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Resource discovery approaches in cloudIoT: a systematic review

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Abstract

The cloud of things (CloudIoT) represents a general system of supporting infrastructure for storing and processing information gathered from smart objects and their communications.There are many resources used to respond to requests in the CloudIoT environment. Therefore, a primary challenge in these systems is resource discovery based on the requests. Discovering and accessing resources, overcoming user constraints, and focusing on dynamic requirements, such as failure nodes, are the most critical issues to be addressed. This paper focuses on several resource discovery mechanisms using the systematic literature review method in the CloudIoT environment. The research aims at analyzing and reviewing studies published from 2016 to 2021 (June) on resource discovery in CloudIoT. The technical classification of resource discovery is based on selected studies by considering architecture, algorithm, middleware, and protocol approaches. These studies are discussed in terms of their main ideas, advantages, and weaknesses. Finally, future research opportunities related to resource discovery in the cloud of things are identified.

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References

  1. Alhaidari F, Atta R, Rachid Z (2020) Cloud of Things: architecture, applications and challenges J Amb Intell Human Comp, 1–19

  2. Elazhary H (2019) Internet of things (iot), mobile cloud, cloudlet, mobile iot, iot cloud, fog, mobile edge, and edge emerging computing paradigms: disambiguation and research directions. J Netw Comput Appl 128:105–1401

    Article  Google Scholar 

  3. Ghosh AM, Katarina G (2020) Edge-cloud computing for internet of things data analytics: embedding intelligence in the edge with deep learning. IEEE Trans Ind Inform 17(3):2191–2200

    Google Scholar 

  4. Stergiou C, Psannis KE, Kim B-G, Gupta B (2018) Secure integration of IoT and cloud computing. Futur Gener Comput Syst 78:964–975

    Article  Google Scholar 

  5. Tian Y, Kaleemullah MM, Rodhaan MA, Song B, Al-Dhelaan A, Ma T (2019) A privacy preserving location service for cloud-of-things system. J Parallel Distrib Comp 123:215–222

    Article  Google Scholar 

  6. Darwish A, Hassanien AE, Elhoseny M, Sangaiah AK, Muhammad K (2019) The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems. J Amb Intell Human Comp 10(10):4151–4166

    Article  Google Scholar 

  7. Li X, Yu Lu, Xianghua Fu, Qi Y (2021) Building the internet of things platform for smart maternal healthcare services with wearable devices and cloud computing. Futur Gener Comput Syst 118:282–296

    Article  Google Scholar 

  8. Aceto G, Persico V, Pescapé A (2020) Industry 4.0 and health: Internet of things, big data, and cloud computing for healthcare 4.0. J Ind Inform Integr 18:100129

    Google Scholar 

  9. Yigitcanlar T, Kankanamge N, Vella K (2021) How are smart city concepts and technologies perceived and utilized? a systematic geo-Twitter analysis of smart cities in Australia. J Urban Technol 28(1–2):135–154

    Article  Google Scholar 

  10. Goudarzi P, Malazi HT, Ahmadi M (2016) Khorramshahr: a scalable peer to peer architecture for port warehouse management system. J Netw Comp Appl 76:49–59

    Article  Google Scholar 

  11. Muniswamaiah, M, Tilak A, Charles CT (2021) Fog computing and the internet of things (IoT): a review. In: 2021 8th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2021 7th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom), pp. 10–12. IEEE

  12. Alrawais A, Alhothaily A, Chunqiang Hu, Cheng X (2017) Fog computing for the internet of things: Security and privacy issues. IEEE Internet Comput 21(2):34–42

    Article  Google Scholar 

  13. Nwogbaga NE, Latip R, Affendey LS, Rahiman ARA (2021) Investigation into the effect of data reduction in offloadable task for distributed IoT-fog-cloud computing. J Cloud Comp 10(1):1–12

    Article  Google Scholar 

  14. Andrade E, Nogueira B, Farias Júnior ID, Araújo D (2021) Performance and availability trade-offs in Fog-Cloud IoT environments. J Netw Syst Manag 29(1):1–27

    Article  Google Scholar 

  15. Bellavista P, Berrocal J, Corradi A, Das SK, Foschini L, Zanni A (2019) A survey on fog computing for the internet of things. Pervasive Mob Comput 52:71–99

    Article  Google Scholar 

  16. Singh S, Chana I (2016) A survey on resource scheduling in cloud computing: Issues and challenges. J grid comp 14(2):217–264

    Article  Google Scholar 

  17. Jennings B, Stadler R (2015) Resource management in clouds: survey and research challenges. J Netw Syst Manage 23(3):567–619

    Article  Google Scholar 

  18. Mustafa S, Nazir B, Hayat A, Madani SA (2015) Resource management in cloud computing: taxonomy, prospects, and challenges. Comput Electr Eng 47:186–203

    Article  Google Scholar 

  19. Nzanywayingoma F, Yang Y (2019) Efficient resource management techniques in cloud computing environment: a review and discussion. Int J Comput Appl 41(3):165–182

