Time-varying Topology Research Articles

STAG-Based QoS Support Routing Strategy for Multiple Missions Over the Satellite Networks

As the typical delay tolerant networks, the satellite networks possess the intermittent connections, the large-scale time delays and the time-varying topologies. Obviously, such features seriously affect the delivery of mission data with certain requirements on the traffic or latency. To decrease the delivery time, existing relay-based contact graph routing (CGR) method selects the earliest reachable path to forward mission data. However, this method cannot guarantee the transmission of a large amount data and wastes the rare contact opportunities. Therefore, in this paper, we focus on the transmission QoS problem of multiple missions over the satellite networks, and design a QoS support routing strategy to achieve multiple flow-maximizing paths with acceptable delivery delays. Especially, with the storage time aggregated graph (STAG), we construct an on-demand mission model to depict both the network dynamic characteristics and the different mission requirements, and then reduce the QoS support problem as a graph-based maximum flow problem. To solve this problem, one STAG-based multiple flow-maximizing routing scheme is proposed to ensure the mission QoS and match the rare network resources, which has low computation complexity. Finally, compared with CGR, simulation results demonstrate the proposed scheme can achieve a higher mission completion rate and resource utilization rate.

Secure and Privacy-Preserving Consensus

Consensus is fundamental for distributed systems since it underpins key functionalities of such systems ranging from distributed information fusion, decision making, to decentralized control. In order to reach an agreement, existing consensus algorithms require each agent to exchange explicit state information with its neighbors. This leads to the disclosure of private state information, which is undesirable in cases where privacy is of concern. In this paper, we propose a novel approach for undirected networks, which can enable secure and privacy-preserving average consensus in a decentralized architecture in the absence of an aggregator or third party. By leveraging partial homomorphic cryptography to embed secrecy in pairwise interaction dynamics, our approach can guarantee convergence to the consensus value (subject to a quantization error) in a deterministic manner without disclosing a node's state to its neighbors. We provide a new privacy definition for dynamical systems, and give a new framework to rigorously prove that a node's privacy can be protected as long as it has at least one legitimate neighbor, which follows the consensus protocol faithfully without attempts to infer other nodes’ states. In addition to enabling resilience to passive attackers aiming to steal state information, the approach also allows easy incorporation of defending mechanisms against active attackers who try to alter the content of exchanged messages. Furthermore, in contrast to existing noise-injection-based privacy-preserving mechanisms that have to reconfigure the entire network when the topology or number of nodes varies, our approach is applicable to dynamic environments with time-varying coupling topologies. This secure and privacy-preserving approach is also applicable to weighted average consensus as well as maximum/minimum consensus under a new update rule. Numerical simulations and comparison with existing approaches confirm the theoretical results. Experimental results on a Raspberry-Pi board based microcontroller network are also presented to verify the effectiveness and efficiency of the approach.

Containment control of fractional-order multi-agent systems with time-varying delays

In this paper, containment control problems of networked fractional-order multi-agent systems with time-varying delays are studied. The normalized directed graphs are employed to characterize the communication topologies. Two sampled-data based containment control protocols are proposed, which can overcome the time-varying delays and switching topologies. It is interestingly found that the decays of the closed-loop systems correspond to the Mittag-Leffler function and its approximation, which are the extensions of the exponential function and its approximation, respectively. Based on the algebraic graph theory, the properties of row-stochastic matrix, and the relation between the topologies and the matrices, some conditions for containment control are established. For the fixed topology, a necessary and sufficient condition is obtained; and for the switching topology, a sufficient condition is provided. Finally, the theoretical results are illustrated by several numerical simulations.

Dynamic topology control in optical satellite networks based on algebraic connectivity

