Source Selection and Resource Allocation in Wireless-Powered Relay Networks: An Adaptive Dynamic Programming-Based Approach

Lyu, Ting; Xu, Haitao; Zhang, Long; Han, Zhu

doi:10.1109/jiot.2023.3321673

Cited by 47 publications

(8 citation statements)

References 52 publications

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“…The expansive path (decoding)includes upsampling blocks that consist of a

transposed convolution that halves the number of feature channels, followed by concatenation with the cropped feature map from the contracting path ( Wang C. et al, 2023 ; Wang Q. et al, 2023 ; Zhang J. et al, 2024 ; Zhang X. et al, 2024 ; Zhao X. et al, 2024 ; Zhao L. et al, 2024 ). This convolution is followed by two

convolutions, each followed by the ReLU activation and normal initialization, which refines the feature map and recover spatial information lost during downsampling ( Lyu et al, 2024 ). The final layer of the model is a

convolution that maps the feature vector at each pixel to the desired number of classes ( Xu et al, 2023 ).…”

Section: Methodsmentioning

confidence: 99%

Remote intelligent perception system for multi-object detection

Alazeb,

Chughtai,

Al Mudawi

et al. 2024

Front. Neurorobot.

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IntroductionDuring the last few years, a heightened interest has been shown in classifying scene images depicting diverse robotic environments. The surge in interest can be attributed to significant improvements in visual sensor technology, which has enhanced image analysis capabilities.MethodsAdvances in vision technology have a major impact on the areas of multiple object detection and scene understanding. These tasks are an integral part of a variety of technologies, including integrating scenes in augmented reality, facilitating robot navigation, enabling autonomous driving systems, and improving applications in tourist information. Despite significant strides in visual interpretation, numerous challenges persist, encompassing semantic understanding, occlusion, orientation, insufficient availability of labeled data, uneven illumination including shadows and lighting, variation in direction, and object size and changing background. To overcome these challenges, we proposed an innovative scene recognition framework, which proved to be highly effective and yielded remarkable results. First, we perform preprocessing using kernel convolution on scene data. Second, we perform semantic segmentation using UNet segmentation. Then, we extract features from these segmented data using discrete wavelet transform (DWT), Sobel and Laplacian, and textual (local binary pattern analysis). To recognize the object, we have used deep belief network and then find the object-to-object relation. Finally, AlexNet is used to assign the relevant labels to the scene based on recognized objects in the image.ResultsThe performance of the proposed system was validated using three standard datasets: PASCALVOC-12, Cityscapes, and Caltech 101. The accuracy attained on the PASCALVOC-12 dataset exceeds 96% while achieving a rate of 95.90% on the Cityscapes dataset.DiscussionFurthermore, the model demonstrates a commendable accuracy of 92.2% on the Caltech 101 dataset. This model showcases noteworthy advancements beyond the capabilities of current models.

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“…The expansive path (decoding)includes upsampling blocks that consist of a

convolution that maps the feature vector at each pixel to the desired number of classes ( Xu et al, 2023 ).…”

Section: Methodsmentioning

confidence: 99%

Remote intelligent perception system for multi-object detection

Alazeb,

Chughtai,

Al Mudawi

et al. 2024

Front. Neurorobot.

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“…Recent works have focused on throughput analysis of wireless-powered IoT networks in various scenarios [11][12][13][14] in different scenarios. Liu et al proposed an optimal transmission policy that maximizes the throughput of a mobile WPCN by optimally pairing the energy consumption of one transmission with the energy harvesting probability, considering the mobility of the energy access point and data access point and the resultant variable distances.…”

Section: Wireless Powered Communication Networkmentioning

confidence: 99%

Enhancing Throughput in IoT Networks: The Impact of Active RIS on Wireless Powered Communication Systems

Hameed,

Koo

2024

Electronics

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This paper investigates the potential of active reconfigurable intelligent surfaces (RIS) to enhance wireless-powered communication networks (WPCNs), addressing the evolving connectivity needs of the internet of things (IoT). Active RIS, capable of amplifying and reflecting signals, offers a solution to surpass the limitations of passive RIS, such as double-fading attenuation, aiming to significantly improve network throughput and coverage. Our research focuses on exploiting the amplification capabilities of active RIS to boost the overall network sum throughput, engaging in a comprehensive optimization of critical network parameters, including time allocation, reflection coefficients, and phase shift matrices specific to active RIS. The formulated problem is non-convex and highly complex due to the coupling of optimization variables. We employed a successive convex approximation algorithm to solve the throughput maximization problem by converting the non-convex constraints into approximated convex constraints and solving them iteratively. Through extensive simulations, we demonstrate that our active RIS-assisted network substantially outperforms networks facilitated by passive RIS, marking a significant advancement in WPCN performance. These findings underscore the potential of active RIS technology in realizing the full capabilities of IoT connectivity.

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“…files, URL content, code snippets for analysis or incident information from other security tools [109]. All the prompts to and the responses generated by the Copilot are saved in a database so there is a full inspection trail for investigators.…”

Section: Microsoft Security Copilotmentioning

confidence: 99%

Generative AI for Cyber Security: Analyzing the Potential of ChatGPT, DALL-E, and Other Models for Enhancing the Security Space

Sai,

Yashvardhan,

Chamola

et al. 2024

IEEE Access

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This research paper intends to provide real-life applications of Generative AI (GAI) in the cybersecurity domain. The frequency, sophistication and impact of cyber threats have continued to rise in today's world. This ever-evolving threat landscape poses challenges for organizations and security professionals who continue looking for better solutions to tackle these threats. GAI technology provides an effective way for them to address these issues in an automated manner with increasing efficiency. It enables them to work on more critical security aspects which require human intervention, while GAI systems deal with general threat situations. Further, GAI systems can better detect novel malware and threatening situations than humans. This feature of GAI, when leveraged, can lead to higher robustness of the security system. Many tech giants like Google, Microsoft etc., are motivated by this idea and are incorporating elements of GAI in their cybersecurity systems to make them more efficient in dealing with ever-evolving threats. Many cybersecurity tools like Google Cloud Security AI Workbench, Microsoft Security Copilot, SentinelOne Purple AI etc., have come into the picture, which leverage GAI to develop more straightforward and robust ways to deal with emerging cybersecurity perils. With the advent of GAI in the cybersecurity domain, one also needs to take into account the limitations and drawbacks that such systems have. This paper also provides some of the limitations of GAI, like periodically giving wrong results, costly training, the potential of GAI being used by malicious actors for illicit activities etc.

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Source Selection and Resource Allocation in Wireless-Powered Relay Networks: An Adaptive Dynamic Programming-Based Approach

Cited by 47 publications

References 52 publications

Remote intelligent perception system for multi-object detection

Remote intelligent perception system for multi-object detection

Enhancing Throughput in IoT Networks: The Impact of Active RIS on Wireless Powered Communication Systems

Generative AI for Cyber Security: Analyzing the Potential of ChatGPT, DALL-E, and Other Models for Enhancing the Security Space

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