Deep Learning Entrusted to Fog Nodes (DLEFN) Based Smart Agriculture

Round 1

Reviewer 1 Report

Describing backgrounds, motivation, introduction were longer than I expected. It would have been great if this article talks straight-to-the-point of the main theme of the paper.

A few typos found in the context. (e.g., equation 2 vs Equation 2)

Otherwise, overall well-written paper.

Author Response

Thank you for the review, their time on reviewing and the valuable comment. Agreeing with the reviewer’s suggestion, we eliminated and condensed a wordy sentence in “abstract” and “introduction”. Furthermore, we added some sentence to highlight on the key objective of the article.

Please see the attachment.

Thank you again for your kind words.

Author Response File: Author Response.docx

Reviewer 2 Report

the authors Lee et al. in their submitted manuscript named "DLEFN: Deep Learning Entrusted to Fog Nodes based Smart Agriculture" presented an efficient way to handle maximum numbers of deep learning applications by taking advantage of maximum available resources on fog nodes while simultaneously reducing the load on the cloud and the network congestion. Their proposed DLEFN promises to deliver the result and showed improvements in number of allowed Deep Learning algorithms, efficiency in bandwidth and in capacity efficiency and cloud overhead. The work is sound and well-presented with sufficient supporting data. I would recommend this work to be accepted.

Author Response

Thank you so much for your kind words.
We really appreciate you taking the time out to share your experience with us.
Thank you again for the review and their time for reviewing.