Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing

IEEE Network

Volumn 32, Issue 1, 2018, Pages 96-101

  Li, He ^a   Ota, Kaoru ^b   Dong, Mianxiong ^b  

^a UNIVERSITY OF AIZU   (Japan)

^b MURORAN INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

INTERNET OF THINGS;

COMPLEX ENVIRONMENTS; EDGE COMPUTING; EDGE NODES; EVALUATION RESULTS; LEARNING TASKS; MULTILAYER STRUCTURES; OPTIMIZATION SOLUTION; PROCESSING CAPABILITY;

DEEP LEARNING;

EID: 85041446895     PISSN: 08908044     EISSN: None     Source Type: Journal    
DOI: 10.1109/MNET.2018.1700202     Document Type: Article

References (15)

1. Z. Fadlullah et al., "State-of-The-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow's Intelligent Network Traffic Control Systems," IEEE Commun. Surveys &Tutorials, DOI: 10.1109/COMST.2017.2707140.
2. N. Kato et al., "The Deep Learning Vision for Heterogeneous Network Traffic Control: Proposal, Challenges, and Future Perspective," IEEE Wireless Commun., vol. 24, no. 3, June 2017, pp. 146-53. DOI: 10.1109/ MWC.2016.1600317WC.
3. S. Verma et al., "A Survey on Network Methodologies for Real-Time Analytics of Massive Iot Data and Open Research Issues," IEEE Commun. Surveys &Tutorials. DOI: 10.1109/ COMST.2017.2694469.
4. L. Li, K. Ota, and M. Dong, "When Weather Matters: IoTBased Electrical Load Forecasting for Smart Grid," IEEE Commun. Mag., vol. 55, no. 10, Oct. 2017, pp. 46-51.
5. T. G. Rodrigues et al., "Hybrid Method for Minimizing Service Delay in Edge Cloud Computing through VM Migration and Transmission Power Control," IEEE Trans. Computers, vol. 66, no. 5, May 2017, pp. 810-19.
6. Y. Zhang et al., "A Survey on Emerging Computing Paradigms for Big Data," Chinese J. Electronics, vol. 26, no. 1, 2017, pp. 1-12.
7. J. Ren et al., "Serving at the Edge: A Scalable IoT Architecture Based on Transparent Computing," IEEE Network, 2017.
8. J. Liu et al., "Energy Consumption Minimization for FiWi Enhanced Lte-A Hetnets with UE Connection Constraint," IEEE Commun. Mag., vol. 54, no. 11, Nov. 2016, pp. 56-62.
9. J. Liu et al., "New Perspectives on Future Smart FiWi Networks: Scalability, Reliability, and Energy Efficiency," IEEE Commun. Surveys &Tutorials, vol. 18, no. 2, 2nd qtr. 2016, pp. 1045-72.
10. N. D. Lane, P. Georgiev, and L. Qendro, "Deepear: Robust Smartphone Audio Sensing in Unconstrained Acoustic Environments Using Deep Learning," Proc. 2015 ACM Int'l. Joint Conf. Pervasive and Ubiquitous Computing, 2015, pp. 283-94.
11. L. Li et al., "Eyes in the Dark: Distributed Scene Understanding for Disaster Management," IEEE Trans. Parallel Distrib. Systems, 2017. DOI: 10.1109/TPDS.2017.2740294.
12. S. Bhattacharya and N. D. Lane, "Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables," Proc. 14th ACM Conf. Embedded Network Sensor Systems CD-ROM, ser. SenSys '16, 2016, pp. 176-89.
13. M. A. Alsheikh et al., "Mobile Big Data Analytics Using Deep Learning and Apache Spark," IEEE Network, vol. 30, no. 3, May/June 2016, pp. 22-29.
14. T. X. Tran et al., "Collaborative Mobile Edge Computing in 5G Networks: New Paradigms, Scenarios, and Challenges," IEEE Commun. Mag., vol. 55, no. 4, Apr. 2017, pp. 54-61.
15. C. Liu et al., "A New Deep Learning-Based Food Recognition System for Dietary Assessment on an Edge Computing Service Infrastructure," IEEE Trans. Services Computing. DOI: 10.1109/TSC.2017.2662008.