    Google Scholar 

  20. Luong NC, Wang P, Niyato D, Wen Y, Han Z (2017) Resource management in cloud networking using economic analysis and pricing models: a survey. IEEE Commun Surv Tutor 19(2):954–1001

    Article  Google Scholar 

  21. Manvi SS, Shyam GK (2014) Resource management for Infrastructure as a Service (IaaS) in cloud computing: a survey. J Netw Comp Appl 41:424–440

    Article  Google Scholar 

  22. Martinez I, Hafid AS, Jarray A (2020) Design, resource management, and evaluation of fog computing systems: a survey. IEEE Internet of Things J 8(4):2494–2516

    Article  Google Scholar 

  23. Ghobaei-Arani M, Souri A, Rahmanian AA (2020) Resource management approaches in fog computing: a comprehensive review. J Grid Comp 18(1):1–42

    Article  Google Scholar 

  24. Nunes, LH, Júlio CE, Alexandre ND, Charith P, and Stephan RM (2016) The effects of relative importance of user constraints in cloud of things resource discovery: a case study. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 245–250

  25. Khalil K, Khalid E, Ahmed A, and Magdy B (2020) A security approach for CoAP-based internet of things resource discovery. In: 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), pp. 1–6. IEEE

  26. Hou L, Zhao S, Xiong X, Zheng K, Chatzimisios P, Hossain MS, Xiang W (2016) Internet of things cloud: architecture and implementation. IEEE Commun Magaz 54(12):32–39

    Article  Google Scholar 

  27. Botta A, De Donato W, Persico V, Pescapé A (2016) Integration of cloud computing and internet of things: a survey. Future Gen Comp Syst 56:684–700

    Article  Google Scholar 

  28. Wu Y (2020) Cloud-edge orchestration for the internet-of-things: Architecture and ai-powered data processing. IEEE Internet of Things J

  29. Xu M, Buyya R (2019) Brownout approach for adaptive management of resources and applications in cloud computing systems: a taxonomy and future directions. ACM Comp Surv (CSUR) 52(1):1–27

    Google Scholar 

  30. Hong C-H, Varghese B (2019) Resource management in fog/edge computing: a survey on architectures, infrastructure, and algorithms. ACM Comp Surv (CSUR) 52(5):1–37

    Google Scholar 

  31. Nazari Jahantigh M, Masoud Rahmani A, Jafari Navimirour N, Rezaee A (2020) Integration of internet of things and cloud computing: a systematic survey. IET Commun 14(2):165–176

    Article  Google Scholar 

  32. Fard MV, Sahafi A, Rahmani AM, Mashhadi PS (2020) Resource allocation mechanisms in cloud computing: a systematic literature review. IET Softw 14(6):638–653

    Article  Google Scholar 

  33. Khalil K, Elgazzar K, Seliem M, Bayoumi M (2020) Resource discovery techniques in the internet of things: a review. Internet of Things 12:100293

    Article  Google Scholar 

  34. Pourghebleh B, Hayyolalam V, Aghaei Anvigh A (2020) Service discovery in the Internet of Things: review of current trends and research challenges. Wireless Netw 26(7):5371–5391

    Article  Google Scholar 

  35. Kianoush S, Raja M, Savazzi S, Sigg S (2018) A cloud-IoT platform for passive radio sensing: challenges and application case studies. IEEE Internet Things J 5(5):3624–3636

    Article  Google Scholar 

  36. Foughali K, Fathallah K, Frihida A (2018) Using Cloud IOT for disease prevention in precision agriculture. Proc Comp Sci 130:575–582

    Article  Google Scholar 

  37. Kaur J, Kaur PD (2018) CE-GMS: a cloud IoT-enabled grocery management system. Electr Comm Res Appl 28:63–72

    Article  Google Scholar 

  38. Abdelwahab S, Hamdaoui B, Guizani M, Znati T (2016) Cloud of things for sensing-as-a-service: architecture, algorithms, and use case. IEEE Internet Things J 3(6):1099–1112

    Article  Google Scholar 

  39. Amiri-Zarandi M, Dara RA, Fraser E (2020) A survey of machine learning-based solutions to protect privacy in the Internet of Things. Comput Secur 96:101921

    Article  Google Scholar 

  40. Gasmi K, Dilek S, Tosun S, Ozdemir S (2022) A survey on computation offloading and service placement in fog computing-based IoT. J Supercomp 78(2):1983–2014

    Article  Google Scholar 

  41. Chowdhury A, Raut SA (2018) A survey study on internet of things resource management. J Netw Comput Appl 120:42–60

    Article  Google Scholar 

  42. Zarrin J, Aguiar RL, Barraca JP (2018) Resource discovery for distributed computing systems: a comprehensive survey. J Parallel Distrib Comp 113:127–166

    Article  Google Scholar 

  43. Tanganelli G, Vallati C, Mingozzi E (2017) Edge-centric distributed discovery and access in the internet of things. IEEE Internet Things J 5(1):425–438

