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A Node Task Assignment Algorithm for Energy Harvesting Wireless Multimedia Sensor Networks
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AI
DOI：10.1109/ICC45041.2023.10279261.png)
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Abstract
En 中文
By using directional sensor nodes such as cameras, wireless multimedia sensor networks are typically used in traffic monitoring, target tracking and other fields. However, most sensor nodes powered by batteries are strictly limited in energy and cannot achieve long-term frontal tracking and monitoring of moving objects. To solve these problems, a model of directed sensor nodes with solar energy harvesting is introduced in this paper, where the energy of such nodes is no longer limited to batteries and the directed sensing area enables better frontal tracking. On this basis, we propose a distributed algorithm for directional task assignment called EN-DADA. EN-DADA is a task assignment algorithm with energy harvesting and orientation awareness, including a task classification phase and node bidding phase. The task is first classified to determine the candidate node set that can execute the task, and then the task assignment is determined according to the monitoring income of each node in the candidate node set. Experimental results show that the proposed task assignment algorithm has advantages in terms of task revenue and network lifetime when using the same energy harvesting model.
Keywords:
Wireless multimedia sensor network
task assignment algorithm
energy harvesting
directed sensing model
Journal
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Papers:
114
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