Smart Sensor Architectures for Multimedia Sensing in IoMT
Today, a wide range of developments and paradigms require the use of embedded systems characterized by restrictions on their computing capacity, consumption, cost, and network connection. The evolution of the Internet of Things (IoT) towards Industrial IoT (IIoT) or the Internet of Multimedia Things...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/5/1400 |
| _version_ | 1798005765374476288 |
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| author | Javier Silvestre-Blanes Víctor Sempere-Payá Teresa Albero-Albero |
| author_facet | Javier Silvestre-Blanes Víctor Sempere-Payá Teresa Albero-Albero |
| author_sort | Javier Silvestre-Blanes |
| collection | DOAJ |
| description | Today, a wide range of developments and paradigms require the use of embedded systems characterized by restrictions on their computing capacity, consumption, cost, and network connection. The evolution of the Internet of Things (IoT) towards Industrial IoT (IIoT) or the Internet of Multimedia Things (IoMT), its impact within the 4.0 industry, the evolution of cloud computing towards edge or fog computing, also called near-sensor computing, or the increase in the use of embedded vision, are current examples of this trend. One of the most common methods of reducing energy consumption is the use of processor frequency scaling, based on a particular policy. The algorithms to define this policy are intended to obtain good responses to the workloads that occur in smarthphones. There has been no study that allows a correct definition of these algorithms for workloads such as those expected in the above scenarios. This paper presents a method to determine the operating parameters of the dynamic governor algorithm called <i>Interactive</i>, which offers significant improvements in power consumption, without reducing the performance of the application. These improvements depend on the load that the system has to support, so the results are evaluated against three different loads, from higher to lower, showing improvements ranging from 62% to 26%. |
| first_indexed | 2024-04-11T12:45:08Z |
| format | Article |
| id | doaj.art-76e15398488b4c9aa8edd71da6dd9ecc |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:45:08Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-76e15398488b4c9aa8edd71da6dd9ecc2022-12-22T04:23:23ZengMDPI AGSensors1424-82202020-03-01205140010.3390/s20051400s20051400Smart Sensor Architectures for Multimedia Sensing in IoMTJavier Silvestre-Blanes0Víctor Sempere-Payá1Teresa Albero-Albero2ITI and Universitat Politècnica de València (UPV), DISCA, EPSA, 03801 Alcoy, SpainITI and Universitat Politècnica de València (UPV), DCOM, ETSIT, 46022 Valencia, SpainITI and Universitat Politècnica de València (UPV), DISCA, EPSA, 03801 Alcoy, SpainToday, a wide range of developments and paradigms require the use of embedded systems characterized by restrictions on their computing capacity, consumption, cost, and network connection. The evolution of the Internet of Things (IoT) towards Industrial IoT (IIoT) or the Internet of Multimedia Things (IoMT), its impact within the 4.0 industry, the evolution of cloud computing towards edge or fog computing, also called near-sensor computing, or the increase in the use of embedded vision, are current examples of this trend. One of the most common methods of reducing energy consumption is the use of processor frequency scaling, based on a particular policy. The algorithms to define this policy are intended to obtain good responses to the workloads that occur in smarthphones. There has been no study that allows a correct definition of these algorithms for workloads such as those expected in the above scenarios. This paper presents a method to determine the operating parameters of the dynamic governor algorithm called <i>Interactive</i>, which offers significant improvements in power consumption, without reducing the performance of the application. These improvements depend on the load that the system has to support, so the results are evaluated against three different loads, from higher to lower, showing improvements ranging from 62% to 26%.https://www.mdpi.com/1424-8220/20/5/1400iomtgovernoredge computingnear sensor computing |
| spellingShingle | Javier Silvestre-Blanes Víctor Sempere-Payá Teresa Albero-Albero Smart Sensor Architectures for Multimedia Sensing in IoMT Sensors iomt governor edge computing near sensor computing |
| title | Smart Sensor Architectures for Multimedia Sensing in IoMT |
| title_full | Smart Sensor Architectures for Multimedia Sensing in IoMT |
| title_fullStr | Smart Sensor Architectures for Multimedia Sensing in IoMT |
| title_full_unstemmed | Smart Sensor Architectures for Multimedia Sensing in IoMT |
| title_short | Smart Sensor Architectures for Multimedia Sensing in IoMT |
| title_sort | smart sensor architectures for multimedia sensing in iomt |
| topic | iomt governor edge computing near sensor computing |
| url | https://www.mdpi.com/1424-8220/20/5/1400 |
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