Topology Duration Optimization for UAV Swarm Network under the System Performance Constraint
Round 1
Reviewer 1 Report
The aim of the paper is to propose a novel topology construction method for UAV swarm network, which takes into account the criterion of topology duration in addition to other important criteria such as network throughput, end-to-end delay, and nodes energy consumption. The paper aims to formulate the topology construction of swarm network as an optimization problem and solve it using a double-head clustering method that considers group similarity of movement, intra- and inter-cluster distance, node forwarding delay, and energy strategy. The proposed method is designed to be effective in constructing network topology for large-scale UAV swarm scenarios, and the paper verifies its effectiveness through simulation results and comparison with representative algorithms.
There is not much new content in the work. a basic/ordinary simulation study for obtaining a swarm formation.
In the literature of the study, there is no serious problem and scientific contribution that has been criticized. The same is true for the method, results and discussion sections. In algorithm and simulation studies, it is not possible to accept the publication unless a serious benchmarking is made, and this situation cannot be ignored / unavoidable in today's studies.
Author Response
We highly thank the reviewer for this comment. In this paper, we focus on the method of network topology construction for UAV swarm based on the criterion of topology stability. In fact, topology stability is a necessary condition for the operation and efficient collaboration of the entire UAV swarm system. Due to the dynamic attributes of swarm system, the relative position or motion relationships of UAV nodes change frequently, which results in dynamically changed swarm network topology. This will lead to high frequency maintenance or reconfiguration of network topology, which will cause a sharp decrease in the efficiency of collaborative tasks. Besides, an unstable network topology will generate isolated offline nodes, which will bring extra difficulty to the flight control of UAV swarm. Therefore, a reliable swarm network topology has a vital influence on the performance of the swarm network and even the operation of whole swarm system.
However, the existing researches do not design the topology construction methods for UAV swarm based on the criterion of topology duration. In this paper, we firstly proposed an optimization model for the network topology construction of UAV swarm, where the topology construction is formulated as a problem of maximizing the topology duration while satisfying the constraints of specific network throughput, end-to-end delay and nodes energy consumption of the system. Then, a novel group trend similarity based double-head clustering method is employed to solve this optimization problem, in which both current status and dynamic trend of the swarm, such us intra- and inter-cluster distance, node forwarding hops, group similarity of movement and energy strategy are fully considered in the proposed method. Simulation results demonstrate the effectiveness and performance advantage of proposed method.
In addition, we added the comparison and analysis of two recently published benchmarking algorithms in the simulations. The first algorithm was published in MDPI in 2022, referring to reference [20] cited in the manuscript, which simultaneously considers the network throughput performance and the energy consumption of nodes in the clustering process. The second algorithm was published in IEEE Access in 2021, i.e., reference [24] cited in this manuscript, where a double cluster heads method based on Political Optimizer is proposed to improve the performance of swarm network.
Note that the related discussion in the revision has also been highlighted in RED color in the document "Resubmitted manuscript with colored markings".
Thank you again for your valuable advice.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The authors of the submitted paper proposed a novel topology construction method for UAV swarm network based on the criterion of topology duration. In the solution of the optimization model, they employed a double-head clustering method, in which key factors affecting network performance are considered comprehensively. The proposed method is superior to the such as MST, K-means, and PSO methods in the performance of topology duration, network throughput, end-to-end delay, and energy consumption balance. I consider the topic of the submitted contribution to be current due to the wide application of UAVs in practice. I have no major objections to the research, which is well described in the submitted paper. The research strategy and results are clearly described. I appreciate the proposal of the topology construction method for the UAV swarm network based on the topology duration criterion. I consider the derived algorithms to be correct. I have no fundamental comments on the approximations adopted in their derivation. The simulation results correspond to the simplifications adopted in the derivation of the topology construction algorithms for the UAV swarm network. References are appropriate. I recommend publishing this post after slight modifications. I recommend adding specific simulation results to the conclusion of the paper and comparing them with other methods.
Minor editing of English language required
Author Response
We are very grateful and completely agree with this comment.
In the revision, we added the comparison and analysis of two recently published algorithms for the topology construction of UAV swarm in the simulations. The first algorithm was published in MDPI in 2022, referring to reference [20] cited in the manuscript, which simultaneously considers the network throughput performance and the energy consumption of nodes in the clustering process. The second algorithm was published in IEEE Access in 2021, i.e., reference [24] cited in this manuscript, where a double cluster heads method based on Political Optimizer is proposed to improve the performance of swarm network.
