Abstract
Cloud internet of things (IoT) is an emerging technology that is already impelling the daily activities of our lives. However, the enormous resources (data and physical features of things) generated from Cloud-enabled IoT sensing devices are lacking suitable managerial approaches. Existing research surveys on Cloud IoT mainly focused on its fundamentals, definitions and layered architecture as well as security challenges. Going by the current literature, none of the existing researches is yet to provide a detailed analysis on the approaches deployed to manage the heterogeneous and dynamic resource data generated by sensor devices in the cloud-enabled IoT paradigm. Hence, to bridge this gap, the existing algorithms designed to manage resource data on various CloudIoT application domains are investigated and analyzed. The emergence of CloudIoT, followed by previous related survey articles in this field, which motivated the current study is presented. Furthermore, the utilization of simulation environment, highlighting the programming languages and a brief description of the simulation packages adopted to design and evaluate the performance of the algorithms are examined. The utilization of diverse network communication protocols and gateways to aid resource dissemination in the cloud-enabled IoT network infrastructure are also discussed. The future work as discussed in previous researches, which pave the way for future research directions in this field is also presented, and ends with concluding remarks.
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References
Botta A, De Donato W, Persico V, Pescape A. Integration of cloud computing and internet of things: a survey. Future generation computer systems, 2016, 56: 684–700
Chang K D, Chen C Y, Chen J L, Chao H. Internet of things and cloud computing for future internet. In: Proceedings of International Conference on Security-Enriched Urban Computing and Smart Grid. 2011, 1–10
Zhou J, Leppanen T, Harjula H, Ylianttila M, Ojala T, Yu C, Jin H. Cloudthings: a common architecture for integrating the internet of things with cloud computing. In: Proceedings of the 17th IEEE International Conference on Computer Supported Cooperative Work in Design. 2013, 651–657
Sundmaeker H, Guillemin P, Friess P, Woelfflé S. Vision and challenges for realising the Internet of Things. Cluster of European Research Projects on the Internet of Things, European Commision, 2010, 3(3): 34–36
Natarajan V, Balasubramanian A, Mishra S, Sridhar R. Security for energy constrained RFID system. In: Proceedings of the 4th IEEE Workshop on Automatic Identification Advanced Technologies. 2005, 181–186
Gupta G S, Mangesh M. G, Parag D T, Jawandhiya P M. Open-source network simulation tools: an overview. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 2013, 2(4): 1629–1635
Dargie W, Poellabauer C. Fundamentals of Wireless Sensor Networks: Theory and Practice. John Wiley & Sons, 2010
Roman S. What are IoT Sensor Devices? see Zenseio Website, 2016
Rimal B P, Jukan A, Katsaros D, Goeleven Y. Architectural requirements for cloud computing systems: an enterprise cloud approach. Journal of Grid Computing, 2011, 9(1): 3–26
Low C, Chen Y, Wu M. Understanding the determinants of cloud computing adoption. Industrial Management & Data Systems, 2011, 111(7): 1006–1023
Cervone H F. An overview of virtual and cloud computing. OCLC Systems & Services: International Digital Library Perspectives, 2010, 26(3): 162–165
Qin L, Feng S, Zhu H. Research on the technological architectural design of geological hazard monitoring and rescue-after-disaster system based on cloud computing and Internet of things. International Journal of System Assurance Engineering and Management, 2018, 9(3): 684–695
Weinberger M. Amazon Web Services: Amazon’s $18 billion cloud business continues to crush Microsoft and Google. see Pulse Website, 2018
Alessio B, Walter D, Valerio P, Antonio P. On the integration of cloud computing and internet of things. In: Proceedings of International Conference on the Future Internet of Things and Cloud. 2014, 23–30
Zaslavsky A, Perera C, Georgakopoulos D. Sensing as a service and big data. In: Proceedings of the International Conference on Advances in Cloud Computing. 2013, 1–6
Andrea P, Roboerto V, Michele F, Rita C. Intelligence video surveillance as a service. In: Proceedings of the Intelligent Multimedia Surveillance. 2013, 1–6
