Uplink Adaptive Multimedia Delivery (UAMD) scheme for Video Sensor Network

Lately Video Sensor Networks (VSN) are increasingly being used in the context of smart cities, smart homes, for environment monitoring, surveillance, etc. In such systems, due to limitations in the video sensor node resources, the most important factor is energy efficiency. Sensing, processing and transmitting are the main contributors to energy consumption in a video sensor node. Among these, wireless transmissions play a dominant role. An effective way for reducing energy consumption is to adjust the active time duration of node's radio transmission. However, this method has the main drawback that affects streaming quality in terms of throughput and delay. So, one of the big challenges when designing an energy-aware VSN is the trade-off between energy consumption and video streaming quality. This paper proposes an Uplink Adaptive Multimedia Delivery (UAMD) scheme that dynamically adjusts the wakeup/ sleep duration of video sensor nodes based on the node remaining battery levels and network traffic conditions. The UAMD algorithm employs a utility function in the decision on duty cycle adjustment process. Simulation results show how UAMD achieves good balance between energy consumption and throughput in comparison with other duty-cycle-based schemes.

[1]  Ian F. Akyildiz,et al.  Wireless multimedia sensor networks: A survey , 2007, IEEE Wireless Communications.

[2]  Gabriel-Miro Muntean,et al.  Energy–Quality–Cost Tradeoff in a Multimedia-Based Heterogeneous Wireless Network Environment , 2013, IEEE Transactions on Broadcasting.

[3]  Dave Evans,et al.  How the Next Evolution of the Internet Is Changing Everything , 2011 .

[5]  Cristina Hava Muntean,et al.  Quality Utility modelling for multimedia applications for Android Mobile devices , 2012, IEEE international Symposium on Broadband Multimedia Systems and Broadcasting.

[6]  Eric Anderson,et al.  X-MAC: a short preamble MAC protocol for duty-cycled wireless sensor networks , 2006, SenSys '06.

[7]  Gabriel-Miro Muntean,et al.  Energy-efficient device-differentiated cooperative adaptive multimedia delivery solution in wireless networks , 2015 .

[8]  Ming-Hsuan Yang,et al.  Traffic modeling and prediction using camera sensor networks , 2010, ICDSC '10.

[9]  Gabriel-Miro Muntean,et al.  eDOAS: Energy-aware device-oriented adaptive multimedia scheme for Wi-Fi offload , 2014, 2014 IEEE Wireless Communications and Networking Conference (WCNC).

[10]  Cristina Hava Muntean,et al.  Energy-Aware Mobile Learning:Opportunities and Challenges , 2014, IEEE Communications Surveys & Tutorials.

[11]  Azzedine Boukerche,et al.  Modeling the sleep interval effects in duty-cycled underwater sensor networks , 2016, 2016 IEEE International Conference on Communications (ICC).

[12]  Jie Li,et al.  Distributed duty cycle control for delay improvement in wireless sensor networks , 2017, Peer-to-Peer Netw. Appl..

[13]  Bambang A. B. Sarif,et al.  Energy efficient video sensor networks for surveillance applications , 2016 .

[14]  Chen Fang,et al.  LC-MAC: An Efficient MAC Protocol for the Long-Chain Wireless Sensor Networks , 2011, 2011 Third International Conference on Communications and Mobile Computing.

[15]  M. Lakshmanan,et al.  AN ADAPTIVE ENERGY EFFICIENT MAC PROTOCOL FOR WIRELESS SENSOR NETWORKS , 2009 .

[16]  Gourab Sen Gupta,et al.  Wireless Sensors for Home Monitoring - A Review , 2008 .

[17]  Chia-han Lee,et al.  On-Line Multi-View Video Summarization for Wireless Video Sensor Network , 2015, IEEE Journal of Selected Topics in Signal Processing.

[18]  CongDuc Pham,et al.  Low cost Wireless Image Sensor Networks for visual surveillance and intrusion detection applications , 2015, 2015 IEEE 12th International Conference on Networking, Sensing and Control.

[19]  Anum Ali,et al.  Energy efficient techniques for M2M communication: A survey , 2016, J. Netw. Comput. Appl..

[20]  Deborah Estrin,et al.  Medium access control with coordinated adaptive sleeping for wireless sensor networks , 2004, IEEE/ACM Transactions on Networking.

[21]  Shengbo Eben Li,et al.  Advanced Machine Learning Approach for Lithium-Ion Battery State Estimation in Electric Vehicles , 2016, IEEE Transactions on Transportation Electrification.

[22]  Gabriel-Miro Muntean,et al.  Adaptive Energy Optimization in Multimedia-Centric Wireless Devices: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[23]  Kin K. Leung,et al.  MAC Essentials for Wireless Sensor Networks , 2010, IEEE Communications Surveys & Tutorials.

[24]  Wei Xie,et al.  An Online-Traffic-Prediction Based Route Finding Mechanism for Smart City , 2015, Int. J. Distributed Sens. Networks.

[25]  Iain E. G. Richardson,et al.  The H.264 Advanced Video Compression Standard , 2010 .

[26]  Anfeng Liu,et al.  A Residual Energy Aware Schedule Scheme for WSNs Employing Adjustable Awake/Sleep Duty Cycle , 2016, Wireless Personal Communications.

[27]  Jae-Ho Lee A Traffic-Aware Energy Efficient Scheme for WSN Employing an Adaptable Wakeup Period , 2013, Wirel. Pers. Commun..

[28]  Gabriel-Miro Muntean,et al.  Battery and Stream-Aware Adaptive Multimedia Delivery for wireless devices , 2010, IEEE Local Computer Network Conference.

[29]  Iain E. Richardson,et al.  The H.264 Advanced Video Compression Standard: Richardson/The H.264 Advanced Video Compression Standard , 2010 .

[30]  Benny Bing H.265/HEVC Standard , 2015 .

[31]  G. W. Reddien Newton–Raphson Methods , 2014 .

[32]  Martin Reisslein,et al.  Towards Efficient Wireless Video Sensor Networks: A Survey of Existing Node Architectures and Proposal for A Flexi-WVSNP Design , 2011, IEEE Communications Surveys & Tutorials.

[33]  Mohsen Guizani,et al.  Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications , 2015, IEEE Communications Surveys & Tutorials.