Requirements for Energy Efficient Edge Computing: A Survey

Väänänen, Olli; Hämäläinen, Timo

doi:10.1007/978-3-030-01168-0_1

Cited by 15 publications

(7 citation statements)

References 34 publications

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“…Väänänen and Hämäläinen [52] defined the concepts of edge, fog, cloud and cloud, however, the authors dedicated an entire section to identifying the advantages of Edge Computing. The authors analyzed the benefits of Edge Computing from the perspective of energy efficiency, security, and privacy.…”

Section: Resultsmentioning

confidence: 99%

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A Review on Edge Computing in Smart Energy by means of a Systematic Mapping Study

et al. 2019

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Context: Smart Energy is a disruptive concept that has led to the emergence of new energy policies, technology projects, and business models. The development of those models is driven by world capitals, companies, and universities. Their purpose is to make the electric power system more efficient through distributed energy generation/storage, smart meter installation, or reduction of consumption/implementation costs. This work approaches Smart Energy as a paradigm that is concerned with systemic strategies involving the implementation of innovative technological developments in energy systems. However, many of the challenges encountered under this paradigm are yet to be overcome, such as the effective integration of solutions within Smart Energy systems. Edge Computing is included in this new technology group. Objective: To investigate developments that involve the use of Edge Computing and that provide solutions to Smart Energy problems. The research work will be developed using the methodology of systematic mapping of literature, following the guidelines established by Kitchenham and Petersen that facilitate the identification of studies published on the subject. Results: Inclusion and exclusion criteria have been applied to identify the relevant articles. We selected 80 papers that were classified according to the type of publication (journal, conferences, or book chapter), type of research (conceptual, experience, or validation), type of activity (implement, validate, analyze) and asset (architecture, framework, method, or models). Conclusion: A complete review has been conducted of the 80 articles that were closely related to the questions posed in this research. To reach the goal of building Edge Computing architectures for Smart Energy environments, several lines of research have been defined. In the future, such architectures will overcome current problems, becoming highly energy-efficient, cost-effective, and capacitated to process and respond in real-time.Electronics 2020, 9, 48 2 of 33 Ledger Technologies (such as blockchain) [15]; or distributed sensing and actuation technologies such as the Internet of Things (IoT) [3,16].Regarding the Internet of Things (IoT), its recognition as a paradigm dates back to 2011-2012, when renowned companies and institutions such as Gartner, Forbes, or Wired started using this term to refer to the emerging technology [17]. The Internet of Things implies the connection of different heterogeneous objects, including buildings, machinery, vehicles, and electronic devices, such as sensors and actuators interconnected by means of communication protocols and forming wireless or wired networks [18] to collect information and extract knowledge [19]. Since then, the scope of IoT has spread throughout a great variety of environments and disciplines, including solutions for development of Smart Cities [20,21], Industry 4.0 [22][23][24], transportation and logistics [25,26], smart homes and hotels [13,27,28] or, more relevant to this research, energy efficiency [8,29,30]. IoT provid...

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Section: Resultsmentioning

confidence: 99%

“…The research of Väänänen and Hämäläinen [52] analyzed the requirements for energy-efficient Edge Computing. The authors identified multiple challenges when applying edge and fog computing in energy efficiency applications.…”

Section: Related Workmentioning

confidence: 99%

A Review on Edge Computing in Smart Energy by means of a Systematic Mapping Study

et al. 2019

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“…It should be considered that NTP protocol consumes many resources and increases latency in industrial environments. In [71] the authors, review the methods used to decrease energy consumption in the WSN sensor networks; The low energy adaptive clustering hierarchy (LEACH) protocol uses some methods and techniques to solve this problem. Modern high-reach and low-consumption IoT networks achieve energy savings using limited bandwidth and intelligent modulation.…”

Section: Energy Consumptionmentioning

confidence: 99%

Fog computing at industrial level, architecture, latency, energy, and security: A review

Caiza

Saeteros

Oñate

et al. 2020

Heliyon

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The industrial applications in the cloud do not meet the requirements of low latency and reliability since variables must be continuously monitored. For this reason, industrial internet of things (IIoT) is a challenge for the current infrastructure because it generates a large amount of data making cloud computing reach the edge and become fog computing (FC). FC can be considered as a new component of Industry 4.0, which aims to solve the problem of big data, reduce energy consumption in industrial sensor networks, improve the security, processing and storage real-time data. It is a promising growing paradigm that offers new opportunities and challenges, beside the ones inherited from cloud computing, which requires a new heterogeneous architecture to improve the network capacity for delivering edge services, that is, providing computing resources closer to the end user. The purpose of this research is to show a systematic review of the most recent studies about the architecture, security, latency, and energy consumption that FC presents at industrial level and thus provide an overview of the current characteristics and challenges of this new technology.

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“…Using some simple and computationally light compression method can be a very effective way to save in energy consumption and thus lengthen the lifetime of battery powered sensor nodes which often operate wirelessly [3].…”

Section: Introductionmentioning

confidence: 99%

Sensor Data Stream on-line Compression with Linearity-based Methods

Väänänen

Hämäläinen

2020

2020 IEEE International Conference on Smart Computing (SMARTCOMP)

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The escalation of the Internet of Things applications has put on display the different sensor data processing methods. The sensor data compression is one of the fundamental methods to reduce the amount of data needed to transmit from the sensor node which is often battery powered and operates wirelessly. Reducing the amount of data in wireless transmission is an effective way to reduce overall energy consumption in wireless sensor nodes. The methods presented and tested are suitable for constrained sensor nodes with limited computational power and limited energy resources. The methods presented are compared with each other using compression ratio and inherent latency. Latency is an important parameter in on-line applications. The improved variation of the linear regression-based method called RT-LRbTC is tested and it has proved to be a potential method to be used in a wireless sensor node with a fixed and predictable latency. The compression efficiency of the compression algorithms is tested with real measurement data sets.

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Requirements for Energy Efficient Edge Computing: A Survey

Cited by 15 publications

References 34 publications

A Review on Edge Computing in Smart Energy by means of a Systematic Mapping Study

A Review on Edge Computing in Smart Energy by means of a Systematic Mapping Study

Fog computing at industrial level, architecture, latency, energy, and security: A review

Sensor Data Stream on-line Compression with Linearity-based Methods

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