Energy-Balanced Multisensory Scheduling for Target Tracking in Wireless Sensor Networks
One important way to extend the lifetime of wireless sensor networks (WSNs) is to manage the sleep scheduling of sensor nodes after they are deployed. Most of the existing works on node scheduling mainly concentrate on nodes which have only one sensor, and they regard a node and its sensor modules a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-10-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/10/3585 |
| _version_ | 1817989581197279232 |
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| author | Juan Feng Hongwei Zhao |
| author_facet | Juan Feng Hongwei Zhao |
| author_sort | Juan Feng |
| collection | DOAJ |
| description | One important way to extend the lifetime of wireless sensor networks (WSNs) is to manage the sleep scheduling of sensor nodes after they are deployed. Most of the existing works on node scheduling mainly concentrate on nodes which have only one sensor, and they regard a node and its sensor modules as a whole to manage sleep scheduling. Few works involve the sensed modules scheduling of the sensor nodes, which have multiple sensors. However, some of the sensed modules (such as video sensor) consume a lot of energy. Therefore, they have less energy efficiency for multisensory networks. In this paper, we propose a distributed and energy-balanced multisensory scheduling strategy (EBMS), which considers the scheduling of both the communication modules and the sensed modules for each node in target tracking WSNs. In EBMS, the network is organized as clustering structures. Each cluster head adaptively assigns a sleep time to its cluster members according to the position of the members. Energy-balanced multisensory scheduling strategy also proposes an energy balanced parameter to balance the energy consumption of each node in the network. In addition, multi-hop coordination scheme is proposed to find the optimal cooperation among clusters to maximize the energy conservation. Experimental results show that EBMS outperformed the state-of-the-art approaches. |
| first_indexed | 2024-04-14T00:48:54Z |
| format | Article |
| id | doaj.art-7df7c2822b9d45b2899d9ddd729e142b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T00:48:54Z |
| publishDate | 2018-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7df7c2822b9d45b2899d9ddd729e142b2022-12-22T02:21:53ZengMDPI AGSensors1424-82202018-10-011810358510.3390/s18103585s18103585Energy-Balanced Multisensory Scheduling for Target Tracking in Wireless Sensor NetworksJuan Feng0Hongwei Zhao1School of Aerospace Science and Technology, Xidian University, Xi’an 710071, ChinaSchool of Electronic Information, Northwestern Polytechnical University, Xi’an 710072, ChinaOne important way to extend the lifetime of wireless sensor networks (WSNs) is to manage the sleep scheduling of sensor nodes after they are deployed. Most of the existing works on node scheduling mainly concentrate on nodes which have only one sensor, and they regard a node and its sensor modules as a whole to manage sleep scheduling. Few works involve the sensed modules scheduling of the sensor nodes, which have multiple sensors. However, some of the sensed modules (such as video sensor) consume a lot of energy. Therefore, they have less energy efficiency for multisensory networks. In this paper, we propose a distributed and energy-balanced multisensory scheduling strategy (EBMS), which considers the scheduling of both the communication modules and the sensed modules for each node in target tracking WSNs. In EBMS, the network is organized as clustering structures. Each cluster head adaptively assigns a sleep time to its cluster members according to the position of the members. Energy-balanced multisensory scheduling strategy also proposes an energy balanced parameter to balance the energy consumption of each node in the network. In addition, multi-hop coordination scheme is proposed to find the optimal cooperation among clusters to maximize the energy conservation. Experimental results show that EBMS outperformed the state-of-the-art approaches.http://www.mdpi.com/1424-8220/18/10/3585target trackingenergy balancemultisensory schedulingWSNs |
| spellingShingle | Juan Feng Hongwei Zhao Energy-Balanced Multisensory Scheduling for Target Tracking in Wireless Sensor Networks Sensors target tracking energy balance multisensory scheduling WSNs |
| title | Energy-Balanced Multisensory Scheduling for Target Tracking in Wireless Sensor Networks |
| title_full | Energy-Balanced Multisensory Scheduling for Target Tracking in Wireless Sensor Networks |
| title_fullStr | Energy-Balanced Multisensory Scheduling for Target Tracking in Wireless Sensor Networks |
| title_full_unstemmed | Energy-Balanced Multisensory Scheduling for Target Tracking in Wireless Sensor Networks |
| title_short | Energy-Balanced Multisensory Scheduling for Target Tracking in Wireless Sensor Networks |
| title_sort | energy balanced multisensory scheduling for target tracking in wireless sensor networks |
| topic | target tracking energy balance multisensory scheduling WSNs |
| url | http://www.mdpi.com/1424-8220/18/10/3585 |
| work_keys_str_mv | AT juanfeng energybalancedmultisensoryschedulingfortargettrackinginwirelesssensornetworks AT hongweizhao energybalancedmultisensoryschedulingfortargettrackinginwirelesssensornetworks |