.

    Luan TH, Gao L, Li Z, Xiang Y, Wei G, Sun L Comput Sci 1–11

  72. 72.

    Mach P, Becvar Z (2017) Mobile edge computing: a survey on architecture and computation offloading. IEEE Commun Surv Tutor 19(3):1628–1656

    Article  Google Scholar 

  73. 73.

    Mahmud R, Buyya R (2019) Fog and edge comp: principles and paradigms, 1st edn

  74. 74.

    Mahmud R, Kotagiri R, Buyya R (2016) Fog computing: a taxonomy, survey and future directions. pp 1–28

  75. 75.

    Mazza D, Tarchi D, Corazza GE (2017) A unified urban mobile cloud computing offloading mechanism for smart cities. IEEE Commun Mag 55(3):30–37

    Article  Google Scholar 

  76. 76.

    Mehta S, Kaur P (2019) Efficient computation offloading in mobile cloud computing with nature-inspired algorithms. Int J Comput Intell Appl 18(4):1950023

    Article  Google Scholar 

  77. 77.

    Mouradian C, Naboulsi D, Yangui S, Glitho RH, Morrow MJ, Polakos PA (2018) A comprehensive survey on fog computing: state-of-the-art and research challenges. IEEE Commun Surv Tutor 20(1):416–464

    Article  Google Scholar 

  78. 78.

    Muniswamaiah M, Tappert CC (2019) Mobile cloud computing in healthcare using dynamic cloudlets for energy-aware consumption. CoRR abs/1908.11501

  79. 79.

    Naha RK, Garg S, Georgakopoulos D, Jayaraman PP, Gao L, Xiang Y, Ranjan R (2018) Fog computing: survey of trends, architectures, requirements, and research directions. IEEE Access 6:47980–48009

    Article  Google Scholar 

  80. 80.

    Nastic S, Rausch T, Scekic O, Dustdar S, Gusev M, Koteska B, Kostoska M, Jakimovski B, Ristov S, Prodan R (2017) A serverless real-time for edge computing. IEEE Internet Comput Internet 21:64–71

    Article  Google Scholar 

  81. 81.

    Nath SB, Gupta H, Chakraborty S, Ghosh SK (2018) A survey of fog computing and communication: current researches and future directions. IEEE Access (i) 1–47

  82. 82.

    Ning H, Li Y, Shi F, Yang LT (2020) Heterogeneous edge computing open platforms and tools for internet of things. Future Gener Comput Syst 106:67–76

    Article  Google Scholar 

  83. 83.

    Noor TH, Zeadally S, Alfazi A, Sheng QZ (2018) Mobile cloud computing: challenges and future research directions. J Net Comput Appl 115:70–85

    Article  Google Scholar 

  84. 84.

    Nunna S, Kousaridas A, Ibrahim M, Dillinger M, Thuemmler C, Feussner H, Schneider A (2015) Enabling real-time context-aware collaboration through 5G and MEC. In: 12th international conference on information technology: new generations. pp 601–605

  85. 85.

    Pang Z, Sun L, Wang Z, Tian E, Yang S (2016) A survey of cloudlet based mobile computing. In: international conference on cloud computing and big data. pp 268–275

  86. 86.

    Patel M, Hu Y, Hédé P, Joubert J, Thornton C, Naughton B, Julian RR, Chan C, Young V, Tan SJ, Lynch D (2014) Mobile edge computing-introductory technical white paper. ETSI White Paper 11(1):1–36

    Google Scholar 

  87. 87.

    Rahimi MR, Ren J, Liu CH, Vasilakos AV, Venkatasubramanian N (2014) Mobile cloud computing: a survey, state of art and future directions. Mobile Netwo Appl 19(2):133–143

    Article  Google Scholar 

  88. 88.

    Ray PP, Dash D, De D (2019) Edge computing for internet of things: a survey, e-healthcare case study and future direction. J Net Comput Appl 140:1–22

    Article  Google Scholar 

  89. 89.

    Ren J, Zhang D, He S, Zhang Y, Li T (2019) A survey on end-edge-cloud orchestrated network computing paradigms: transparent computing, mobile edge computing, fog computing, and cloudlet. ACM Comput Surv 52(6):1–36

    Article  Google Scholar 

  90. 90.

    Roman R, Lopez J, Mambo M (2018) Mobile edge computing, Fog et al.: a survey and analysis of security threats and challenges. Future Gener Comput Syst 78:680–698

    Article  Google Scholar 

  91. 91.

    Sabella D, Vaillant A, Kuure P, Rauschenbach U, Giust F (2016) Mobile-edge computing architecture: the role of MEC in the internet of things. IEEE Consum Electron Mag 5(4):84–91

    Article  Google Scholar 

  92. 92.

    Sangal SMHKVAL (2015) Analysis of cloudlet completion time during attack on smart grid cloud. Int J Cloud Comput 4:356–376

    Google Scholar 

  93. 93.

    Satyanarayanan M (2017) The emergence of edge computing. Computer 50(1):30–39

    Article  Google Scholar 

  94. 94.