Jing Q, Athanasios V V, Jiafu W, Jingwei D Q. Security of the Internet of Things: perspectives and challenges. Wireless Networks, 2014, 20(8): 2481–2501
Al-Fuqaha A, Guizani M, Mohammadi M, Aledhari M, Ayyash M. Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Communications Surveys & Tutorials, 2015, 17(4): 2347–2376
Li S, Da Xu L, Zhao S. The internet of things: a survey. Information Systems Frontiers, 2015, 17(2): 243–259
Reinl P, Holzschuher F, Pfizer F. Docker cluster management for the cloud-survey result and own solution. Journal of Grid Computing, 2016, 14(2): 265–282
Herald L. Technologies for web and cloud service interaction: a survey. Service-Oriented Computing and Applications, 2016, 10(2): 71–110
Botta A, De Donato W, Persico V, Pescapé A. Integration of cloud computing and the internet of things: a survey. Future Generation Computer Systems, 2016, 56: 684–700
Cavalcante E, Jorge P, Marcelo P A, Maia P, Roniceli M, Thais B, Flavia C D, Paulo F P. On the interplay of the internet of things and cloud computing: a systematic mapping study. Computer Communications, 2016, 89: 17–33
Aitsaadi N, Boutaba R, Takahashi Y. Cloudification of the Internet of Things. Annals of Telecommunications, 2017, 72(2): 1–2
Ngu A H, Gutierrez M, Metsis V, Nepal S, Sheng Q. IoT middleware: a survey on issues and enabling technologies. IEEE Internet of Things, 2017, 4(1): 1–20
Ray P P. A survey of IoT cloud platforms. Future Computing and Informatics Journal, 2017, 1(1): 35–46
Tayeb S, Latifi S, Kim Y. A survey on IoT communication and computation frameworks: an industrial perspective. In: Proceedings of the 7th IEEE Annual Computing and Communication Workshop and Conference. 2017, 1–8
Gonzalez-Martínez J A, Bote-Lorenzo M L, Gómez-Sánchez E, Cano-Parra R. Cloud computing and education: a state-of-the-art survey. Computers & Education, 2015, 80: 132–151
Diallo O, Rodrigues J J P C, Sene M, Niu J. Real-rime query processing optimization for cloud-based wireless body area networks. Information Sciences, 2014, 284: 84–94
Luo S, Ren B. The monitoring and managing application of cloud computing based on internet of things. Computer Methods and Programs Biomedicine, 2016, 130: 154–161
Sareen S, Sood S K, Gupta S K. IoT-based cloud framework to control the ebola virus outbreak. Journal of Ambient Intelligence and Human Computing, 2016, 12: 1–18
Lin C H, Hsiu P C, Hsieh C K. Dynamic backlight scaling optimization: a cloud-based energy-saving service for mobile streaming applications. IEEE Transactions on Computers, 2014, 63(2): 335–348
Mendes L D P, Rodrigues J P C, Lioret J, Sandra S. Cross-layer dynamic admission control for cloud-based multimedia sensor networks. IEEE Systems Journal, 2014, 8(1): 235–246
Hong S N, Kim J. Joint coding and stochastic data transmission for uplink cloud radio access networks. IEEE Communications Letters, 2014, 18(9): 1619–1622
Kim J. Energy-efficient dynamic packet downloading for medical IoT platforms. IEEE Transactions on Industrial Informatics, 2015, 11(6): 1653–1659
Abawajy J H, Hassan M M. Federated internet of things and cloud computing pervasive patient health monitoring system. IEEE Communication Magazine, 2017, 55(1): 48–53
Shi X, Hao Y, Zeng D, Wang L, Hossain M S, et al. Cloud-assisted mood fatigue detection system. Mobile Networks and Applications, 2016, 21(5): 744–752
Yang C, Shen W, Lin T, Wang X. IoT-enabled dynamic service selection across multiple manufacturing clouds. Manufacturing Letters, 2016, 7: 22–25
Jutila M. An adaptive edge router enabling internet of things. IEEE Internet of Things Journal, 2016, 3(6): 1061–1069
Kumrai T, Ota K, Dong M, Kishigami J, Sung D K. Multi-objective optimization in cloud brokering systems for connected internet of things. IEEE Internet of Things Journal, 2017, 4(2): 404–413
Hossain M S, Muhammad G. Cloud-assisted industrial internet of things (IIoT)-enabled framework for health monitoring. Computer Networks, 2016, 101: 192–202
Ray P P. Internet of things cloud enabled MISSENARD index measurement for indoor occupants. Elsevier Measurement, 2016, 92: 152–165
Wang Y, Lin X, Pedram M. A nested two stage game-based optimization framework in mobile cloud computing system. In: Proceedings of the 7th IEEE International Symposium on Service-Oriented System Engineering. 2013, 494–502
Kim S. Nested game-based computation offloading scheme for mobile cloud IoT systems. EURASIP Journal on Wireless Communications and Networking, 2015, 1: 229
