With the development of intelligent applications, more and more intelligent applications are computation intensive, data intensive and delay sensitive. Compared with traditional cloud computing, edge computing can reduce communication delay by offloading computing tasks to edge cloud. Furthermore, with the complexity of computing scenarios in edge cloud, deep learning based on computation offloading scheme has attracted wide attention. However, all the learning-based offloading scheme does not consider the where and how to run the offloading scheme itself. Thus, in this paper, we consider the problem of running the learning-based computation offloading scheme for the first time and propose the learning for smart edge architecture. Then, we give the computation offloading optimization problem of mobile devices under multi-user and multi edge cloud scenarios. Furthermore, we propose cognitive learning-based computation offloading (CLCO) scheme for this problem. Finally, experimental results show that compared with other offloading schemes, the CLCO scheme has lower task duration and energy consumption.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Liu H, Zhang B, Su X, Ma J, Wang W, Leung K (2017) Energy-aware Participant Selection for Smartphone-enabled Mobile Crowd Sensing. IEEE Syst J 11(3):1435–1446
Lu H, Li Y, Chen M, Kim H, Serikawa S (2018) Brain intelligence: go beyond artificial intelligence. Mob Netw Appl 23(2):368–375
Hao Y, Peng L, Hu H, Hassan M, Atif A (2017) Energy harvesting based body area networks for smart health. Sensors 17(7):1602
Zhang Y, Gravina R, Lu H, Massimo V, Giancarlo F (2018) PEA: Parallel Electrocardiogram-based authentication for smart healthcare systems. J Netw Comput Appl 117:10–16
Liu H, Zhao J, Zhang H, Guo S, Leung K, Crowcroft J (2017) Energy-Efficient Event detection by participatory sensing under budget constraints. IEEE Syst J 11(4):2490–2501
Liu H, Fan J, Hui P, Wu J, Leung K (2015) Towards QoI and Energy-Efficiency in Participatory Crowdsourcing. IEEE Trans Veh Technol 64(10):4684–4700
Ge X, Yang B, Ye J, Mao G, Wang C, Han T (2015) Spatial spectrum and energy efficiency of random cellular networks. IEEE Trans Commun 63(3):1019–1030
Jiang H, Cai C, Ma X, Yang Y, Liu Q (2018) Smart home based on WiFi sensing: a survey. IEEE Access 6(6):13317– 13325
Ge X, Pan L, Li Q, Mao G, Tu S (2017) Multi-Path Cooperative communications networks for augmented and virtual reality transmission. IEEE Trans Multimed 19(10):2345–2358
Yu N, Kaoru S (2018) Per-Flow Throughput fairness in ring aggregation network with multiple edge routers. Big Data Cogn Comput 2(3):17
Chen M, Hao Y, Hu L, Hossain M, Ghoneim A (2018) An adaptive fusion strategy for distributed information estimation over cooperative Multi-Agent networks. IEEE Trans Inf Theory 63(5):3076–3091
Tian D, Zhou J, Sheng Z (2017) Edge-cocaco: Towards Joint Optimization of Computation, Caching and Communication on Edge Cloud. IEEE Wirel Commun 25(3):21–27
Tong L, Li Y, Gao W (2016) A hierarchical edge cloud architecture for mobile computing. Proceedings of the 35th IEEE Conference on Computer Communications, pp 399–400
Chen X, Jiao L, Li W, Fu X (2016) Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Transactions on Networking 24(5):2795–2808
Hany F, Robert J, Gary B (2018) Fog Computing and the Internet of Things: A Review. Big Data Cogn Comput 2(2):10
Chen M, Hao Y, Hu L, Huang K, Lau NK (2017) Green andMobilityaware Caching in 5G Networks. IEEE Trans Wirel Commun 16(12):8347–8361
Wang C, Li Y, Jin D (2014) Mobility-Assisted Opportunistic computation offloading. IEEE Commun Lett 18(10):1779–1782
Chen M, Hao Y, Li Y, Lai C, Wu D (2015) On the computation offloading at ad hoc cloudlet: architecture and service modes. IEEE Commun Mag 53(6):18–24
Chun B, Ihm S, Maniatis P, Naik M, Patti A (2011) Clonecloud: elastic execution between mobile device and cloud. Proceedings of the sixth conference on Computer systems, pp 301–314
Barbera M, Kosta S, Mei A, Stefa J (2013) To offload or not to offload the bandwidth and energy costs of mobile cloud computing. INFOCOM, 2013 Proceedings IEEE, pp 1285–1293
Kollenstart M, Harmsma E, Langius E, Andrikopo V, Lazovik A (2018) Adaptive provisioning of heterogeneous cloud resources for big data processing. Big Data Cogn Comput 2(3):15
Chen M, Hao Y, Qiu M, Song J, Wu D, Humar I (2016) Mobility-aware Caching and Computation Offloading in 5G Ultradense Cellular Networks. Sensors 16(7):974–987
Li Y, Wang W (2014) Can mobile cloudlets support mobile applications?. In: 2014 Proceedings IEEE INFOCOM, pp 1060–1068
Chen M, Qian Y, Hao Y, Song L (2018) Data-Driven Computing and Caching in 5G Networks: Architecture and Delay Analysis. IEEE Wirel Commun 25(1):70–75
Joel RB (2018) A deep learning model of perception in Color-Letter synesthesia. Big Data Cogn Comput 2 (1):8
Chen M, Hao Y (2018) Task Offloading for Mobile Edge Computing in Software Defined Ultra-dense Network. IEEE J Sel Areas Commun 36(3):587–597
Sun Y, Zhou S, Xu J (2017) EMM: Energy-Aware mobility management for mobile edge computing in ultra dense networks. IEEE J Sel Areas Commun 35(11):2637–2646
Daniel B (2018) Reimaging research methodology as data science. Big Data Cogn Comput 2(1):4
Zheng Y, Wu D, Ke Y, Yang C, Chen M, Zhang G (2017) EMM: Energy-Aware mobility management for mobile edge computing in ultra dense networks. ZhangYuanhuan2017 27(8):1777–1789
Wang D, Peng Y, Ma X, Ding W, Jiang H, Liu J (2018) Adaptive Wireless Video Streaming based on Edge Computing: Opportunities and Approaches. IEEE Transactions on Services Computing. https://doi.org/10.1109/TSC.2018.2828426
Chen X, Pu L, Gao L, Wu W, Wu D (2017) Exploiting massive D2D collaboration for Energy-Efficient mobile edge computing. IEEE Wirel Commun 24(4):64–71
Kang K, Chen L, Yi H, Wang B, Sha M (2017) Real-Time Information derivation from big sensor data via edge computing. Big Data Cogn Comput 1(1):5
Chen M, Miao Y, Hao Y, Hwang K (2017) Narrow band internet of things. IEEE Access 5:20557–20577
Ge X, Sun Y, Gharavi H, John T (2018) Joint Optimization of Computation and Communication Power in Multi-user Massive MIMO Systems. IEEE Trans Wirel Commun 17(6):4051–4063
The authors would like to extend their sincere appreciation to the Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia, for funding this research group No. (RG-1437-042).
About this article
Cite this article
Hao, Y., Jiang, Y., Hossain, M.S. et al. Learning for Smart Edge: Cognitive Learning-Based Computation Offloading. Mobile Netw Appl 25, 1016–1022 (2020). https://doi.org/10.1007/s11036-018-1119-7
- Computation offloading
- Cognitive learning
- Edge computing