Cooperative and Control of Dynamic Complex Networks

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 8650

Special Issue Editors


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Guest Editor
College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
Interests: large-scale heterogeneous dynamic networks; computer networks; wireless networks and complex networks
College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
Interests: mobile communication networks; Internet of Things; complex networks; maritime communication; edge computation and resource management

Special Issue Information

Dear Colleagues,

Complex networks have attracted a great deal of attention among both academic and industry communities due to their prominent advantages, such as abundant application scenarios. Many application scenarios require consideration of the dynamical behaviors of complex networks, calling for the re-design of many research concepts. Network controllability is one of the most important target problems to be tackled for dynamic complex networks to achieve objectives such as stabilization and synchronization. In addition, cooperation among nodes of complex networks is useful for controllability, synchronization, task completion, etc.

The aim of this Special Issue is to focus on the cooperation and control of dynamic complex networks, and to bring together researchers, industry practitioners, and individuals working on critical issues in dynamic complex networks to share their new ideas, latest findings, and state-of-the-art results. Potential topics to be covered by the Issue include, but are not limited to:

  • Control algorithm design (e.g., adaptive control, pinning control, and impulsive control);
  • Synchronization control of dynamic complex networks;
  • Control protocol/controller design;
  • Network controllability analysis;
  • Network models for cooperation and control;
  • Application of artificial intelligence techniques to cooperation and control of dynamic complex networks;
  • Community detection for cooperation and control;
  • Cooperation and control of complex network survivability;
  • Performance evaluation and simulation.

We look forward to receiving your contributions.

Prof. Dr. Shengming Jiang
Dr. Yanli Xu
Guest Editors

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Keywords

  • control algorithm design (e.g., adaptive control, pinning control, and impulsive control)
  • synchronization control of dynamic complex networks
  • control protocol/controller design
  • network controllability analysis
  • network models for cooperation and control
  • application of artificial intelligence techniques to cooperation and control of dynamic complex networks
  • community detection for cooperation and control
  • cooperation and control of complex network survivability
  • performance evaluation and simulation

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Published Papers (6 papers)

