Exponential Consensus of Multiple Agents Over Dynamic Network Topology: Controllability, Connectivity, and Compactness

Due to the constantly moving of the communication nodes in the combat environment, the network topology is highly dynamic and uncertain. However, combat missions require the real-time and accurate information transmission through the military network. Therefore, it is of great practical interest to design a dynamic routing algorithm to accommodate to the dynamic and uncertain network topology. We consider this important problem in this paper and make the following contributions. First, a semi-Markov prediction model is proposed to predict the uncertainty of the network topology. Second, a dynamic resource table is updated by the formation transition probability which can describe the dynamic of the network topology. Third, the Q-learning method is applied to find an optimal routing in the uncertain and dynamic network topology. Numerical experiments demonstrate that the proposed method can predict the network topology more accurate and obtain an optimal routing strategy with improved transmission efficiency and reduced transmission delay.KeywordsMilitary networksSemi-MarkovTopology predictionQ-learningDynamic routing

Mobile Ad-hoc network is an autonomous system of mobile wireless nodes connected dynamically without any preexisting infrastructure. Here, since the nodes are mobile, the network topology changes rapidly and unpredictably over time. The QoS routing has challenging problems due to the network's dynamic topology. The multi hop wireless forwarding of packets, broadcasting of control traffic and the fact that, all nodes may act as routers in addition to being sources and sinks of data in ad-hoc networks produce different queuing behaviour. Hence, a scheduling algorithm to schedule the packets based on their respective priorities improves the performance of the network. Here, we present an efficient fuzzy based priority scheduler for mobile ad-hoc networks, to determine the priority of the packets. The performance of this scheduler is studied using GloMoSim and evaluated in terms of quantitative metrics such as packet delivery ratio, average end to end delay and throughput. It is found that the scheduler provides overall improvement in the performance of the system with various mobility models, when tested with different routing protocols.

Due to the high mobility and dynamic topology of the FANET network, maintaining communication links between UAVs is a challenging task. The topology of these networks is more dynamic than traditional mobile networks, which raises challenges for the routing protocol. The existing routing protocols for these networks partly fail to detect network topology changes. Few methods have recently been proposed to overcome this problem due to the rapid changes of network topology. We try to solve this problem by designing a new dynamic routing method for a group of UAVs using Hybrid SDN technology (SDN and a distributed routing protocol) with a highly dynamic topology. Comparison of the proposed method performance and two other algorithms is simulated. The simulation results show that the proposed method has better results than traditional algorithms in the package delivery ratio, average end to end delay, packet loss, throughput and normalized routing Load.

In this paper, we propose a machine learning-based mobility robustness optimization algorithm to optimize handover parameters for seamless mobility under dynamic small-cell networks. Small cells can be arbitrarily deployed, portable, and turned on and off to fulfill wireless traffic demands or energy efficiency. As a result, the small-cell network topology dynamically varies challenging network optimization, especially handover optimization. Previous studies have only considered dynamics due to user mobility in a specific static network topology. To optimize handovers under dynamic network topologies, together with user mobility, we propose an algorithm consisting of two steps: topology adaptation and mobility adaptation. To adapt to a dynamic topology, the algorithm obtains prior knowledge, which presents a belief distribution of the optimal handover parameters, for the current network topology as coarse optimization. In the second step, the algorithm fine-tunes the handover parameters to adapt to user mobility based on reinforcement learning, which utilizes the knowledge obtained during the first step. Under a dynamic small-cell network, we showed that the proposed algorithm reduced adaptation time to 4.17% of the time needed by a comparative machine-based algorithm. Furthermore, the proposed algorithm improved the user satisfaction rate to 416.7% compared to the previous work.

Choral singing requires the coordination of physiological subsystems within and across individuals. Previously, we suggested that the choir functions as a superordinate system that imposes boundary conditions on the dynamic features of the individual singers and found reliable differences in the network topography by analyzing within- and cross-frequency couplings (WFC and CFC, respectively). Here, we further refine our analyses to investigate hyper-frequency network (HFN) topology structures (i.e., the layout or arrangement of connections) using a graph-theoretical approach. In a sample of eleven singers and one conductor engaged in choral singing (aged between 23 and 56 years, and including five men and seven women), we calculated phase coupling (WFC and CFC) between respiratory, cardiac, and vocalizing subsystems across ten frequencies of interest. All these couplings were used for construction of HFN with nodes being a combination of frequency components and subsystems across choir participants. With regard to the network topology measures, we found that clustering coefficients (CCs) as well as local and global efficiency were highest and characteristic path lengths, correspondingly, were shortest when the choir sang a canon in parts as compared to singing it in unison. Furthermore, these metrics revealed a significant relationship to individual heart rate, as an indicator of arousal, and to an index of heart rate variability indicated by the LF/HF ratio (low and high frequency, respectively), and reflecting the balance between sympathetic and parasympathetic activity. In addition, we found that the CC and local efficiency for groups singing the same canon part were higher than for groups of singers constructed randomly post hoc, indicating stronger neighbor–neighbor connections in the former. We conclude that network topology dynamics are a crucial determinant of group behavior and may represent a potent biomarker for social interaction.

