Energy aware edge computing: A survey

Jiang, Congfeng; Fan, Tiantian; Gao, Honghao; Shi, Weisong; Liu, Liangkai; Cérin, Christophe; Wan, Jian

doi:10.1016/j.comcom.2020.01.004

Cited by 111 publications

(41 citation statements)

References 93 publications

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“…Furthermore, given that wireless sensor nodes are highly constrained in terms of computation capacity and energy consumption, the promising capacity of edge computing has not been evaluated here due to the gap between the hardware capacity of connected devices involved in each of the networks (WSN and IoT) [ 51 ] and those developed using a state-of-the-art energy-aware hardware design for edge computing by Jiang et al [ 52 ]. Even though paradigms and algorithmic propositions emerging from edge computing could be of interest, issues such as portability efforts and shrinking requirements needed to transfer software from medium- to high-performance computing units to highly energy-constrained and low-power computation capacity hardware targets led the authors to focus this study on distributed computing for low-cost/low-performance, battery-powered and highly constrained sensor networks [ 37 ].…”

Section: Distributed Computing In Wireless Sensor Networkmentioning

confidence: 99%

A Distributed Computing Solution Based on Distributed Kalman Filter for Leak Detection in WSN-Based Water Pipeline Monitoring

Nkemeni

Mieyeville

Tsafack

2020

Sensors

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Wireless Sensor Network (WSN) applications that favor more local computations and less communication can contribute to solving the problem of high power consumption and performance issues plaguing most centralized WSN applications. In this study, we present a fully distributed solution, where leaks are detected in a water distribution network via only local collaborations between a sensor node and its close neighbors, without the need for long-distance transmissions via several hops to a centralized fusion center. A complete approach that includes the design, simulation, and physical measurements, showing how distributed computing implemented via a distributed Kalman filter improves the accuracy of leak detection and the power consumption is presented. The results from the physical implementation show that distributed data fusion increases the accuracy of leak detection while preserving WSN lifetime.

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Section: Distributed Computing In Wireless Sensor Networkmentioning

confidence: 99%

A Distributed Computing Solution Based on Distributed Kalman Filter for Leak Detection in WSN-Based Water Pipeline Monitoring

Nkemeni

Mieyeville

Tsafack

2020

Sensors

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“…In [53], the advancements and importance of energy-aware technologies for core network operation were explicitly discussed. Energy-efficient initiatives for edge computing systems and relevant research challenges were outlined in [54]. Buzzi et al [8] identified the key aspects of energy-aware network design.…”

Section: Related Survey Papers a Survey Articles On Green Communmentioning

confidence: 99%

“…Although MEC reduces content transmission delay and energy requirements, the limited storage and computing capacity at the network edge calls for the smart utilization of networking resources [54], [119]. In addition, the changing user demand influences the requirements for MEC and caching services.…”

Section: E Green Techniques Combining Mobile Edge Computing and Cachingmentioning

confidence: 99%

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A Review on Green Caching Strategies for Next Generation Communication Networks

et al. 2020

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In recent years, the ever-increasing demand for networking resources and energy, fueled by the unprecedented upsurge in Internet traffic, has been a cause for concern for many service providers. Content caching, which serves user requests locally, is deemed to be an enabling technology in addressing the challenges offered by the phenomenal growth in Internet traffic. Conventionally, content caching is considered as a viable solution to alleviate the backhaul pressure. However, recently, many studies have reported energy cost reductions contributed by content caching in cache-equipped networks. The hypothesis is that caching shortens content delivery distance and eventually achieves significant reduction in transmission energy consumption. This has motivated us to conduct this study and in this article, a comprehensive survey of the state-of-the-art green caching techniques is provided. This review paper extensively discusses contributions of the existing studies on green caching. In addition, the study explores different cache-equipped network types, solution methods, and application scenarios. We categorically present that the optimal selection of the caching nodes, smart resource management, popular content selection, and renewable energy integration can substantially improve energy efficiency of the cache-equipped systems. In addition, based on the comprehensive analysis, we also highlight some potential research ideas relevant to green content caching.

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“…From servers to mobile devices, the demand for computing and energy is growing. Because mobile devices are often limited in terms of computing power, storage capacity and energy consumption [46]. And this technology is also widely used in scenarios other than medical care, such as smart cities, smart transportation and smart homes [47]- [51].…”

Section: Introductionmentioning

confidence: 99%

MobileAid: A Fast and Effective Cognitive Aid System on Mobile Devices

Liu

et al. 2020

IEEE Access

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Elderly people generally suffer from diseases with cognitive decline, which seriously affect their daily lives. To assist these patients with cognitive decline, numerous cognitive aid devices have been researched and designed. Although these devices can achieve the aid tasks, they have to face two difficulties: low accuracy and long latency. In this paper, we present MobileAid, an aid system implemented on the mobile device, which assists cognitive decline patients to recognize objects. The key idea of this system is a two-step lightweight neural network design for the target recognition: context recognition and object recognition, and this design achieves low time delay. Considering the great success of convolutional neural networks in object recognition, we design a lightweight convolutional neural network by the combination of pooling layers and activation functions selection, which achieves high accuracy. Furthermore, we apply the depthwise separable convolution to reduce the resource consumption for deploying the system on mobile devices. The results of the extensive experiments we have conducted show that MobileAid can achieve 95% high accuracy and 90ms low time delay with few resource consumption.

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Energy aware edge computing: A survey

Cited by 111 publications

References 93 publications

A Distributed Computing Solution Based on Distributed Kalman Filter for Leak Detection in WSN-Based Water Pipeline Monitoring

A Distributed Computing Solution Based on Distributed Kalman Filter for Leak Detection in WSN-Based Water Pipeline Monitoring

A Review on Green Caching Strategies for Next Generation Communication Networks

MobileAid: A Fast and Effective Cognitive Aid System on Mobile Devices

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