Advertisement

Mobile Networks and Applications

, Volume 23, Issue 6, pp 1586–1596 | Cite as

Joint Active Duty Scheduling and Encoding Rate Allocation Optimized Performance of Wireless Multimedia Sensor Networks in Smart Cities

  • Nguyen-Son Vo
  • Thanh-Hieu Nguyen
  • Huu Khuong Nguyen
Article

Abstract

In smart cities, wireless multimedia sensor networks (WMSNs) and mobile cellular networks (MCNs) play an important role in surveillance and management of living environment, i.e., structural health of buildings, urban transportation, and potential locations of future crime, etc. In WMSNs, the camera sensors (CSs) can capture multimedia contents from particular areas, packetize and transmit them to the cluster heads (CHs). The multimedia contents are then forwarded by the CHs, to the base stations (BSs) in MCNs, for monitoring and analyzing. The most challenge is that on the one hand, streaming multimedia contents at high data rate for high playback quality demands, particularly in video streaming applications, requires huge amounts of bandwidth and energy consumption. On the other hand, the wireless channels are inherently varying lossy and the CSs and the CHs are equipped with energy- and bandwidth-constrained capacity. In addition, due to high density of CSs deployed, many of them may monitor the same areas and capture many videos with high correlation, causing numerous redundant contents sent to the BSs, and thus more energy and bandwidth resources are wasted. In this paper, we propose a joint active duty scheduling and encoding rate allocation (ADS-ERA) model to optimize the performance of WMSNs. In particular, the ADS-ERA can minimize the capture, packetization, and transmission energy consumption while satisfying given limited bandwidth and high playback quality constraints. The ADS optimization problem is formulated and solved for optimally scheduling the active duties of the CSs to capture the videos with minimum capture energy consumption. By taking into the constraints on bandwidth and quality, we further formulate the ERA optimization problem and solve it for optimally allocating the encoding rates of the captured videos so that they are packetized and transmitted with minimum energy consumption. Simulation results demonstrate that the proposed ADS-EAR model significantly gain high performance of WMSNs, i.e., minimizing the consumption of capture, packetization, and transmission energy, utilizing the limited bandwidth efficiently, and providing high playback quality of received videos at the BSs.

Keywords

Energy efficiency Encoding rate optimization Resource efficiency Smart Cities Scheduling scheme Wireless multimedia sensor networks 

Notes

Acknowledgments

This work was supported in part by the Newton Institutional Link under Grant ID 172719890.

