Distributed Energy Management for Multiuser Mobile-Edge Computing Systems With Energy Harvesting Devices and QoS Constraints

Zhang, Guanglin; Chen, Yan; Shen, Zhirong; Wang, Lin

doi:10.1109/jiot.2018.2875909

Cited by 68 publications

(22 citation statements)

References 36 publications

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“…jointly optimizes the local computing resources, channel allocation, and transmission power for obtaining an optimal trade-off between energy consumption and execution latency. In [30], the authors formulate the energy management problem as a stochastic optimization programming by using the Lyapunov optimization-based online algorithm. Wu et al [31] propose a delay-aware computation offloading algorithm with joint optimization of secrecy-provisioning, computation workload, and radio resource allocation to minimize the overall execution delay.…”

Section: B Related Workmentioning

confidence: 99%

Computation Offloading for Distributed Mobile Edge Computing Network: A Multiobjective Approach

Sufyan

Banerjee

2020

IEEE Access

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Mobile edge computing (MEC) is emerging as a cornerstone technology to address the conflict between resource-constrained smart devices (SDs) and the ever-increasing computational demands of the mobile applications. MEC enables the SDs to offload computational-intensive tasks to the nearby edge nodes for providing better quality-of-services (QoS). The recently proposed offloading strategies, mainly consider a centralized approach for a limited number of SDs. However, with the growing popularity of the SDs, these offloading models may have the scalability issue and can be susceptible to single point failure. Although there are few distributed offloading models in the literature, they ignore the vast computational resources of the cloud, load sharing between the MEC servers, and other optimization parameters. Toward this end, we propose an efficient computation offloading scheme for a distributed load sharing MEC network in cooperation with cloud computing to enhance the capabilities of the SDs. We formulate a nonlinear multiobjective optimization problem by applying queuing theory to model the execution delay, energy consumption, and payment cost for using edge and cloud services. To solve the formulated problem, we propose a stochastic gradient descent (SGD) algorithm based solution approach to jointly optimize the offloading probability and transmission power of the SDs for finding an optimal trade-off between energy consumption, execution delay, and cost of the SDs. Finally, we perform extensive simulations to demonstrate the effectiveness of the proposed offloading scheme. Moreover, compared to the other solutions, the proposed scheme is scalable and outperforms the existing schemes.

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Section: B Related Workmentioning

confidence: 99%

Computation Offloading for Distributed Mobile Edge Computing Network: A Multiobjective Approach

Sufyan

Banerjee

2020

IEEE Access

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“…It is vital to optimize the quality of service (QoS) during video transmission through WMMDs. It is supposed to sense, retrieve, store, process, transmit, and communicate information from a source node to a destination node as shown in Fig.1.The sensor-enabled systems for video transmission can monitor a diverse range of applications, such as surveillance, emergency response (e.g., fire-fighting), military, transportation systems, smart cities, and healthcare, among others [2], [3].…”

Section: Introductionmentioning

confidence: 99%

“…It is predicted from the CISCO service provider team that media, especially video, streaming plays a prominent role in the current technological era with significant traffic contribution of up to 60% and this is expected to reach up to 80% by the end of 2019 [3]. This paper focuses on cloud-based deployment, QoS optimization and interpretation while transmitting video through WM-MDs.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we make the following contributions: In [1], a detailed survey highlighting the innovative and emerging technological trends and practices is presented [2]. Latest and emerging network generation is expected to bring a paradigm shift in the delivery of a voluminous of media content at remote locations [3]. Remarkable and self-aware technologies are the cornerstone for every domain with the help of Internet of things (IoT)-based trends [4], [5], but QoS optimization during media transmission is not focused.…”

Section: Introductionmentioning

confidence: 99%

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Toward Convergence of AI and IoT for Energy-Efficient Communication in Smart Homes

Sodhro

Gurtov

Zahid

et al. 2021

IEEE Internet Things J.

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The convergence of Artificial Intelligence (AI) and Internet of Things (IoT) promotes the energy efficient communication in smart homes. Quality of Service (QoS) optimization during video streaming through wireless micro medical devices (WMMD) in smart healthcare homes is the main purpose of this research. This paper contributes in four distinct ways. First,to propose a novel Lazy Video Transmission Algorithm (LVTA). Second, a novel Video Transmission Rate Control Algorithm (VTRCA) is proposed. Third, a novel cloud-based video transmission framework is developed. Fourth, the relationship between buffer size and performance indicators i.e., peak-to-mean ratio (PMR), energy (i.e., encoding and transmission) and standard deviation is investigated while comparing the LVTA, VTRCA, and Baseline approaches. Experimental results demonstrate that the reduction in encoding (32%, 35.4%) and transmission (37%, 39%) energy drains, PMR (5, 4), and standard deviation (3dB, 4dB) for VTRCA and LVTA, respectively, is greater than that obtained by Baseline during video streaming through WMMD.

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“…These are known as MEC servers. Considering the emerging 5G network with its fast data transmission capability and the development of new MEC servers with even more powerful computing resources, MEC-enabled IoT should well-accommodate the emerging demands and their related quality-of-service (QoS) requirements while reducing the pressure on UE, backbone networks, and central cloud infrastructures [11].…”

Section: Introductionmentioning

confidence: 99%

Joint time delay and energy optimization with intelligent overclocking in edge computing

Wang

Xiong

Chen

et al. 2020

Sci. China Inf. Sci.

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Distributed Energy Management for Multiuser Mobile-Edge Computing Systems With Energy Harvesting Devices and QoS Constraints

Cited by 68 publications

References 36 publications

Computation Offloading for Distributed Mobile Edge Computing Network: A Multiobjective Approach

Computation Offloading for Distributed Mobile Edge Computing Network: A Multiobjective Approach

Toward Convergence of AI and IoT for Energy-Efficient Communication in Smart Homes

Joint time delay and energy optimization with intelligent overclocking in edge computing

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