Interference-Aware Wireless Networks for Home Monitoring and Performance Evaluation

Ding, Fei; Song, Aiguo; Zhang, Dengyin; Tong, En; Pan, Zhiwen; You, Xiaohu

doi:10.1109/tase.2017.2778303

Cited by 10 publications

(5 citation statements)

References 42 publications

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“…With the sensor network, which is a key component of IoT, data can be quickly processed first on local end devices [27]. The connection of IoT devices can support many applications, including Healthcare system [31], [32], [33], Smart city / Smart home [34], [35], [36], Video surveillance [37], [38], [39] and so on. In terms of mobile devices, though some researchers categorize them into edge devices due to its computation power [40], [41], [42], we consider their energy limitation and treat them as end devices in the following discussion.…”

Section: Distributed Computing Hierarchymentioning

confidence: 99%

Towards Ubiquitous Intelligent Computing: Heterogeneous Distributed Deep Neural Networks

Zhang

Song

Lin

et al. 2022

IEEE Trans. Big Data

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For the pursuit of ubiquitous computing, distributed computing systems containing the cloud, edge devices, and Internet-of-Things devices are highly demanded. However, existing distributed frameworks do not tailor for the fast development of Deep Neural Network (DNN), which is the key technique behind many intelligent applications nowadays. Based on prior exploration on distributed deep neural networks (DDNN), we propose Heterogeneous Distributed Deep Neural Network (HDDNN) over the distributed hierarchy, targeting at ubiquitous intelligent computing. While being able to support basic functionalities of DNNs, our framework is optimized for various types of heterogeneity, including heterogeneous computing nodes, heterogeneous neural networks, and heterogeneous system tasks. Besides, our framework features parallel computing, privacy protection and robustness, with other consideration for the combination of heterogeneous distributed system and DNN. Extensive experiments demonstrate that our framework is capable of utilizing hierarchical distributed system better for DNN and tailoring DNN for real-world distributed system properly, which is with low response time, high performance, and better user experience.Manuscript received xx, xx; revised xx, xx.

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Section: Distributed Computing Hierarchymentioning

confidence: 99%

Towards Ubiquitous Intelligent Computing: Heterogeneous Distributed Deep Neural Networks

Zhang

Song

Lin

et al. 2022

IEEE Trans. Big Data

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“…interference management under the condition of multisignal coexistence have attracted much attention recently [88,89]. With the advent of 5G and the IoT commercialization, crowded spectrum resources are likely to lead to an overlap of multiple signals.…”

Section: Coexistence Signal Recognition Signal Monitoring Andmentioning

confidence: 99%

A Survey on Deep Learning Techniques in Wireless Signal Recognition

Dong

Zhou

et al. 2019

Wireless Communications and Mobile Computing

109

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Wireless signal recognition plays an important role in cognitive radio, which promises a broad prospect in spectrum monitoring and management with the coming applications for the 5G and Internet of Things networks. Therefore, a great deal of research and exploration on signal recognition has been done and a series of effective schemes has been developed. In this paper, a brief overview of signal recognition approaches is presented. More specifically, classical methods, emerging machine learning, and deep leaning schemes are extended from modulation recognition to wireless technology recognition with the continuous evolution of wireless communication system. In addition, the opening problems and new challenges in practice are discussed. Finally, a conclusion of existing methods and future trends on signal recognition is given.

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“…Cloud Fog Automation has been proposed by the researchers from ABB to implement advanced control algorithms and machine intelligence down to the Level-1 control loops, by more powerful and cost-effective computation infrastructure of cloud and fog computing with flexible load distribution and resilient redundancy [3] [4]. Wireless networks, notably for cloud fog automation, provide greater flexibility for the autonomous system, streamline design and installation procedures, and reduce maintenance requirements [5], [6], [7]. In industrial applications where the controllers and actuators are deployed through a wireless link, wireless networks also make it possible to implement remote control [8], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Latency-Aware Control for Wireless Cloud Fog Automation: Framework and Case Study

Lyu¹,

Bengtsson²,

Nilsson³

et al. 2023

Preprint

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<p>The development of wireless communication has indeed promoted cloud fog automation in the industry. It also introduces new issues of reliability and latency for control systems. This study sought to investigate the impacts of the commonly used industrial wireless network on the control performance parameters using a ball-and-beam (BB) time-critical balancing control system. An internal model control-inspired latency-aware wireless control framework (IMC-LA) is presented and employed in the BB system to handle the time delays and instability-creating elements introduced by wireless communication. A preliminary control assessment of the BB system under two new-generation wireless technologies, Wi-Fi 6 and 5G, was delivered. The correlation between network performance and control performance was analyzed statistically compared to the wired Ethernet condition. Test results show a dramatic decrease in position error after utilizing the proposed latency-aware wireless control framework. This study provides practical insights into the potential impacts of industrial wireless networks on control systems with a workable latency-aware wireless control approach.</p>

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Interference-Aware Wireless Networks for Home Monitoring and Performance Evaluation

Cited by 10 publications

References 42 publications

Towards Ubiquitous Intelligent Computing: Heterogeneous Distributed Deep Neural Networks

Towards Ubiquitous Intelligent Computing: Heterogeneous Distributed Deep Neural Networks

A Survey on Deep Learning Techniques in Wireless Signal Recognition

Latency-Aware Control for Wireless Cloud Fog Automation: Framework and Case Study

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