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Research

13 pages, 3099 KiB  
Article
Power Allocation Based on Multi-Agent Deep Deterministic Policy Gradient for Underwater Acoustic Communication Networks
by Xuan Geng and Xinyu Hui
Electronics 2024, 13(2), 295; https://doi.org/10.3390/electronics13020295 - 9 Jan 2024
Viewed by 1109
Abstract
This paper proposes a reinforcement learning-based power allocation for underwater acoustic communication networks (UACNs). The objective function is formulated as maximizing channel capacity under constraints of maximum power and minimum channel capacity. To solve this problem, a multi-agent deep deterministic policy gradient (MADDPG) [...] Read more.
This paper proposes a reinforcement learning-based power allocation for underwater acoustic communication networks (UACNs). The objective function is formulated as maximizing channel capacity under constraints of maximum power and minimum channel capacity. To solve this problem, a multi-agent deep deterministic policy gradient (MADDPG) approach is introduced, where each transmitter node is considered as an agent. Given the definition of a Markov decision process (MDP) model for this problem, the agents learn to collaboratively maximize the channel capacity by deep deterministic policy gradient (DDPG) learning. Specifically, the power allocation of each agent is obtained by a centralized training and distributed execution (CTDE) method. Simulation results show the sum rate achieved by the proposed algorithm approximates that of the fractional programming (FP) algorithm and improves by at least 5% compared with the DQN (deep Q-learning network) -based power allocation algorithm. Full article
(This article belongs to the Special Issue Cooperative and Control of Dynamic Complex Networks)
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15 pages, 2160 KiB  
Article
Energy Efficiency Optimization for a V2X Network with NOMA-CoMP Enabled
by Woping Xu and Junhui Tian
Electronics 2023, 12(22), 4590; https://doi.org/10.3390/electronics12224590 - 10 Nov 2023
Viewed by 1100
Abstract
With the increase in user density, it is a challenging problem to achieve higher energy efficiency in vehicle-to-everything (V2X) networks, where frequent handovers occur as users continuously pass through dense cell boundaries. In this paper, we investigate this energy efficiency problem in a [...] Read more.
With the increase in user density, it is a challenging problem to achieve higher energy efficiency in vehicle-to-everything (V2X) networks, where frequent handovers occur as users continuously pass through dense cell boundaries. In this paper, we investigate this energy efficiency problem in a highly dense V2X network. To alleviate inter-cell interference and reduce unnecessary handovers, we propose a user-centric network architecture and utilize the coordinated multiple point (CoMP) transmission scheme to enable multiple roadside access points (APs) simultaneously to service one mobile vehicle. Moreover, non-orthogonal multiple access (NOMA) is adopted to enable multiple users to multiplex within one sub-channel and improve system spectrum reuse. Accordingly, the energy efficiency optimization problem is formulated as system achievable rate per joule, with the constraints of the AP transmit power budget, vehicle minimum rate requirements, and the maximum number of CoMP APs serving each vehicle. To solve the optimization problem, a semi-dynamic CoMP AP clustering algorithm is proposed to select a group of CoMP APs to serve a vehicle user by considering the vehicle–AP channel coefficients and the vehicle user’s achievable energy efficiency. Then, with the selected CoMP AP clusters, a bidirectional selection algorithm is applied via NOMA to select multiple vehicle users multiplexing within one sub-channel and perform sub-channel power allocation. Numerical simulations are presented to verify the effectiveness of the proposed algorithms, and the results show the improvements offered by the proposed algorithms compared to current benchmarks. Full article
(This article belongs to the Special Issue Cooperative and Control of Dynamic Complex Networks)
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23 pages, 1178 KiB  
Article
Novel Synchronization Criteria for Non-Dissipative Coupled Networks with Bounded Disturbances and Time-Varying Delays of Unidentified Bounds via Impulsive Sampling Control
by Hongguang Fan, Kaibo Shi, Yanan Xu, Rui Zhang, Shuai Zhou and Hui Wen
Electronics 2023, 12(19), 4175; https://doi.org/10.3390/electronics12194175 - 8 Oct 2023
Viewed by 908
Abstract
The μsynchronization issues of non-dissipative coupled networks with bounded disturbances and mixed delays are studied in this article. Different from existing works, three kinds of time delays, including internal delays, coupling delays, and impulsive sampling delays, have unidentified bounds and even [...] Read more.
The μsynchronization issues of non-dissipative coupled networks with bounded disturbances and mixed delays are studied in this article. Different from existing works, three kinds of time delays, including internal delays, coupling delays, and impulsive sampling delays, have unidentified bounds and even evolve towards infinity over time, making the concerned network more practical. Considering μstability theory and impulse inequality techniques, a hybrid non-delayed and time-delayed impulsive controller including both current and historical state information is designed, and several novel sufficient conditions are derived to make nonlinear complex networks achieve μsynchronization. Moreover, not only can the constriction of dissipative coupling conditions on network topology be relaxed, but also the restriction of various time delays on impulsive intervals can be weakened, which makes the theoretical achievements in this article more general than the previous achievements. Ultimately, numerical simulations confirm the effectiveness of our results. Full article
(This article belongs to the Special Issue Cooperative and Control of Dynamic Complex Networks)
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21 pages, 4567 KiB  
Article
Robustness of Double-Layer Group-Dependent Combat Network with Cascading Failure
by Jintao Yu, Bing Xiao and Yuzhu Cui
Electronics 2023, 12(14), 3061; https://doi.org/10.3390/electronics12143061 - 13 Jul 2023
Cited by 2 | Viewed by 1109
Abstract
The networked combat system-of-system (CSOS) is the trend of combat development with the innovation of technology. To achieve the combat effectiveness, studying the ability of CSOS to cope with external interference is of great importance. Here we report a modeling method of CSOS [...] Read more.