Optical communication satellite network (OCSN) has been considered as a superior solution to broadband applications of space-based information, since it has many advantages such as less power and higher data rate. Topology framework is the foundation and premise of OCSN. However, frail links, limited number of optical transceivers and time-varying network topology deteriorate topology control of OCSN. Usually, topology control is mainly conducted in processes of network bootstrapping and reconfiguring. In this paper, we investigate the problem of topology control about constructing robust spanning trees in above two processes. The model of OCSN based on space-time graph is built and link availability is proposed to represent the capability of choosing strong links. In addition, the algebraic connectivity is introduced to measure robustness of spanning trees and then the problem is formulated as a 0-1 multi-objective mixed-integer nonlinear programming (MOMINP) which has been proven to be NP-hard. For fast constructing a spanning tree with large algebraic connectivity and average edge weight in OCSN, we develop the subtree merging and maximizing algebraic connectivity (SMMAC) algorithm that is effectively appropriate for distributed network bootstrapping and reconfiguring, and also discuss the properties of SMMAC containing outcome existence, computational complexity and approximation analysis in this paper. The algorithm is based on a primal decomposition method to solve MOMINP, which can efficiently reduce the complexity. Some simulations are conducted in two typical distributed scenarios. Results demonstrate that, compared with other alternative algorithms, our algorithm can significantly improve algebraic connectivity of OCSN and meanwhile guarantee relatively high average edge weight for the topology of OCSNs.

Scalable Distributed Filtering for a Class of Discrete-Time Complex Networks Over Time-Varying Topology.

This article is concerned with the distributed filtering problem for a class of discrete complex networks over time-varying topology described by a sequence of variables. In the developed scalable filtering algorithm, only the local information and the information from the neighboring nodes are used. As such, the proposed filter can be implemented in a truly distributed manner at each node, and it is no longer necessary to have a certain center node collecting information from all the nodes. The aim of the addressed filtering problem is to design a time-varying filter for each node such that an upper bound of the filtering error covariance is ensured and the desired filter gain is then calculated by minimizing the obtained upper bound. The filter is established by solving two sets of recursive matrix equations, and thus, the algorithm is suitable for online application. Sufficient conditions are provided under which the filtering error is exponentially bounded in mean square. The monotonicity of the filtering error with respect to the coupling strength is discussed as well. Finally, an illustrative example is presented to demonstrate the feasibility and effectiveness of our distributed filtering strategy.

Efficient topology control for time-varying spacecraft networks with unreliable links

In spacecraft networks, the time-varying topology, intermittent connectivity, and unreliable links make management of the network challenging. Previous works mainly focus on information propagation or routing. However, with a large number of nodes in the future spacecraft networks, it is very crucial regarding how to make efficient network topology controls. In this article, we investigate the topology control problem in spacecraft networks where the time-varying topology can be predicted. We first develop a model that formalizes the time-varying spacecraft network topologies as a directed space–time graph. Compared with most existing static graph models, this model includes both temporal and spatial topology information. To capture the characteristics of practical network, links in our space–time graph model are weighted by cost, efficiency, and unreliability. The purpose of our topology control is to construct a sparse (low total cost) structure from the original topology such that (1) the topology is still connected over space–time graph; (2) the cost efficiency ratio of the topology is minimized; and (3) the unreliability parameter is lower than the required bound. We prove that such an optimization problem is NP-hard. Then, we provide five heuristic algorithms, which can significantly maintain low topology cost efficiency ratio while achieving high reliable connectivity. Finally, simulations have been conducted on random space networks and hybrid low earth orbit/geostationary earth orbit satellite-based sensor network. Simulation results demonstrate the efficiency of our model and topology control algorithms.

Impulsive synchronization of time-scales complex networks with time-varying topology

The dynamics of complex networks (CNs) often does not appear as absolutely continuous or discrete, but more likely to be mixed. This paper discusses the asymptotic synchronization problem of a class of complex networks with time-varying topology (TTCNs). Based on time scales theory, it obtains a general scale-type synchronization condition for TTCNs on different time scales. An impulsive control method is taken into account when the condition is not met, and a scale-type criterion is established in such situation. Simulations verify the effectiveness of our conclusions on different time scales, including continuous, discrete with arbitrary step, and mixed cases.

Energy-efficiently collaborative data downloading in optical satellite networks

Optical satellite networks (OSNs) play an important role in space applications since it has some merits such as high data rate and low power. Increasing space applications generate a large amount of data on satellite so that satellites have difficulty in downloading these data to ground station timely under the limited contact window. Thus, we investigate the problem of collaborative data downloading in this paper. The problem lies on how to energy-efficiently offload data by optical inter-satellite links (ISLs) to balance data load and downlink capacity of each satellite so as to improve the performance of data downloading in OSN. Firstly, we develop the Cost Time-evolving Graph (CTEG) to describe optical transmission cost and the time-varying topology of OSN. Secondly, the problem of collaborative data downloading is formulated as Multi-objective Mixed-integer Linear Programming (MOMILP) which is proven to be NP-hard. For reducing computational complexity, we divide the scheduling time into multiple stages and propose the Multi-stage Collaborative Scheduling Algorithm (MCSA) which operates on a slot-by-slot basis. Simulations are conducted in the joint platform and results demonstrate that, compared with CoDld and No-ISLs, the MCSA provides a relatively high data throughput meanwhile drastically reducing energy consumption generated by data offloading.

