Abstract
With the development of the Internet of Things (IoT) technologies, many kinds of IoT devices as well as many colorful IoT applications are emerging and absorb great attention. In this paper, we propose U-MEC, a Mobile Edge Computing framework deployed in ultra dense networks, in order to narrow the resource gap between the constantly increasing demand of IoT applications and the restricted supply of IoT devices. To improve the energy efficiency of this framework, we present a comprehensive model and formulate a mixed integer programming problem to capture task offloading, user association and base station switching. We propose an online scheduling algorithm, which exploits current system information only by invoking Lyapunov optimization, Lagrange multiplier, and sub-gradient techniques. The simulation results show that our framework achieves a better performance in energy consumption compared with several benchmark schemes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
We will study the multi-application case in the future work.
- 2.
\(\overline{\lambda _i}\) indicates the average amount of data required to sense by device i in each time slot, which is decided by the application manager offline.
References
Chen, X., Jiao, L., Li, W., Fu, X.: Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans. Netw. 24, 2795–2808 (2016)
Feng, M., Mao, S., Jiang, T.: Boost: base station on-off switching strategy for energy efficient massive MIMO HetNets. In: IEEE INFOCOM (2016)
Feng, M., Mao, S., Jiang, T.: Base station on-off switching in 5G wireless networks: approaches and challenges. IEEE Wirel. Commun. 24, 46–54 (2017)
Ge, X., Tu, S., Mao, G., Wang, C.X., Han, T.: 5G ultra-dense cellular networks. IEEE Wirel. Commun. 23, 72–79 (2016)
Keränen, A., Ott, J., Kärkkäinen, T.: The one simulator for DTN protocol evaluation. In: ACM International Conference on Simulation Tools and Techniques (2009)
Kim, J.M., Kim, Y.G., Chung, S.W.: Stabilizing CPU frequency and voltage for temperature-aware DVFS in mobile devices. IEEE Trans. Comput. 64, 286–292 (2015)
Kim, Y., Kwak, J., Chong, S.: Dual-side optimization for cost-delay tradeoff in mobile edge computing. IEEE Trans. Veh. Technol. 67, 1765–1781 (2018)
Kwak, J., Kim, Y., Lee, J., Chong, S.: DREAM: dynamic resource and task allocation for energy minimization in mobile cloud systems. IEEE J. Sel. Areas Commun. 33, 2510–2523 (2015)
Mao, Y., Zhang, J., Letaief, K.B.: Dynamic computation offloading for mobile-edge computing with energy harvesting devices. IEEE J. Sel. Areas Commun. 34, 3590–3605 (2016)
Ng, D.W.K., Schober, R.: Resource allocation and scheduling in multi-cell OFDMA systems with decode-and-forward relaying. IEEE Trans. Wirel. Commun. 10, 2246–2258 (2011)
Schrijver, A.: Theory of Linear and Integer Programming. Wiley, Hoboken (1998)
Wang, S., Zhang, X., Zhang, Y., Wang, L., et al.: A survey on mobile edge networks: convergence of computing, caching and communications. IEEE Access 5, 6757–6779 (2017)
Yan, M., Chan, C.A., Li, W., et al.: Network energy consumption assessment of conventional mobile services and over-the-top instant messaging applications. IEEE J. Sel. Areas Commun. 34, 3168–3180 (2016)
You, C., Huang, K., Chae, H., Kim, B.H.: Energy-efficient resource allocation for mobile-edge computation offloading. IEEE Trans. Wirel. Commun. 16, 1397–1411 (2017)
Yu, N., Miao, Y., Mu, L., Du, H., Huang, H., Jia, X.: Minimizing energy cost by dynamic switching on/off base stations in cellular networks. IEEE Trans. Wirel. Commun. 15, 7457–7469 (2016)
Acknowledgement
This work was supported by the National Natural Science Foundation of China (No. 61702288, 61702287), the Fundamental Research Funds for the Central Universities (No. 7933), the Natural Science Foundation of Tianjin in China (No. 16JCQNJC00700), and the Open Project Fund of Guangdong Key Laboratory of Big Data Analysis and Processing (No. 2017003).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Yu, B., Pu, L., Xie, Q., Xu, J., Zhang, J. (2018). U-MEC: Energy-Efficient Mobile Edge Computing for IoT Applications in Ultra Dense Networks. In: Chellappan, S., Cheng, W., Li, W. (eds) Wireless Algorithms, Systems, and Applications. WASA 2018. Lecture Notes in Computer Science(), vol 10874. Springer, Cham. https://doi.org/10.1007/978-3-319-94268-1_51
Download citation
DOI: https://doi.org/10.1007/978-3-319-94268-1_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94267-4
Online ISBN: 978-3-319-94268-1
eBook Packages: Computer ScienceComputer Science (R0)