Advertisement

Soft Computing

, Volume 23, Issue 15, pp 6051–6063 | Cite as

A channel-aware expected energy consumption minimization strategy in wireless networks

  • Gaocai Wang
  • Qifei ZhaoEmail author
  • Tianxiao Xie
  • Guojun WangEmail author
Foundations
  • 53 Downloads

Abstract

With the rapid development of wireless network technology, energy saving has become a very important topic to build a green network in wireless networks. Due to the time-varying characteristics of the channel, it is possible to obtain a higher utilization for energy by using the channel with good state in wireless communications. From the view of the data transmission energy consumption of the whole wireless network, this paper proposes an expected energy consumption minimization strategy (E2CMS) for data transmission based on the optimal stopping theory. Considering the maximum transmission delay and a given receiving power, E2CMS delays data to transmit until the best expected channel state is detected. In order to solve the problem, firstly, we construct an energy consumption minimization problem with quality of service constraints. Then, we prove that E2CMS is a pure threshold strategy by the optimal stopping theory and obtain the power threshold by solving a fixed-point equation using backward induction. Finally, simulations are performed in a typical small-scale fading channel model. E2CMS is compared with a variety of different transmission scheduling strategies. The results show that E2CMS has lower average energy consumption for per unit data and significantly improves the network performance.

Keywords

Wireless networks Channel awareness Optimal stopping theory Data transmission Energy consumption optimization 

Notes

Funding

This study was funded by the National Natural Science Foundation of China under Grant Nos. 61562006 and 61632009, 61772233, in part by the Natural Science Foundation of Guangxi Province under Grant No. 2016GXNSFBA380181.

Compliance with ethical standards

Conflict of interest

We declare that we have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Bhuiyan MZA, Wu J, Wang G, Wang T, Hassan M (2017) e-Sampling: event-sensitive autonomous adaptive sensing and low-cost monitoring in networked sensing systems. ACM Trans Auton Adapt Syst 12(1):1–29CrossRefGoogle Scholar
  2. Castiglione A, D’Arco P, De Santi A, Russo R (2017) Secure group communication schemes for dynamic heterogeneous distributed computing. Future Gener Comput Syst 74:313–324CrossRefGoogle Scholar
  3. Ferguson T (2006) Optimal stopping and applications. http://www.math.ucla.edu/tom/Stopping/Contents.html
  4. Freeman PR (1983) The secretary problem and its extensions: a review. Int Stat Rev 51(51):189–206MathSciNetzbMATHCrossRefGoogle Scholar
  5. Jing Z, Chen D, Nguyen HV et al (2014) Cross-layer aided energy-efficient opportunistic routing in Ad Hoc networks. IEEE Trans Commun 62(2):522–535CrossRefGoogle Scholar
  6. Kohan M, Khotanlou H, Nassiri M (2013) An efficient mechanism for data rate adaptation in wireless LAN’s. Adv Comput Sci Int J 2(3):19–25Google Scholar
  7. Li CP, Neely MJ (2007) Energy-optimal scheduling with dynamic channel acquisition in wireless downlink. In: The 46th IEEE conference on decision and control, pp 1140–1147Google Scholar
  8. Lin C, Tian Y, Yao M (2011) Green network and green evaluation: energy conservation mechanism, model and evaluation (in Chinese). J Comput 34(4):593–612Google Scholar
  9. Liu B, Lin C, Jiang X et al (2008) Performance analysis of sleep scheduling schemes in sensor networks using stochastic Petri net. In: Proceedings of the international conference on communications (ICC), pp 4278–4283Google Scholar
  10. Peng Y, Wang G, Huang S et al (2016) Optimization strategy of data transmission energy consumption based on optimal stopping theory in mobile networks (in Chinese). J Comput 39(6):1162–1175MathSciNetGoogle Scholar
  11. Poulakis MI, Panagopoulos AD, Constantinou P (2013) Channel-aware opportunistic transmission scheduling for energy-efficient wireless links. IEEE Trans Veh Technol 62(1):192–204CrossRefGoogle Scholar
  12. Qin X, Berry R (2003) Exploiting multiuser diversity for medium access control in wireless networks. IEEE INFOCOM, San Francisco, pp 1084–1094Google Scholar
  13. Shen X, Agrawal S (2006) Kernel density estimation for an anomaly based intrusion detection system. In: International conference on machine learning, models, technologies and applications, Las Vegas, pp 161–167Google Scholar
  14. Simon MK, Alouini MS (2005) Digital communications over fading channels. Wiley, HobokenGoogle Scholar
  15. Van Phan C (2014) A game-theoretic framework for opportunistic transmission in wireless networks. In: Proceedings of the 2014 IEEE fifth international conference on communications and electronics (ICCE 2014), Danang, pp 150–154Google Scholar
  16. Wang L, Wong K-K, Jin S, Zheng G, Heath RW (2018) A new look at physical layer security, caching, and wireless energy harvesting for heterogeneous ultra-dense networks. IEEE Commun Mag 56(6):49–55CrossRefGoogle Scholar
  17. Weng C, Chen C, Chen P et al (2013) Design of an energy-efficient cross-layer protocol for mobile ad hoc networks. IET Commun 7(3):217–228CrossRefGoogle Scholar
  18. Yang W, Wang G, Bhuiyan MZA, Choo K-KR (2017) Hypergraph partitioning for social networks based on information entropy modularity. J Netw Comput Appl 86:59–71CrossRefGoogle Scholar
  19. Yue G, Zhou X, Wang X (2004) Performance comparisons of channel estimation techniques in multipath fading CDMA. IEEE Trans Wirel Commun 3(3):716–724CrossRefGoogle Scholar
  20. Zhang F, Antonio FA, Wang L et al (2012) Network energy consumption system model and energy efficiency algorithm. J Comput 35(3):603–615Google Scholar
  21. Zheng D, Ge W-Y, Zhang J-S (2009) Distributed opportunistic scheduling for ad hoc networks with random access: an optimal stopping approach. IEEE Trans Inf Theory 55(1):205–222MathSciNetzbMATHCrossRefGoogle Scholar
  22. Zhou Z, Dong M, Ota K, Wang G, Yang L (2015) Energy-efficient resource allocation for D2D communications underlaying cloud-RAN based LTE-A networks. IEEE Internet Things J 3(3):428–438CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Electrical EngineeringGuangxi UniversityNanningPeople’s Republic of China
  2. 2.School of Computer Science and TechnologyGuangzhou UniversityGuangzhouPeople’s Republic of China

Personalised recommendations