[논문 리뷰] Edge Intelligence: Architectures, Challenges, and Applications

Dianlei Xu, Tong Li외 6명|arXiv (Cornell University)|2020. 03. 26.

Privacy-Preserving Technologies in Data참고 문헌 336인용 수 123

한 줄 요약

네 가지 핵심 edge 인텔리전스 구성요소—edge caching, edge training, edge inference, edge offloading—를 식별하고 아키텍처, 기법, 열린 도전과제의 다차원적 분류를 제공하는 포괄적 설문.

ABSTRACT

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.

연구 동기 및 목표

• 지연 시간을 줄이고 대역폭을 절약하며 프라이버시를 보호하기 위해 edge-enabled AI를 촉진한다.
• 캐싱, 트레이닝, 추론, 오프로깅의 네 가지 엣지 인텔리전스 구성요소에서 최첨단을 체계적으로 분류한다.
• 엣지 접근 방식과 전통적 클라우드 중심 인텔리전스를 비교하고 트레이드오프를 논의한다.
• 데이터, 모델, 프라이버시, 인센티브 분야의 향후 연구 방향을 요약한다.

제안 방법

• 네 가지 구성요소 프레임워크: edge caching, edge training, edge inference, and edge offloading를 제안한다.
• 각 구성요소에 대한 다차원 분류를 제공하며, 실용적 문제, 기법, 목표, 성능, 단점을 포함한다.
• 응용 시나리오, 방법론, 결과의 관점에서 문헌을 분석한다.
• 데이터 부족, 데이터 일관성, 모델 적응성, 프라이버시 및 보안, 인센티브 메커니즘 등 향후 연구 방향과 이슈를 논의한다.

실험 결과

연구 질문

• RQ1 What are the fundamental components and architectures of edge intelligence and how do they interact?
• RQ2 What practical problems, techniques, and goals characterize edge caching, edge training, edge inference, and edge offloading?
• RQ3 How does edge intelligence compare to traditional cloud-centric intelligence in terms of latency, privacy, and bandwidth?
• RQ4 What are the key open issues and future directions in data, models/algorithms, privacy, security, and incentives?

주요 결과

• Identifies four key components of edge intelligence: edge caching, edge training, edge inference, and edge offloading.
• Provides a systematic, multi-dimensional classification and taxonomy for each component.
• Documents open issues and proposes five future research directions: data scarcity, data consistency, model/algorithm adaptability, privacy and security, and incentive mechanisms.
• Reviews growth trends and motivations, noting a rapid rise in edge training, inference, and offloading since 2014.
• Highlights that edge computing enables ultra-low latency, reduced end-device energy use, and scalable deployment through edge-cloud collaboration.