Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Cluster Computing
Erschienen in: Cluster Computing 1/2019

30.07.2018

Cloud and IoT-based emerging services systems

verfasst von: Sugam Sharma, Victor Chang, U. Sunday Tim, Johnny Wong, Shashi Gadia

Erschienen in: Cluster Computing | Ausgabe 1/2019

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The emerging services and analytics advocate the service delivery in a polymorphic view that successfully serves a variety of audience. The amalgamation of numerous modern technologies such as cloud computing, Internet of Things (IoT) and Big Data is the potential support behind the emerging services Systems. Today, IoT, also dubbed as ubiquitous sensing is taking the center stage over the traditional paradigm. The evolution of IoT necessitates the expansion of cloud horizon to deal with emerging challenges. In this paper, we study the cloud-based emerging services, useful in IoT paradigm, that support the effective data analytics. Also, we conceive a new classification called CNNC {Clouda, NNClouda} for cloud data models; further, some important case studies are also discussed to further strengthen the classification. An emerging service, data analytics in autonomous vehicles, is then described in details. Challenges and recommendations related to privacy, security and ethical concerns have been discussed.
Vorheriger Artikel A survey of privacy preserving schemes in IoE enabled Smart Grid Advanced Metering Infrastructure
Nächster Artikel A design and development of an intelligent jammer and jamming detection methodologies using machine learning approach

