Hostname: page-component-7b7567c8c-2rsb6 Total loading time: 0 Render date: 2026-11-12T18:49:00.195Z Has data issue: false hasContentIssue false
  • English
  • Français

FINITE CAPACITY ENERGY PACKET NETWORKS

Published online by Cambridge University Press:  19 April 2017

Yasin Murat Kadioglu
Yasin Murat Kadioglu*
Affiliation:
Intelligent Systems and Networks Group, Department of Electrical and Electronic Engineering, Imperial College, London SW7 2BT, UK E-mail: y.kadioglu14@imperial.ac.uk
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

This paper surveys research on mathematical models that predict the performance of digital devices that operate with intermittent energy sources. The approach taken in this work is based on the “Energy Packet Network” paradigm where the arrival of data to be processed or transmitted, and the energy to operate the system, are modeled as discrete random processes. Our assumption is that these devices will capture energy from intermittent ambient sources such as vibrations, heat or light, and capture it onto electrical energy that may be stored in batteries or capacitors. The devices consume this energy intermittently for processing and for wired or wireless transmission. Thus, both the arrival of energy to the device, and the devices workload, are modeled as random processes. Based on these assumptions, we discuss probability models based on Markov chains that can be used to predict the effective rates at which such devices operate. We also survey related work that models networks of such systems.

Keywords

data packetsenergy harvestingenergy packet networksMarkov chainsrandom walk

Information

Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 31 , Special Issue 4: G-Networks and their Applications , October 2017, pp. 477 - 504
Copyright
Copyright © Cambridge University Press 2017 

