Centralized Task Allocation and Alignment Based on Constraint Table and Alignment Rules

Round 1

Reviewer 1 Report

The paper presents a set of algorithms for allocating and aligning tasks using constraint tables and rules. While the existing methods incorporated into the approach are described extensively, it is unclear where the actual novel contributions made are.

1. There are many portions with unclear writing and meanings throughout the paper. Some of the more severe potions include
Abstract: For real-world application... in the techniques'
Introduction:
- According to this flow (what does flow mean?)... using the UxVs.
- the temporal constraints by just relationship, not permitted time and computation

2. In the Introduction, CBBA and CCBBA are shown to have several shortcomings. Game theory is then introduced as an alternative that addresses some of these shortcomings. The method used in this paper however still relies heavily on CBBA and CCBBA instead of game theory, without much justification. This severely undermines the motivation for the work.

3. While task allocation has been motivated well in the Introduction, very little attention is given to task alignment, and the motivation for the scoring schemes.

4. It is assumed that the temporal constraints are set `realistically' - how is this quantified? This is important for algorithm users to verify if this approach is applicable.

5. The central contribution of the work (i.e., the TACTAR algorithm in Section 5) needs to be clarified and motivated better, as well as related to the algorithms in the previous sections to show what it is addressing and how it contributes to the approach.

6. Check the numbering of the last section.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Summary

This paper proposed an algorithm for task allocation and alignment problems based on constraint tables and alignment rules. Furthermore, the authors conducted a series of simulations to validate the feasibility of the proposed method.

Some minor issues should be addressed to improve the quality of this paper.

1. The difference between CBBA and CCBBA seems to be insufficiently clear. Please clarify in more detail.

2. Since equation (2) includes four equations, the indicator of equation (2) on page 2 confuses the reader.

3. Some latest related works about task allocation should be discussed. For example,  “Reusing Delivery Drones for Urban Crowdsensing”, IEEE Transactions on Mobile Computing, 2022. “A Comparative Approach to Resurrecting the Market of MOD Vehicular Crowdsensing”, IEEE International Conference on Computer Communications, 2022.

4. There are some mistakes in the equation (2), such as \forall j \in, J.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Most of my comments have been addressed well. One last minor point however remains: it is still not immediately obvious that game theory is not fast enough, as mentioned in the clarification. More emphasis should be given on this point, since so much more explanation has been given for the strengths of the game theory approach versus its one weakness.

Furthermore, please indicate changes more obviously within the manuscript (e.g., with different coloured text) so it is easier to locate the changes made.

Author Response

Please see the attachment.

Author Response File: Author Response.docx