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Chang, V.: An overview, examples and impacts offered by Emerging services and analytics in cloud computing. Int. J. Inf. Manag. (2015)
2.
Chang, V., Ramachandran, M., Wills, G., Walters, R.J., Kantere, V., Li, C.S.: ESaaSA 2015-Proceedings of the International Workshop on Emerging Software as a Service and Analytics, Lisbon, Portugal, May 20–22 (2015)
3.
Chen, Y.K.: Challenges and opportunities of internet of things. In: Design Automation Conference (ASP-DAC), 2012 17th Asia and South Pacific, pp. 383–388. IEEE (2012)
4.
Monnier, O.: A smarter grid with the Internet of Things. Texas Instruments White Paper (2013)
5.
Zanella, A., Bui, N., Castellani, A., Vangelista, L., Zorzi, M.: Internet of things for smart cities. IEEE Internet Things J. 1(1), 22–32 (2014)CrossRef
6.
Hwang, J.-S., Young, O.G., Choe, H.: Smart Cities Seoul: a case study ITU-T technology watch report, Seoul, Feb 2013, p. 8 (2013)
7.
Farooq, M.U., Waseem, M., Khairi, A., Mazhar, S.: A critical analysis on the security concerns of Internet of Things (IoT). Int. J. Comput. Appl. 111(7), 1–6 (2015)
8.
Evans, D.: The Internet of Things: how the next evolution of the internet is changing everything. Cisco (2011) Accessed 4 Apr 2018
9.
Villars, R.L., Olofson, C.W., Eastwood, M.: Big Data: What it is and why you should care. IDC White Paper. IDC, Framingham (2011)
10.
Curino, C., Jones, E.P., Popa, R.A., Malviya, N., Wu, E., Madden, S., Zeldovich, N.: Relational cloud: a database-as-a-service for the cloud. In: 5th Biennial Conference on Innovative Data Systems Research, CIDR, January 9–12. Asilomar, California (2011)
11.
Seibold, M., Kemper, A.: Database-as-a-Service. Datenbank-Spektrum 12(1), 59–62 (2012)CrossRef
12.
13.
Ashton, K.: That “Internet of Things” thing. RFiD J 22(7), 97–114 (2009)
14.
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—CERP IoT (2010)
15.
16.
O’Leary, D.E.: Artificial intelligence and Big Data. IEEE Intell. Syst. 28(02), 96–99 (2013)CrossRef
17.
18.
19.
Hilbert, M., López, P.: The world’s technological capacity to store, communicate, and compute information. Science 332(6025), 60–65 (2011)CrossRef
20.
Nugent, A., Halper, F., Kaufman, M.: Big data for dummies. Wiley, New York (2013)
21.
Sharma, S., Tim, U.S., Wong, J., Gadia, S., Sharma, S.: A brief review on leading big data models. Data Sci. J. 13, 138–157 (2014)CrossRef
22.
Sharma, S., Shandilya, R., Patnaik, S., Mahapatra, A.: Leading NoSQL models for handling Big Data: a brief review, Int. J. Bus. Inf. Syst. 18(4) (2015a)
23.
Sharma, S., Tim, U.S., Gadia, S., Wong, J., Shandilya, R., Peddoju, S.: Classification and comparison of NoSQL big data models. Int. J. Big Data Intell. 2(2) (2015b)
24.
Sharma, S.: Expanded cloud plumes hiding Big Data ecosystem. Future Gener. Comput. Syst. 59, 63–92 (2016).CrossRef
25.
Cerri, D., Della Valle, E., Marcos, D.D.F., Giunchiglia, F., Naor, D., Nixon, L., Simperl, E.: Towards knowledge in the cloud. In: On the Move to Meaningful Internet Systems: OTM 2008 Workshops, pp. 986–995 (2008)
26.
Chen, C.P., Zhang, C.Y.: Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Inf. Sci. 275, 314–347 (2014)CrossRef
27.
Kumar, V.: Customer lifetime value: the path to profitability. Now Publishers Inc, Breda (2008)
28.
Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. J. Internet Serv. Appl. 1(1), 7–18 (2010)CrossRef
29.
Antcheva, I., Ballintijn, M., Bellenot, B., Biskup, M., Brun, R., Buncic, N., et al.: ROOT—a C++ framework for petabyte data storage, statistical analysis and visualization. Comput. Phys. Commun. 180(12), 2499–2512 (2009)CrossRef
30.
Miner, G., Nisbet, R., Elder, J.: Handbook of statistical analysis and datamining applications. Academic Press, Cambridge (2009)MATH
31.
LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S., Kruschwitz, N.: Big data, analytics and the path from insights to value. MIT Sloan Manag. Rev. 52(2), 21 (2011)
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
Schneider, R.D.: Hadoop for Dummies. John Willey & sons, New York (2012)
46.
Anderson, J., Bendiken, A., Huckabee, J.: Dydra: networks made friendly, A product of Datagraph, Inc. http://​dydra.​com. Accessed 17 Apr 2015
