Skosana, Vusi JAbu-Mahfouz, Adnan MI2022-05-092022-05-092021-12Skosana, V. & Abu-Mahfouz, A.M. 2021. Energy-efficient sensing matrices for Wireless Multimedia Sensor Networks: A Review. http://hdl.handle.net/10204/12408 .978-1-6654-1091-5978-1-6654-1092-2DOI: 10.1109/FoNeS-AIoT54873.2021.00021http://hdl.handle.net/10204/12408Wireless Multimedia Sensor Networks (WMSN) hold the key to unlocking the next generation of video surveillance applications. They operate under energy-constrained environments but Compressive Sensing (CS) is a tool that can help overcome these challenges. Sensing matrices are critical in delivering the promise of CS, there are different types and each has its benefits and costs. In this paper, these sensing matrices are compared and the strengths and weaknesses were highlighted. It was found that deterministic sensing matrices held the most promise as they gave better recovery accuracy than dense random matrices while being more efficient but, work still needs to be done to evaluate the energy cost of their implementation.AbstractenCompressive sensingImage compressionMatrixSensingWireless Multimedia Sensor NetworksConference PresentationSkosana, V., & Abu-Mahfouz, A. M. (2021). Energy-efficient sensing matrices for Wireless Multimedia Sensor Networks: A Review. http://hdl.handle.net/10204/12408Skosana, V, and Adnan MI Abu-Mahfouz. "Energy-efficient sensing matrices for Wireless Multimedia Sensor Networks: A Review." <i>2021 International Conference on Forthcoming Networks and Sustainability in AIoT Era (FoNeS-AIoT), Nicosia, Turkey, 27-28 December 2021</i> (2021): http://hdl.handle.net/10204/12408Skosana V, Abu-Mahfouz AM, Energy-efficient sensing matrices for Wireless Multimedia Sensor Networks: A Review; 2021. http://hdl.handle.net/10204/12408 .TY - Conference Presentation AU - Skosana, V AU - Abu-Mahfouz, Adnan MI AB - Wireless Multimedia Sensor Networks (WMSN) hold the key to unlocking the next generation of video surveillance applications. They operate under energy-constrained environments but Compressive Sensing (CS) is a tool that can help overcome these challenges. Sensing matrices are critical in delivering the promise of CS, there are different types and each has its benefits and costs. In this paper, these sensing matrices are compared and the strengths and weaknesses were highlighted. It was found that deterministic sensing matrices held the most promise as they gave better recovery accuracy than dense random matrices while being more efficient but, work still needs to be done to evaluate the energy cost of their implementation. DA - 2021-12 DB - ResearchSpace DP - CSIR J1 - 2021 International Conference on Forthcoming Networks and Sustainability in AIoT Era (FoNeS-AIoT), Nicosia, Turkey, 27-28 December 2021 KW - Compressive sensing KW - Image compression KW - Matrix KW - Sensing KW - Wireless Multimedia Sensor Networks LK - https://researchspace.csir.co.za PY - 2021 SM - 978-1-6654-1091-5 SM - 978-1-6654-1092-2 T1 - Energy-efficient sensing matrices for Wireless Multimedia Sensor Networks: A Review TI - Energy-efficient sensing matrices for Wireless Multimedia Sensor Networks: A Review UR - http://hdl.handle.net/10204/12408 ER -25609