1 |
G. Zhang and R. Li, "Fog computing architecture-based data acquisition for WSN applications," China Communications, vol. 14, no. 11, pp. 69-81, Nov. 2017.
DOI
|
2 |
S. Ghiasi, A. Srivastava, X. Yang, and Sarrafzadeh, "Optimal energy aware clustering in sensor networks," Sensors, vol. 2, no. 7, pp. 258- 269, 2002.
DOI
|
3 |
V. Chatzigiannakis and S. Papavassiliou, "Diagnosing Anomalies and Identifying Faulty Nodes in Sensor Networks," IEEE Sensors Journal, vol. 7, no. 5, pp. 637-645, May 2007.
DOI
|
4 |
P. Poekaew and P. Champrasert, "Adaptive-PCA: An event-based data aggregation using principal component analysis for WSNs," in Proc. of 2015 International Conference on Smart Sensors and Application (ICSSA), Kuala Lumpur, pp. 50-55, 2015.
|
5 |
A. Rooshenas, H. R. Rabiee, A. Movaghar and M. Y. Naderi, "Reducing the data transmission in Wireless Sensor Networks using the Principal Component Analysis," in Proc. of 2010 Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Brisbane, QLD, pp. 133-138, 2010.
|
6 |
Weng J, Zhang Y, Hwang W S., "Candid covariance-free incremental principal component analysis," IEEE Transactions on Pattern Analysis & Machine Intelligence, 25(8), 1034-1040, 2003.
DOI
|
7 |
T. Yu, X. Wang and A. Shami, "Recursive Principal Component Analysis-Based Data Outlier Detection and Sensor Data Aggregation in IoT Systems," IEEE Internet of Things Journal, vol. 4, no. 6, pp. 2207-2216, Dec. 2017.
DOI
|
8 |
Hou, Ranran, et al., "Incremental PCA based online model updating for multivariate process monitoring," in Proc. of the 10th World Congress on Intelligent Control and Automation IEEE, 2012.
|
9 |
M. Chiang and T. Zhang, "Fog and IoT: An Overview of Research Opportunities," IEEE Internet of Things Journal, vol. 3, no. 6, pp. 854-864, Dec. 2016.
DOI
|
10 |
J. Moon, S. Cho, S. Kum and S. Lee, "Cloud-Edge Collaboration Framework for IoT data analytics," in Proc. of 2018 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, pp. 1414-1416, 2018.
|
11 |
B. Tang et al., "Incorporating Intelligence in Fog Computing for Big Data Analysis in Smart Cities," IEEE Transactions on Industrial Informatics, vol. 13, no. 5, pp. 2140-2150, Oct. 2017.
DOI
|
12 |
T. Tuor, S. Wang, T. Salonidis, B. J. Ko and K. K. Leung, "Demo abstract: Distributed machine learning at resource-limited edge nodes," in Proc. of IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Honolulu, HI, pp. 1-2, 2018.
|
13 |
T. N. Gia, M. Jiang, A. Rahmani, T. Westerlund, P. Liljeberg and H. Tenhunen, "Fog Computing in Healthcare Internet of Things: A Case Study on ECG Feature Extraction," in Proc. of 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, Liverpool, pp. 356-363, 2015.
|
14 |
O. Akrivopoulos, I. Chatzigiannakis, C. Tselios and A. Antoniou, "On the Deployment of Healthcare Applications over Fog Computing Infrastructure," in Proc. of 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), Turin, pp. 288-293, 2017.
|
15 |
S. Dey and A. Mukherjee, "Implementing Deep Learning and Inferencing on Fog and Edge Computing Systems," in Proc. of 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Athens, pp. 818-823, 2018.
|
16 |
Pooranian Z, Conti M, Yu C M, "RARE: Defeating Side Channels based on Data-Deduplication in Cloud Storage," in Proc. of Infocom Workshops Ccsna, 2018.
|
17 |
Wang Yi, "Analysis on the Application Mode of Cloud Computing in the Internet of Things," Telecommunications Science, 27(12), 26-30, 2011.
|
18 |
B. Li, D. Yao and Y. Qian, "Incremental principal component analysis method on online network anomaly detection," in Proc. of 2013 International Conference on Information and Network Security (ICINS 2013), Beijing, pp. 1-6, 2013.
|
19 |
M. Samuel, "Intel Lab Data," Massachusetts Avenue, Boston, MA, USA, and MIT, Cambridge, U.K., pp. 12-31, 2012.
|
20 |
Zhou Zhihua, Machine Learning, Tsinghua University Press, Beijing, pp. 33-34, 2016.
|
21 |
F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, "Fog computing and its role in the Internet of Things," in Proc. of 1st Ed. MCC Workshop Mobile Cloud Comput. (MCC), New York, NY, USA, pp. 13-16, 2012.
|
22 |
M. Taneja and A. Davy, "Resource aware placement of IoT application modules in fog-cloud computing paradigm," in Proc. of IFIP/IEEE Symp. Integr. Netw. Service Manage. (IM), pp. 1222-1228, 2017.
|
23 |
D. Borthakur, H. Dubey, N. Constant, L. Mahler and K. Mankodiya, "Smart fog: Fog computing framework for unsupervised clustering analytics in wearable Internet of Things," in Proc. of 2017 IEEE Global Conference on Signal and Information Processing (Global SIP), Montreal, QC, pp. 472-476, 2017.
|
24 |
V. Chatzigiannakis and S. Papavassiliou, "Diagnosing Anomalies and Identifying Faulty Nodes in Sensor Networks," IEEE Sensors Journal, vol. 7, no. 5, pp. 637-645, May 2007.
DOI
|
25 |
P. Verma and S. K. Sood, "Fog Assisted-IoT Enabled Patient Health Monitoring in Smart Homes," IEEE Internet of Things Journal, vol. 5, no. 3, pp. 1789-1796, June 2018.
DOI
|
26 |
A. Akbar et al., "Real-Time Probabilistic Data Fusion for Large-Scale IoT Applications," IEEE Access, vol. 6, pp. 10015-10027, 2018.
DOI
|
27 |
S. Ji, S. Yuan, T. Ma and C. Tan, "Distributed Fault Detection for Wireless Sensor Based on Weighted Average," in Proc. of 2010 Second International Conference on Networks Security, Wireless Communications and Trusted Computing, Wuhan, Hubei, pp. 57-60, 2010.
|