    Article  Google Scholar 

  44. Mecibah R, Badis D, Ali Y, and Mohamed A (2018)A scalable semantic resource discovery architecture for the internet of things. In: International Conference on Computer Science and its Applications, pp. 37–47. Springer, Cham

  45. Pradhan M, Filippo P, Mauro T (2019) Dynamic resource discovery and management for edge computing based on SPF for HADR operations. In: 2019 International Conference on Military Communications and Information Systems (ICMCIS), pp. 1–6. IEEE

  46. Caturano F, Jaime J, Simon PR (2019) Automated discovery of CoAP-enabled IoT devices. In: 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN), pp. 396–401. IEEE

  47. Murturi I, Cosmin A, Christos T, Schahram D (2019) Edge-to-edge resource discovery using metadata replication. In: 2019 IEEE 3rd International Conference on Fog and Edge Computing (ICFEC), pp. 1–6. IEEE

  48. Zarrin J, Rui LA, João PB (2017) HARD: Hybrid adaptive resource discovery for jungle computing. J Netw Comp Appl. 90: 42–73

  49. Djamaa B, Yachir A, Richardson M (2017) Hybrid CoAP-based resource discovery for the internet of things. J Ambient Intell Humaniz Comput 8(3):357–372

    Article  Google Scholar 

  50. Thanikaivel B, Venkatalakshmi K, Kannan A (2021) Optimized mobile cloud resource discovery architecture based on dynamic cognitive and intelligent technique. Microproc Microsyst 81:103716

    Article  Google Scholar 

  51. Taneja M, Alan D (2017) Resource aware placement of IoT application modules in fog-cloud computing paradigm. In: 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), pp. 1222–1228. IEEE

  52. Navimipour NJ, Keshanchi B, Milani FS (2017) Resources discovery in the cloud environments using collaborative filtering and ontology relations. Electr Comm Res Appl 26:89–100

    Article  Google Scholar 

  53. Nunes LH, Julio CE, Charith P, Stephan RM, and Alexandre CBD (2018) The elimination-selection based algorithm for efficient resource discovery in Internet of Things environments. In: 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC), pp. 1–7. IEEE

  54. Osamy W, Khedr AM, Salim A (2019) ADSDA: adaptive distributed service discovery algorithm for internet of things based mobile wireless sensor networks. IEEE Sens J 19(22):10869–10880

    Article  Google Scholar 

  55. Kalantary S, Akbari Torkestani J, Shahidinejad A (2021) Resource discovery in the internet of things integrated with fog computing using Markov learning model. J Supercomp 77(12):13806–13827

    Article  Google Scholar 

  56. Nunes Luiz Henrique, Estrella Julio Cezar, Perera Charith, Reiff-Marganiec Stephan, Delbem Alexandre Claudio Botazzo (2017) Multi criteria IoT resource discovery: a comparative analysis. Softw Pract Exper. 47(10):1325–1341

    Article  Google Scholar 

  57. Moorthy RS, Pabitha P (2020) A novel resource discovery mechanism using sine cosine optimization algorithm in cloud. In: 2020 4th international conference on intelligent computing and control systems (ICICCS), pp. 742–746. IEEE

  58. Bharti M, Kumar R, Saxena S, Jindal H (2020) Optimal resource selection framework for internet-of-things. Comput Electr Eng 86:106693

    Article  Google Scholar 

  59. Farahzadi A, Shams P, Rezazadeh J, Farahbakhsh R (2018) Middleware technologies for cloud of things: a survey. Digital Commun Netw 4(3):176–188

    Article  Google Scholar 

  60. Perera C, Vasilakos AV (2016) A knowledge-based resource discovery for internet of things. Knowl-Based Syst 109:122–136

    Article  Google Scholar 

  61. Caglar F, Shashank S, Aniruddha G, and Xenofon K (2016) Intelligent, performance interference-aware resource management for iot cloud backends. In: 2016 IEEE First International Conference on Internet-of-Things Design and Implementation (IoTDI), pp. 95–105. IEEE

  62. Bharti M, Saxena S, Kumar R (2020) A middleware approach for reliable resource selection on Internet-of-Things. Int J Commun Syst 33(5):e4278

    Article  Google Scholar 

  63. Albalas F, Wail M, Majd AS (2017) Aft: Adaptive fibonacci-based tuning protocol for service and resource discovery in the internet of things. In: 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC), pp. 177–182. IEEE

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Authors and Affiliations

  1. Department of Computer Engineering, Dezfoul Branch, Dezfoul, Iran

    Parisa Goudarzi & Mohammad Mosleh

  2. Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan

    Amir Masoud Rahmani

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  1. Parisa Goudarzi
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  2. Amir Masoud Rahmani
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  3. Mohammad Mosleh
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Correspondence to Amir Masoud Rahmani.

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Goudarzi, P., Rahmani, A.M. & Mosleh, M. Resource discovery approaches in cloudIoT: a systematic review. J Supercomput 78, 17202–17230 (2022). https://doi.org/10.1007/s11227-022-04541-0

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