CDCS: Cluster-Based Distributed Compressed Sensing to Facilitate QoS Routing in Cognitive Video Sensor Networks
Compressed sensing based in-network compression methods which minimize data redundancy are critical to cognitive video sensor networks. However, most existing methods require a large number of sensors for each measurement, resulting in significant performance degradation in energy efficiency and qua...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-03-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/21/4/345 |
| _version_ | 1797999186836193280 |
|---|---|
| author | Hang Shen Lingli Li Tianjing Wang Guangwei Bai |
| author_facet | Hang Shen Lingli Li Tianjing Wang Guangwei Bai |
| author_sort | Hang Shen |
| collection | DOAJ |
| description | Compressed sensing based in-network compression methods which minimize data redundancy are critical to cognitive video sensor networks. However, most existing methods require a large number of sensors for each measurement, resulting in significant performance degradation in energy efficiency and quality-of-service satisfaction. In this paper, a cluster-based distributed compressed sensing scheme working together with a quality-of-service aware routing framework is proposed to deliver visual information in cognitive video sensor networks efficiently. First, the correlation among adjacent video sensors determines the member nodes that participate in a cluster. On this basis, a sequential compressed sensing approach is applied to determine whether enough measurements are obtained to limit the reconstruction error between decoded signals and original signals under a specified reconstruction threshold. The goal is to maximize the removal of unnecessary traffic without sacrificing video quality. Lastly, the compressed data is transmitted via a distributed spectrum-aware quality-of-service routing scheme, with an objective of minimizing energy consumption subject to delay and reliability constraints. Simulation results demonstrate that the proposed approach can achieve energy-efficient data delivery and reconstruction accuracy of visual information compared with existing quality-of-service routing schemes. |
| first_indexed | 2024-04-11T11:00:41Z |
| format | Article |
| id | doaj.art-41480087030e4c198b3b1a16c535bd48 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T11:00:41Z |
| publishDate | 2019-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-41480087030e4c198b3b1a16c535bd482022-12-22T04:28:38ZengMDPI AGEntropy1099-43002019-03-0121434510.3390/e21040345e21040345CDCS: Cluster-Based Distributed Compressed Sensing to Facilitate QoS Routing in Cognitive Video Sensor NetworksHang Shen0Lingli Li1Tianjing Wang2Guangwei Bai3College of Computer Science and Technology, Nanjing Tech University, Nanjing 211816, ChinaCollege of Computer Science and Technology, Nanjing Tech University, Nanjing 211816, ChinaCollege of Computer Science and Technology, Nanjing Tech University, Nanjing 211816, ChinaCollege of Computer Science and Technology, Nanjing Tech University, Nanjing 211816, ChinaCompressed sensing based in-network compression methods which minimize data redundancy are critical to cognitive video sensor networks. However, most existing methods require a large number of sensors for each measurement, resulting in significant performance degradation in energy efficiency and quality-of-service satisfaction. In this paper, a cluster-based distributed compressed sensing scheme working together with a quality-of-service aware routing framework is proposed to deliver visual information in cognitive video sensor networks efficiently. First, the correlation among adjacent video sensors determines the member nodes that participate in a cluster. On this basis, a sequential compressed sensing approach is applied to determine whether enough measurements are obtained to limit the reconstruction error between decoded signals and original signals under a specified reconstruction threshold. The goal is to maximize the removal of unnecessary traffic without sacrificing video quality. Lastly, the compressed data is transmitted via a distributed spectrum-aware quality-of-service routing scheme, with an objective of minimizing energy consumption subject to delay and reliability constraints. Simulation results demonstrate that the proposed approach can achieve energy-efficient data delivery and reconstruction accuracy of visual information compared with existing quality-of-service routing schemes.https://www.mdpi.com/1099-4300/21/4/345spatial correlationquality-of-servicedistributed compressed sensinginformation theorycognitive video sensor networks |
| spellingShingle | Hang Shen Lingli Li Tianjing Wang Guangwei Bai CDCS: Cluster-Based Distributed Compressed Sensing to Facilitate QoS Routing in Cognitive Video Sensor Networks Entropy spatial correlation quality-of-service distributed compressed sensing information theory cognitive video sensor networks |
| title | CDCS: Cluster-Based Distributed Compressed Sensing to Facilitate QoS Routing in Cognitive Video Sensor Networks |
| title_full | CDCS: Cluster-Based Distributed Compressed Sensing to Facilitate QoS Routing in Cognitive Video Sensor Networks |
| title_fullStr | CDCS: Cluster-Based Distributed Compressed Sensing to Facilitate QoS Routing in Cognitive Video Sensor Networks |
| title_full_unstemmed | CDCS: Cluster-Based Distributed Compressed Sensing to Facilitate QoS Routing in Cognitive Video Sensor Networks |
| title_short | CDCS: Cluster-Based Distributed Compressed Sensing to Facilitate QoS Routing in Cognitive Video Sensor Networks |
| title_sort | cdcs cluster based distributed compressed sensing to facilitate qos routing in cognitive video sensor networks |
| topic | spatial correlation quality-of-service distributed compressed sensing information theory cognitive video sensor networks |
| url | https://www.mdpi.com/1099-4300/21/4/345 |
| work_keys_str_mv | AT hangshen cdcsclusterbaseddistributedcompressedsensingtofacilitateqosroutingincognitivevideosensornetworks AT linglili cdcsclusterbaseddistributedcompressedsensingtofacilitateqosroutingincognitivevideosensornetworks AT tianjingwang cdcsclusterbaseddistributedcompressedsensingtofacilitateqosroutingincognitivevideosensornetworks AT guangweibai cdcsclusterbaseddistributedcompressedsensingtofacilitateqosroutingincognitivevideosensornetworks |