Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.4
2.3
Journals
Mathematics
Special Issues
Computational Methods for Network Optimization and Security
share announcement

Computational Methods for Network Optimization and Security

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 21 August 2026 | Viewed by 5779

Editors

Dr. Qin Wang

E-Mail Website
Guest Editor
School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Interests: blockchain; low-altitude intelligent networks; spectrum sensing and sharing; game-theoretic resource optimization
Dr. Yang Liu

E-Mail Website
Guest Editor
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
Interests: 6G mobile communications; internet of things; multi-scenario channel modeling; intelligent transmission technology
Special Issues, Collections and Topics in MDPI journals
Dr. Ziye Jia

E-Mail Website
Guest Editor Assistant
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: space-air-ground network; UAV swarm; aerial access network; low-altitude economy

Special Issue Information

Dear Colleagues,

This Special Issue of Mathematics will explore “Computational Methods for Network Optimization and Security” in depth, addressing the critical demands of increasingly complex, heterogeneous, and intelligent network environments. We invite original research and review papers that develop or apply advanced computational methods, mathematical modeling, and intelligent algorithms to enhance the performance, resilience, and trustworthiness of modern networks. The scope of this Special Issue spans network performance optimization, including but not limited to resource allocation, intelligent traffic routing, topological design, and dynamic load balancing, alongside computational approaches for securing networks across multiple layers, including AI-driven intrusion detection systems, the optimization of cryptographic protocols, and threat modeling based on graph theory. Covered topics range from innovative heuristic algorithms for large-scale network optimization to quantum-secure computational methods for security. We also encourage contributions in the following related domains:

  • Dynamic Resource Scheduling and Load Balancing;
  • Network Slicing and Quality of Service (QoS) Assurance;
  • Topology Optimization and Link Configuration;
  • Multi-Access Edge Computing (MEC) and Task Offloading Optimization;
  • Green Networking and Energy Efficiency Optimization;
  • Multi-Hop Communication Optimization in IoT/Vehicle Networks;
  • Joint Communication-Computing-Storage Optimization;
  • Blockchain-based Security Mechanisms;
  • Secure and Efficient Utilization of Network Resources.

We look forward to receiving your contributions.

Dr. Qin Wang
Dr. Yang Liu
Guest Editors

Dr. Ziye Jia
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational methods
  • network optimization
  • network security
  • algorithm design
  • cybersecurity resilience
  • blockchain

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 1036 KB  
Article
Dual-Scale Grid-Based Adaptive Trajectory Planning for UAVs in Urban Low-Altitude Airspace
by Xin Zhang, Guang Cheng, Chao Wang, Yu Liu, Guanwang Jiang and Ziye Jia
Mathematics 2026, 14(15), 2700; https://doi.org/10.3390/math14152700 (registering DOI) - 28 Jul 2026
Abstract
With the rapid growth of the low-altitude economy, the number of unmanned aerial vehicles (UAVs) has grown rapidly. It is challenging to plan substantial UAV trajectories in complex urban low-altitude airspace, considering the airspace capacity, inter-vehicle safety, and communication reliability. To deal with [...] Read more.
With the rapid growth of the low-altitude economy, the number of unmanned aerial vehicles (UAVs) has grown rapidly. It is challenging to plan substantial UAV trajectories in complex urban low-altitude airspace, considering the airspace capacity, inter-vehicle safety, and communication reliability. To deal with this challenge, we propose a dual-scale grid-based trajectory planning approach that separates the global routing and local refinement. Specifically, we discretize the three-dimensional airspace into coarse macro-grids for capacity-constrained routing and high-quality communication-aided fine micro-grids for collision-free trajectory refinement. Both consider an altitude-dependent energy model. To handle the complex dual-scale grid trajectory planning of UAVs, we propose a priority-driven dual-grid Theta* with adaptive relaxation (DGTAR) to balance the global planning efficiency and local obstacle avoidance accuracy. First, we design a priority-driven capacity allocation mechanism to enforce safe separation among UAVs. Then, a combined planner is proposed, which integrates a Theta*-enhanced global search with a sampling-based refinement algorithm, invoking on-demand boundary relaxation to ensure the feasibility. Simulation results reveal that the proposed method DGTAR achieves reductions in many aspects compared with benchmark mechanisms, while maintaining a high planning success rate in congested scenarios. Full article
(This article belongs to the Special Issue Computational Methods for Network Optimization and Security)
►▼ Show Figures

Figure 1

17 pages, 750 KB  
Article
Efficient Computational Algorithms for Non-Convex Constrained Beamforming in Heterogeneous IoV Backhaul Networks
by Haowen Zheng, Zeyu Wang, Chun Zhu, Haifeng Tang and Xinyi Hui
Mathematics 2026, 14(8), 1372; https://doi.org/10.3390/math14081372 - 19 Apr 2026
Viewed by 369
Abstract
The rapid expansion of the Internet of Vehicles (IoV) necessitates high-capacity backhaul connectivity, yet the deployment of such networks under strict hardware and power constraints poses significant computational challenges for network optimization. To address this challenge, this paper investigates a joint transmit–receive beamforming [...] Read more.
The rapid expansion of the Internet of Vehicles (IoV) necessitates high-capacity backhaul connectivity, yet the deployment of such networks under strict hardware and power constraints poses significant computational challenges for network optimization. To address this challenge, this paper investigates a joint transmit–receive beamforming optimization problem for narrowband wireless backhaul in IoV networks under constant-modulus constraints. Unlike ideal digital architectures, we focus on cost-effective analog phase shifters, which introduce strictly non-convex constant-modulus constraints, rendering the optimization problem mathematically intractable for standard solvers. Since the resulting problem is highly non-convex, we develop two structured numerical methods: an iterative alternating optimization (AO) method and a joint optimization (JO) method, where AO employs auxiliary WMMSE-guided alternating updates together with constant-modulus projection, while JO jointly updates both beamformers over the constant-modulus feasible set. We compare their achievable sum-rate performance with that of a CDO-based benchmark and analyze their dominant computational costs through representative Big-O complexity expressions. Furthermore, we examine the effect of SVD-based and random feasible initializations on empirical convergence behavior, runtime, and final achievable performance. Simulation results demonstrate that the proposed computational methods significantly improve achievable sum-rate performance compared with the CDO benchmark. Moreover, SVD-based initialization provides a more structured starting point and generally leads to better convergence behavior and lower runtime than random feasible initialization. The empirical timing results further show that AO exhibits faster empirical convergence and requires lower runtime, whereas JO achieves better final sum-rate performance after more iterations. Full article
(This article belongs to the Special Issue Computational Methods for Network Optimization and Security)
►▼ Show Figures

Figure 1

20 pages, 951 KB  
Article
Resilient Collaborative Control Method for Transportation Hubs Considering Communication Reliability
by Haifeng Tang, Yongchao Fan, Ying Zhang and Zeyu Wang
Mathematics 2026, 14(6), 982; https://doi.org/10.3390/math14060982 - 13 Mar 2026
Viewed by 415
Abstract
As traffic demand increases and intelligent transportation systems continue to develop, traffic signal control must operate reliably in complex and heterogeneous network environments, especially under communication instability. Traditional approaches often lack sufficient resilience when facing packet loss, delay, and other communication disturbances. This [...] Read more.
As traffic demand increases and intelligent transportation systems continue to develop, traffic signal control must operate reliably in complex and heterogeneous network environments, especially under communication instability. Traditional approaches often lack sufficient resilience when facing packet loss, delay, and other communication disturbances. This study proposes a resilient collaborative control (RCC) method for transportation hubs that explicitly considers communication reliability. A multi-layer computational framework is developed to support real-time mapping and interaction between physical and virtual networks. A fuzzy-logic-based communication state perception model is introduced to guide adaptive control-mode switching. To improve network-level performance, a recovery-oriented optimization algorithm is applied for dynamic load balancing across the hub area. Co-simulation results show that, compared with traditional adaptive control, the proposed method reduces average vehicle delay by 42.3%, increases network speed by 52.3%, shortens recovery time by 63%, and improves the resilience index to 0.87. These results support the effectiveness of the proposed framework within the evaluated co-simulation setting. Full article
(This article belongs to the Special Issue Computational Methods for Network Optimization and Security)
►▼ Show Figures