References

1. Abdelrahman, O.H. & Gelenbe, E. (2016). A diffusion model for energy harvesting sensor nodes. In IEEE 24th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS’16), IEEEXplore, pp. 154–158.Google Scholar
2. Alippi, C. & Galperti, C. (2008). An adaptive system for optimal solar energy harvesting in wireless sensor network nodes. IEEE Transactions on Circuits and Systems I: Regular Papers, 55(6): 1742–1750.Google Scholar
3. Berl, A., Gelenbe, E., Di Girolamo, M., Giuliani, G., De Meer, H., Dang, M.Q., & Pentikousis, K. (2010). Energy-efficient cloud computing. The Computer Journal 53(7): 1045–1051.Google Scholar
4. Ceran, E.T. & Gelenbe, E. (2016). Energy packet model optimisation with approximate matrix inversion. In Proceedings of the 2nd International Workshop on Energy-Aware Simulation, ACM, p. 4.Google Scholar
5. Francois, F., Abdelrahman, O.H., & Gelenbe, E. (2015). Impact of signaling storms on energy consumption and latency of LTE user equipment. In High Performance Computing and Communications (HPCC), 2015 IEEE 7th International Symposium on Cyber space Safety and Security (CSS), 2015 IEEE 12th International Conference on Embedded Software and Systems (ICESS), 2015 IEEE 17th International Conference on, IEEE, pp. 1248–1255.Google Scholar
6. Francois, F., Abdelrahman, O.H., & Gelenbe, E. (2016). Towards assessment of energy consumption and latency of LTE uses during signaling storms. In Information Sciences and Systems 2015, Springer, pp. 45–55.Google Scholar
7. Gelenbe, E. (1993). G-networks by triggered customer movement. Journal of applied probability 30(03): 742–748.Google Scholar
8. Gelenbe, E. (1993). G-networks with signals and batch removal. Probability in the Engineering and Informational Sciences 7: 335–342.Google Scholar
9. Gelenbe, E. (2011). Energy packet networks: ICT based energy allocation and storage—(invited paper). In GreeNets, (Rodrigues, J.J.P.C., Zhou, L., Chen, M., & Kailas, A. Eds.), volume 51 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Springer, pp. 186–195.Google Scholar
10. Gelenbe, E. (2012). Energy packet networks: adaptive energy management for the cloud. In Proceedings of the 2nd International Workshop on Cloud Computing Platforms (CloudCP’12). ACM, New York, NY, USA, Article 1, 5 pages.Google Scholar
11. Gelenbe, E. (2014). Adaptive management of energy packets. In 2014 IEEE 38th International Computer Software and Applications Conference Workshops (COMPSACW), IEEE, pp. 1–6.Google Scholar
12. Gelenbe, E. (2014). Error and energy when communicating with spins. In Signal and Information Processing (GlobalSIP), 2014 IEEE Global Conference on, IEEE, pp. 784–787.Google Scholar
13. Gelenbe, E. (2014). A sensor node with energy harvesting. SIGMETRICS Performance Evaluation Review 42(2): 37–39.Google Scholar
14. Gelenbe, E. (2015). Errors and power when communicating with spins. IEEE Transactions on Emerging Topics in Computing 3(4): 483–488.Google Scholar
15. Gelenbe, E. (2015). Synchronising energy harvesting and data packets in a wireless sensor. Energies 8(1): 356–369.Google Scholar
16. Gelenbe, E. (2016). Agreement in spins and social networks. ACM SIGMETRICS Performance Evaluation Review 44(2): 15–17.Google Scholar
17. Gelenbe, E. & Caseau, Y. (2015). The impact of information technology on energy consumption and carbon emissions. Ubiquity 2015 (June), Article 1.Google Scholar
18. Gelenbe, E. & Ceran, E.T. (2015). Central or distributed energy storage for processors with energy harvesting. In Sustainable Internet and ICT for Sustainability (SustainIT), 2015, IEEE, pp. 1–3.CrossRefGoogle Scholar
19. Gelenbe, E. & Ceran, E.T. (2016). Energy packet networks with energy harvesting. IEEE Access 4: 1321–1331.Google Scholar
20. Gelenbe, E., Gesbert, D., Gunduz, D., Kulah, H., & Uysal-Biyikoglu, E. (2013). Energy harvesting communication networks: Optimization and demonstration (the e-crops project). In Digital Communications-Green ICT (TIWDC), 2013 24th Tyrrhenian International Workshop on, IEEE, pp. 1–6.Google Scholar
21. Gelenbe, E. & Gunduz, D. (2013). Optimum power level for communications with interference. In 2013 24th Tyrrhenian International Workshop on Digital Communications-Green ICT (TIWDC), IEEE, pp. 1–6.Google Scholar
22. Gelenbe, E. & Kadioglu, Y.M. (2015). Energy loss through standby and leakage in energy harvesting wireless sensors. In 20th IEEE International Workshop on Computer Aided Modelling and Design of Communication Links and Networks (CAMAD), Guildford, 2015, pp. 231–236.Google Scholar
23. Gelenbe, E. & Kadioglu, Y.M. (2016). Performance of an autonomous energy harvesting wireless sensor. In Information Sciences and Systems 2015, Springer, pp. 35–43.CrossRefGoogle Scholar
24. Gelenbe, E. & Lent, R. (2012). Optimising server energy consumption and response time. Theoretical and Applied Informatics 24(4): 257.Google Scholar
25. Gelenbe, E. & Lent, R. (2013). Energy–qos trade-offs in mobile service selection. Future Internet 5(2): 128–139.Google Scholar
26. Gelenbe, E., Lent, R., & Douratsos, M. (2012). Choosing a local or remote cloud. In Network Cloud Computing and Applications (NCCA), 2012 Second Symposium on, IEEE, pp. 25–30.Google Scholar
27. Gelenbe, E. & Mahmoodi, T. (2011). Energy–aware routing protocol in the cognitive packet network. In International Conference on Smart Grids, Green Communications, and IT Energy–aware Technologies (Energy 2011), Venice, Italy, 22–27 May 2011, pp. 7–12.Google Scholar
28. Gelenbe, E. & Marin, A. (2015). Interconnected wireless sensors with energy harvesting. In International Conference on Analytical and Stochastic Modeling Techniques and Applications, Springer International Publishing, pp. 87–99.Google Scholar
29. Gelenbe, E. & Morfopoulou, C. (2011). A framework for energy-aware routing in packet networks. The Computer Journal 54(6): 850–859.Google Scholar
30. Goldsmith, A. (2005). Wireless communications. 1st ed. New York, USA: Cambridge University Press.Google Scholar
31. Kadioglu, Y.M. (2016). Energy consumption model for data processing and transmission in energy harvesting wireless sensors. In Computer and Information Sciences – 31st International Symposium, ISCIS, Proceedings, Kraków, Poland, 27–28 October, pp. 117–125.Google Scholar
32. Kadioglu, Y.M. & Gelenbe, E. (2016). Packet transmission with k energy packets in an energy harvesting sensor. In Proceedings of the 2nd International Workshop on Energy-Aware Simulation (ENERGY-SIM ’16), New York, NY, USA: ACM, pp. 1: 1–1: 6.Google Scholar
33. Meshkati, F., Poor, H.V., Schwartz, S.C., & Mandayam, N.B. (2005). An energy-efficient approach to power control and receiver design in wireless data networks. IEEE Transactions on Communications, 53(11): 1885–1894.CrossRefGoogle Scholar
34. Newcombe, L. (2008). Data centre energy efficiency metrics: Existing and proposed metrics to provide effective understanding and reporting of data centre energy. BCS, Swindon, UK: British Computer Society.Google Scholar
35. Rodoplu, V. & Meng, T.H. (2007). Bits-per-joule capacity of energy-limited wireless networks. IEEE Transactions on Wireless Communications, 6(3): 857–865.Google Scholar
36. Seah, W.K., Eu, Z.A., & Tan, H.-P. (2009). Wireless sensor networks powered by ambient energy harvesting (wsn-heap)-survey and challenges. In 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology, 2009. Wireless VITAE 2009, IEEE, pp. 1–5.Google Scholar
37. Żoładek, H. (2000). The topological proof of Abel–Ruffini theorem. Topological Methods in Nonlinear Analysis 16: 253–265.Google Scholar