Clustering in cognitive radio for multimedia streaming over wireless Sensor networks

Streaming over multimedia WSN (MWSN) in urban environment is challenging due to many issues among which spectrum scarcity and high radio interference. Such conditions make it difficult to ensure high bandwidth, low transmission delay and low packet losses required for real time multimedia streaming applications. In this paper, we propose COMUS a COgnitive radio solution for MUltimedia streaming over wireless Sensor networks which uses both cognitive radio technology and clustering mechanism to enhance spectrum and energy efficiency. In COMUS we consider clustering the MWSN nodes into different clusters to ensure low energy consumption. Furthermore, based on the nodes geographical position and the actual and the forecasted channel availability, we aim to ensure stable clusters forming. The multimedia streaming from a particular source node to the sink node, require a physical channel selection to perform the corresponding routing task. Thus, in COMUS we propose an efficient channel selection to prevent frequent channel switching which considers the PU (Primary User) activity forecasts. Our simulation results show that COMUS outperforms the two existing pioneering mechanisms called SEARCH and SCEEM and this in terms of providing higher video quality (PSNR and frame rate), lower end-to-end transmission delay and lower frame loss ratio under varied spectrum conditions.

[1]  Claire Cardie,et al.  Proceedings of the Eighteenth International Conference on Machine Learning, 2001, p. 577–584. Constrained K-means Clustering with Background Knowledge , 2022 .

[2]  A. Rachedi,et al.  Channel bonding in cognitive radio wireless sensor networks , 2012, 2012 International Conference on Selected Topics in Mobile and Wireless Networking.

[3]  K. J. Ray Liu,et al.  Advances in cognitive radio networks: A survey , 2011, IEEE Journal of Selected Topics in Signal Processing.

[4]  Dan Klein,et al.  From Instance-level Constraints to Space-Level Constraints: Making the Most of Prior Knowledge in Data Clustering , 2002, ICML.

[5]  Vijay K. Bhargava,et al.  Design of OMC-MAC: An Opportunistic Multi-Channel MAC with QoS Provisioning for Distributed Cognitive Radio Networks , 2011, IEEE Transactions on Wireless Communications.

[6]  Len Tashman,et al.  ARIMA: The Models of Box and Jenkins , 2013 .

[7]  Toufik Ahmed,et al.  ViCoV: Efficient video streaming for cognitive radio VANET , 2014, Veh. Commun..

[8]  Wei Tu,et al.  Distributed scheduling scheme for video streaming over multi-channel multi-radio multi-hop wireless networks , 2010, IEEE Journal on Selected Areas in Communications.

[9]  Bu-Sung Lee,et al.  Explore and Model Better I-Frames for Video Coding , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[10]  Xuemin Shen,et al.  Delay Performance Analysis for Supporting Real-Time Traffic in a Cognitive Radio Sensor Network , 2011, IEEE Trans. Wirel. Commun..

[11]  D. B. Preston Spectral Analysis and Time Series , 1983 .

[12]  R. B. Patel,et al.  EECDA: Energy Efficient Clustering and Data Aggregation Protocol for Heterogeneous Wireless Sensor Networks , 2011, Int. J. Comput. Commun. Control.

[13]  Reza Monsefi,et al.  An adaptive Cross-Layer multichannel QoS-MAC protocol for cluster based wireless multimedia sensor networks , 2009, 2009 International Conference on Ultra Modern Telecommunications & Workshops.

[14]  Ian F. Akyildiz,et al.  A survey on wireless multimedia sensor networks , 2007, Comput. Networks.

[15]  Vijay K. Bhargava,et al.  Medium access control in distributed cognitive radio networks , 2011, IEEE Wireless Communications.

[16]  Dongmei Zhao,et al.  Quality of Service Performance of a Cognitive Radio Sensor Network , 2010, 2010 IEEE International Conference on Communications.

[17]  Makoto Takizawa,et al.  A Survey on Clustering Algorithms for Wireless Sensor Networks , 2010, 2010 13th International Conference on Network-Based Information Systems.

[18]  Marco Di Felice,et al.  SEARCH: A routing protocol for mobile cognitive radio ad-Hoc networks , 2009, 2009 IEEE Sarnoff Symposium.

[19]  Ian F. Akyildiz,et al.  CRAHNs: Cognitive radio ad hoc networks , 2009, Ad Hoc Networks.

[20]  Özgür B. Akan,et al.  A Spectrum-Aware Clustering for Efficient Multimedia Routing in Cognitive Radio Sensor Networks , 2014, IEEE Transactions on Vehicular Technology.

[21]  Bechir Hamdaoui,et al.  A Survey on Energy-Efficient Routing Techniques with QoS Assurances for Wireless Multimedia Sensor Networks , 2012, IEEE Communications Surveys & Tutorials.

[22]  Özgür B. Akan,et al.  Delay-sensitive and multimedia communication in cognitive radio sensor networks , 2012, Ad Hoc Networks.

[23]  F. Richard Yu,et al.  Energy-Efficient Resource Allocation for Heterogeneous Cognitive Radio Networks with Femtocells , 2012, IEEE Transactions on Wireless Communications.

[24]  F. Richard Yu,et al.  Spectrum sharing and resource allocation for energy-efficient heterogeneous cognitive radio networks with femtocells , 2012, 2012 IEEE International Conference on Communications (ICC).