A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market

We propose a dynamic network model where two mechanisms control the probability of a link between two nodes: (i) the existence or absence of this link in the past, and (ii) node-specific latent variables (dynamic fitnesses) describing the propensity of each node to create links. Assuming a Markov dynamics for both mechanisms, we propose an Expectation-Maximization algorithm for model estimation and inference of the latent variables. The estimated parameters and fitnesses can be used to forecast the presence of a link in the future. We apply our methodology to the e-MID interbank network for which the two linkage mechanisms are associated with two different trading behaviors in the process of network formation, namely preferential trading and trading driven by node-specific characteristics. The empirical results allow to recognize preferential lending in the interbank market and indicate how a method that does not account for time-varying network topologies tends to overestimate preferential linkage.

Event-Triggered Optimal Active Power Control in Islanded Microgrid With Variable Demand and Time-Varying Communication Topology

For islanded microgrids, traditional droop-based control leads to frequency deviation and low economic efficiency. To resolve this issue, a novel event-triggered optimal active power control strategy, which achieves frequency regulation and economic operation in a fully distributed way, is proposed in this paper. Frequency deviations are monitored and used to trigger the load estimation algorithm to track changes in power demand. Based on estimated demand values, a distributed economic dispatch (ED) algorithm, which only requires that the communication topology satisfies the jointly connected condition, is proposed to calculate the optimal generation references for the generators. The ED algorithm has the finite-time convergence property, which is rigorously proved by Lyapunov theory, LaSalle’s invariance principle and homogeneous property. Based on optimal generation references, the parameters of droop controller are reset in real time, which makes microgrid achieve frequency restoration and economic operation simultaneously. Finally, several case studies are presented to illustrate the effectiveness of the proposed control strategy.

Distributed fault diagnosis of networked dynamical systems with time-varying topology

In this paper, the distributed fault diagnosis (DFD) of networked dynamical systems with time-varying connected topologies, e.g., wireless sensor networks in harsh environments, is considered. Specifically, two essential problems are focused on, which are faced in extending the the decomposition-based adaptive DFD approach to such topology-varying systems. The problems introduced by the time-varying topologies are, respectively, decomposition schemes deterioration and pre-training difficulties. The causes of the two problems are detailed and addressed in our work. First, for the decomposition schemes deterioration problem, a multi-agent dynamics-based online distributed decomposition algorithm are developed, so that a decent decomposed network structure for such topology-varying network can be maintained. Second, to alleviate the pre-training difficulties in topology-varying systems, a fault detection method is proposed, which avoids the need for pre-training. The distributed decomposition algorithm is proved to converge in finite steps, and the proposed fault detection method is verified both theoretically and experimentally.

Temporal Netgrid Model-Based Dynamic Routing in Large-Scale Small Satellite Networks

In recent years, Small Satellite Networks (SSNs) are attracting increasing attention due to its economical prospects and advantages in high bandwidth and low latency. More and more companies and organizations are planning to construct large-scale SSNs to provide global services. In this way, the traffic pattern is more complicated because of random transmission requirements and stochastic packet generations/arrivals, and routing faces more challenges due to network scale and limited resource in small satellites. Traditional satellite routing algorithms, which attempt to exploit the predictable satellite's trajectory with time-discrete graph models, cannot handle these challenges. In this paper, we propose a novel Temporal Netgrid Model (TNM) to portray the time-varying topology of large-scale SSNs. In TNM, the whole space is divided into small cubes (i.e., netgrids) and then, satellites can be located by netgrids instead of coordinates. By doing so, we can construct a network topology for random traffic routing. Furthermore, an efficient Netgrid-based Shortest Path Routing (NSR) algorithm is proposed based on TNM; NSR attempts to find the optimal path from source netgrid to any other reachable netgrids. In this way, the routing complexity is significantly reduced. We also develop a Large-scale Satellite Network Simulator (LSNS) to validate our study. The results show that NSR achieves a significant reduction in computational complexity as well as near-optimal routing performance in terms of end-to-end delay and packet drop rate in scenarios of large-scale SSNs compared with existing satellite routing algorithms.

