An Energy-Efficient Sensing Matrix for Wireless Multimedia Sensor Networks
A measurement matrix is essential to compressed sensing frameworks. The measurement matrix can establish the fidelity of a compressed signal, reduce the sampling rate demand, and enhance the stability and performance of the recovery algorithm. Choosing a suitable measurement matrix for Wireless Mult...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/10/4843 |
| _version_ | 1827740115077693440 |
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| author | Vusi Skosana Adnan Abu-Mahfouz |
| author_facet | Vusi Skosana Adnan Abu-Mahfouz |
| author_sort | Vusi Skosana |
| collection | DOAJ |
| description | A measurement matrix is essential to compressed sensing frameworks. The measurement matrix can establish the fidelity of a compressed signal, reduce the sampling rate demand, and enhance the stability and performance of the recovery algorithm. Choosing a suitable measurement matrix for Wireless Multimedia Sensor Networks (WMSNs) is demanding because there is a sensitive weighing of energy efficiency against image quality that must be performed. Many measurement matrices have been proposed to deliver low computational complexity or high image quality, but only some have achieved both, and even fewer have been proven beyond doubt. A Deterministic Partial Canonical Identity (DPCI) matrix is proposed that has the lowest sensing complexity of the leading energy-efficient sensing matrices while offering better image quality than the Gaussian measurement matrix. The simplest sensing matrix is the basis of the proposed matrix, where random numbers were replaced with a chaotic sequence, and the random permutation was replaced with random sample positions. The novel construction significantly reduces the computational complexity as well time complexity of the sensing matrix. The DPCI has lower recovery accuracy than other deterministic measurement matrices such as the Binary Permuted Block Diagonal (BPBD) and Deterministic Binary Block Diagonal (DBBD) but offers a lower construction cost than the BPBD and lower sensing cost than the DBBD. This matrix offers the best balance between energy efficiency and image quality for energy-sensitive applications. |
| first_indexed | 2024-03-11T03:20:25Z |
| format | Article |
| id | doaj.art-734e342dbd604aa8bb7b756447d02465 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T03:20:25Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-734e342dbd604aa8bb7b756447d024652023-11-18T03:13:26ZengMDPI AGSensors1424-82202023-05-012310484310.3390/s23104843An Energy-Efficient Sensing Matrix for Wireless Multimedia Sensor NetworksVusi Skosana0Adnan Abu-Mahfouz1Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0028, South AfricaDepartment of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0028, South AfricaA measurement matrix is essential to compressed sensing frameworks. The measurement matrix can establish the fidelity of a compressed signal, reduce the sampling rate demand, and enhance the stability and performance of the recovery algorithm. Choosing a suitable measurement matrix for Wireless Multimedia Sensor Networks (WMSNs) is demanding because there is a sensitive weighing of energy efficiency against image quality that must be performed. Many measurement matrices have been proposed to deliver low computational complexity or high image quality, but only some have achieved both, and even fewer have been proven beyond doubt. A Deterministic Partial Canonical Identity (DPCI) matrix is proposed that has the lowest sensing complexity of the leading energy-efficient sensing matrices while offering better image quality than the Gaussian measurement matrix. The simplest sensing matrix is the basis of the proposed matrix, where random numbers were replaced with a chaotic sequence, and the random permutation was replaced with random sample positions. The novel construction significantly reduces the computational complexity as well time complexity of the sensing matrix. The DPCI has lower recovery accuracy than other deterministic measurement matrices such as the Binary Permuted Block Diagonal (BPBD) and Deterministic Binary Block Diagonal (DBBD) but offers a lower construction cost than the BPBD and lower sensing cost than the DBBD. This matrix offers the best balance between energy efficiency and image quality for energy-sensitive applications.https://www.mdpi.com/1424-8220/23/10/4843chaotic sequencesenergy efficiencyimage qualitysensing matrixwireless multimedia sensor networkwireless sensor network |
| spellingShingle | Vusi Skosana Adnan Abu-Mahfouz An Energy-Efficient Sensing Matrix for Wireless Multimedia Sensor Networks Sensors chaotic sequences energy efficiency image quality sensing matrix wireless multimedia sensor network wireless sensor network |
| title | An Energy-Efficient Sensing Matrix for Wireless Multimedia Sensor Networks |
| title_full | An Energy-Efficient Sensing Matrix for Wireless Multimedia Sensor Networks |
| title_fullStr | An Energy-Efficient Sensing Matrix for Wireless Multimedia Sensor Networks |
| title_full_unstemmed | An Energy-Efficient Sensing Matrix for Wireless Multimedia Sensor Networks |
| title_short | An Energy-Efficient Sensing Matrix for Wireless Multimedia Sensor Networks |
| title_sort | energy efficient sensing matrix for wireless multimedia sensor networks |
| topic | chaotic sequences energy efficiency image quality sensing matrix wireless multimedia sensor network wireless sensor network |
| url | https://www.mdpi.com/1424-8220/23/10/4843 |
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