We investigate the use of distributed PID actions to achieve consensus and synchronization in networks of homogeneous and heterogeneous agents. We first analyze the case of distributed PID control on networks with heterogeneous nodes described by first-order linear systems. Convergence of the strategy is proved using appropriate state transformations and Lyapunov functions. Then, we propose a multiplex proportional-integral approach, for solving consensus problems in networks of heterogeneous n-dimensional node dynamics affected by constant disturbances. The proportional and integral actions are deployed on two different layers across the network, each with its own topology. Furthermore, the contribution of the network topology and node dynamics have been systematically separated giving some sufficient conditions guaranteeing convergence. Finally, an extension to networks of identical nonlinear node dynamics is presented. We provide local and global stability analysis together with a detailed performance assessment where heterogeneity among nodes and disturbances are considered. The effectiveness of the theoretical results is illustrated via its application to a representative power grid model recently presented in the literature and also for synchronization in networks of chaotic circuits.

Recently, there has been a growing interest in network controllability for determining the number and placement of controllers. In the framework of linear dynamics, this paper regarding the network controllability studies both the formulation of the control input matrix and the influence of the linear nodal dynamics, with precise guidelines derived on how to design the input matrix. This design strategy takes the column vectors of the input matrix as the basis to maximize the controllable subspace. In the one-dimensional case, it is found that nodal dynamics with high degrees of heterogeneity dominate the network topology, but identical nodal dynamics have no effect on the network controllability. In the higher dimensional case, it reveals why controllable network topology and controllable nodal dynamics together are still insufficient to ensure the controllability of the whole network, with a feasible solution to the problem presented based on a suitably designed input matrix. All characteristics of the network topology and nodal dynamics are integrated into the input matrix formulation, which plays an essential role in determining the network controllability.

The bioactive components of Astragalus membranaceus and Salvia miltiorrhiza improved cardiac and renal function in chronic heart failure (CHF) and chronic kidney disease (CKD), respectively. However, the common regulating molecular mechanisms remain unclear. The aim of this study was to investigate these mechanisms using bioinformatics, network topology, and molecular dynamics simulation techniques. The active components and target sites of A. membranaceus and S. miltiorrhiza were obtained from the Traditional Chinese Medicine Systems Pharmacology database. The targets of CKD and CHF were obtained from various databases for a protein-protein interaction analysis. The Gene Ontology (GO) function and Kyotoencyclopedia of genes and genomes (KEGG) pathway enrichment of intersection targets were analyzed by using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database. Molecular docking and dynamic simulations were conducted on the core ingredients and targets. The diagnostic efficiency of the key targets was evaluated by using receiver-operating characteristic (ROC) curves. A total of 70 active ingredients and 158 common targets were found. The top five core targets were AKT1, STAT3, TP53, MAPK1, and RELA. The GO enrichment analysis included apoptosis and oxidative stress. The KEGG pathway enrichment results indicated that the drug pair regulated the AGE-receptor for AGE signaling pathway, fluid shear stress and atherosclerosis, and the IL-17 signaling pathway. Molecular docking and dynamic simulations confirmed that the core ingredients had good affinity and stability with the key targets. The ROC curves confirmed the accuracy of every key target for identifying CKD and CHF and demonstrated that combining them improves diagnosis. The combination of A. membranaceus and S. miltiorrhiza proved effective for the treatment of CKD and CHF through various components, targets, and mechanisms. Moreover, it may predict the diagnostic value of key targets, providing a reference for clinical diagnostic applications.

