The Many Faces of Edge Intelligence

Peltonen, Ella; Ahmad, Ijaz; Aral, Atakan; Capobianco, Michele; Ding, Aaron Yi; Gil‐Castiñeira, Felipe; Gilman, Ekaterina; Harjula, Erkki; Jurmu, Marko; Karvonen, Teemu; Kelanti, Markus; Leppänen, Teemu; Lovén, Lauri; Mikkonen, Tommi; Mohan, Nitinder; Nurmi, Petteri; Pirttikangas, Susanna; Sroka, Paweł; Tarkoma, Sasu; Yang, Tingting

doi:10.1109/access.2022.3210584

Cited by 27 publications

(10 citation statements)

References 70 publications

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“…ML at the edge (popularly known as edge ML [236] or edge intelligence [272]) refers to the training and use of ML models across the computing continuum: on UEs, edge nodes, and cloud servers, rather than only in cloud-based centralized setups. his is a rapidly evolving field with prominent innovation opportunities and impact [273], [274].…”

Section: B ML At the Edgementioning

confidence: 99%

Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

López,

Rosabal,

Ruiz-Guirola

et al. 2023

IEEE Open J. Commun. Soc.

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Technology solutions must effectively balance economic growth, social equity, and environmental integrity to achieve a sustainable society. Notably, although the Internet of Things (IoT) paradigm constitutes a key sustainability enabler, critical issues such as the increasing maintenance operations, energy consumption, and manufacturing/disposal of IoT devices have long-term negative economic, societal, and environmental impacts and must be efficiently addressed. This calls for self-sustainable IoT ecosystems requiring minimal external resources and intervention, effectively utilizing renewable energy sources, and recycling materials whenever possible, thus encompassing energy sustainability. In this work, we focus on energy-sustainable IoT during the operation phase, although our discussions sometimes extend to other sustainability aspects and IoT lifecycle phases. Specifically, we provide a fresh look at energysustainable IoT and identify energy provision, transfer, and energy efficiency as the three main energyrelated processes whose harmonious coexistence pushes toward realizing self-sustainable IoT systems. Their main related technologies, recent advances, challenges, and research directions are also discussed. Moreover, we overview relevant performance metrics to assess the energy-sustainability potential of a certain technique, technology, device, or network, together with target values for the next generation of wireless systems, and discuss protocol, integration, and implementation issues. Overall, this paper offers insights that are valuable for advancing sustainability goals for present and future generations.

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Section: B ML At the Edgementioning

confidence: 99%

Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

López,

Rosabal,

Ruiz-Guirola

et al. 2023

IEEE Open J. Commun. Soc.

Self Cite

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“…Data gathered from the mining site from various sensors, mobile equipment, and monitoring devices can help in localized decision-making for autonomous operations, leveraging Arti icial Intelligence (AI). Edge computing and AI have recently become highly complementary [10,11,12], enabling diverse services, resulting in Edge Intelligence (EI). Edge platforms, thus, offer unique opportunities for localized AI-based operations in NGMining.…”

Section: Intelligent Edge For Ngminingmentioning

confidence: 99%

Edge computing for critical environments: Vision and existing solutions

Ijaz Ahmad,

Andrea Gentili,

Rupender Singh

et al. 2023

ITU J-FET

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With the increasing connectivity of critical infrastructures through wireless technologies, the importance of edge computing is increasing manyfolds. Edge computing has become an important phenomenon combining the strengths of distributed computing technologies with those of telecommunication technologies. Therefore, new technological breakthroughs are happening in the realm of edge computing for critical environments, such as the next generation underground and open-pit mining. Since the mining technologies are moving at a faster pace towards digitization leveraging connectivity technologies, edge computing plays a crucial role in bringing computing and connectivity into mines to provide latency- and security-critical services on site. In this article, we study how edge computing fulfills the needs of critical environments, focusing on mining environments, and provides important insight into future research directions.

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“…EI [1] can improve the process of the collection of solar radiation data and make the estimation of solar panel performance more efficient and more accurate. In addition, the utilization of edge intelligence can make solar radiation data collection more massive and can be enabled on the sensor nodes without solar panels.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Using Machine Learning Techniques for Estimating Solar Panel Performance in Edge Sensor Devices

Dobrilovic,

Pekez,

Ognjenovic

et al. 2024

Applied Sciences

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The importance of the usage of renewable energy sources in powering wireless sensor nodes in IoT and sensor networks grows together with the increasing number of utilized sensor nodes. Considering the other types of renewable energy sources, solar power differs as the most suitable one and emerges as the major source for powering sensor nodes. Thus, the consideration of using sensor nodes and collected sensor data for estimating solar panel performances and therefore solar power potential can improve the efforts in this direction. This paper presents the methodology for implementing edge intelligence on wireless sensor nodes for solar panel output voltage estimation and forecasting. The methodology covers the usage of the Python Scikit-learn package and micromlgen library for the implementation of edge intelligence on Arduino clone-based sensor nodes, particularly the development boards based on the ESP8266 chips. Scikit-learn is used for analyzing the efficiency of various regressors on collected solar data. The micromlgen library is then used for implementing those regressors on Arduino and clone nodes. The prediction of solar panel voltage generation is based on a single-sensor reading—UV or BH1750 light sensor. The Random Forest and Decision Tree regressors are implemented on the ESP8266-based development board—Wemos D1 R2. The estimation accuracy of the RF model is an MSE of approximately 0.10, MAE of 0.07 for UV and 0.04 for BH1750, and an R2 of approximately 0.93 for both UV and BH1750 light sensors. The Decision Tree model has a lower accuracy with an MSE between 0.13 and 0.14, MAE of 0.07 for UV and 0.04 for BH1750, and R2 of 0.90 and 0.89 for the UV and BH1750 sensors, respectively. The methodology and its efficiency are presented and discussed in this paper.

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The Many Faces of Edge Intelligence

Cited by 27 publications

References 70 publications

Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

Edge computing for critical environments: Vision and existing solutions

Analysis of Using Machine Learning Techniques for Estimating Solar Panel Performance in Edge Sensor Devices

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