Energy-Aware Mobile Edge Computing and Routing for Low-Latency Visual Data Processing

Trinh, Huy; Calyam, Prasad; Chemodanov, Dmitrii; Yao, Shizeng; Lei, Qing; Gao, Fan; Palaniappan, Kannappan

doi:10.1109/tmm.2018.2865661

Cited by 58 publications

(23 citation statements)

References 16 publications

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“…The objective of the green-RPL is to minimize carbon footprint subject to path delay, path energy, residual energy, and idle time. Trinh et al in [239] presented SPIDER, a sustainable policy-based intelligence-driven edge routing algorithm based on MEC to detect geographical obstacles using DL to aid the routing engine to efficiently offload the data and route the priority data in an emergency for energy conservation. The proposed work presents a facial recognition use case in a disaster scenario.…”

Section: ) Energy-aware Routingmentioning

confidence: 99%

Multimedia Internet of Things: A Comprehensive Survey

et al. 2020

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The immense increase in multimedia-on-demand traffic that refers to audio, video, and images, has drastically shifted the vision of the Internet of Things (IoT) from scalar to Multimedia Internet of Things (M-IoT). IoT devices are constrained in terms of energy, computing, size, and storage memory. Delaysensitive and bandwidth-hungry multimedia applications over constrained IoT networks require revision of IoT architecture for M-IoT. This paper provides a comprehensive survey of M-IoT with an emphasis on architecture, protocols, and applications. This article starts by providing a horizontal overview of the IoT. Then, we discuss the issues considering the characteristics of multimedia and provide a summary of related M-IoT architectures. Various multimedia applications supported by IoT are surveyed, and numerous use cases related to road traffic management, security, industry, and health are illustrated to show how different M-IoT applications are revolutionizing human life. We explore the importance of Quality-of-Experience (QoE) and Quality-of-Service (QoS) for multimedia transmission over IoT. Moreover, we explore the limitations of IoT for multimedia computing and present the relationship between the M-IoT and emerging technologies including event processing, feature extraction, cloud computing, Fog/Edge computing and Software-Defined-Networks (SDNs). We also present the need for better routing and Physical-Medium Access Control (PHY-MAC) protocols for M-IoT. Finally, we present a detailed discussion on the open research issues and several potential research areas related to emerging multimedia communication in IoT. INDEX TERMS Multimedia Internet of Things (M-IoT), multimedia communication, Internet of Multimedia Things (IoMT), multimedia computing, Quality-of-Experience (QoE), Quality-of-Service (QoS), multimedia routing, medium access control (MAC).

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Section: ) Energy-aware Routingmentioning

confidence: 99%

Multimedia Internet of Things: A Comprehensive Survey

et al. 2020

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“…Computer vision and machine learning models are extensively used to analyze video streams. Thus, relatively powerful computing resources are required at the edge, with multi-core processors of at least 2.7 GHz [143][144][145] and powerful GPUs (e.g., in [144,146,147]). However, recent advances in machine learning allow not only edge servers but also resource-constrained UEs to perform complex computer vision tasks.…”

Section: A Overviewmentioning

confidence: 99%

Video Caching, Analytics, and Delivery at the Wireless Edge: A Survey and Future Directions

Jedari

Premsankar

Illahi

et al. 2021

IEEE Commun. Surv. Tutorials

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“…Trinh et al [23] study the potential of edge computing to address the energy management related applications for limited power-restricted IoT devices while providing low-latency processing of high-resolution visual data. To address the trade-off between processing throughput and energy efficiency, they proposed a smart-driven edge routing based on a sustainable strategy algorithm that uses machine learning.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Resource Provisioning Strategy for Reduced Power Consumption in Edge Computing

Fang¹,

Chen²,

Lu³

2020

Applied Sciences

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Edge computing is an emerging paradigm that settles some servers on the near-user side and allows some real-time requests from users to be directly returned to the user after being processed by these servers settled on the near-user side. In this paper, we focus on saving the energy of the system to provide an efficient scheduling strategy in edge computing. Our objective is to reduce the power consumption for the providers of the edge nodes while meeting the resources and delay constraints. We propose a two-stage scheduling strategy which includes the scheduling and resource provisioning. In the scheduling stage, we first propose an efficient scheme based on the branch and bound method. In order to reduce complexity, we propose a heuristic algorithm that guarantees users’ deadlines. In the resource provisioning stage, we first approach the problem by virtualizing the edge nodes into master and slave nodes based on the sleep power consumption mode. After that, we propose a scheduling strategy through balancing the resources of virtual nodes that reduce the power consumption and guarantees the user’s delay as well. We use iFogSim to simulate our strategy. The simulation results show that our strategy can effectively reduce the power consumption of the edge system. In the test of idle tasks, the highest energy consumption was 27.9% lower than the original algorithm.

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Energy-Aware Mobile Edge Computing and Routing for Low-Latency Visual Data Processing

Cited by 58 publications

References 16 publications

Multimedia Internet of Things: A Comprehensive Survey

Multimedia Internet of Things: A Comprehensive Survey

Video Caching, Analytics, and Delivery at the Wireless Edge: A Survey and Future Directions

Energy-Efficient Resource Provisioning Strategy for Reduced Power Consumption in Edge Computing

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