A novel general type-2 fuzzy controller for fractional-order multi-agent systems under unknown time-varying topology

In this paper, a robust adaptive control scheme is proposed for the leader following control of a class of fractional-order multi-agent systems (FMAS). The asymptotic stability is shown by a linear matrix inequality (LMI) approach. The nonlinear dynamics of the agents are assumed to be unknown. Moreover, the communication topology among the agents is assumed to be unknown and time-varying. A deep general type-2 fuzzy system (DGT2FS) using restricted Boltzmann machine (RMB) and contrastive divergence (CD) learning algorithm is proposed to estimate uncertainties. The simulation studies presented indicate that the proposed control method results in good performance under time-varying topology, unknown dynamics and external disturbances. The effectiveness of the proposed DGT2FS is verified also on modeling problems with high dimensional real-world data sets.

Optimality in the Sense of Arm Current Distribution of MMC VSC-HVDC

This letter investigates the optimality of the modular multilevel converter (MMC) voltage source converters high-voltage direct current (VSC-HVDC), which constitutes a linear time-invariant (LTI) system for sufficiently large number of submodules per valve. The contribution of this letter is the mathematical proof that MMC VSC-HVDC is the optimal LTI topology in the sense of arm current distribution. Namely, the findings of this work show that MMC VSC-HVDC attains an optimal limit in the way the ac and dc components of the converter arm currents are distributed in the field of linear converter topologies, and may only be outperformed by nonlinear or time-varying VSC-HVDC topologies.

Time-Expanded Graph-Based Resource Allocation Over the Satellite Networks

In this letter, we propose a transceiver resource allocation scheme based on the time-expanded graph (TEG) for the satellite networks. Due to the time-varying topology of the satellite networks, the design of the efficient transceiver resource allocation requires multiple nodes to make joint decision across time. To overcome this problem, the distribution of the transceiver resources is first depicted by the TEG. Then, we add some virtual nodes and edges in the TEG to represent the resources, and obtain the modified TEG, which can transform the nodes’ transceiver resources into the link constraints. Finally, the joint resource allocation will be implemented through solving the max-flow problem over the modified TEG. Numerical simulation results will also be presented to verify our proposed scheme.

Heuristic computing methods for contact plan design in the spatial-node-based Internet of Everything

To satisfy the increasing demands of high-speed transmission, high-efficiency computing, and real-time communications in the high-dynamic and heterogeneous networks, the Contact Plan Design (CPD) has attracted continuous attention in recent years, especially for the spatial-node-based Internet of Everything (IoE). In this paper, we study the NP-hardness of contact scheduling and the attenuation of atmospheric precipitation in the spatial-node-based IoE. Two heuristic computing methods for contact plan design are proposed by comprehensively considering the time-varying topology, the intermittent connectivity, and the adaptive transmission in different weather conditions, which are named Contact Plan Design-Particle Swarm Optimization (CPD-PSO) and Contact Plan Design-Greedy algorithm with the Minimum Delivery Time (CPD-GMDT) separately. For the population-based algorithm, CPD-PSO not only solves the CPD problem with a limited-resource condition, but also dynamically adjusts the search scope to ensure the continuous searching capability of the algorithm. For the CPD-GMDT that makes CP decisions based on the current state, the algorithm uses the idea of greedy algorithm to schedule Satellite-Platform Links (SPLs) and Inter Satellite Links (ISLs) respectively using the strategies of optimal matching and load balancing. The simulation results show that the proposed CPD-PSO outperforms Contact Plan Design-Genetic Algorithm (CPD-GA) in terms of fitness and delivery time, and CPD-GMDT presents better overall delay than Fair Contact Plan (FCP).

