A Resource-Based Dynamic Pricing and Forced Forwarding Incentive Algorithm in Socially Aware Networking

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The paper presents an innovative approach to incentivize selfish nodes in socially aware networking (SAN) through a resource-based dynamic pricing and forced forwarding incentive algorithm (DFIA). The proposal introduces virtual currency and blockchain technology to ensure transparency and immutability in transaction records. The approach appears promising and well-researched, with significant potential to enhance network performance by increasing message delivery probability and reducing average latency. 

The experimental section of the paper primarily compares the performance of the proposed DFIA algorithm with three scenarios:

1. Without any incentive mechanism (S + selfish nodes)
2. Using DFIA incentive algorithm (S + selfish nodes + DFIA)
3. Using RSIA incentive algorithm (S + selfish nodes + RSIA)
4. Using STIA incentive algorithm (S + selfish nodes + STIA)

The results are evaluated based on three metrics:

1. Message Delivery Probability
2. Average Latency
3. Network Overhead

These results indicate that DFIA outperforms in increasing message delivery probability and reducing average latency, although it incurs higher network overhead due to the use of blockchain technology.

Strengths

1. Multi-dimensional Evaluation: The experiments assess several key performance metrics, providing a comprehensive view of the algorithms' strengths and weaknesses.
2. Robust Parameter Settings: The experiments cover a range of different selfish node percentages and message TTL values, ensuring a broad evaluation of the algorithms under various network conditions.

Limitations

1. Limited Algorithm Comparison: Only two existing algorithms (RSIA and STIA) are compared. This might not be sufficient to fully demonstrate the superiority of DFIA. Including more existing algorithms, especially those widely cited and used in the literature, would provide a more thorough evaluation.
2. Single Scenario: The experiments are conducted in a relatively controlled simulation environment. Real-world applications often involve more complex and dynamic network conditions. More diverse scenarios should be considered to better evaluate the robustness and adaptability of DFIA.
3. Limited Performance Metrics: While message delivery probability, average latency, and network overhead are important, additional metrics such as energy consumption, computational overhead, and network throughput would provide a more comprehensive evaluation.

Recommendations

1. Expand Algorithm Comparison: Include a wider range of existing incentive algorithms for comparison, particularly those that have shown strong performance in related studies. This would help to better contextualize the performance of DFIA.
2. Diversify Experimental Scenarios: Consider a wider variety of network environments and real-world application scenarios, such as different mobility models, node densities, and network scales, to fully assess the algorithm’s effectiveness and stability.
3. Increase Performance Metrics: Introduce additional performance metrics, such as energy consumption, computational overhead, and network throughput, to offer a more detailed assessment of the algorithm’s impact on the network.

 

While the current experimental comparison provides a good initial demonstration of DFIA’s strengths, it appears somewhat limited in scope. Expanding the range of algorithms, scenarios, and performance metrics considered would allow for a more comprehensive and rigorous evaluation, better showcasing the potential and robustness of the proposed DFIA algorithm.

Comments on the Quality of English Language

The quality of the English language in the paper is generally good. The paper is mostly clear and comprehensible, with a well-structured presentation of ideas. However, there are some areas where improvements can be made to enhance readability and clarity.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Please also see the attachments.

Under the socially aware networking structure with the assumptions such that the nodes in the network typically exhibit selfish behaviors because of resource constraints and social correlation , this work proposed a resource-based dynamic pricing and forced forwarding incentive algorithm (named DFIA). The starting point of the work is Equation (1). In other words, in order to solve the problem of the real world given by Equation (1), four main assumptions (page 4) were selected in this paper. Also, two algorithms were proposed as a solution, and their performance was analyzed. As an interesting topic, it could be evaluated that many related studies are being conducted in the Introduction contents. However, it seems that the performance evaluation results of the proposed algorithm and the answers to the following questions about the given experimental environment should be clearly described.

1. There should be a review comment of the validity of mathematical modeling for the real world given by the four assumptions and Equation (1). First, a realistic discussion and review results of the four main assumptions are needed. If possible, it is necessary to summarize the review results of the cases in which the problem is realistically important and applied in practice in previous research results. And, if a given assumption is not allowed, the importance of the problem and the limitations of its reality should be described.

2. If the given assumptions are accepted, it is a review opinion on the reliability and validity of the problem-solving method. It is evaluated that various methods have been proposed to solve a  given problem in the Introduction content. Therefore, a comparative review with existing research results must be carried out. Otherwise, if it is first in the world to present an algorithm in this work, it is necessary to admit that there is a limit to the reality of a given problem. In addition, the performance of the two proposed algorithms must be compared with the optimal algorithm. It must be proved that the optimal solution can be guaranteed by all enumeration methods or mathematical methods for small-sized problems.

3. In addition, if the proposed problem is an NP-hard or NP-complete problem, the problem should be proved. Therefore, it must be explained that there is no choice but to solve the given problem with a heuristic algorithm. And, it is necessary to explain the limitations of application of existing algorithms and the superiority of the proposed method in comparison.

4. It is a question about the results of Figure 4 through Figure 9. As a natural result, it can be seen that the ‘S+selfish’ scenario with high overhead performs well. This is evaluated as a very natural result. Although the implications of this result could be seen in Chapter 4, it is insufficient to grasp the excellence of the proposed algorithm. The implications of the result and its importance should be described more clearly.

5. It should be more clearly explained that the given scenario and the parameter values in Table 1 are data of a realistic environment. Rather than just the data given in the existing literature, it is necessary to explain together how useful the parameter is applied in a certain environment and this problem is realistically used.

6. Finally, the contents of the abstract and conclusion are too poor. In particular, in the conclusion section, the importance, adopted assumptions, and limitations of the given problem should be explained in more detail. In addition, before concluding, it is necessary to summarize the implications of the research results and the performance of the algorithm for problem-solving results.

Comments on the Quality of English Language

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

To confirm that the revision was made by reflecting the review comments.

Truly yours.