The week's pick
Random Articles
Reseach Article
A Fuzzy Logic Adaptive Image Compression Level using Cross-Layering in Wireless Multimedia Sensor Networks
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 174 - Number 28 |
| Year of Publication: 2021 |
| Authors: Mohammed Ameen A. Abdo, Ala Eldin Abdallah Awouda, Yousif Elfatih Yousif |
10.5120/ijca2021921200
|
Mohammed Ameen A. Abdo, Ala Eldin Abdallah Awouda, Yousif Elfatih Yousif . A Fuzzy Logic Adaptive Image Compression Level using Cross-Layering in Wireless Multimedia Sensor Networks. International Journal of Computer Applications. 174, 28 ( Apr 2021), 18-24. DOI=10.5120/ijca2021921200
Abstract
The growing interest in Wireless Sensor Networks (WSNs) with the rapid growth in micro-electronics technology has made it possible to deliver multimedia content over Wireless Multimedia Sensor Networks (WMSNs). There are several main peculiarities that make the delivery of multimedia content over WMSN challenging. Most of these are due to the processing, timing, and other quality of service requirements. Furthermore, WMSNs are susceptible to rapid degradation since they deal with large amount of data that require processing and transmission power. In this paper, a cross-layer design approach is proposed to overcome such challenges. In the proposed model, the concept of cross-layering and fuzzy logic has been exploited to monitor the network conditions and control the amount of the multimedia data in order to utilize the available resources efficiently and improve the applications Quality of Service (QoS). The simulation results have shown better resource utilization, stability, and fairness in quality metrics consideration. The proposed model has shown to be efficient compared to the conventional scheme in terms of bandwidth utilization, power consumption, delay, loss, and images quality.
References
- M. H. Alsharif, S. Kim, and N. Kuruoğlu, "Energy harvesting techniques for wireless sensor networks/radio-frequency identification: A review," Symmetry, vol. 11, no. 7, pp. 865-865, 2019.
- L. Li, Y. Liu, J. Wang, T. Wu, and P. Zhou, "Partially observed cross-layer optimization for vehicular communications," vol. 31, no. 1, p. e3398, 2018.
- V. J. I. J. o. A. R. i. C. E. Jindal and Technology, "History and architecture of wireless sensor networks for ubiquitous computing," vol. 7, no. 2, pp. 214-217, 2018.
- A. K. Dwivedi and A. Sharma, "FEECA: Fuzzy based Energy Efficient Clustering Approach in Wireless Sensor Network," EAI Endorsed Transactions on Scalable Information Systems, vol. 7, no. 27, 2020.
- I. P. Ktistakis, G. Goodman, and C. Shimizu, "Methods for optimizing fuzzy inference systems," in Advances in Data Science: Methodologies and Applications: Springer, 2020, pp. 97–116-97–116.
- Y. Zur and A. Adler, "Deep Learning of Compressed Sensing Operators with Structural Similarity Loss," arXiv preprint arXiv:1906.10411, 2019.
- L. B. Bhajantri, "A Comprehensive Survey on Data Aggregation in Wireless Sensor Networks," 2018.
- R. Song, Q. Wei, and W. Xiao, "ADP-based optimal sensor scheduling for target tracking in energy harvesting wireless sensor networks," Neural Computing and Applications, vol. 27, no. 6, pp. 1543–1551-1543–1551, 2016.
- S. Q. Mahdi, S. K. Gharghan, and M. A. Hasan, "FPGA-Based neural network for accurate distance estimation of elderly falls using WSN in an indoor environment," Measurement, vol. 167, pp. 108276-108276, 2021.
- Z.-J. Wang, Z.-H. Zhan, and J. Zhang, "Solving the energy efficient coverage problem in wireless sensor networks: A distributed genetic algorithm approach with hierarchical fitness evaluation," Energies, vol. 11, no. 12, pp. 3526-3526, 2018.
- T. Khampeerpat and C. Jaikaeo, "Mobile sensor relocation for nonuniform and dynamic coverage requirements," IEICE TRANSACTIONS on Information and Systems, vol. 100, no. 3, pp. 520–530-520–530, 2017.
- D. Sahin and H. M. Ammari, "Programming Languages, Network Simulators, and Tools," in The Art of Wireless Sensor Networks: Springer, 2014, pp. 739–788-739–788.
- M. Bakni, L. M. M. Chacón, Y. Cardinale, G. Terrasson, and O. Curea, "WSN simulators evaluation: an approach focusing on energy awareness," arXiv preprint arXiv:2002.06246, 2020.
- K. A. Ngo, T. T. Huynh, and D. T. Huynh, "Simulation wireless sensor networks in castalia," in Proceedings of the 2018 International Conference on Intelligent Information Technology, pp. 39–44-39–44.
- Z. Herczeg, Á. Kiss, D. Schmidt, N. Wehn, and T. Gyimóthy, "XEEMU: An improved XScale power simulator," in International Workshop on Power And Timing Modeling, Optimization and Simulation, pp. 300–309-300–309.
- S. Sohoni, R. Min, Z. Xu, and Y. Hu, "A study of memory system performance of multimedia applications," ACM SIGMETRICS Performance Evaluation Review, vol. 29, no. 1, pp. 206–215-206–215, 2001.
Index Terms
Keywords