IoT big data analytics for smart homes with fog and cloud computing
Tóm tắt
Từ khóa
Tài liệu tham khảo
El-Sayed, 2018, Edge of things: The big picture on the integration of edge, IoT and the cloud in a distributed computing environment, IEEE Access, 6, 1706, 10.1109/ACCESS.2017.2780087
Rashidi, 2011, Discovering activities to recognize and track in a smart environment, IEEE Trans. Knowl. Data Eng., 23, 527, 10.1109/TKDE.2010.148
Yassine, 2017, Mining human activity patterns from smart home big data for health care applications, IEEE Access, 5, 13131, 10.1109/ACCESS.2017.2719921
Muhammad, 2018, Edge computing with cloud for voice disorders assessment and treatment, IEEE Commun. Mag., 56, 60, 10.1109/MCOM.2018.1700790
S. Singh, A. Yassine, Mining energy consumption behavior patterns for households in smart grid, IEEE Trans. Emerg. Top. Comput. http://dx.doi.org/10.1109/TETC.2017.2692098.
Marjani, 2017, Big IoT data analytics: Architecture, opportunities, and open research challenges, IEEE Access, 5, 5247, 10.1109/ACCESS.2017.2689040
A. Mebrek, L. Merghem-Boulahia, M. Esseghir, Efficient green solution for a balanced energy consumption and delay in the IoT-Fog-Cloud computing, in: IEEE 16th International Symposium on Network Computing and Applications (NCA), Cambridge, MA, 2017, pp. 1–4, 2017.
A.S. Chhabra, T. Choudhury, A.V. Srivastava, A. Aggarwal, Prediction for big data and IoT in 2017, in: International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS) Dubai, 2017, pp. 181–187.
Y. Ge, X. Liang, Y.C. Zhou, Z. Pan, G.T. Zhao, Y.L. Zheng, Adaptive analytic service for real-time internet of things applications, in: IEEE International Conference on Web Services (ICWS), San Francisco, CA, 2016, pp. 484–491.
Pouladzadeh, 2015, A virtualization mechanism for real-time multimedia-assisted mobile food recognition application in cloud computing, Cluster Comput., 18, 10991110, 10.1007/s10586-015-0468-2
Hossain, 2016, Big data-driven service composition using parallel clustered particle swarm optimization in mobile environment, IEEE Trans. Serv. Comput., 9, 806, 10.1109/TSC.2016.2598335
Muhammad, 2017, A facial-expression monitoring system for improved healthcare in smart cities, IEEE Access, 5, 10871, 10.1109/ACCESS.2017.2712788
Al-Ali, 2017, A smart home energy management system using IoT and big data analytics approach, IEEE Trans. Consum. Electron., 63, 426, 10.1109/TCE.2017.015014
A. Berouine, F. Lachhab, Y.N. Malek, M. Bakhouya, R. Ouladsine, A smart metering platform using big data and IoT technologies, in: 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), Rabat, 2017, pp. 1–6.
Hossain, 2018, Cloud-assisted secure video transmission and sharing framework for smart cities, Future Gener. Comput. Syst., 83, 596, 10.1016/j.future.2017.03.029
He, 2018, Multitier fog computing with large-scale IoT data analytics for smart cities, IEEE Internet Things J., 5, 677, 10.1109/JIOT.2017.2724845
Cai, 2017, IoT-based big data storage systems in cloud computing: Perspectives and challenges, IEEE Internet Things J., 4, 75, 10.1109/JIOT.2016.2619369
G. Poghosyan, I. Pefkianakis, P. Le Guyadec, V. Christophides, Extracting usage patterns of home IoT devices, in: IEEE Symposium on Computers and Communications (ISCC), Heraklion, 2017, pp. 1318–1324.
Kang, 2017, Internet of everything: A large-scale autonomic IoT gateway, IEEE Trans. Multi-Scale Comput. Syst., 3, 206, 10.1109/TMSCS.2017.2705683
M. Sultan, K.N. Ahmed, SLASH: Self-learning and adaptive smart home framework by integrating IoT with big data analytics, in: Computing Conference, London, 2017, pp. 530–538.
M.M. Rathore, A. Ahmad, A. Paul, IoT-based smart city development using big data analytical approach, in: IEEE International Conference on Automatica (ICA-ACCA), Curico, 2016, pp. 1–8.
Hossain, 2016, Audio-visual emotion recognition using multi-directional regression and ridgelet transform, J. Multimodal User Interfaces, 10, 325, 10.1007/s12193-015-0207-2
J. Lohokare, R. Dani, A. Rajurkar, A. Apte, An IoT ecosystem for the implementation of scalable wireless home automation systems at smart city level, in: TENCON 2017 IEEE Region 10 Conference, Penang, 2017, pp. 1503–1508.
