Tracking dynamic piecewise-constant network topologies via adaptive tensor factorization

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 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.

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.

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.

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.

The electrical power grid is a critical infrastructure that plays a key role in supporting modern society. The reliability of power systems needs to be continuously maintained to deliver high-quality electric services. Due to the tremendous amounts of potential investment demanded for constructing new electricity transmission facilities, electric utilities need economical solutions that can enable them to supply electricity to their customers in a cost-effective and reliable way. Dynamic thermal rating (DTR) and network topology optimization (NTO) technologies aim to maximize the use of existing transmission assets and to provide flexible ways to enhance reliability of the power system. In this study, the DTR and NTO are incorporated into the power grid reliability assessment procedure using the sequential Monte Carlo simulation. Multiple case studies are carried out based on the modified IEEE RTS-79 and IEEE RTS-96 systems, accounting for long-term multiarea weather conditions. The numerical results indicate that with the incorporation of DTR and NTO, the reliability of power systems can be improved. The effect of these methods is especially significant for power grids with lower electricity delivery capabilities.

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.

Lots of data is generated around us in today's big data age. Much of this data is time-varying or dynamic, such as the social network connections that change across time or dynamic functional brain connectivity networks constructed across multiple subjects. In these dynamic (time-varying) networks, it is important to reduce the large amount of data into a few meaningful descriptors. One way to achieve this goal is to detect change points or anomalies in the connectivity patterns across time. Recently, there has been an interest in describing the time-varying network activity as a tensor and detecting the anomalies in terms of the changes in the subspaces of the tensor along each mode [1], [2]. However, the current approaches to tensor decomposition are not robust to non-Gaussian noise, outliers, and corruption in the data. For this reason, a robust low-rank tensor recovery algorithm similar to robust principal components analysis (RPCA) has been recently proposed. In this paper, we employ higher order robust PCA (HoRPCA) for tracking dynamic networks in time and detecting anomalies using a subspace distance measure. The proposed approach assumes that most real life networks are low-rank in nature and considers a low-rank plus sparse tensor decomposition at each time point. The subspaces corresponding to each mode and each time point are described through a projection operator and the subspace distance is quantified through a Hausdorff distance measure. The proposed framework is evaluated on both simulated networks and dynamic functional connectivity brain networks.

In this paper, we investigate the problem of securing exponentially fast consensus (exponential consensus for short) for identical agents with finite-dimensional linear system dynamics over dynamic network topology. Our aim is to find the weakest possible conditions that guarantee exponentially fast consensus using a Lyapunov function consisting of a sum of terms of the same functional form. We first investigate necessary conditions, starting by examining the system (both agent and network) parameters. It is found that controllability of the linear agents is necessary for reaching consensus. Then, to work out necessary conditions incorporating the network topology, we construct a set of Laplacian matrix-valued functions. The precompactness of this set of functions is shown to be a significant generalization of existing assumptions on network topology, including the common assumption that the edge weights are bounded piecewise constant functions or continuous functions. With the aid of such a precompactness assumption and restricting the Lyapunov function to one consisting of a sum of terms of the same functional form, we prove that a joint <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(\delta, T)$</tex-math></inline-formula> -connectivity condition on the network topology is necessary for exponential consensus. Finally, we investigate how the above two “necessities” work together to guarantee exponential consensus. To partially address this problem, we define a synchronization index to characterize the interplay between agent parameters and network topology. Based on this notion, it is shown that by designing a proper feedback matrix and under the precompactness assumption, exponential consensus can be reached globally and uniformly if the joint <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(\delta,T)$</tex-math></inline-formula> -connectivity and controllability conditions are satisfied, and the synchronization index is not less than one.

We study the time complexity of single and multi token broadcast in adversarial dynamic radio networks. Initially, k tokens (which are k pieces of information) are distributed among the n nodes of a network and all the tokens need to be disseminated to all the nodes in the network. We first consider the single-token broadcast problem (i.e., the case k=1). By presenting upper and lower bounds, we show that the time complexity of single-token broadcast depends on the amount of stability and connectivity of the dynamic network topology and on the adaptiveness of the adversary providing the dynamic topology. Then, we give two generic algorithms which allow to transform generalized forms of single-token broadcast algorithms into multi-token broadcast (k-token broadcast) algorithms. Based on these generic algorithms, we obtain k-token broadcast algorithms for a number of different dynamic network settings. For one of the modeling assumptions, our algorithm is complemented by a lower bound which shows that the upper bound is close to optimal.