Figure 1

21 pages, 669 KB  
Article
An Elevation-Aware Large-Scale Channel Model for UAV Air-to-Ground Links
by Naier Xia, Yang Liu and Yu Yu
Mathematics 2025, 13(21), 3377; https://doi.org/10.3390/math13213377 - 23 Oct 2025
Cited by 2 | Viewed by 3739
Abstract
This paper addresses the issue of existing research that fails adequately capture the spatiotemporal nonstationarity caused by the building of occlusion and flight dynamics in air-to-ground channels from unmanned aerial vehicles (UAVs) in urban scenarios. This study focuses on the angular-altitude correlations of [...] Read more.
This paper addresses the issue of existing research that fails adequately capture the spatiotemporal nonstationarity caused by the building of occlusion and flight dynamics in air-to-ground channels from unmanned aerial vehicles (UAVs) in urban scenarios. This study focuses on the angular-altitude correlations of three key metrics: path loss (PL), shadow fading, and the Ricean K-factor. A dynamic path-loss model incorporating the look-down angle is proposed, an exponential decay model for the shadow-fading standard deviation is constructed, and a model for the angle-dependent variation of the Ricean K-factor is established based on line-of-sight probability. Simulations were conducted in two urban-geometry scenarios using WinProp to evaluate the combined effects of flight altitude and elevation angle. The results indicate that path loss decreases and subsequently stabilizes with increasing elevation angle, the shadow-fading standard deviation decreases significantly, and the Ricean K-factor increases with angle and saturates at high angles, in agreement with theoretical predictions. These models are more adaptable to UAV mobility scenarios than traditional fixed exponential models and provide a useful basis for UAV link planning and system optimization in urban environments. Full article
(This article belongs to the Special Issue Computational Methods for Network Optimization and Security)
►▼ Show Figures

Figure 1

12 pages, 520 KB  
Article
A Collaborative Optimization Scheme for Beamforming and Power Control in MIMO-Based Internet of Vehicles
by Haifeng Tang, Fan Ding, Haitao Zhao, Jingyi Wu and Xinyi Hui
Mathematics 2025, 13(18), 2927; https://doi.org/10.3390/math13182927 - 10 Sep 2025
Cited by 1 | Viewed by 953
Abstract
Driven by advancements in communication technology, the Internet of Vehicles (IoV) has gained significant importance. Its capability for real-time information exchange and processing substantially enhances data transmission performance within multi-node distributed systems. Among core physical layer transmission technologies, beamforming and power allocation are [...] Read more.
Driven by advancements in communication technology, the Internet of Vehicles (IoV) has gained significant importance. Its capability for real-time information exchange and processing substantially enhances data transmission performance within multi-node distributed systems. Among core physical layer transmission technologies, beamforming and power allocation are crucial for optimizing system efficiency. However, the real-time joint optimization of the transmitter, receiver, and power allocation in MIMO-based IoV systems remains insufficiently addressed in existing research. To bridge this gap, this paper proposes a framework for the real-time joint optimization of beamforming and power allocation, aiming to maximize transmission efficiency while satisfying constant modulus constraints and power limitations. The proposed framework decomposes the problem and utilizes the CVX library to obtain a local optimum for the joint scheme. The simulation results show that compared with traditional beamforming methods, this scheme has better performance in multiple indicators, increasing the transmission rate of the system by 43%, having faster convergence speed, and improving spectral efficiency. Thus, this study achieves real-time joint optimization of MIMO beamforming and power allocation for IoV scenarios, providing crucial technical support for related designs. Full article
(This article belongs to the Special Issue Computational Methods for Network Optimization and Security)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop