Wireless Video Surveillance: A Survey

Ye, Yun; Ci, Song; Katsaggelos, Aggelos K.; Liu, Yanwei; Qian, Yi

doi:10.1109/access.2013.2282613

Cited by 65 publications

(4 citation statements)

References 104 publications

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“…Again, only the most crucial information is transmitted to the base station by selecting the most lifetime-efficient route amongst thousands of potential alternative routes. Nonetheless, there are numerous other applications [3], [4] for the employment of our NL maximization framework, including environmental monitoring [56], surveillance [57], smart water quality monitoring, smart environment sensing, smart metering, smart agricultural applications, health monitoring and smart cities [58], just to name a few.…”

Section: Application Scenariosmentioning

confidence: 99%

Network-Lifetime Maximization of Wireless Sensor Networks

et al. 2015

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Network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance for extending the duration of the operations in the battery-constrained wireless sensor networks (WSNs). In this paper, we consider a two-stage NL maximization technique conceived for a fully-connected WSN, where the NL is strictly dependent on the source node's (SN) battery level, since we can transmit information generated at the SN to the destination node (DN) via alternative routes, each having a specific route lifetime (RL) value. During the first stage, the RL of the alternative routes spanning from the SN to the DN is evaluated, where the RL is defined as the earliest time, at which a sensor node lying in the route fully drains its battery charge. The second stage involves the summation of these

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Section: Application Scenariosmentioning

confidence: 99%

Network-Lifetime Maximization of Wireless Sensor Networks

et al. 2015

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“…In fact, wireless 3D image transmission is readily implementable by performing the SAII and VCR at the transmitter and receiver, respectively. Nonetheless, a sophisticated system design is required to transmit a large number of high-resolution 2D elemental images through the wireless channel and display a 3D image with a high reconstruction performance while overcoming wireless impairments [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Image Transmission of Integral Imaging through Wireless MIMO Channel

Lim

Cho

2023

Sensors

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For the reconstruction of high-resolution 3D digital content in integral imaging, an efficient wireless 3D image transmission system is required to convey a large number of elemental images without a communication bottleneck. To support a high transmission rate, we herein propose a novel wireless three-dimensional (3D) image transmission and reception strategy based on the multiple-input multiple-output (MIMO) technique. By exploiting the spatial multiplexing capability, multiple elemental images are transmitted simultaneously through the wireless MIMO channel, and recovered with a linear receiver such as matched filter, zero forcing, or minimum mean squared error combiners. Using the recovered elemental images, a 3D image can be reconstructed using volumetric computational reconstruction (VCR) with non-uniform shifting pixels. Although the received elemental images are corrupted by the wireless channel and inter-stream interference, the averaging effect of the VCR can improve the visual quality of the reconstructed 3D images. The numerical results validate that the proposed system can achieve excellent 3D reconstruction performance in terms of the visual quality and peak sidelobe ratio though a large number of elemental images are transmitted simultaneously over the wireless MIMO channel.

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“…However, these studies are not applicable to our case because they do not rely on any necessary assumptions regarding the amount of the data being communicated, the requirement of feedback for control, or the remote processing and detection of events. Works like [6]- [9] that contains these assumptions are mostly based on the quickest detection of the events. Many of them do not take the aspect of energy consumption into consideration, a perspective which is becoming increasingly important.…”

Section: Introductionmentioning

confidence: 99%

Energy-Optimal Sampling for Edge Computing Feedback Systems: Aperiodic Case

Moothedath

2022

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)

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We study the problem of optimal sampling in an edge-based video analytics system (VAS), where sensor samples collected at a terminal device are offloaded to a back-end server that processes them and generates feedback for a user. Sampling the system with the maximum allowed frequency results in the timely detection of relevant events with minimum delay. However, it incurs high energy costs and causes unnecessary usage of network and compute resources via communication and processing of redundant samples. On the other hand, an infrequent sampling result in a higher delay in detecting the relevant event, thus increasing the idle energy usage and degrading the quality of experience in terms of responsiveness of the system. We quantify this sampling frequency trade-off as a weighted function between the number of samples and the responsiveness. We propose an energy-optimal aperiodic sampling policy that improves over the state-of-the-art optimal periodic sampling policy. Numerically, we show the proposed policy provides a consistent improvement of more than 10% over the state-of-the-art.

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Wireless Video Surveillance: A Survey

Cited by 65 publications

References 104 publications

Network-Lifetime Maximization of Wireless Sensor Networks

Network-Lifetime Maximization of Wireless Sensor Networks

Three-Dimensional Image Transmission of Integral Imaging through Wireless MIMO Channel

Energy-Optimal Sampling for Edge Computing Feedback Systems: Aperiodic Case

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