Mobile systems are gaining more and more importance, and new promising paradigms like Mobile Cloud Computing are emerging. Mobile Cloud Computing provides an infrastructure where data storage and processing could happen outside the mobile node. Specifically, there is a major interest in the use of the services obtained by taking advantage of the distributed resource pooling provided by nearby mobile nodes in a transparent way. This kind of systems is useful in application domains such as emergencies, education and tourism. However, these systems are commonly based on dynamic network topologies, in which disconnections and network partitions can occur frequently, and thus the availability of the services is usually compromised. Techniques and methods from Autonomic Computing can be applied to Mobile Cloud Computing to build dependable service models taking into account changes in the context. In this work, a context-aware software architecture is proposed to support the availability of the services deployed in mobile and dynamic network environments. The proposal is based on a service replication scheme together with a self-configuration approach for the activation/hibernation of the replicas of the service depending on relevant context information from the mobile system. To that end, an election algorithm has been designed and implemented.

The lifetime of wireless sensor networks are constrained by its limited battery capacity. Therefore, the lifetime is widely regard as a bottleneck of technique of wireless sensor network. Recently, the emerging breakthrough in wireless power transfer technique is expected to eliminate the power constraint bottleneck. In this paper, we propose an intelligent wireless charging vehicle (IWCV) strategy to resolve above problem in a dynamic and scalable approach. The IWCV strategy includes an intelligent routing strategy to traverse the sensor network topology and charging their battery to prolong their lifetime. What makes IWCV different to previous studies is that IWCV can still work even if the topology changes by re-computing the traversing route and stop time for each node in a relative short amount of time, compared with the time needed to find the shortest Hamiltoaian cycle. With the scalable and dynamic feature of IWCV, one can change their sensor network topology without down time to reconfigure the wireless charging vehicle while still maintain low energy consumption during traveling and charging in dynamic network topologies.

The rapid development of Internet of Vehicles (IoV) presents critical challenges in secure storage and sharing of massive vehicular data. Traditional blockchain's full-node storage paradigm incurs prohibitive overhead in resource-constrained vehicular environments, while existing coded blockchain schemes struggle with IoV's dynamic network topology and fluctuating node population. This paper proposes an adaptive rateless coded blockchain architecture tailored for dynamic IoV environments, employing systematic Raptor codes for block compression, VRF-based lightweight consensus, and dynamic parameter adjustment that adapts redundancy in real-time without re-encoding existing blocks. The scheme achieves an average compression ratio of 97.88% while ensuring decoding failure probability below 10^-12, significantly reducing storage overhead and demonstrating robustness in dynamic vehicular networks.

It is very difficult to find feasible QoS (Quality of service) routes in the mobile ad hoc networks (MANETs), because of the nature constrains of it, such as dynamic network topology, wireless communication link and limited process capability of nodes. In order to reduce average cost in flooding path discovery scheme of the traditional MANETs routing protocols and increase the probability of success in finding QoS feasible paths and we proposed a heuristic and distributed route discovery method named RLGAMAN that supports QoS requirement for MANETs in this study. This method integrates a distributed route discovery scheme with a reinforcement learning (RL) method that only utilizes the local information for the dynamic network environment; and the route expand scheme based on genetic algorithms (GA) method to find more new feasible paths and avoid the problem of local optimize. We investigate the performance of the RLGAMAN by simulation experiment bed in NS2. Compared with traditional method, the experiment results showed the network performance is improved obviously and RLGAMAN is efficient and effective.

Future Internet technologies and the deployment of mobile and nomadic services enable complex communications networks, that have a highly dynamic behavior. This naturally engenders route-discovery problems under changing conditions over these networks, but the temporal variations in the topology of dynamic networks are not effectively captured in a classical graph model. In this paper, we use evolving graphs, which help capture the dynamic characteristics of such networks, in order to compute multicast trees with minimum overall transmission time for a class of wireless mobile dynamic networks. We first show that computing different types of strongly connected components in evolving digraphs is NP-Hard, and then propose a polynomial-time algorithm to build all rooted directed Minimum Spanning Trees in strongly connected dynamic networks. These results open new avenues for the implementation of Internet spanning-tree based protocols over highly dynamic network infrastructures.