Role-Based User Allocation Driven by Criticality in Edge Computing

Liu, Ensheng; Zheng, Liping; He, Qiang; Lai, Phu; Xu, Benzhu; Zhang, Gaofeng

doi:10.1109/tsc.2023.3280498

Cited by 3 publications

(3 citation statements)

References 33 publications

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“…ML technology has attracted significant attention for empowering intelligent solutions in various wireless networks, spanning mobile communication networks [1], wireless sensor networks [2], transportation systems [3], and non-terrestrial networks (NTNs) [4]. It proves instrumental in addressing intricate challenges within wireless communication, such as resource management [5], data offloading to edge networks [6], spectrum management [7], routing [8], and user server allocation [9], among others. Various ML techniques offer effective solutions to these complex problems.…”

Section: Technological Backgroundmentioning

confidence: 99%

Real-World Implementation and Performance Analysis of Distributed Learning Frameworks for 6G IoT Applications

Naseh,

Abdollahpour,

Tarchi

2024

Information

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This paper explores the practical implementation and performance analysis of distributed learning (DL) frameworks on various client platforms, responding to the dynamic landscape of 6G technology and the pressing need for a fully connected distributed intelligence network for Internet of Things (IoT) devices. The heterogeneous nature of clients and data presents challenges for effective federated learning (FL) techniques, prompting our exploration of federated transfer learning (FTL) on Raspberry Pi, Odroid, and virtual machine platforms. Our study provides a detailed examination of the design, implementation, and evaluation of the FTL framework, specifically adapted to the unique constraints of various IoT platforms. By measuring the accuracy of FTL across diverse clients, we reveal its superior performance over traditional FL, particularly in terms of faster training and higher accuracy, due to the use of transfer learning (TL). Real-world measurements further demonstrate improved resource efficiency with lower average load, memory usage, temperature, power, and energy consumption when FTL is implemented compared to FL. Our experiments also showcase FTL’s robustness in scenarios where users leave the server’s communication coverage, resulting in fewer clients and less data for training. This adaptability underscores the effectiveness of FTL in environments with limited data, clients, and resources, contributing valuable information to the intersection of edge computing and DL for the 6G IoT.

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Section: Technological Backgroundmentioning

confidence: 99%

Real-World Implementation and Performance Analysis of Distributed Learning Frameworks for 6G IoT Applications

Naseh,

Abdollahpour,

Tarchi

2024

Information

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“…A temperature data I/F module detects fire situations by analyzing the temperature data. The smart edge gateway collects data from the temperature, movement, fire detection, and electrical leak detector sensors and delivers information directly to the occupant app in the event of an emergency [34]. The gateway collects information from the ZigBee-based sensors and provides information to servers and occupant apps through wired LAN communication and Wi-Fi.…”

Section: Occupant Detecting Sensor Inframentioning

confidence: 99%

“…The existing system uses LTE-based communication from the gateway to the server, which can cause data transmission delays [31]. The proposed system transmits data to the cloud server in real time through the Virtual Edge Gateway (VEG) internal network depicted in Figure 20 [34]. The VEG uses an internal network as a virtual server.…”

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confidence: 99%

Smart Fire Safety Management System (SFSMS) Connected with Energy Management for Sustainable Service in Smart Building Infrastructures

Park,

Lee,

Jang

et al. 2023

Buildings

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The scale of human accidents and the resultant damage has increased due to recent large-scale urban (building) fires, meaning there is a need to devise an effective strategy for urban disasters. In the event of a fire, it is difficult to evacuate in the early stages due to the loss of detection function, difficulty in securing visibility, and confusion over evacuation routes. Accordingly, for rapid evacuation and rescue, it is necessary to build a city-level fire safety service and digital system based on smart technology. In addition, both forest and building fires emit a large amount of carbon dioxide, which is the main cause of global warming. Therefore, we need to prepare both energy and fire management to achieve carbon neutrality by 2030. In this study, we developed an AI-based smart fire safety system for efficient urban integrated management using a city-based fire safety architecture. In addition, we designed a fire management infrastructure and an energy management system for buildings. The proposal was demonstrated by building a test bed in the A building, and the AR-based mobile/web application was tested for optimized evacuation management. Furthermore, AI-based fire detection and the optimal evacuation of occupants were implemented through deep learning-based fire information data analysis. As a result, this paper presents four points for safety and energy management, and we demonstrate that the optimization of occupant evacuation ability and energy saving can be achieved. We also analyze the efficiency of the data transfer rate to prevent data communication delays by using Virtual Edge Gateway (VEG) management. In the future, we expect that the appearance of future fire and energy management buildings through this research will produce more accurate data prediction technology and the development of cutting-edge smart technology in smart city infrastructures.

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UGV-awareness task placement in edge-cloud based urban intelligent video systems

Zhang,

Li,

et al. 2024

Cluster Comput

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Role-Based User Allocation Driven by Criticality in Edge Computing

Cited by 3 publications

References 33 publications

Real-World Implementation and Performance Analysis of Distributed Learning Frameworks for 6G IoT Applications

Real-World Implementation and Performance Analysis of Distributed Learning Frameworks for 6G IoT Applications

Smart Fire Safety Management System (SFSMS) Connected with Energy Management for Sustainable Service in Smart Building Infrastructures

UGV-awareness task placement in edge-cloud based urban intelligent video systems

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