A survey on edge computing for wearable technology

Jin, Xinqi; Li, Lingkun; Dang, Fan; Chen, Xinlei

doi:10.1016/j.dsp.2021.103146

Cited by 36 publications

(12 citation statements)

References 96 publications

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“…Dynamic Voltage and Frequency Scaling (DVFS) defines several levels of frequency at which a processor can operate, where lower frequencies become slower to save power [12], [15]. Sensors and wearable devices that use small-sized batteries can also be optimized to save power while delivering functionalities including sensing, storage, and computation [6]. The work in [5] combines the use of DVFS and DPM for optimizing performance and energy savings of single-board computers using Pareto frontier for multi-criteria optimization of several computing-intensive kernels.…”

Section: Related Workmentioning

confidence: 99%

“…In this case, it is more efficient to send these data to more powerful resources nearby (placed on the edge of the network) for faster processing (i.e., with low latency) [4], [5]. Another example is wearable technologies, which can depend on energy-and resource-constrained mobile devices or low-end servers placed nearby to process their data streams [6].…”

Section: Introductionmentioning

confidence: 99%

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Balancing Performance and Energy Consumption of Bagging Ensembles for the Classification of Data Streams in Edge Computing

Cassales

Gomes

Bifet

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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In recent years, the Edge Computing (EC) paradigm has emerged as an enabling factor for developing technologies like the Internet of Things (IoT) and 5G networks, bridging the gap between Cloud Computing services and end-users, supporting low latency, mobility, and location awareness to delay-sensitive applications. An increasing number of solutions in EC have employed machine learning (ML) methods to perform data classification and other information processing tasks on continuous and evolving data streams. Usually, such solutions have to cope with vast amounts of data that come as data streams while balancing energy consumption, latency, and the predictive performance of the algorithms. Ensemble methods achieve remarkable predictive performance when applied to evolving data streams due to several models and the possibility of selective resets. This work investigates a strategy that introduces short intervals to defer the processing of mini-batches. Well balanced, our strategy can improve the performance (i.e., delay, throughput) and reduce the energy consumption of bagging ensembles to classify data streams. The experimental evaluation involved six state-of-art ensemble algorithms (OzaBag, OzaBag Adaptive Size Hoeffding Tree, Online Bagging ADWIN, Leveraging Bagging, Adaptive RandomForest, and Streaming Random Patches) applying five widely used machine learning benchmark datasets with varied characteristics on three computer platforms. As a result, our strategy can significantly reduce energy consumption in 96% of the experimental scenarios evaluated. Despite the trade-offs, it is possible to balance them to avoid significant loss in predictive performance.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Balancing Performance and Energy Consumption of Bagging Ensembles for the Classification of Data Streams in Edge Computing

Cassales

Gomes

Bifet

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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show abstract

“…As mentioned in the introduction, edge computing is an up-andcoming technology. Next to smartphones [39] and wearable devices [16], there also exists devices created specifically for edge computing. For example, NVIDIA released the NVIDIA Jetson [24], which is designed specifically for AI and ML on the edge, with the most powerful Jetson device boasting up to 1602 frames per seconds [23] when using MobileNet-V1.…”

Section: Single Board Computermentioning

confidence: 99%

Video- and Location-based Analysis of Cycling Routes for Safety Measures and Fan Engagement

Dumez

Prevost

Slembrouck

et al. 2022

Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports

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Video-based analysis of cycling races can provide a lot of information that can be used to keep cycling interesting for the fans and improve cyclists' safety. In this paper, we propose a solution to collect and process the metadata of cycling races. The idea is to use edge computing, by collecting data from a car in front of the race and processing this data using a tailor-made setup. Our solution consists of a camera to record video, and a GPS module to map the corresponding locations. Both data streams are offered to a single board computer. The video frames are used for crowd size classification to roughly estimate the number of spectators present along the race route. Moreover, we use the same footage to recognize cyclists' names on the road's surface to determine the location of fans of specific cyclists to create metadata around fan engagement. The tailor-made system performs the processing of the video frames and the results are sent to a web server using a cellular network connection. A web application was created to visualize the crowd size and the location of cyclists' names on the road's surface. CCS CONCEPTS• Computing methodologies → Scene understanding; Video summarization.

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“…Regarding smart wearable devices, their popularity has rapidly increased lately due to their wearability [103]. Their light weight and compact size limit their computing capabilities; thus, MEC applications may offer a great range of possibilities to increase their computing power [104].…”

Section: Edge Computing Applicationsmentioning

confidence: 99%

Modeling of a Generic Edge Computing Application Design

Roig

Alcaraz

Gilly

et al. 2021

Sensors

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Edge computing applications leverage advances in edge computing along with the latest trends of convolutional neural networks in order to achieve ultra-low latency, high-speed processing, low-power consumptions scenarios, which are necessary for deploying real-time Internet of Things deployments efficiently. As the importance of such scenarios is growing by the day, we propose to undertake two different kind of models, such as an algebraic models, with a process algebra called ACP and a coding model with a modeling language called Promela. Both approaches have been used to build models considering an edge infrastructure with a cloud backup, which has been further extended with the addition of extra fog nodes, and after having applied the proper verification techniques, they have all been duly verified. Specifically, a generic edge computing design has been specified in an algebraic manner with ACP, being followed by its corresponding algebraic verification, whereas it has also been specified by means of Promela code, which has been verified by means of the model checker Spin.

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A survey on edge computing for wearable technology

Cited by 36 publications

References 96 publications

Balancing Performance and Energy Consumption of Bagging Ensembles for the Classification of Data Streams in Edge Computing

Balancing Performance and Energy Consumption of Bagging Ensembles for the Classification of Data Streams in Edge Computing

Video- and Location-based Analysis of Cycling Routes for Safety Measures and Fan Engagement

Modeling of a Generic Edge Computing Application Design

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