    Satyanarayanan M, Bahl P, Cáceres R, Davies N (2009) The case for VM-based cloudlets in mobile computing. IEEE Pervasive Comput 8(4):14–23

    Article  Google Scholar 

  95. 95.

    Shahzadi S, Iqbal M, Dagiuklas T, Qayyum ZU (2017) Multi-access edge computing: open issues, challenges and future perspectives. J Cloud Comput 6(1):30

    Article  Google Scholar 

  96. 96.

    Shi W, Cao J, Zhang Q, Li Y, Xu L (2016) Edge computing: vision and challenges. IEEE Internet Things J 3(5):637–646

    Article  Google Scholar 

  97. 97.

    Shi W, Dustdar S (2016) The promise of edge computing. Computer 49:78–81

    Article  Google Scholar 

  98. 98.

    Simoens P, Xiao Y, Pillai P, Chen Z, Ha K, Satyanarayanan M (2013) Scalable crowd-sourcing of video from mobile devices. In: 11th annual international conference on mobile systems, applications, and services, (MobiSys ’13). p 139

  99. 99.

    Sinaeepourfard A, Krogstie J, Petersen SA, Ahlers D (2019) F2c2c-dm: a fog-to-cloudlet-to-cloud data management architecture in smart city. In: 2019 IEEE 5th world forum on internet of things (WF-IoT). pp 590–595

  100. 100.

    Sinky H, Hamdaoui B (2016) Cloudlet-aware mobile content delivery in wireless urban communication networks. In: 2016 IEEE global communications conference, GLOBECOM 2016, Washington, DC, USA, December 4–8, 2016, IEEE. pp 1–7

  101. 101.

    Sittón-Candanedo I, Alonso R, Rodríguez-González S, Coria J, de la Prieta F (2019) Edge computing architectures in industry 4.0: a general survey and comparison. In: 14th International conference on soft computing models in industrial and environmental applications (SOCO 2019), vol 950. pp 121–131

  102. 102.

    Sneps-Sneppe M, Namiot D (2016) On mobile cloud for smart city applications. CoRR

  103. 103.

    Song Y, Yau SS, Yu R, Zhang X, Xue G (2017) An approach to qos-based task distribution in edge computing networks for iot applications. In: IEEE international conference on edge computing. IEEE Computer Society, pp 32–39

  104. 104.

    Sonmez C, Ozgovde, A, Ersoy, C (2017) EdgeCloudSim: an environment for performance evaluation of edge computing systems. In: 2nd International conference on fog and mobile edge computing, (FMEC’17). pp 39–44

  105. 105.

    Stojmenovic I, Wen S (2014) The fog computing paradigm: scenarios and security issues. In: Federated conference on computer science and information systems, vol 2. pp 1–8

  106. 106.

    Sun C, Li H, Li X, Wen J, Xiong Q, Zhou W (2020) Convergence of recommender systems and EC: a comprehensive survey. IEEE Access 8:47118–47132

    Article  Google Scholar 

  107. 107.

    Sun X, Ansari N (2017) Latency aware workload offloading in the cloudlet network. IEEE Commun Lett 21(7):1481–1484

    Article  Google Scholar 

  108. 108.

    Taleb T, Samdanis K, Mada B, Flinck H, Dutta S, Sabella D (2017) On multi-access edge computing: a survey of the emerging 5G network edge cloud architecture and orchestration. IEEE Commun Surv Tutor 19(3):1657–1681

    Article  Google Scholar 

  109. 109.

    Tawalbeh LA, Bakheder W, Mehmood R, Song H (2016) Cloudlet-based mobile cloud computing for healthcare applications. In: IEEE global communications conference, (GLOBECOM). pp 1–6

  110. 110.

    Tran TX, Hajisami A, Pandey P, Pompili D (2017) Collaborative mobile edge computing in 5G networks: new paradigms, scenarios, and challenges. IEEE Commun Mag 55(4):54–61

    Article  Google Scholar 

  111. 111.

    Tuli S, Basumatary N, Gill SS, Kahani M, Arya RC, Wander GS, Buyya R (2020) HealthFog: an ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated IoT and fog computing environments. Future Gener Comput Syst 104:187–200

    Article  Google Scholar 

  112. 112.

    Vaidya S, Ambad P, Bhosle S (2018) Industry 4.0–a Glimpse. Procedia Manuf 20:233–238

    Article  Google Scholar 

  113. 113.

    Vaquero LM, Rodero-Merino L (2014) Finding your way in the fog. ACM SIGCOMM Comput Commun Rev 44(5):27–32

    Article  Google Scholar 

  114. 114.

    Varshney P, Simmhan Y (2017) Demystifying fog computing: characterizing architectures, applications and abstractions. In: IEEE 1st International conference on fog and edge computing (ICFEC’17). pp 115–124

  115. 115.

    Wang S, Zhang X, Zhang Y, Wang L, Yang J, Wang W (2017) A survey on mobile edge networks: convergence of computing, caching and communications. IEEE Access SS Secur Anal Intell CPS 5:6757–6779

    Google Scholar 

  116. 116.

