Surveillance-Aware Uplink Scheduling for Cellular Networks

Wang, Chih-Hang; Kuo, Jian-Jhih; Yang, De-Nian; Chen, Wen-Tsuen

doi:10.1109/tmc.2018.2827054

Cited by 8 publications

(1 citation statement)

References 26 publications

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“…This research offered the use of smart camera that has been equipped by the capability of image classification such convolutional neural networks [1]. There has been many works related to video surveillances that enable the successful of the objective by adjusting protocols [2], video coding [3], and the use various networks [4][5]. Protocol adjustments offer UDP modifications or TCP optimization for fast but reliable image transfer.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network Approach and Implementation Sensor Design for Detecting Object

Suherman

Trasya

Ihsan

et al. 2022

J. Phys.: Conf. Ser.

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This research offered sensor design for detecting animal for any applications uses convolutional neural network within JeVois smart camera. The camera is connected to communication processor through serial connection. By using 900 images for training, the sensor has been able to detect the intended animal presence and send the result to end node. The acuracy achieves 95 %, with 90.91% precision, recall 100% and F score of 95.24 %. Even dough the power consumption increases from 2.2 W to 4.08 W, the detection is potentially reduces number of the transmitter images.

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Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network Approach and Implementation Sensor Design for Detecting Object

Suherman

Trasya

Ihsan

et al. 2022

J. Phys.: Conf. Ser.

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Efficient scheduling of video camera sensor networks for IoT systems in smart cities

Naeem

Ejaz

Iqbal

et al. 2019

Trans Emerging Tel Tech

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Video camera sensor networks (VCSN) has numerous applications in smart cities, including vehicular networks, environmental monitoring, and smart houses. Scheduling of video camera sensor networks (VCSN) can reduce the computational complexity, increase energy efficiency, and enhance throughput for the Internet of things (IoT) systems. In this paper, we apply the iterative low‐complexity probabilistic evolutionary method for scheduling video cameras to maximize throughput in VCSNs for IoT systems. Scheduling of video cameras in VCSNs to maximize throughput is a combinatorial optimization problem whose computational complexity increases exponentially with the increase in the number of video cameras. We propose an iterative probabilistic method named as cross‐entropy optimization (CEO), which is an evolutionary algorithm. The combinatorial optimization problems can be solved using the CEO which is a generalized Monte Carlo technique. The proposed method updates its selected population (video cameras) at each iteration based on the Kullback Leibler (KL) distance/divergence. The KL distance/divergence is minimized using the probability distribution obtained from the learned from the group of selected samples of better solutions found in the previous iterations. The effectiveness of the CEO is verified in terms of optimality and simplicity through simulations. In addition, the results of the CEO are better than the suboptimal algorithms (ie, best norm‐based algorithm, genetic algorithm, and capacity upper‐bound–based greedy algorithm) and maximum of 2%‐3% deviation from the exhaustive search (optimal) with less complexity. The trade‐off between CEO and optimal is the computational complexity.

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A QoS Aware Uplink Scheduler for IoT in Emergency Over LTE/LTE-A Networks

Zhang

et al. 2019

Software Engineering Research, Management and Applications

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Surveillance-Aware Uplink Scheduling for Cellular Networks

Cited by 8 publications

References 26 publications

Convolutional Neural Network Approach and Implementation Sensor Design for Detecting Object

Convolutional Neural Network Approach and Implementation Sensor Design for Detecting Object

Efficient scheduling of video camera sensor networks for IoT systems in smart cities

A QoS Aware Uplink Scheduler for IoT in Emergency Over LTE/LTE-A Networks

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