Energy-aware scheduling for information fusion in wireless sensor network surveillance

Existing routing protocols may not be applicable in UAV networks because of their dynamic network topology and lack of accurate position information. In this paper, an enhanced flooding-based routing protocol is designed based on random network coding (RNC) and clustering for swarm UAV networks, enabling the efficient routing process without any routing path discovery or network topology information. RNC can naturally accelerate the routing process, with which in some hops fewer generations need to be transmitted. To address the issue of numerous hops and further expedite routing process, a clustering method is leveraged, where UAV networks are partitioned into multiple clusters and generations are only flooded from representatives of each cluster rather than flooded from each UAV. By this way, the amount of hops can be significantly reduced. The technical details of the introduced routing protocol are designed. Moreover, to capture the dynamic network topology, the Poisson cluster process is employed to model UAV networks. Afterwards, stochastic geometry tools are utilized to derive the distance distribution between two random selected UAVs and analytically evaluate performance. Extensive simulation studies are conducted to prove the validation of performance analysis, demonstrate the effectiveness of our designed routing protocol, and reveal its design insight.

As the scale of the network increases, especially wireless and mobile networks comes into fashion, the problem of network dynamic topology management turns into a focus topic in the network world. When network information changes, frequent route computing and network state updates can cause computing and traffic overhead, respectively. Therefore, minimum of influence to network structure has been identified as one of the key issues in designing dynamic network routing protocols. A novel protocol, which is based on Aggregation Tree Routing Model (ATRM) as well as an effective ball-and-string algorithm, is introduced in this paper which aims to minimize the alteration of network topology and to speed up the minimum cost route computation. This routing protocol is applicable to all kinds of networks, including Internet, ad hoc and wireless sensor networks. The simulations show that our protocol not only results in significantly lower overhead, but also gives high levels of dynamic network topology management performance while keeping practical.

Mobile ad hoc networks (MANET) are collections of self-organizing mobile nodes with dynamic topologies and have no fixed infrastructure. MANET do not have centralized administration, here nodes act as both host as well as router and communicate by forwarding packets for each other in multi hop way, because of the fundamental characteristics like, the open medium, dynamic network topology, lack of centralized monitoring and management these networks are particularly vulnerable to various types of attacks. One of the routing protocol being used for wireless network is Ad hoc on demand distance vector (AODV). Black hole is one of the attack that is launched on AODV. In this attack, the attacker claims the shortest route to the destination and hinders the communication by dropping all the packets. In this paper, a new solution against the Black Hole attack is proposed. This paper demonstrates a timer based detection approach for identifying black hole node. In network layer we proposed a Timer based method to overhear the next node action. The simulation results using EXata-Cyber shows that in a dynamic network most of the malicious nodes can be detected and that results in an improved packet delivery ratio.

Broadcast of critical information such as emergency traffic messages in vehicular ad hoc networks (VANETs) has to address jamming with dynamic network topology. In this paper, we propose a deep reinforcement learning based cooperative power control scheme for VANET broadcast against reactive jammers who can observe the ongoing broadcast states. The neural episodic control based cooperative power control scheme uses the convolutional neural network and differentiate neural dictionary to accelerate the learning speed for the VANETs with dynamic topology. Simulation results have shown that the proposed scheme can effectively improve the packet delivery rate and reduce the energy consumption of the broadcast compared with other power control schemes.

Future data communication networks show three emerging trends: increasing size of networks, increasing traffic volumes and dynamic network topologies. Efficient network management solutions are required that are scalable, can cope with large, and increasing, traffic volumes and provide decentralised and adaptive routing strategies that cope with the dynamics of the network topology. Routing strategies are an important aspect of network management as they have a significant influence on the overall network performance. This paper introduces the preliminary studies for FUNNet, a new routing algorithm inspired by the kingdom of Fungi. Fungi form robust, resilient and responsive networks and these networks change topology as a consequence of changes in local conditions. Fungi are capable of expanding in size as they self-regulate and optimise the balance between exploration and exploitation which is dependent on the transport of the internal resource, i.e. ‘traffic’, within the network. FUNNet exploits the biological processes that are responsible for simulating fungal networks in a bio-inspired routing protocol. The initial results are positive and suggest that fungal metaphors can improve network management, although further evaluation of more complex scenarios is required.

The multicast routing protocols used in wire network, such as distance vector multicast routing protocol(DVMRP), multicast open shortest path first(MOSPF), core based tree(CBT), do not run very well in ad hoc network because the dynamic net topology may destroy the distribution tree. Besides, distribution tree usually needs global routing structure. Which produces extra processing cost because the dynamic topology will introduce frequent exchange of routing vector or routing state between nodes. To deal with these problems multicast forwarding group routing protocol (MFGRP) which is based on route grid is designed on basis of dynamic source routing protocol (DSR). MFGRP ensures the multicast packets are sent to destination through dynamic topology network using multicast route grid.