47.
Pan, J.Z.: Resource description framework. In: Handbook on Ontologies, pp. 71–90. Springer Berlin Heidelberg, (2009)
48.
49.
50.
51.
52.
53.
Chang, V., Walters, R., Wills, G.: Business integration-as-a-service. Int. J. Cloud Appl. Comput. 2(1), 16–40 (2012)
54.
Chang, V.: Business integration as a service: computational risk analysis for small and medium enterprises adopting SAP. Int. J. Next-Gener. Comput. 4(3) (2013)
55.
Sano, M.D.: Business intelligence-as-a-Service: a new approach to manage business processes in the cloud. In: WETICE Conference (WETICE), 2014 IEEE 23rd International, pp. 155–160. IEEE (2014)
56.
Chang, V.: The business intelligence as a service in the cloud. Future Gener. Comput. Syst. 37, 512–534 (2014)CrossRef
57.
Chang, V., Walters, R.J., Wills, G.: The development that leads to the Cloud computing business framework. Int. J. Inf. Manage. 33(3), 524–538 (2013)CrossRef
58.
Zulkernine, F., Martin, P., Zou, Y., Bauer, M., Gwadry-Sridhar, F., and Aboulnaga, A.: Towards cloud-based analytics-as-a-Service (CLAaaS) for big data analytics in the cloud. In: Big Data (BigData Congress), IEEE International Congress on, pp. 62–69. IEEE (2013)
59.
Vu, Q.H., Pham, T.V., Truong, H.L., Dustdar, S., Asal, R.: Demods: a description model for data-as-a-service. In: Advanced Information Networking and Applications (AINA), 2012 IEEE 26th International Conference on, pp. 605–612. IEEE (2012)
60.
Nepal, S., Chen, S., Yao, J., Thilakanathan, D.: DIaaS: data integrity-as-a-Service in the cloud. In: Cloud Computing (CLOUD), 2011 IEEE International Conference on, pp. 308–315. IEEE (2011)
61.
Liu, R., Wang, H.W., Monreale, A., Pedreschi, D., Giannotti, F., Guo, W.: AUDIO: An integrity auditing framework of outlier-mining-as-a-service systems. In: Machine Learning and Knowledge Discovery in Databases, pp. 1–18. Springer Berlin Heidelberg (2012)
62.
63.
Gunawi, H.S., Do, T., Hellerstein, J.M., Stoica, I., Borthakur, D., Robbins, J.: Failure-as-a-Service (faas): A cloud service for large-scale, online failure drills, p. 3. University of California, Berkeley (2011)
64.
Shende, J.R.G.: Forensics-as-a-Service. Cybercrime and cloud forensics: applications for investigation processes: applications for investigation processes, p. 266 (2012)
65.
Takabi, H., Joshi, J.B.: Policy management-as-a-Service: an approach to manage policy heterogeneity in cloud computing environment. In: System Science (HICSS), 2012 45th Hawaii International Conference on, pp. 5500–5508. IEEE (2012)
66.
Baliga, A., Chen, X., Coskun, B., de los Reyes, G., Lee, S., Mathur, S., & Van der Merwe, J.E: VPMN: virtual private mobile network towards mobility-as-a-service. In: Proceedings of the second international workshop on Mobile cloud computing and services, pp. 7–12. ACM (2011)
67.
Cherrier, S., Ghamri-Doudane, Y.M.: The “object-as-a-service” paradigm. In: Global Information Infrastructure and Networking Symposium (GIIS), pp. 1–7. IEEE (2014)
68.
Pawar, P.S., Sajjad, A., Dimitrakos, T., Chadwick, D.W.: Security-as-a-Service in multi-cloud and federated cloud environments. In: Trust Management IX, pp. 251–261. Springer International Publishing (2015)
69.
Sheng, X., Tang, J., Xiao, X., Xue, G.: Sensing-as-a-Service: challenges, solutions and future directions. IEEE Sens. J. 13(10), 3733–3741 (2013)CrossRef
70.
Distefano, S., Merlino, G., Puliafito, A.: Sensing and actuation-as-a-Service: a new development for clouds. In Network Computing and Applications (NCA), 11th IEEE International Symposium, pp. 272–275 (2012b)
71.
Dash, S.K., Mohapatra, S., Pattnaik, P.K.: A survey on applications of wireless sensor network using cloud computing. Int. J. Comput. Sci. Eng. Technol. 1(4), 50–55 (2010)
72.
Rao, B.P., Saluia, P., Sharma, N., Mittal, A., Sharma, S.V.: Cloud computing for Internet of Things & sensing based applications. In Sensing Technology (ICST), 2012 Sixth International Conference on, pp. 374–380. IEEE (2012)
73.
