Abstract
AbstractThe availability of advanced wireless sensor nodes enable us to use video processing techniques in a wireless sensor network (WSN) platform. Such paradigm can be used to implement video sensor networks (VSNs) that can serve as an alternative to existing video surveillance applications. However, video processing requires tremendous resources in terms of computation and transmission of the encoded video. As the most widely used video codec, H.264/AVC comes with a number of advanced encoding tools that can be tailored to suit a wide range of applications. Therefore, in order to get an optimal encoding performance for the VSN, it is essential to find the right encoding configuration and setting parameters for each VSN node based on the content being captured. In fact, the environment at which the VSN is deployed affects not only the content captured by the VSN node but also the node’s performance in terms of power consumption and its life-time. The objective of this study is to maximize the lifetime of the VSN by exploiting the trade-off between encoding and communication on sensor nodes. In order to reduce VSNs’ power consumption and obtain a more balanced energy consumption among VSN nodes, we use a branch and bound optimization techniques on a finite set of encoder configuration settings called configuration IDs (CIDs) and a fairness-based scheme. In our approach, the bitrate allocation in terms of fairness ratio per each node is obtained from the training sequences and is used to select appropriate encoder configuration settings for the test sequences. We use real life content of three different possible scenes of VSNs’ implementation with different levels of complexity in our study. Performance evaluations show that the proposed optimization technique manages to balance VSN’s power consumption per each node while the nodes’ maximum power consumption is minimized. We show that by using that approach, the VSN’s power consumption is reduced by around 7.58% in average.
Highlights
The advances in VLSI, sensors and wireless communication technologies have provided us with miniature devices that have low computational power and communication capabilities
We propose an algorithm to reduce the maximum power consumption video sensor network (VSN) nodes by extending our previous work in [12] and [13]
For the purpose of the analysis, a large number of real-life captured videos of simulated VSN scenes settings with different activity levels are used in this paper
Summary
The advances in VLSI, sensors and wireless communication technologies have provided us with miniature devices that have low computational power and communication capabilities. These devices can be organized to form a network called wireless sensor network (WSN). A WSN is typically used to measure physical attributes of the monitored environment and send the information to a central device that usually has unlimited resources. The information gathered at the central device, usually called the sink node, can be used by human operator or any additional machine/software to. Wireless video sensor network (VSN) has the potential to improve the ability to develop user-centric surveillance applications to monitor and prevent harmful events [4,5]. Maximizing the power efficiency of coding and transmission operations in VSNs is very important
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