Landscape of IoT Patterns
February 26, 2019 Β· Declared Dead Β· π International Workshop on Software Engineering Research & Practices for the Internet of Things
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Hironori Washizaki, Nobukazu Yoshioka, Atsuo Hazeyama, Takehisa Kato, Haruhiko Kaiya, Shinpei Ogata, Takao Okubo, Eduardo B. Fernandez
arXiv ID
1902.09718
Category
cs.SE: Software Engineering
Citations
17
Venue
International Workshop on Software Engineering Research & Practices for the Internet of Things
Last Checked
4 months ago
Abstract
Patterns are encapsulations of problems and solutions under specific contexts. As the industry is realizing many successes (and failures) in IoT systems development and operations, many IoT patterns have been published such as IoT design patterns and IoT architecture patterns. Because these patterns are not well classified, their adoption does not live up to their potential. To understand the reasons, this paper analyzes an extensive set of published IoT architecture and design patterns according to several dimensions and outlines directions for improvements in publishing and adopting IoT patterns.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted