Video Compressed Sensing framework for Wireless Multimedia Sensor Networks using a combination of multiple matrices☆
Graphical abstract
Introduction
A WMSN consists of simple and low-cost sensor nodes that are used for a variety of applications, such as environmental monitoring, healthcare monitoring and surveillance applications. In the case of surveillance applications, sensor nodes with multimedia capability are deployed in the area of interest to detect anomalies. These sensor nodes will capture and transmit the surveillance video wirelessly to the network operator. Transmission of multimedia data that range from several megabytes to a few gigabytes is a challenging task because it imposes requirements for large amounts of storage and high bandwidth for transmission. These challenges can be overcome by using an emerging technique called compressed sensing (CS), which asserts that the original signal can be reconstructed from a small number of measurements [1]. CS can be applied to sparse signals or compressible signals. The original signal is made sparse using a transform basis, and a measurement matrix is applied to the sparse signal to obtain the measurements. These measurements are transmitted for reconstruction at the receiver side. There are many reconstruction algorithms, such as basis pursuit, greedy algorithms and iterative algorithms, for reconstructing the original signal from the measurement vector.
The objective of this paper is to implement VCS based on a DWT–DCT hybrid approach and to propose efficient measurement matrices for VCS. This VCS framework is based on a block-based approach; CS is applied to all of the blocks to obtain the measurements. The performance of the system with the proposed measurement matrices is evaluated by analysing the PSNR, percentage of reduction in samples, storage, energy complexity, transmission energy and end-to-end delay.
The rest of the paper is organized as follows. Section 2 provides a brief survey of related works. Section 3 provides a brief description of the CS technique, and Section 4 provides details about VCS based on the DCT, DWT and DWT–DCT hybrid approach. This section also explains in detail the proposed measurement matrices and orthogonal matching pursuit (OMP) algorithm, whereas Section 5 discusses the simulation and experimental results in detail; finally, Section 6 gives the conclusions and proposed future work.
Section snippets
Related works
This section provides a brief discussion of works related to VCS along with the advantages and limitations of each technique.
Akyildiz et al. [2] provided a survey of techniques used in the algorithms, hardware and protocols for WMSNs. Different architectures of WMSNs are explored along with their advantages and disadvantages. Different varieties of low-resolution and medium-resolution image sensor nodes and their collaborative processing are also explained. The authors have also discussed the
Compressed sensing
Compressed sensing is a new approach in which sensing and compression are performed simultaneously, resulting in a significant reduction in sampling and computation cost at the sensor. CS theory states that the original signal can be reconstructed from very few measurements; hence, it is also called sub-Nyquist sampling. This technique ultimately reduces the complexity of the process, requires much less storage capacity and uses less bandwidth. CS provides accurate recovery of the signal
Video compressed sensing framework
This section discusses the implementation of the proposed VCS based on the DWT–DCT hybrid approach and compares it with the VCS based on DCT and DWT approaches. Efficient measurement matrices are proposed and implemented that are further compared with the Gaussian measurement matrix. In this framework, measurement matrices are proposed using a combination of multiple matrices. The block-based approach is adopted for VCS in order to reduce the size of the measurement matrix that is used to
Results and discussion
The DWT–DCT approach-based VCS using the proposed Combination matrix and Hybrid matrix is implemented in MATLAB and the performance of the measurement matrix is analysed in terms of PSNR, storage and energy complexity. The measurements obtained from the VCS framework are transmitted in real time using TelosB motes. Experimental analysis is carried out by evaluating the quality of reconstruction, transmission energy and end-to-end delay.
Conclusions and future work
A VCS framework based on the DWT–DCT hybrid approach is implemented using the proposed Combination matrix and Hybrid matrix. The performance of the measurement matrix is evaluated in terms of PSNR, storage and energy complexity. Transmission energy and delay are analysed experimentally with the help of TelosB nodes. From the results, it is concluded that the proposed matrices are memory and energy efficient while maintaining an acceptable range of PSNR. The DWT–DCT hybrid approach yields better
Aasha Nandhini Sukumaran is a Junior Research Fellow at SSN College of Engineering, India. She received the B.E. degree in Electronics and Communication Engineering from Rajalakshmi Engineering College, India, in 2010 and the M.E. degree in Communication Systems from SSN College of Engineering, India, in 2012. Her research interests include security issues and compressive sensing in wireless sensor networks.
References (26)
- et al.
A survey on wireless multimedia sensor networks
IEEE Wireless Commun
(2007) - et al.
Motion-aware decoding of compressed-sensed video
IEEE Trans Circ Syst Video Technol
(2013) - et al.
Multi-focus image fusion for visual sensor networks in DCT domain
Comput Electr Eng
(2011) - et al.
An introduction to compressive sampling
IEEE Signal Process Mag
(2008) Imaging via compressive sampling
IEEE Signal Process Mag
(2008)- et al.
Structured compressed sensing: from Theory to applications
IEEE Trans Signal Process
(2011) - Gan Lu. Block based compressed sensing of natural images. In: 15th international conference on digital signal...
- Han Bing, Wu Feng, Wu Depang. Image representation by compressed sensing. In: 15th IEEE international conference on...
- Yu Lei, Barbot Jean-Pierre, Zheng Gang, Sun Hong. Toeplitz-structured chaotic sensing matrix for compressive sensing....
- et al.
Blind compressed sensing
IEEE Trans Inform Theory
(2011)
Cited by (33)
Fruit and vegetable disease detection and classification: Recent trends, challenges, and future opportunities
2024, Engineering Applications of Artificial IntelligenceA new image size reduction model for an efficient visual sensor network
2019, Journal of Visual Communication and Image RepresentationEfficient compressed sensing-based security approach for video surveillance application in wireless multimedia sensor networks
2017, Computers and Electrical EngineeringCompressed sensing based foreground detection vector for object detection in Wireless Visual Sensor Networks
2017, AEU - International Journal of Electronics and CommunicationsCitation Excerpt :CS is applied to the difference frame which results in differenced compressive measurements. In this work binDCT [7] and hybrid measurement matrix [14] is used as the basis and sensing matrix for the CS process. BinDCT is used instead of DCT to reduce the computational complexity.
A smart agriculturing IoT system for banana plants disease detection through inbuilt compressed sensing devices
2023, Multimedia Tools and Applications
Aasha Nandhini Sukumaran is a Junior Research Fellow at SSN College of Engineering, India. She received the B.E. degree in Electronics and Communication Engineering from Rajalakshmi Engineering College, India, in 2010 and the M.E. degree in Communication Systems from SSN College of Engineering, India, in 2012. Her research interests include security issues and compressive sensing in wireless sensor networks.
Radha Sankararajan is a Professor and Head of the department of ECE, SSN College of Engineering, Chennai, India. She is the recipient of the IETE – S.K. Mitra Memorial Award (2006) from the IETE Council of India and the CTS – SSN Best Faculty Award (2007, 2009) for outstanding academic performance. Her research interests include security, architecture issues of mobile ad hoc networks, WSNs and cognitive radios.
Kishore Rajendiran is working as an Associate Prof., in the department of ECE, SSN College of Engineering, Chennai, India. He received the B.E. degree in Electronics and Communication Engineering from Vellore Engineering College, Madras University, in 1998, the M.Tech. degree in Communication Systems from Pondicherry University, in 2003 and the Ph.D. degree from Anna University, in 2012. His research interests include security issues in wireless networks, compressive sensing and image fusion.
- ☆
Reviews processed and recommended for publication to the Editor-in-Chief by Associate Editor Dr. S. Rajavelu.