Edge AI: A survey

Singh, Raghubir; Gill, Sukhpal Singh

doi:10.1016/j.iotcps.2023.02.004

Cited by 104 publications

(44 citation statements)

References 190 publications

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“…28 Latency is an issue with cloud computing; hence we will develop an intelligent algorithm that will offload the latency sensitive microservices over the fog/edge node without any delay and normal services over the cloud platform in the future. 29 Artificial Intelligence technique will play the vital role to decide the offloading platform at the runtime. 30…”

Section: Discussionmentioning

confidence: 99%

“…The limitation of the proposed framework is that authors did not test the performance of the proposed approach in real cloud environment and did not consider all the network parameters constraints 28 . Latency is an issue with cloud computing; hence we will develop an intelligent algorithm that will offload the latency sensitive microservices over the fog/edge node without any delay and normal services over the cloud platform in the future 29 . Artificial Intelligence technique will play the vital role to decide the offloading platform at the runtime 30 …”

Section: Discussionmentioning

confidence: 99%

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QoS‐aware resource scheduling using whale optimization algorithm for microservice applications

et al. 2023

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Microservices is a structural approach, where multiple small set of services are composed and processed independently with lightweight communication mechanism. To accomplish the end‐user demand in minimum delay and cost without violating the service level agreement (SLA) constraints and overhead is a challenging issue in cloud computing. In addition, existing framework tries to deploy the microservice over the best computing resource for latency‐sensitive applications, but long boot‐time, and low resource utilization still remains a challenging task. To find the solution for aforementioned issues, we propose a Quality of Service (QoS) aware resource allocation model based on a Fine‐tuned Sunflower Whale Optimization Algorithm (FSWOA) that find the best resources for microservice deployment and fulfill the objectives of users as well as service provider. The proposed technique deploys the container‐based services over the physical machine based upon the capacity, to execute the micro services by utilizing the CPU and memory maximally. The proposed work aims is to distribute the workload in efficient manner and avoid the wastage of resources that leads to optimize the QoS parameters. The experimental results conducted in simulation environment demonstrates that proposed approach perform superior over baseline approaches and reduces the time, memory consumption, CPU consumption, and service cost up to 4.26%, 11.29%, 17.07% and 24.22% compared to SFWAO, GA, PSO and ACO.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

QoS‐aware resource scheduling using whale optimization algorithm for microservice applications

et al. 2023

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“…Therefore, the recent NAS methods have been mainly focused on developing strategies to reduce the energy and time consumption of NAS, by Early stopping, Weight sharing, One-shot and zero-shot methods, etc. [38] Figure 2. Overview of the current AutoML and NAS pipeline.…”

Section: Figurementioning

confidence: 99%

“…In the rapidly evolving landscape of artificial intelligence, a paradigm shift is being observed in the deployment of AI models, emphasizing the importance of positioning these systems near the end‐user, at the very edge of the network. This strategic placement, in close proximity to where the data originates, is known as Edge AI [ 38 ] and increasingly recognized for its critical role in promoting environmental sustainability. The implementation of Edge AI brings a plethora of additional advantages that are pivotal in today's interconnected world.…”

Section: Introductionmentioning

confidence: 99%

Retrosynthetic Life Cycle Assessment: A Short Perspective on the Sustainability of Integrating Thermoplastics and Artificial Intelligence Into Composite Systems

Yaghoubi,

Kumru

2024

Advanced Sustainable Systems

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Over the past 30 years, the polymer composite industry has flourished, producing advanced structural materials for the aviation, energy, and transportation sectors. However, the use of crosslinked thermoset matrices has been linked to significant end‐of‐life challenges, presenting a critical issue for the industry. Moreover, the industry is characterized by numerous labor‐intensive processes. In alignment with Industry 4.0 principles, two major routes have been identified to enhance sustainability: the utilization of high‐performance thermoplastic matrices and the integration of artificial intelligence in manufacturing. Nevertheless, there are substantial concerns regarding the life cycle assessment of these technologies, which are not accounted for in the initial calculations, including the environmental footprint of polymer synthesis and energy requirements for training AI. This perspective aims to address potential and significant CO2 emissions from chemical feedstocks and the high computing requirements of these new technologies.

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“…This capability has two main benefits: on one hand it improves the autonomy of the drone swarm that doesn't rely on the communications infrastructure. On the other hand, there is also a communications efficiency improvement because the network doesn't exchange raw data but high-level semantic information [9], [10]. Drone classification powered by Artificial Intelligence (AI) can be considered as an enabling technology for such a system.…”

Section: Introductionmentioning

confidence: 99%

On-board drone classification with Deep Learning and System-on-Chip implementation

Di Cecio,

Manco,

Gigante

2024

J. Phys.: Conf. Ser.

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In recent years the increasing use of drones has raised significant concerns on safety and make them dramatic threats to security. To address these worries Counter-UAS Systems (CUS) are capturing the interest of research and of industry. Consequently, the development of effective drone detection technologies has become a critical research focus. The proposed work explores the application of edge computing to drone classification. It tunes a Deep Learning model, You Only Look Once (YOLO), and implements it on a Field Programmable Gate Array (FPGA) technology. FPGAs are considered advantageous over conventional processors since they enable parallelism and can be used to create high-speed, low-power, and low-latency circuit designs and so to satisfy the stringent Size, weight and Power (SWaP) requirements of a drone-based implementation. In details, two different YOLO neural networks YOLO v3 and v8 are trained and evaluated on a large data set constructed with drones’ images at various distances. The two models are then implemented on a System-on-Chip (SoC). In order to demonstrate the feasibility of a drone on board image Artificial Intelligence processing, the evaluation assesses the accuracy of classification and the computational performances such as latency.

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Edge AI: A survey

Cited by 104 publications

References 190 publications

QoS‐aware resource scheduling using whale optimization algorithm for microservice applications

QoS‐aware resource scheduling using whale optimization algorithm for microservice applications

Retrosynthetic Life Cycle Assessment: A Short Perspective on the Sustainability of Integrating Thermoplastics and Artificial Intelligence Into Composite Systems

On-board drone classification with Deep Learning and System-on-Chip implementation

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