Zhu C, Sheng Z, Leung V C M, Shu L, Yang L T. Toward offering more useful data reliably to mobile cloud from wireless sensor network. IEEE Transactions on Emerging Topics in Computing, 2014, 3(1): 84–94
Qu T, Lei S P, Wang Z Z, Nie D X, Chen X, George Q H. IoT-based realtime production logistics synchronization system under smart cloud manufacturing. The International Journal of Advanced Manufacturing Technology, 2016, 84(1–4): 147–164
Narman H S, Hossain M S, Atiquzzaman M, Shen H. Scheduling internet of things applications in cloud computing. Annals of Telecommunications, 2017, 72(1–2): 79–93
Yang C, Shen W, Lin T, Wang X. IoT-enabled dynamic service selection across multiple manufacturing clouds. Manufacturing Letters, 2016, 7: 22–25
Yang C, Lan S, Shen W, Huang G Q, Wang X, Lin T. Towards product customization and personalization in IoT-enabled cloud manufacturing. Cluster Computing, 2017, 20(2): 1717–1730
Georgakopoulos D, Fazia P P, Jayaraman M, Massimo V, Rajiv R. Internet of things and edge cloud computing roadmap for manufacturing. IEEE Cloud Computing, 2016, 3(4): 66–73
Roopaei M, Rad P, Choo K K R. Cloud of things in smart agriculture: intelligent irrigation monitoring by thermal imaging. IEEE Cloud Computing, 2017, 4(1): 10–15
Chen Y S, Chen Y R. Context-oriented data acquisition and integration platform for internet of things. In: Proceedings of IEEE Conference on Technologies and Applications of Artificial Intelligence. 2012, 103–108
Fazio M, Puliafito A. Cloud4sens: a cloud-based architecture for sensor controlling and monitoring. IEEE Communications Magazine, 2015, 53(3): 41–47
Mitton N, Papavassiliou S, Puliafito A, Trivedi K S. Combining cloud and sensors in a smart city environment. EURASIP Journal on Wireless Communications and Networking, 2012, 1: 1–10
Zhu C, Leung V C M, Yang L T, Hu X, Shu L. Collaborative location-based sleep scheduling to integrate wireless sensor networks with mobile cloud computing. In: Proceedings of IEEE Globecom Workshops. 2013, 452–457
Paul H, Fliege J, Dekorsy A. In-network-processing: distributed consensus-based linear estimation. IEEE Communications Letters, 2012, 17(1): 59–62
Abdelwahab S, Hamdaoui B, Guizani M, Znati T. Cloud of things for sensing-as-a-service: architecture, algorithms, and use case. IEEE Internet of Things Journal, 2016, 3(6): 1099–1112
Ali A M M, Ahmad N M, Amin A H M. Cloudlet-based cyber foraging framework for distributed video surveillance provisioning. In: Proceedings of the 4th World Congress on Information and Communication Technologies. 2014, 199–204
Alsmirat M A, Jararweh Y, Obaidat I, Gupta B B. Internet of surveillance: a cloud supported large-scale wireless surveillance system. The Journal of Supercomputing, 2017, 73(3): 973–992
Madria S, Kumar V, Dalvi R. Sensor cloud: a cloud of virtual sensors. IEEE Software, 2013, 31(2): 70–77
Lawson V, Ramaswamy L. Data quality and energy management tradeoffs in sensor service clouds. In: Proceedings of IEEE International Congress on Big Data. 2015, 749–752
Pham T N, Tsai M F, Nguyen D B, Dow C R, Deng D J. A cloud-based smart-parking system based on Internet-of-Things technologies. IEEE Access, 2015, 3: 1581–1591
Liu Q, Ma Y, Alhussein M, Zhang Y, Peng L. Green data center with IoT sensing and cloud-assisted smart temperature control system. Computer Networks, 2016, 101: 104–112
Atif Y, Ding J, Jeusfeld M A. Internet of things approach to cloud-based smartcarparking. Procedia ComputerScience, 2016, 98: 193–198
Dinh T, Kim Y An efficient interactive model for on-demand sensing-as-a-servicesof sensor-cloud. Sensors, 2016, 16(7): 992
Yu J, Kim M, Bang H C, Bae S H, Kim S J. IoT as a applications: cloud-based building management systems for the internet ofthings. Multimedia Tools and Applications, 2016, 75(22): 14583–14596
Barcelo M, Correa A, Llorca J, Tulino A M, Vicario J L, Morell A. IoT-cloud service optimization in next generation smart environments. IEEE Journal on Selected Areas in Communications, 2016, 34(12): 4077–4090
Li C, Wei W, Li J, Song W. A cloud-based monitoring system via face recognition using Gaborand CS-LBP features. The Journal of Supercomputing, 2017, 73(4): 1532–1546
Cament L A, Galdames F J, Bowyer K W, Perez C A. Face recognition under pose variation with local Gabor features enhanced by active shape and statistical models. Pattern Recognition, 2015, 48(11): 3371–3384
