Security and Machine Learning Adoption in IoT: A Preliminary Study of IoT Developer Discussions

Authors: Gias Uddin

2021 3rd International Workshop on Software Engineering Research & Practices for the Internet of Things (SERP4IoT)

Results of the summarizing process for the arXiv paper: 2104.00634v1

  • Comprehensive Summary
  • Key points
  • Layman's Summary
  • Blog article
The summary is not ready yet
Summaries already available in other languages: fr

Abstract: Internet of Things (IoT) is defined as the connection between places and physical objects (i.e., things) over the internet/network via smart computing devices. Traditionally, we learn about the IoT ecosystem/problems by conducting surveys of IoT developers/practitioners. Another way to learn is by analyzing IoT developer discussions in popular online developer forums like Stack Overflow (SO). However, we are aware of no such studies that focused on IoT developers' security and ML-related discussions in SO. This paper offers the results of preliminary study of IoT developer discussions in SO. We find around 12% of sentences contain security discussions, while around 0.12% sentences contain ML- related discussions. We find that IoT developers discussing security issues frequently inquired about how the shared data can be stored, shared, and transferred securely across IoT devices and users. We also find that IoT developers are interested to adopt deep neural network-based ML models into their IoT devices, but they find it challenging to accommodate those into their resource-constrained IoT devices. Our findings offer implications for IoT vendors and researchers to develop and design novel techniques for improved security and ML adoption into IoT devices.

Submitted to arXiv on 01 Apr. 2021

Tools for this paper

Explain it differently 3 free rewrites left today
Export BibTeX · RIS (Zotero, Mendeley, EndNote) · Markdown (Notion, Obsidian) · Premium
Citation alerts Premium
Journal club Slides to present it Premium
For journalists Press kit and hype check Premium

Ask questions about this paper to our AI assistant

You can also chat with multiple papers at once here.

⚠The license of the paper does not allow us to build upon its content and the AI assistant only knows about the paper metadata rather than the full article.

Figures and plots

Ask about a figure by number or use its question button to request visual analysis. Up to two figures and four image panels per question. Values read from plots are approximate; check the original data.

AI assistant instructions?

Assess the quality of the AI-generated content by voting

Score: 0

Why do we need votes?

Votes are used to determine whether we need to re-run our summarizing tools. If the count reaches -10, our tools can be restarted.

The previous summary was created more than a year ago and can be re-run (if necessary) by clicking on the Run button below.

⚠The license of this specific paper does not allow us to build upon its content and the summarizing tools will be run using the paper metadata rather than the full article. However, it still does a good job, and you can also try our tools on papers with more open licenses.

Similar papers summarized with our AI tools

Navigate through even more similar papers through a

tree representation

Disclaimer: The AI-based summarization tool and virtual assistant provided on this website may not always provide accurate and complete summaries or responses. We encourage you to carefully review and evaluate the generated content to ensure its quality and relevance to your needs.