The networked combat system-of-system (CSOS) is the trend of combat development with the innovation of technology. To achieve the combat effectiveness, studying the ability of CSOS to cope with external interference is of great importance. Here we report a modeling method of CSOS from the perspective of complex networks and explore the robustness of the combat network based on this. Firstly, a more realistic double-layer heterogeneous dependent combat network model is established. Then, the conditional group dependency situation is considered to design failure rules for dependent failure, and the coupling relation between the double-layer subnets is analyzed for overload failure. Based on this, the initial load and capacity of the node are defined, respectively, as well as the load redistribution strategy and the status judgment rules for the cascading failure model. Simulation experiments are carried out by changing the attack modes and different parameters, and the results show that the robustness of the combat network can be effectively improved by improving the tolerance limit of one-way dependency of the functional net, the node capacity of the functional subnet, and the tolerance of the overload state. The conclusions of this paper can provide a useful reference for network structure optimization and network security protection in the military field. Full article
(This article belongs to the Special Issue Cooperative and Control of Dynamic Complex Networks)
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16 pages, 4863 KiB  
Article
Performance Analysis of a Reconfigurable-Intelligent-Surfaces-Assisted V2V Communication System
by Kainan Li, Siyuan Zhou and Guoping Tan
Electronics 2023, 12(11), 2383; https://doi.org/10.3390/electronics12112383 - 24 May 2023
Cited by 3 | Viewed by 1969
Abstract
Novel reconfigurable Intelligent Surfaces (RISs) area technology can improve the communication performance by changing the wireless transmission environment. Introducing RIS technology into Vehicle-to-Vehicle (V2V) communication environments can enhance the communication reliability by creating Line-of-Sight (LoS) communication links, thereby effectively improving the communication performance. [...] Read more.
Novel reconfigurable Intelligent Surfaces (RISs) area technology can improve the communication performance by changing the wireless transmission environment. Introducing RIS technology into Vehicle-to-Vehicle (V2V) communication environments can enhance the communication reliability by creating Line-of-Sight (LoS) communication links, thereby effectively improving the communication performance. However, in RIS-assisted V2V large-scale communication networks, the stochasticity of network nodes and random interference can impact performance. In this article, we examine the outage communication transmission performance of an RIS-assisted V2V communication network. We select the signal transmission mode based on the obstacle presence between vehicles and use the stochastic geometry theory to calculate the probabilities of the two modes: direct mode and RIS-assisted mode. By deriving the communication distance distribution and the aggregate interference distribution, we evaluate V2V communication in the direct mode to assess its transmission performance in two scenarios and obtain the overall outage probability. The numerical results demonstrate a better performance in RIS-assisted V2V networks, with an improved optimal phase shift scheme over that of the original V2V network. Monte Carlo simulation validated our analytical findings. Full article
(This article belongs to the Special Issue Cooperative and Control of Dynamic Complex Networks)
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29 pages, 10210 KiB  
Article
Joint Edge Computing and Caching Based on D3QN for the Internet of Vehicles
by Geng Chen, Jingli Sun, Qingtian Zeng, Gang Jing and Yudong Zhang
Electronics 2023, 12(10), 2311; https://doi.org/10.3390/electronics12102311 - 20 May 2023
Cited by 1 | Viewed by 1570
Abstract
With the Internet of Vehicles (IOV), a lot of self-driving vehicles (SDVs) need to handle a variety of tasks but have very seriously limited computing and storage resources, meaning they cannot complete intensive tasks timely. In this paper, a joint edge computing and [...] Read more.
With the Internet of Vehicles (IOV), a lot of self-driving vehicles (SDVs) need to handle a variety of tasks but have very seriously limited computing and storage resources, meaning they cannot complete intensive tasks timely. In this paper, a joint edge computing and caching based on a Dueling Double Deep Q Network (D3QN) is proposed to solve the problem of the multi-task joint edge calculation and caching process. Firstly, the processes of offloading tasks and caching them to the base station are modeled as optimization problems to maximize system revenues, which are limited by system latency and energy consumption as well as cache space for computing task constraints. Moreover, we also take into account the negative impact of the number of unfinished tasks in relation to the optimization problem—the higher the number of unfinished tasks, the lower the system revenue. Secondly, we use the D3QN algorithm together with the cache models to solve the formulated NP-hard problem and select the optimal caching and offloading action by adopting an e-greedy strategy. Moreover, two cache models are proposed in this paper to cache tasks, namely the active cache, based on the popularity of the task, and passive cache, based on the D3QN algorithm. Additionally, tasks which deal with cache space are updated by computing the expulsion value based on type of popularity. Finally, simulation results show that the proposed algorithm has good performance in terms of the latency and energy consumption of the system and that it improves utilization of cache space and reduces the probability of unfinished tasks. Compared to the Deep Q Network with caching policy, with the Double Deep Q Network with caching policy and Dueling Deep Q Network with caching policy, the system revenue of the proposed algorithm is improved by 65%, 35% and 66%, respectively. The scenario of the IOV proposed in this article can be expanded to larger-scale IOV systems by increasing the number of SDVs and base stations, and the content caching and download functions of the Internet of Things can also be achieved through collaboration between multiple base stations. However, only the cache model is focused on in this article, and the design of the replacement model is not good enough, resulting in a low utilization of cache resources. In future work, we will analyze how to make joint decisions based on multi-agent collaboration for caching, offloading and replacement in IOV scenarios with multiple heterogeneous services to support different Vehicle-to-Everything services. Full article
(This article belongs to the Special Issue Cooperative and Control of Dynamic Complex Networks)
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