Advances in Image Processing, Reliability, and Artificial Intelligence: Data Centred-Techniques and Applications in Edge Computing provides a clear outlook of the mechanisms, risks, challenges, and opportunities in system reliability for image processing and AI applications running on edge devices. It provides Best Known Configuration (BKC) and Methods (BKM) while discussing trends and future works based on current research. The content serves as a reference for practitioners and provides a state-of-the-art for researchers in the area. It provides foundations to analyse and replicate different applications through use cases. It tackles concerns for how reliability aspects (i.e., fault tolerance, availability, maturity, and recoverability) are addressed for applications running in an environment that is not fully controlled and exposed to environmental variations. - Provides an analysis of current challenges and trends in systems reliability, AI, and image processing in edge computing for supporting different data-driven decision-making strategies- Considers the challenges and opportunities regarding data sovereignty, sustainability, model lifecycle and AI ethics in edge computing- Explains strategies and trends for monitoring and meta-monitoring AI deployments and system reliability in edge computing- Addresses the top concerns in the reliability, AI, and image processing in edge computing for supporting distributed decision-making- Describes an industry perspective for different verticals, outlining trends and future research directions

Advances in Image Processing, Reliability, and Artificial Intelligence: Data Centred-Techniques and Applications in Edge Computing provides a clear outlook of the mechanisms, risks, challenges, and opportunities in system reliability for image processing and AI applications running on edge devices. It provides Best Known Configuration (BKC) and Methods (BKM) while discussing trends and future works based on current research. The content serves as a reference for practitioners and provides a state-of-the-art for researchers in the area. It provides foundations to analyse and replicate different applications through use cases. It tackles concerns for how reliability aspects (i.e., fault tolerance, availability, maturity, and recoverability) are addressed for applications running in an environment that is not fully controlled and exposed to environmental variations. - Provides an analysis of current challenges and trends in systems reliability, AI, and image processing in edge computing for supporting different data-driven decision-making strategies- Considers the challenges and opportunities regarding data sovereignty, sustainability, model lifecycle and AI ethics in edge computing- Explains strategies and trends for monitoring and meta-monitoring AI deployments and system reliability in edge computing- Addresses the top concerns in the reliability, AI, and image processing in edge computing for supporting distributed decision-making- Describes an industry perspective for different verticals, outlining trends and future research directions

Advances in Image Processing, Reliability, and Artificial Intelligence: Data Centred-Techniques and Applications in Edge Computing provides a clear outlook of the mechanisms, risks, challenges, and opportunities in system reliability for image processing and AI applications running on edge devices. It provides Best Known Configuration (BKC) and Methods (BKM) while discussing trends and future works based on current research. The content serves as a reference for practitioners and provides a state-of-the-art for researchers in the area. It provides foundations to analyse and replicate different applications through use cases. It tackles concerns for how reliability aspects (i.e., fault tolerance, availability, maturity, and recoverability) are addressed for applications running in an environment that is not fully controlled and exposed to environmental variations. - Provides an analysis of current challenges and trends in systems reliability, AI, and image processing in edge computing for supporting different data-driven decision-making strategies- Considers the challenges and opportunities regarding data sovereignty, sustainability, model lifecycle and AI ethics in edge computing- Explains strategies and trends for monitoring and meta-monitoring AI deployments and system reliability in edge computing- Addresses the top concerns in the reliability, AI, and image processing in edge computing for supporting distributed decision-making- Describes an industry perspective for different verticals, outlining trends and future research directions