Note that the related discussion in the revision has also been highlighted in RED color in the document "Resubmitted manuscript with colored markings".
Thank you again for your valuable comment, which helped us improve the quality of our manuscript.
Author Response File: Author Response.pdf
Reviewer 3 Report
In this paper, the authors proposed a novel topology construction method for UAV swarm network based on the criterion of topology duration is proposed. Specially, the topology construction of swarm network is formulated as an optimization problem of maximizing the topology duration while satisfying the constraints of certain network throughput, end-to-end delay, and nodes energy consumption. Then, a double-head clustering method is employed to solve this problem, in which group similarity of movement, intra- and inter-cluster distance, node forwarding delay, and energy strategy are comprehensively taken into account.
M. A. Ouamri, R. Alkanhel, C. Gueguen, M. A. Alohali and S. S. M. Ghoneim, "Modeling and analysis of uav-assisted mobile network with imperfect beam alignment," Computers, Materials & Continua, vol. 74, no.1, pp. 453–467, 2023.
D. Alkama, M. A. Ouamri, M. S. Alzaidi, R. N. Shaw, M. Azni and S. S. M. Ghoneim, "Downlink Performance Analysis in MIMO UAV-Cellular Communication With LOS/NLOS Propagation Under 3D Beamforming," in IEEE Access, vol. 10, pp. 6650-6659, 2022
The article is of good quality, I accept the paper after minor revision
Author Response
Thank you very much for your good suggestion.
We have carefully studied the references provided, which are of great help for us to further understand the relevant models and methods of UAV swarm system therein.
In the revision, we added several references to enhance the stringency of the paper, which are shown as follows:
9. Ouamri, M.A.; Alkanhel, R.; Gueguen, C.; Alohali, M.A.; Ghoneim, S.S.M. Modeling and analysis of uav-assisted mobile network with imperfect beam alignment. CMC-Computers, Materials & Continua 2023, 74, 453–467
10. Ouamri, M.A.; Ote¸steanu, M.E.; Barb, G.; Gueguen, C. Coverage analysis and efficient placement of drone-BSs in 5G networks. Engineering Proceedings 2022, 14, 18.
13. Alkama, D.; Ouamri, M.A.; Alzaidi, M.S.; Shaw, R.N.; Azni, M.; Ghoneim, S.S.M. Downlink performance analysis in MIMO UAV-cellular communication with LOS/NLOS propagation under 3D beamforming. IEEE Access 2022, 10, 6650–6659.
33. Ouamri, M.A.; Alkanhel, R.; Singh, D.; El-Kenaway, E.S.M.; Ghoneim, S.S.M. Double deep q-network method for energy efficiency and throughput in a uav-assisted terrestrial network. International Journal of Computer Systems Science & Engineering 2023, 46, 73–92.
Note that this revision has also been highlighted in YELLOW color in the document "Resubmitted manuscript with colored markings".
Thank you again for your valuable suggestion.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
This paper is just an advanced work on simulation, and it is possible to prove its contribution and show that the findings are recompiled. In addition, it is necessary to share the relevant simulations, data and models, taking into account blind review, in order to prevent unethical situations such as stigma, theft, plagiarism, etc., and to ensure the reliability of the study.
Only traditional algorithms have been preferred for benchmarking, this does not indicate that the study has made a significant contribution. It would be better to try the proposed algorithms in similar or competing studies. In the last revision, the authors cited 4 papers from the same author group (somewhat suspicious and worrying, are they really necessary?), but there is no benchmarking with these publications. I strongly recommend that the authors replace them with more serious sources on this subject.
Author Response
Point 1: This paper is just an advanced work on simulation, and it is possible to prove its contribution and show that the findings are recompiled.
Response 1:
This paper investigates the problem of network topology construction for UAV swarm systems. Based on the fact that, in the dynamic UAV swarm scenarios, topology stability is an important factor that has to be considered, a novel group trend similarity based double-head clustering method is proposed to perform the network topology construction for UAV swarm systems, which is based on the criterion of topology stability while fully take into account the specific performance of network throughput, end-to-end delay and nodes energy consumption of the system. None of the current studies use the topology stability as a criterion for the topology construction of swarm network.