Chatterjee S, Misra S. Dynamic and adaptive data caching mechanism for virtualization within sensor-cloud. In: Proceedings of IEEE International Conference on Advanced Networks and Telecommuncations Systems. 2014, 1–6
Dinh T, Kim Y, Lee H. A location-based interactive model of internet of things and cloud (IoT-Cloud) for mobile cloud computing applications. Sensors, 2017, 17(3): 489
Wang W, Wang Q, Sohraby K. Multimedia sensing as a service (MSaaS): exploring resource saving potentials of at cloud-edge IoT and fogs. IEEE Internet of Things Journal, 2016, 4(2): 487–495
Qin L, Feng S, Zhu H. Research on the technological architectural design of geological hazard monitoring and rescue-after-disaster system based on cloud computing and Internet of things. International Journal of System Assurance Engineering and Management, 2018, 9(3): 684–695
Imran M, Said A M, Hasbullah H. A survey of simulators, emulators and testbeds for wireless sensor networks. In: Proceedings of International Symposium on Information Technology. 2010, 897–902
Fishman G S. Discrete-event Simulation: Modeling, Programming, and Analysis. Springer Science & Business Media, 2013
NSNAM, what is NS-3? see Nsnam Website, 2017
Goyal T, Singh A, Agrawal A. Cloudsim: simulator for cloud computing infrastructure and modeling. Procedia Engineering, 2012, 38: 3566–3572
Chandrakant N, Bijil A P, Puneeth P, Shenoy P D, Venugopal K R. WSN integrated cloud computing for the then-care system (NCS) using middleware services. International Journal of Innovative Technology and Exploring Engineering, 2013, 4: 2278–3075
Berrahal S, Boudriga N, Bagula A. Cooperative sensor-clouds for public safety services in infrastructure-less areas. In: Proceedings of the 22nd Asia-Pacific Conference on Communications. 2016, 222–229
Siraj S, Gupta A, Badgujar R. Network simulation tools survey. International Journal of Advanced Research in Computer and Communication Engineering, 2012, 1(4): 199–206
Vieira A, Dias L, Guilherme P, Jose O. Comparison of simo and arena simulation tools. see Repositorium Website, 2018
Bhushan S B, Reddy C H P. A QoS aware cloud service composition algorithm for geo-distributed multi cloud domain. International Journal of Intelligent Engineering and Systems, 2016, 9(4): 147–156
TinyOS. TOSSIM. see Tinyos Website, 2018
Zio E. The Monte Carlo Simulation Method for System Reliability and Risk Analysis. Springer, 2013
Ozturk O. Introduction to XMPP protocol and developing online collaboration applications using open source software and libraries. In: Proceedings of IEEE International Symposium on Collaborative Technologies and Systems. 2010, 21–25
TechTarget. IoT agenda. see Techtarget Website, 2018
Liu Q, Ma Y, Alhussein M, Zhang Y, Peng L. Green data center with IoT sensing and cloud-assisted smart temperature control system. Computer Networks, 2016, 101: 104–112
Rama G. Report: AWS market share is triple Azure’s. see Awsinsider Website, 2017
Acknowledgements
The Authors would like to appreciate the support of the Research Management Centre (RMC) Universiti Teknologi Malaysia with the research grant (QJ130000.2451.07G48). We would like to express our sincere thanks to all researchers who devoted their time and knowledge to the completeness of this research project.
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Edje E. Abel is currently a PhD scholar at the School of Computing, faculty of Engineering, Universiti Teknologi Malaysia, Malaysia. He obtained his MSc (Information Systems Management) in 2010 and BSc (Network Computing) in 2009, in Brunel University, UK. His area of interest is cloud Internet of Things, grid computing, network computing, and information systems management.
Muhammad Shafie Abd Latiff received his PhD degree in 2000 from Bradford University, UK. He is a professor and currently the Head of Pervasive Computing Research Group at the School of Computing, faculty of Engineering, Universiti Teknologi Malaysia (UTM), Malaysia. His research interests are in computer networks with the focus generally on routing protocol, grid, and cloud computing and wireless sensor networks.
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Abel, E.E., Latiff, M.S.A. The utilization of algorithms for cloud internet of things application domains: a review. Front. Comput. Sci. 15, 153502 (2021). https://doi.org/10.1007/s11704-019-9056-6
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DOI: https://doi.org/10.1007/s11704-019-9056-6