References

  1. 1.
    Sethi RK (2016) The role of telecommunications in smart Cities. GlobalLogic Inc., San JoseGoogle Scholar
  2. 2.
    Misra S, Reisslein M, Xue G (2008) A survey of multimedia streaming in wireless sensor networks. IEEE Commun Surv Tutorials 10(4):18–39CrossRefGoogle Scholar
  3. 3.
    Hamaguchi K, Ma Y, Takada M, Nishijima T, Shimura T (2012) Telecommunication systems in smart cities. Hitachi Review 61(3): 152–158Google Scholar
  4. 4.
    Aguirre E, et al. (2017) Design and implementation of context aware applications with wireless sensor network support in urban train transportation environments. IEEE Sens J 17 (1):169–178CrossRefGoogle Scholar
  5. 5.
    Ehsan S, Hamdaoui B (2012) A survey on energy-efficient routing techniques with QoS assurances for wireless multimedia sensor networks. IEEE Commun Surv Tutorials 14(2):265–278CrossRefGoogle Scholar
  6. 6.
    Shen H, Bai G (2016) Routing in wireless multimedia sensor networks: A survey and challenges ahead. J Netw Comput Appl 71:30–49CrossRefGoogle Scholar
  7. 7.
    Kandris D, Tsagkaropoulos M, Politis I, Tzes A, Kotsopoulos S (2011) Energy efficient and perceived QoS aware video routing over wireless multimedia sensor networks. Journal Ad Hoc Networks 9(4):591–607CrossRefGoogle Scholar
  8. 8.
    Dai R, Wang P, Akyildiz IF (2012) Correlation-aware QoS routing with differential coding for wireless video sensor networks. IEEE Trans. on Multimedia 14(5):1469–1479CrossRefGoogle Scholar
  9. 9.
    Spachos P, Toumpakaris D, Hatzinakos D (2015) QoS and energy-aware dynamic routing in wireless multimedia sensor networks. In: Proceedings IEEE ICC. IEEE, London, pp 6935–6940Google Scholar
  10. 10.
    Hamrioui S, Lorenz P (2016) Eq-AODV: Energy and QoS supported AODV for better performance in WMSNs. In: Proceedings IEEE ICC. IEEE, London, pp 1–6Google Scholar
  11. 11.
    Al-Turjman F, Hasan MZ (2017) Optimized multi-constrained quality-of-service multipath routing approach for multimedia sensor networks. IEEE Sens J 99:1–12Google Scholar
  12. 12.
    Wang P, Dai R, Akyildiz IF (2013) A differential coding-based scheduling framework for wireless multimedia sensor networks. IEEE Trans Multimedia 15(3):684–697CrossRefGoogle Scholar
  13. 13.
    Alaei M, Barcelo-Ordinas JM (2010) Priority-based node selection and scheduling for wireless multimedia sensor networks. In: Proceedings IEEE wireless and mobile computing, networking and communications, Niagara Falls, Canada, pp 151–158Google Scholar
  14. 14.
    Kim AN, Gurses E (2008) Power-congestion-distortion optimized scheduling in wireless video sensor networks. In: Proceedings IEEE international symposium on wireless communication systems. IEEE, Reykjavik, pp 573–577Google Scholar
  15. 15.
    Hao H, Wang K, Ji H, Li X, Zhang H (2015) Utility-based scheduling algorithm for wireless multi-media sensor networks. In: Proceedings IEEE personal, indoor, and mobile radio communications. IEEE, Hong Kong, pp 1052–1056Google Scholar
  16. 16.
    Guo L, Ning Z, Song Q, Zhang L, Jamalipour A (2017) A QoS-oriented high-efficiency resource allocation scheme in wireless multimedia sensor networks. IEEE Sens J 17(5):1538–1548CrossRefGoogle Scholar
  17. 17.
    Shah GA, Liang W, Akan OB (2012) Cross-layer framework for QoS support in wireless multimedia sensor networks. IEEE Trans Multimedia 14(5):1442–1455CrossRefGoogle Scholar
  18. 18.
    Kader MEEDAE, Youssif AAA, Ghalwash AZ (2016) Energy aware and adaptive cross-layer scheme for video transmission over wireless sensor networks. IEEE Sens J 16(21):7792–7802CrossRefGoogle Scholar
  19. 19.
    Vo N-S, Ha D-B, Canberk B, Zhang J (2016) Green two-tiered wireless multimedia sensor systems: An energy, bandwidth, and quality optimization framework. IET Commun 10(18):2543– 2550CrossRefGoogle Scholar
  20. 20.
    Nguyen T-H, Vo N-S, Huynh B-C, Nguyen HM, Huynh D-T (2017) Joint time switching and rate allocation optimization for energy efficiency in wireless multimedia sensor networks. In: Proceeding international conference on recent advances in signal processing, telecommunications & computing. IEEE, Da Nang, pp 175–180Google Scholar
  21. 21.
    Albanese A, Blomer J, Edmonds J, Luby M, Sudan M (1996) Priority encoding transmission. IEEE Trans Inform Theory 42(6):1737–1744MathSciNetCrossRefGoogle Scholar
  22. 22.
    Puri R, Ramchandran K (1999) Multiple description source coding using forward error correction codes. In: Proceedings 33rd Asilomar Conf. signals, Syst., Comput. IEEE, Pacific Grove, pp 342–346Google Scholar
  23. 23.
    Chou PA, Wang HJ, Padmanabhan VN (2003) Layered multiple description coding. In: Proceedings packet video workshop. IEEE, Nantes, pp 1–7Google Scholar
  24. 24.
    Du X, Vo N-S, Cheng W, Duong TQ, Shu L (2013) Joint replication density and rate allocation optimization for vod systems over wireless mesh networks. IEEE Trans Circuits Syst Video Technol 23(7):1260–1273CrossRefGoogle Scholar
  25. 25.
    Boyce JM, Ye JCY, Ramasubramonian AK (2016) Overview of SHVC: Scalable extensions of the high efficiency video coding standard. IEEE Trans Circuits Syst Video Technol 26(1):20–34CrossRefGoogle Scholar
  26. 26.
    Li X, Zhou F, Du J (2013) LDTS: A lightweight and dependable trust systemfor clusteredwireless sensor networks. IEEE Trans Inf Forensics Secur 8(6):924–935CrossRefGoogle Scholar
  27. 27.
    Xiang W, Zhu C, Siew CK, Xu Y, Liu M (2009) Forward error correction-based 2-d layered multiple description coding for error-resilient h.264 svc video transmission. IEEE Trans Circuits Syst Video Technol 19(12):1730–1738CrossRefGoogle Scholar
  28. 28.
    Breslau L, Cao P, Fan L, Phillips G, Shenker S (1999) Web caching and zipf-like distributions: evidence and implications, In: Proceedings of IEEE INFOCOM. IEEE, New York, pp 126–134Google Scholar
  29. 29.
    Hou YT, Shi Y, Sherali HD (2013) Applied optimization methods for wireless networks, 1st edn. Cambridge University Press, UKGoogle Scholar
  30. 30.
    Zhang Y (1995) Solving large-scale linear programs by interior-point methods under the matlab environment, Department of Mathematics and Statistics, University of Maryland, Baltimore County, Baltimore, MD, Technical Report TR96-01Google Scholar
  31. 31.
    Mehrotra S (1992) On the implementation of a primal-dual interior point method. SIAM J Optim 2:575–601MathSciNetCrossRefGoogle Scholar
  32. 32.
    Fang T, Chau L-P (2006) GOP-Based channel rate allocation using genetic algorithm for scalable video streaming over error-prone networks. IEEE Trans Image Process 15(6):1323–1330CrossRefGoogle Scholar
  33. 33.
    HM Reference Software Version 12.0. http://hevc.hhi.fraunhofer.de

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Nguyen-Son Vo
    • 1
  • Thanh-Hieu Nguyen
    • 1
  • Huu Khuong Nguyen
    • 2
  1. 1.Duy Tan UniversityDa NangVietnam
  2. 2.Ho Chi Minh City University of TransportHo Chi Minh CityVietnam

Personalised recommendations