Clustering is an important research area in network topology construction, which has two critical steps, i.e., clustering and cluster head election. Most relevant studies are focused on these two steps. In this paper, an innovative design is made for the above steps by jointly considering the characteristics of the swarm system and the operation mechanism of the clustering network. The main innovations and contributions of this paper are as follows:
1) A novel model of network topology construction for UAV swarm systems is proposed, in which the topology construction is formulated as a problem of maximizing the topology duration while satisfying the constraints of specific network throughput, end-to-end delay and nodes energy consumption of the system.
2) A novel group trend similarity based double-head clustering method (GTSC) is proposed to solve the optimization problem. The proposed method takes full account of the current state and movement trends of the swarm system. In the process of clustering, based on the group mobility trend similarity of the swarm system, clusters are formed with nodes in close proximity and with similar mobility trend in order to maximize the topology duration. In the process of cluster head election, we take full account of mobility similarity, energy consumption strategies, and communication performance related factors including distance between nodes and inter-cluster hops.
3) In the simulations, we employed algorithms with fast convergence speed MST and K-means, representative swarm intelligence algorithm PSO that is capable of achieving approximate optimal solution, a newest algorithm IWCL where factors of residual energy ratio, adaptive node degree, relative mobility and average distance are considered, and a state-of-the-art double cluster heads based algorithm DCM with load-balancing to perform the performance comparisons. The proposed GTSC method is more superior to the above algorithms in the performance of topology duration, network throughput, end-to-end delay and energy consumption balance.
Note that this revision has also been highlighted in RED color in the document "Resubmitted manuscript with colored markings".
Point 2: In addition, it is necessary to share the relevant simulations, data and models, taking into account blind review, in order to prevent unethical situations such as stigma, theft, plagiarism, etc., and to ensure the reliability of the study.
Response 2:
Academic ethics and scientific rigor are the top and most important requirements for all academic studies in our institution, there is not any fraud or plagiarism at all in this work. All work in this work is absolutely authentic, reliable and original. All the conclusions in the paper are based on rational analysis in the description of method, and all the results have theoretical basis. In case of any doubt, we can provide the source code for originality assessment and academic arbitration.
Point 3: Only traditional algorithms have been preferred for benchmarking, this does not indicate that the study has made a significant contribution. It would be better to try the proposed algorithms in similar or competing studies.
Response 3:
Of particular note is that in the last revision, we have added two state-of-the-art algorithms, IWLC and DCM with load-balancing for benchmarking, while retaining the traditional algorithms.
IWLC is proposed in 2022, it is a novel location-based K-means++ clustering algorithm where factors including residual energy ratio, adaptive node degree, relative mobility and average distance are considered in clustering process.
DCM with load-balancing is proposed in 2021, it is a double cluster heads based topology construction method which utilizes the latest swarm intelligence algorithm, PO algorithm for clustering. It takes parameters of position, speed, moving direction, height variation and link quality into account in the clustering process while the load-balancing of cluster heads is also considered.
Point 4: In the last revision, the authors cited 4 papers from the same author group (somewhat suspicious and worrying, are they really necessary?), but there is no benchmarking with these publications. I strongly recommend that the authors replace them with more serious sources on this subject.
Response 4:
It is worth mentioning that the 4 papers you refer to were not used for benchmarking. References [9] and [10] are used to introduce the application scenarios of the UAV swarm system. Reference [13] is introduced to illustrate the influence of topology construction for UAV swarm systems in practical applications. Reference [33] is introduced to illustrate that the location relationship between communication nodes has a huge impact on network throughput.
In the newest revision, we have replaced the original references [9] and [10] to two survey literature as follows.
- Wang, J.; Jiang, C.; Han, Z.; Ren, Y.; Maunder, R.G.; Hanzo, L. Taking drones to the next level: Cooperative distributed unmanned-aerial-vehicular networks for small and mini drones. IEEE Vehicular Technology Magazine 2017, 12, 73–82.
- Bekmezci, I.; Sahingoz, O.K.; Temel, Ş. Flying ad-hoc networks (FANETs): A survey. Ad Hoc Networks 2013, 11, 1254–1270.
Author Response File: 
Author Response.docx
