Energy-Efficient Scheduling for Hybrid Tasks in Control Devices for the Internet of Things
In control devices for the Internet of Things (IoT), energy is one of the critical restriction factors. Dynamic voltage scaling (DVS) has been proved to be an effective method for reducing the energy consumption of processors. This paper proposes an energy-efficient scheduling algorithm for IoT cont...
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| Format: | Article |
| Language: | English |
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MDPI AG
2012-08-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/12/8/11334 |
| _version_ | 1798000024834015232 |
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| author | Guojun Dai Yifan Wu Zhigang Gao Haixia Xia |
| author_facet | Guojun Dai Yifan Wu Zhigang Gao Haixia Xia |
| author_sort | Guojun Dai |
| collection | DOAJ |
| description | In control devices for the Internet of Things (IoT), energy is one of the critical restriction factors. Dynamic voltage scaling (DVS) has been proved to be an effective method for reducing the energy consumption of processors. This paper proposes an energy-efficient scheduling algorithm for IoT control devices with hard real-time control tasks (HRCTs) and soft real-time tasks (SRTs). The main contribution of this paper includes two parts. First, it builds the Hybrid tasks with multi-subtasks of different function Weight (HoW) task model for IoT control devices. HoW describes the structure of HRCTs and SRTs, and their properties, e.g., deadlines, execution time, preemption properties, and energy-saving goals, <em>etc</em>. Second, it presents the Hybrid Tasks’ Dynamic Voltage Scaling (HTDVS) algorithm. HTDVS first sets the slowdown factors of subtasks while meeting the different real-time requirements of HRCTs and SRTs, and then dynamically reclaims, reserves, and reuses the slack time of the subtasks to meet their ideal energy-saving goals. Experimental results show HTDVS can reduce energy consumption about 10%–80% while meeting the real-time requirements of HRCTs, HRCTs help to reduce the deadline miss ratio (DMR) of systems, and HTDVS has comparable performance with the greedy algorithm and is more favorable to keep the subtasks’ ideal speeds. |
| first_indexed | 2024-04-11T11:13:40Z |
| format | Article |
| id | doaj.art-14dfe8ff5c214940ab6c2c06a0ddbc62 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T11:13:40Z |
| publishDate | 2012-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-14dfe8ff5c214940ab6c2c06a0ddbc622022-12-22T04:27:19ZengMDPI AGSensors1424-82202012-08-01128113341135910.3390/s120811334Energy-Efficient Scheduling for Hybrid Tasks in Control Devices for the Internet of ThingsGuojun DaiYifan WuZhigang GaoHaixia XiaIn control devices for the Internet of Things (IoT), energy is one of the critical restriction factors. Dynamic voltage scaling (DVS) has been proved to be an effective method for reducing the energy consumption of processors. This paper proposes an energy-efficient scheduling algorithm for IoT control devices with hard real-time control tasks (HRCTs) and soft real-time tasks (SRTs). The main contribution of this paper includes two parts. First, it builds the Hybrid tasks with multi-subtasks of different function Weight (HoW) task model for IoT control devices. HoW describes the structure of HRCTs and SRTs, and their properties, e.g., deadlines, execution time, preemption properties, and energy-saving goals, <em>etc</em>. Second, it presents the Hybrid Tasks’ Dynamic Voltage Scaling (HTDVS) algorithm. HTDVS first sets the slowdown factors of subtasks while meeting the different real-time requirements of HRCTs and SRTs, and then dynamically reclaims, reserves, and reuses the slack time of the subtasks to meet their ideal energy-saving goals. Experimental results show HTDVS can reduce energy consumption about 10%–80% while meeting the real-time requirements of HRCTs, HRCTs help to reduce the deadline miss ratio (DMR) of systems, and HTDVS has comparable performance with the greedy algorithm and is more favorable to keep the subtasks’ ideal speeds.http://www.mdpi.com/1424-8220/12/8/11334IoTcontrol deviceshybrid tasksDVSslowdown factorsslack time |
| spellingShingle | Guojun Dai Yifan Wu Zhigang Gao Haixia Xia Energy-Efficient Scheduling for Hybrid Tasks in Control Devices for the Internet of Things Sensors IoT control devices hybrid tasks DVS slowdown factors slack time |
| title | Energy-Efficient Scheduling for Hybrid Tasks in Control Devices for the Internet of Things |
| title_full | Energy-Efficient Scheduling for Hybrid Tasks in Control Devices for the Internet of Things |
| title_fullStr | Energy-Efficient Scheduling for Hybrid Tasks in Control Devices for the Internet of Things |
| title_full_unstemmed | Energy-Efficient Scheduling for Hybrid Tasks in Control Devices for the Internet of Things |
| title_short | Energy-Efficient Scheduling for Hybrid Tasks in Control Devices for the Internet of Things |
| title_sort | energy efficient scheduling for hybrid tasks in control devices for the internet of things |
| topic | IoT control devices hybrid tasks DVS slowdown factors slack time |
| url | http://www.mdpi.com/1424-8220/12/8/11334 |
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