{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T05:35:14Z","timestamp":1782970514985,"version":"3.54.5"},"reference-count":31,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T00:00:00Z","timestamp":1661299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NPRP","award":["NPRP13S-0212-200345"],"award-info":[{"award-number":["NPRP13S-0212-200345"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A wireless vision sensor network (WVSN) is built by using multiple image sensors connected wirelessly to a central server node performing video analysis, ultimately automating different tasks such as video surveillance. In such applications, a large deployment of sensors in the same way as Internet-of-Things (IoT) devices is required, leading to extreme requirements in terms of sensor cost, communication bandwidth and power consumption. To achieve the best possible trade-off, we propose in this paper a new concept that attempts to achieve image compression and early image recognition leading to lower bandwidth and smart image processing integrated at the sensing node. A WVSN implementation is proposed to save power consumption and bandwidth utilization by processing only part of the acquired image at the sensor node. A convolutional neural network is deployed at the central server node for the purpose of progressive image recognition. The proposed implementation is capable of achieving an average recognition accuracy of 88% with an average confidence probability of 83% for five subimages, while minimizing the overall power consumption at the sensor node as well as the bandwidth utilization between the sensor node and the central server node by 43% and 86%, respectively, compared to the traditional sensor node.<\/jats:p>","DOI":"10.3390\/s22176348","type":"journal-article","created":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T02:55:34Z","timestamp":1661309734000},"page":"6348","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Progressive Early Image Recognition for Wireless Vision Sensor Networks"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8887-9029","authenticated-orcid":false,"given":"AlKhzami","family":"AlHarami","sequence":"first","affiliation":[{"name":"Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha P.O. Box 34110, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4381-6056","authenticated-orcid":false,"given":"Abubakar","family":"Abubakar","sequence":"additional","affiliation":[{"name":"Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha P.O. Box 34110, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Visual Computing Group, Microsoft Research Asia, Beijing 100080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amine","family":"Bermak","sequence":"additional","affiliation":[{"name":"Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha P.O. 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