Reinforcement-Learning-Based Cooperative Adaptive Cruise Control of Buses in the Lincoln Tunnel Corridor with Time-Varying Topology

The exclusive bus lane (XBL) is one of the most popular bus transit systems in the U.S. The Lincoln Tunnel utilizes an XBL through the tunnel in the AM peak period. This paper proposes a novel data-driven cooperative adaptive cruise control (CACC) algorithm that aims to minimize a cost function for connected and autonomous buses along the XBL. Different from existing model-based CACC algorithms, the proposed approach employs the idea of reinforcement learning, which does not rely on accurate knowledge of bus dynamics. Considering a time-varying topology, where each autonomous vehicle can only receive information from preceding vehicles that are within its communication range, a distributed controller is learned real-time by online headway, velocity, and acceleration data collected from the system trajectories. The convergence of the proposed algorithm and the stability of the closed-loop system are rigorously analyzed. The effectiveness of the proposed approach is demonstrated using a well-calibrated Paramics microscopic traffic simulation model of the XBL corridor. The simulation results show that the travel time in the autonomous version of the XBL are close to the present day travel time even when the bus volume is increased by 30%.

Information Correlated Lévy Walk Exploration and Distributed Mapping Using a Swarm of Robots

In this work, we present a novel distributed method for constructing an occupancy grid map of an unknown environment using a swarm of robots with global localization capabilities and limited inter-robot communication. The robots explore the domain by performing Levy walks in which their headings are defined by maximizing the mutual information between the robot's estimate of its environment in the form of an occupancy grid map and the distance measurements that it is likely to obtain when it moves in that direction. Each robot is equipped with laser range sensors, and it builds its occupancy grid map by repeatedly combining its own distance measurements with map information that is broadcast by neighboring robots. Using results on average consensus over time-varying graph topologies, we prove that all robots' maps will eventually converge to the actual map of the environment. In addition, we demonstrate that a technique based on topological data analysis, developed in our previous work for generating topological maps, can be readily extended for adaptive thresholding of occupancy grid maps. We validate the effectiveness of our distributed exploration and mapping strategy through a series of 2D simulations and multi-robot experiments.

Data offloading in IoT environments: modeling, analysis, and verification

Recent years have seen a significant growth in Internet of Things (IoT) technology consisting of a large number of devices embedded with sensors and deployed to perform monitoring and actuation tasks. The IoT devices collect large volumes of data that is usually uploaded to cloud to perform analytics and predictions. One of the main challenges in IoT is the transportation of large-scale data collected over a period of time at a remote site. Cellular networks are already facing explosive growth of mobile data traffic due to the proliferation of smart devices and traffic-intensive applications. An alternate solution is to perform the data offloading, where a portion of data can be offloaded from primary links and transferred using opportunistic terminal-to-terminal (T2T) network that relies on direct communication between mobile users, without any need for an infrastructure backbone. However, such approach may lead to data loss and delay if dynamics of time-varying topology and mobility of nodes is not taken care of. To address this challenge, we propose three prediction-based offloading schemes that exploit the mobility patterns and temporal contacts of nodes to predict future data transfer opportunities. We have utilized the High-level Petri Nets to model and formally analyzed the communication processes of the proposed schemes. The new symbolic model verifier (NuSMV) has been employed for the verification of the three schemes against the identified constraints. The verification results affirm the correctness and scalability of the models. The protocols are evaluated with performance metrics, such as the delivery ratio, latency, and overhead. Our results indicate significant improvement in performance compared to existing approaches.

Adaptive deep dynamic programming for integrated frequency control of multi-area multi-microgrid systems

To reduce the frequency deviation of a multi-area multi-microgrid system, a framework of integrated frequency control is designed in this paper, which can replace load frequency control (LFC) and generation command dispatch (GCD).Then an adaptive deep dynamic programming (ADDP) scheme is proposed for the integrated frequency control. The ADDP contains three deep neural networks, i.e., deep prediction neural network, deep critic neural network and deep action neural network. Deep prediction neural network is applied to predict the next state of the multi-area multi-microgrid system from the previous states and the previous actions. Deep critic neural network is employed in the evaluations of the performance of the deep action neural network. Deep action neural network is introduced to simultaneously provide generation commands for all the LFC units in the multi-area multi-microgrid system. The ADDP is compared with other 157 algorithms under six case studies, i.e., basic situation, plug-and-play, communication failure, all-day long disturbance, time-varying topology and parameters varying. The other 157 algorithms consist of adaptive dynamic programming and 156 combined algorithms, which combined with 12 control algorithms for the controller of LFC and 13 optimization algorithms for GCD. Simulation results verify the effectiveness and superiority of the ADDP for integrated frequency control of a multi-area multi-microgrid system.