Disclosing Edge Intelligence: A Systematic Meta-Survey
Abstract
:1. Introduction
- Comprehensiveness: The research methodology we applied to perform our systematic EI literature review followed the PRISMA guidelines [4], a formal protocol consisting of well-defined and reproducible steps centered on clear criteria for the selection of the target articles, aiming at high level of homogeneity and quality [5];
- Effectiveness: Given the infeasibility of an exhaustive study of the whole EI literature, we opted for a systematic review performed in the form of a tertiary study, a well-established approach, also known as meta-analysis [6], which has the purpose of aggregating and generalizing the main results from large collections of thematically related secondary studies (reviews, surveys, roadmaps, white papers, etc.).
2. Background
3. Research Methodology
3.1. Objectives
- (RQ1) What are current definitions or interpretations of EI?
- (RQ2) Are there specific reference architectures that help to enable intelligence at the edge?
- (RQ3) What are the main topics (intended as broad subjects or themes) addressed by EI?
- (RQ4) What are the key techniques (intended as enabling, implementation methods) of EI?
- (RQ5) What are the pursued goals, the on-going efforts, and the future challenges for EI?
3.2. Search Strategy
3.3. Eligibility Criteria
- The work is a literature review, survey, or mapping study that specifically delves into the EI realm;
- A model or at least a formal definition of EI is proposed or adopted;
- The work uses EI as the main element of the proposed solution;
- Either EI-based or EI-enabling architectures or techniques are presented.
- The work is too vertical on use cases (ranging from individual domains such as autonomous vehicles [23] and the smart grid [24] to general IoT-based applications [25]), techniques (e.g., information fusion for EI [26], neural-network-based self-learning architecture [27]), algorithm customization (e.g., combination of blockchain and k-means algorithm [28]), or model/platform optimization [20,29];
3.4. Study Selection
4. Literature Review
- Evolutionary EI definitions, which “simply” mean EI as the next stage of current edge computing [14,48,49,50], where edge nodes self-process their own data, being empowered through lightened AI algorithms (individually measured in terms of <accuracy, latency, energy, memory footprint> [48]) or pre-trained models (intended as “pluggable AI capabilities for edge computers” [51]);
- Revolutionary EI definitions, which propose EI as a new paradigm combining (“the amalgam”, “the marriage”, “the confluence”) both novel and existing approaches, techniques, and tools from different areas (mainly from edge computing and AI, but also approximate computing, cognitive science, etc.) and realizing a fully distributed intelligence among end devices, edge nodes, and cloud servers [12,15,35,47,51,52,53,54].
- Knowledge Discovery and Data mining (KDD), which encompasses all aspects pertaining to the extraction of valuable insights and patterns from the vast data generated by end devices;
- Hardware platforms and software frameworks, namely those commercial devices and software tools that concretely allow enabling intelligence at the network edge;
- Service, which encompasses all non-functional aspects (from service placement, composition, and orchestration to mobility, offloading, caching, etc.) related to the support and maintenance of IoT services at the edge layer;
- Interoperability, which focuses on those methods and mechanisms enabling different devices, systems, and networks to be readily connected and exchange information.
- Edge inference, which covers all techniques for near-real-time inference, i.e., as close as possible to the data sources;
- Edge training, which encompasses all techniques that aid in training complex ML models on constrained and resource-limited devices;
- Modeling, which encompasses all techniques that aid in designing ML models’ architectures suitable for resource-limited devices;
- Management, which encompasses all techniques that aid in managing the vast amount of real-time data at the edge layer;
- Collaboration, which includes techniques that aim to improve the interoperability between nodes across the edge-to-cloud continuum.
- Promoting programming and software platforms for EI [12,48], as well as lightweight OS for the edge devices [48]; with respect to the former, the most well-known are IoT Edge Microsoft Azure https://azure.microsoft.com/it-it/products/iot-edge/, accessed on 30 January 2023, Cisco Edge Intelligence https://www.cisco.com/c/en/us/solutions/internet-of-things/edge-intelligence.html, accessed on 30 January 2023, AWS IoT Greengrass https://aws.amazon.com/greengrass/, accessed on 30 January 2023, IoT Core https://cloud.google.com/iot-core?hl=it, accessed on 30 January 2023, Google Coral https://coral.ai/, accessed on 30 January 2023, NVIDIA Jetson https://www.nvidia.com/it-it/autonomous-machines/embedded-systems/, accessed on 30 January 2023, and Open VINO https://docs.openvino.ai/latest/index.html, accessed on 30 January 2023, while obvious OS candidates for edge devices are open-source and Linux-based such as Wind River, Android Things, or RedHat (whereas there are others as well such as Azure RTOS, VxWorks, FreeRtos, etc.);
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Title | Year | Cit. # | SLR | EI Definition | Ref. Architecture | Topics Addressed | Key Techniques | Hardware Tools | Software Tools | Use Cases |
---|---|---|---|---|---|---|---|---|---|---|
Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review[7] | 2022 | 8 | Yes | No | No | - KDD - Interoperability - Platforms/ Frameworks | - Edge Training - Edge Inference - Modeling - Collaboration - (indirect contributions) | - Edge devices - Processors | - ML on the continuum - Data Analytics on the continuum - Simulation and Emulation systems | - Healthcare - Smart Factory - Smart Agriculture - Smart Cities - Automotive |
Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing[12] | 2019 | 703 | No | Revolutionary: “The marriage of edge computing and AI” | 2-layer | - KDD - Platforms/ Frameworks | - Edge Training - Edge Inference | Edge devices | - Systems and Frameworks on EI Model Training, Inference | - Smart Factory - Smart City - Smart Home - Entertainment |
The Many Faces of Edge Intelligence[14] | 2022 | 0 | No | Evolutionary: “An emerging computing paradigm that enables AI functionalities at the network edge” | 3-layer | Outlook | No | No | No | - Smart City - Automotive |
Edge Intelligence[15] | 2019 | 0 | No | Revolutionary: “Edge computing with machine learning and advanced networking capabilities” | 2-layer | Standardization | No | Edge devices | No | - Smart Factory - Smart City - Public Safety |
Edge Intelligence: Empowering Intelligence to the Edge of Network[35] | 2021 | 19 | No | Revolutionary: “A set of connected systems and devices for data collection, caching, processing and analysis proximity to where data are captured based on AI.” | 2-layer | - KDD - Service | - Edge Training - Edge Inference - Modeling - Management | No | No | - Smart Factory - Smart City - Healthcare |
Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence[47] | 2020 | 239 | No | Revolutionary: “The integration of edge computing and AI” | No | - KDD - Service | - Edge Inference - Management | No | No | - Automotive - Smart Home - Smart City |
OpenEI: An open framework for edge intelligence[48] | 2019 | 47 | No | Evolutionary: “The capability to enable edges to execute AI algorithms” | No | - Platforms/ Frameworks - Interoperability | - Edge Training - Edge Inference - Modeling | Hardware modules | - Running environments - Edge-based deep learning packages | - Automotive - Smart Home - Healthcare - Public Safety |
Edge Intelligence: Challenges and Opportunities[49] | 2020 | 1 | No | Evolutionary: “The next stage of edge computing, which allows to run AI applications at the edge of the network” | 3-layer | - KDD - Platforms/ Frameworks - Service | - Edge Training - Edge Inference - Modeling | - Edge AI chips - Edge Computing Platforms | - Edge AI programming libraries | - Automotive - Smart factory - Smart city - Healthcare |
Artificial Intelligence in the IoT Era: A Review of Edge AI Hardware and Software[50] | 2022 | 2 | No | Evolutionary: “The modern trend of moving artificial intelligence computation near to the origin of data sources” | No | - Platforms/ Frameworks - Outlook | Edge Inference | - Hardware devices - NVIDIA Jetson devices | - ML Frameworks - Mobile SDK - Software for MCU - Model conversion lib. for MCU | No |
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey[51] | 2020 | 486 | No | Revolutionary: “The combination of edge computing and AI” | 2-layer | - KDD - Platforms/ Frameworks - Service - Outlook | - Edge Training - Edge Inference - Management - Collaboration | AI hardware for Edge Computing | Edge frameworks for DL | - Smart city - Automotive - Smart home - Smart factory |
Edge Intelligence: A Robust Reinforcement of Edge Computing and Artificial Intelligence[52] | 2021 | 0 | No | Revolutionary: “The combination of edge computing and AI” | No | - KDD - Service | No | No | No | - Automotive - Military |
Roadmap for edge AI: A Dagstuhl Perspective[53] | 2022 | 8 | No | Revolutionary: “A fast evolving domain that merges edge computing and AI“ | No | Outlook | No | No | No | - Automotive - Entertainment - Smart Factory - Healthcare |
Edge Intelligence: Concepts, Architectures, Applications, and Future Directions[54] | 2022 | 4 | No | Revolutionary: “The confluence of edge computing with machine learning, or artificial intelligence in the broad sense” | 2-layer | - KDD - Platforms/ Frameworks - Outlook | Edge training | Edge devices | Edge intelligence frameworks | - Automotive - Entertainment - Smart home - Smart city - Smart factory |
Edge Intelligence: The Convergence of Humans, Things, and AI[55] | 2019 | 25 | No | Revolutionary: “A new paradigm in which intelligence is gradually be pushed from the cloud closer to the edge” | No | - Outlook - Platforms/ Frameworks | No | Edge AI chips and modules | - Sw for AI lifecycle managing - Edge Computing platforms | - Smart City - Automotive - Healthcare - Corporate |
Source | Criteria |
---|---|
Database | ScienceDirect, Scopus, IEEEXplore, ACM Digital Library, Web of Science |
Date of publication | 2011–2023 |
Keywords | - Edge intelligence/AI - Embedded intelligence/AI - On-device intelligence/AI |
Language | English |
Type of Publication | Article, conference paper, book chapter, review, survey |
Inclusion Criteria | - Secondary studies on EI - Formal definitions, models, and perspectives of EI - Relevant EI architectures and techniques - EI as the main element of the proposed solution |
Exclusion Criteria | - Too vertical contributions on use-cases or single techniques - The EI term used as a buzzword or improperly - The EI-related contribution is very limited |
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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Barbuto, V.; Savaglio, C.; Chen, M.; Fortino, G. Disclosing Edge Intelligence: A Systematic Meta-Survey. Big Data Cogn. Comput. 2023, 7, 44. https://doi.org/10.3390/bdcc7010044
Barbuto V, Savaglio C, Chen M, Fortino G. Disclosing Edge Intelligence: A Systematic Meta-Survey. Big Data and Cognitive Computing. 2023; 7(1):44. https://doi.org/10.3390/bdcc7010044
Chicago/Turabian StyleBarbuto, Vincenzo, Claudio Savaglio, Min Chen, and Giancarlo Fortino. 2023. "Disclosing Edge Intelligence: A Systematic Meta-Survey" Big Data and Cognitive Computing 7, no. 1: 44. https://doi.org/10.3390/bdcc7010044