Hossain, 2018, Emotion-aware connected healthcare big data towards 5G, IEEE Internet Things J., 5, 2399, 10.1109/JIOT.2017.2772959
F. Mehdipour, B. Javadi, A. Mahanti, FOG-Engine: Towards big data analytics in the fog IEEE 14th intl conf on dependable, autonomic and secure computing, in: 14th Intl Conf on Pervasive Intelligence and Computing, Auckland, 2016, pp. 640–646.
J. He, J. Wei, K. Chen, Z. Tang, Y. Zhou, Y. Zhang, Multi-tier fog computing with large-scale IoT data analytics for smart cities, IEEE Internet Things J. vol. PP, no. 99, pp. 1–1. http://dx.doi.org/10.1109/JIOT.2017.2724845.
N.M. Gonzalez, et al., Fog computing: Data analytics and cloud distributed processing on the network edges, in: 35th International Conference of the Chilean Computer Science Society (SCCC), Valparaiso, 2016, pp. 1–9.
H. Cao, M. Wachowicz, S. Cha, Developing an edge computing platform for real-time descriptive analytics, in: IEEE International Conference on Big Data (Big Data), Boston, MA, 2017, pp. 4546–4554.
J. Li, T. Zhang, J. Jin, Y. Yang, D. Yuan, L. Gao, Latency estimation for fog-based internet of things, in: 27th International Telecommunication Networks and Applications Conference (ITNAC), Melbourne, VIC, 2017, pp. 1–6.
Hou, 2016, Internet of things cloud: architecture and implementation, IEEE Comm. Magazine, 54, 32, 10.1109/MCOM.2016.1600398CM
H.J. Hong, P.H. Tsai, A.C. Cheng, M.Y.S. Uddin, N. Venkatasubramanian, C.H. Hsu, Supporting internet-of-things analytics in a fog computing platform, in: IEEE International Conference on Cloud Computing Technology and Science (CloudCom) Hong Kong, 2017, pp. 138–145.
Patel, 2017, On using the intelligent edge for IoT analytics, IEEE Intell. Syst., 32, 64, 10.1109/MIS.2017.3711653
Yang, 2017, IoT stream processing and analytics in the fog, IEEE Commun. Mag., 55, 21, 10.1109/MCOM.2017.1600840
Rahman, 2018, Semantic multimedia fog computing and IoT environment: sustainability perspective, IEEE Comm. Magazine, 56, 80, 10.1109/MCOM.2018.1700907
S. Singh, A. Yassine, IoT big data analytics with fog computing for household energy management in smart grids, in: SGIoT 2018 - 2nd EAI International Conference on Smart Grid and Internet of Things, Niagara Falls, Canada, 2018.
Singh, 2018, Big data mining of energy time series for behavioral analytics and energy consumption forecasting, Energies, 11, 452, 10.3390/en11020452
J.Y. Kim, H.J. Lee, J.Y. Son, J.H. Park, Smart home web of objects-based IoT management model and methods for home data mining, in: 17th Asia-Pacific Network Operations and Management Symposium (APNOMS), Busan, 2015, pp. 327–331.
Han, 2004, Mining frequent patterns without candidate generation: A frequent-pattern tree approach, Data Min. Knowl. Discov., 8, 5387, 10.1023/B:DAMI.0000005258.31418.83
Yassine, 2015, Smart meters big data: Game theoretic model for fair data sharing in deregulated smart grids, IEEE Access, 3, 2743, 10.1109/ACCESS.2015.2504503
A. Yassine, S. Shirmohammadi, Measuring user’s privacy payoff using intelligent agents, in: Computational Intelligence for Measurement Systems and Applications, 2009, CIMSA’09. IEEE International Conference on, pp. 169–174.
Paverd, 2014, vol. 8448, 1
Lin, 2017, Green video transmission in the mobile cloud networks, IEEE Trans. Circuits Syst. Video Technol., 27, 159, 10.1109/TCSVT.2016.2539618
A. Yassine, S. Shirmohammadi, Privacy and the market for private data: a negotiation model to capitalize on private data, in: IEEE/ACS International Conference on Computer Systems and Applications, Doha, 2008, pp. 669–678.
Makonin, 2015, AMPds2 - Almanac of Minutely Power dataset: Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014, Sci. Data, 3, 1
A. Yassine, Implementation challenges of automatic demand response for households in smart grids, in: 3rd International Conference on Renewable Energies for Developing Countries (REDEC), Zouk Mosbeh, 2016, pp. 1–6.
J. Han, J. Pei, Y. Yin, Mining frequent patterns without candidate generation, in: 2000 ACM SIGMOD International Conference on Management of Data USA, 2000, pp. 1 12.
Sugar, 2003, Finding the number of clusters in a data set: An information theoretic approach, J. Am. Stat. Assoc., 98, 750, 10.1198/016214503000000666
Rousseeuw, 1987, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, J. 702 Comput. Appl. Math., 20, 53, 10.1016/0377-0427(87)90125-7
M. Taneja, A. Davy, Resource aware placement of IoT application modules in Fog-Cloud Computing Paradigm, in: IFIP/IEEE Symposium on Integrated Network and Service Management (IM), Lisbon, 2017, pp. 1222–1228.