    Wang T, Luo H, Zheng X, Xie M (2019) Crowdsourcing mechanism for trust evaluation in CPCS based on intelligent mobile edge computing. ACM Trans Intell Syst Technol 10(6):62:1–62:19

    Article  Google Scholar 

  117. 117.

    Wang Y, Chen IR, Wang DC (2015) A survey of mobile cloud computing applications: perspectives and challenges. Wirel Pers Commun 80(4):1607–1623

    Article  Google Scholar 

  118. 118.

    Wang Y, Pan Y (2015) Cloud-dew architecture: realizing the potential of distributed database systems in unreliable networks. In: Proceedings of the international conference on parallel and distributed processing techniques and applications (PDPTA). p 85

  119. 119.

    Yang B, Chai WK, Pavlou G, Katsaros KV (2016) Seamless support of low latency mobile applications with NFV-enabled mobile edge-cloud. In: 5th IEEE international conference on cloud networking, (CloudNet). pp 136–141

  120. 120.

    Yao D, Yu C, Yang LT, Jin H (2019) Using crowdsourcing to provide qos for mobile cloud computing. IEEE Trans Cloud Comput 7(2):344–356

    Article  Google Scholar 

  121. 121.

    Yassine A, Hossain MS, Muhammad G, Guizani M (2020) Cloudlet-based intelligent auctioning agents for truthful autonomous electric vehicles energy crowdsourcing. IEEE Trans Veh Technol 69(5):5457–5466

    Article  Google Scholar 

  122. 122.

    Yi S, Hao Z, Qin Z, Li Q (2016) Fog computing: platform and applications. In: 3rd Workshop on hot topics in web systems and technologies. pp 73–78

  123. 123.

    Yi S, Li C, Li Q (2015) A survey of fog computing: concepts, applications and issues. In: Workshop on mobile big data-mobidata ’15. pp 37–42

  124. 124.

    Yogi MK, Chandrasekhar K, Kumar GV (2017) Mist computing: principles, trends and future direction. SSRG Int J Comput Sci Eng 4(7):19–21

    Article  Google Scholar 

  125. 125.

    Yousefpour A, Fung C, Nguyen T, Kadiyala K, Jalali F, Niakanlahiji A, Kong J, Jue JP (2019) All one needs to know about fog computing and related edge computing paradigms: a complete survey. J Syst Architect 98:289–330

  126. 126.

    Yu J, Lee N, Pyo CS, Lee YS (2018) WISE: web of object architecture on IoT environment for smart home and building energy management. J Supercomput 74(9):4403–4418

    Article  Google Scholar 

  127. 127.

    Zhang J, Chen B, Zhao Y, Cheng X, Hu F (2018) Data security and privacy-preserving in edge computing paradigm: survey and open issues. IEEE Access 6:18209–18237

    Article  Google Scholar 

  128. 128.

    Zhang J, Zhou Z, Li S, Gan L, Zhang X, Qi L, Xu X, Dou W (2018) Hybrid computation offloading for smart home automation in mobile cloud computing. Pers Ubiquitous Comput 22(1):121–134

    Article  Google Scholar 

  129. 129.

    Zhang K, Mao Y, Leng S, He Y, Zhang Y (2017) Mobile-edge computing for vehicular networks. IEEE Veh Technol Mag 12:36–44

    Article  Google Scholar 

  130. 130.

    Zhang Y (2004) Transparence computing: concept, architecture and example. Chin J Electron 32(12):169–174

    Google Scholar 

  131. 131.

    Zhang Y, Niyato D, Wang P (2015) Offloading in mobile cloudlet systems with intermittent connectivity. IEEE Trans Mob Comput 14(12):2516–2529

    Article  Google Scholar 

  132. 132.

    Zhuang W, Jamalipour A, Bai F, Vinel A (2017) Emerging technologies, applications, and standardizations for connecting vehicles. IEEE Veh Technol Mag 12(2):23–25

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the European Regional Development Fund (FEDER), through the Regional Operational Programme of Lisbon (POR LISBOA 2020) and the Competitiveness and Internationalization Operational Programme (COMPETE 2020) of the Portugal 2020 framework [Project 5G with Nr. 024539 (POCI-01-0247-FEDER-024539)]. We also acknowledge the support from the MobiWise project: from mobile sensing to mobility advising (P2020 SAICTPAC/0011/2015), co-financed by COMPETE 2020, Portugal 2020-POCI, European Regional Development Fund of European Union, and the Portuguese Foundation of Science and Technology.

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Correspondence to Gonçalo Carvalho.

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Carvalho, G., Cabral, B., Pereira, V. et al. Edge computing: current trends, research challenges and future directions. Computing (2021). https://doi.org/10.1007/s00607-020-00896-5

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Keywords

  • Edge computing
  • Fog computing
  • Cloudlet computing
  • Multi-access edge computing
  • Mobile cloud computing

Mathematics Subject Classification

  • 68-02