Patel, H., Patel, D., Chaudhari, J., Patel, S., Prajapati, K.: Tradeoffs between performance and security of cryptographic primitives used in storage-as-a-Service for cloud computing. In: Proceedings of the CUBE International Information Technology Conference, pp. 557–560. ACM (2012)
74.
Distefano, S., Merlino, G., Puliafito, A.: Enabling the cloud of things. In: Innovative mobile and internet services in ubiquitous computing (IMIS), 2012 Sixth International Conference on, pp. 858–863 (2012a)
75.
Prati, A., Vezzani, R., Fornaciari, M., Cucchiara, R.: Intelligent video surveillance as a service. In: Intelligent Multimedia Surveillance, pp. 1–16. Springer Berlin Heidelberg (2013)
76.
Mark, C., Magnus, E., Jonathan, P.H., Richard, M.M.: Autonomous driving in urban environments: approaches, lessons and challenges. Philos Trans 1928, 4649 (2010)
77.
78.
Xie, B., Li, K., Wang, J., Zhao, S.: Vehicle networking concept and its automotive industry applications based on‘triple network convergence’. J. Autom. Saf. Engergy 4, 348 (2013)
79.
O’Leary, D.E.: Ethics for Big Data and analytics. IEEE Intell. Syst. pp. 81–84 (2016)
80.
Kord, D.: Ethics of Big Data. O’Rilly Media Inc, Beijing (2012)
81.
Joy, J., Gerla, M.: Internet of vehicles and autonomous connected car—privacy and security issues. In: ICCCN, Vancouver, IEEE (2017)
82.
China Automotive: Acquisdata Industry SnapShot: China—Automotive (7614), p. 1 (2017)
83.
Guo, L., Li, Q., Ye, T., Wu, J., Li, J., Dong, M., Ota, K.: A secure mechanism for big data collection in large scale internet of vehicle. IEEE Internet Things J. 4(2), 10 (2017)CrossRef
84.
Datta, S.K., Haerri, J., Bonnet, C., Ferreira Da Costa, R.: Vehicles as connected resources: opportunities and challenges for the future. IEEE Veh. Technol. Mag. 12(2), 26 (2017)CrossRef
85.
86.
87.
88.
89.
Madria, S., Kumar, V., Dalvi, R.: Sensor cloud: a cloud of virtual sensors. IEEE Softw 31(2), 70–77 (2014)CrossRef
90.
Chang, V., Wills, G.: A model to compare cloud and non-cloud storage of Big Data. Future Gener. Comput. Syst. 57, 56–76 (2015)CrossRef
91.
Chang, V., Kuo, Y.H., Ramachandran, M.: Cloud computing adoption framework–a security framework for business clouds. Future Gener. Comput. Syst. 27, 24–41 (2015)
92.
West, J., Goldenberg, S.: Apple defends green credentials of cloud computing services. Guardian Newspaper Group, 17 (2012)
93.
David, S., Sun, Z.: Meta-analysis of big data security and privacy: scholarly literature gaps. IEEE Consum. Electron. Mag., pp. 4035 (2016)
94.
Applin, S.: Autonomous vehicle ethics: stock or custom? IEEE Consumer Electron. Mag. 3, 108 (2017)CrossRef
95.
Ring, T.: Feature: connected cars—the next targe tfor hackers. Netw Secur 2015, 11–16 (2015)CrossRef
96.
97.
Crayton, T.J., Meier, B.M.: Autonomous vehicles: developing a public health research agenda to frame the future of transportation policy. J. Transp. Health 6, 245–252 (2017)CrossRef
98.
Fleetwood, J.: Public health, ethics, and autonomous vehicles. A J Public Health 4, 532 (2017)CrossRef
99.
Claus, S., Silk, N., Wiltshire, C.: Potential impacts of autonomous vehicles on the UK insurance sector. Bank Engl Q Bull 57(1), 40–48 (2017)
Metadaten
Titel
Cloud and IoT-based emerging services systems
verfasst von
Sugam Sharma
Victor Chang
U. Sunday Tim
Johnny Wong
Shashi Gadia
Publikationsdatum
30.07.2018
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe 1/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-018-2821-8

Weitere Artikel der Ausgabe 1/2019

Cluster Computing 1/2019 Zur Ausgabe

OriginalPaper

RFTL: improving performance of selective caching-based page-level FTL through replication

OriginalPaper

Quantum virtual machine: power and performance management in virtualized web servers clusters

OriginalPaper

A fog based load forecasting strategy for smart grids using big electrical data

OriginalPaper

Efficient searchable public key encryption against keyword guessing attacks for cloud-based EMR systems

OriginalPaper

A design and development of an intelligent jammer and jamming detection methodologies using machine learning approach

OriginalPaper

A simulation provenance data management system for efficient job execution on an online computational science engineering platform

Premium Partner

    Bildnachweise