Mobile fog: a programming model for large-scale applications on the internet of things

By Kirak Hong, D. Lillethun, U. Ramachandran, Beate Ottenwälder, B. Koldehofe. 2013

545 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Computer Vision and Pattern Recognition

Citations in the corpus

Practical Distributed Learning: Secure Machine Learning with Communication-Efficient Local Updates (2019, background): “There is growing literature on the practical applications of edge and fog computing (Garcia Lopez et al., 2015; Hong et al., 2013) in various scenarios such as smart home or sensor networks.”
Federated Multi-Task Learning (2017, background): “Computing SQL-like queries across distributed, low-powered nodes is a decades-long area of research that has been explored under the purview of query processing in sensor networks, computing at the edge, and fog computing [31, 11, 32, 7, 17, 14].”
Federated Learning: Challenges, Methods, and Future Directions (2019, background): “es across distributed, low-powered devices is a decades-long area of research that has been explored under the purview of query processing in sensor networks, computing at the edge, and fog computing [12, 29, 40, 49, 74]. Recent works have also considered training machine learning models centrally b”
Asynchronous Federated Optimization (2019, background): “Edge computing (Garcia Lopez et al., 2015; Hong et al., 2013) is increasingly applied in various scenarios such as smart home, wearable devices, and sensor networks.”
Mobile Edge Computing: A Survey (2018, background): “The application can also be helpful for face recognition, for example, if someone commits a crime then his photo can be transferred to these intelligent cameras to trace the culprit [35] [38] [35].”
An Overview of Service Placement Problem in Fog and Edge Computing (2020, background): “[16, 163] C 3 3 [145, 149] C 3 3 3 [7, 10, 78, 86, 87, 143, 161, 162] Di 3 [73, 150] Di 3 3 3”
Twenty Security Considerations for Cloud-Supported Internet of Things (2016, unknown): “Fog computing [87], [89] describes more of a distributed computation approach, akin to edge [90] or grid computing, where certain service functionality is composed from among “things” and cloud services, at various levels, data flowing where appropriate (e.”
iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments (2016, methodology): “[10], wherein they proposed an API for fog applications and modeled a CCTV-based vehicle tracking system using that it.”
Finding your Way in the Fog: Towards a Comprehensive Definition of Fog Computing (2014, background): “The presence of small functional units (droplets) in more locations (devices) calls for the right abstractions to be in place, so that programmers do not need to deal with these difficult issues [12].”
Edge Computing for the Internet of Things: A Case Study (2018, methodology): “A similar concept is presented in [28], with edge-devices (including smartphones and connected vehicles), routers and on-demand dedicated compute instances employed for processing data in the fog platform.”
Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions (2018, background): “[94] proposed a high-level programming model that supports large-scale geospatially distributed time-sensitive applications.”
From Cloud to Fog Computing: A Review and a Conceptual Live VM Migration Framework (2017, background): “[33] described a video surveillance application that requires a three-level hierarchy system to perform motion detection with smart camera, face recognition with fog computing instances, and identity aggregation with cloud computing instances.”
All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey (2018, methodology): “[199] propose a PaaS model called Mobile Fog that provides a programming abstraction and allows applications to easily use fog resources while supporting dynamic scaling at runtime.”
A Survey of Fog Computing: Concepts, Applications and Issues (2015, background): “[22] propose a high-level programming model for future Internet applications with on-demand scaling, which are large-scale geospatially distributed and latency sensitive.”
A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges (2017, methodology): “The ﬁrst programming architecture is called mobile-fog and is proposed by Hong et al. [51].”
Survey on fog computing: architecture, key technologies, applications and open issues (2017, background): “…transportation, smart home, Internet of Vehicles, etc.), the resources and service interfaces of fog nodes are highly dynamic and heterogeneous at different levels of architecture hierarchy to address the requirements of widely distributed applications that need low latency (Hong et al., 2013).”
Resource Management in Fog/Edge Computing (2018, background): “ting and recent fog/edge computing paradigms. Tasks that require prompt reaction are processed in fog/edge nodes whereas complex or long-term analysis tasks are performed at more powerful cloud nodes [138], [139], [140]. Mobile Fog facilitates hierarchical fog/edge computing. It enables easy communi”
Fog Computing: Principles, Architectures, and Applications (2016, methodology): “Mobile Fog [10], an API for developing futuristic applications which leverage the large-scale, geo-distribution and low latency guarantee provided by the fog computing infrastructure.”
Assessment of the Suitability of Fog Computing in the Context of Internet of Things (2018, methodology): “The devices of the fog computing tier of are “intelligent” in terms of their computational and storage ability [41], [43].”