Communication Network Topology Research Articles

A Distance-Based Data-Mule Scheduling Technique for Lesser Nodal Delay in Wireless Sensor Network

Nodal delay in wireless sensor network is an indisputable factor in the medium of communication. Factor such as changeability of communication devices, network topologies, packet-sizes, and transmission rate demands to develop data-mule queue scheduling technique. Our proposed data-mule scheduling technique accomplish this through simulations using standard software written in C# by controlling data-mule schedules that collects data from all the nodes connected to the hop. The scheme identifies the hierarchical positions of static source nodes and the distance of mobile source nodes from the hop with rescheduling based on the newly acquired distances. Source nodes applied with data-mule scheduling technique resulted to lower nodal delay. Transmission of packet-data is efficiently and effectively improved.

Consensusability of Discrete-Time Multiagent Systems With Communication Delay and Packet Dropouts

In this paper, we study the mean-square consensusability problem of discrete-time linear multiagent systems over directed networks with constant communication delay and packet dropouts. When the packet dropouts in different communication channels are identical, the consensusability problem is transformed into a simultaneous mean-square stabilization problem of systems with constant time delay and packet dropout. When the packet dropouts are nonidentical, we build the dynamics over edges based on the notion of compressed edge Laplacian for directed graphs to separate the packet dropouts from the network topology. Sufficient consensusability conditions are obtained for both cases in terms of the communication delay, the packet dropout rates, the communication network topology, and the agent dynamics.

Identification of Key Nodes Considering Different Roles of Power Businesses in Power Communication System and Power System

In the power communication network, the importance of the power businesses are the key to evaluate the key nodes of the power communication system. As a special network of power system, when determining the importance of power businesses, the different roles of power businesses in power communication system and power system should be considered. Based on analytic hierarchy process (AHP), entropy weight method and weight degradation method, this paper presents a method to identify the key nodes of power communication network. Firstly, the roles of power businesses in power communication network and power system are analyzed, and a multi-level evaluation model is set up. The evaluation indexes are quantified in the form of quantitative assignment, and the importance of power businesses are evaluated based on improved AHP and entropy weight method. Secondly, on the premise of getting the importance of power businesses, combining with the topology of power communication network to improve the weight degradation method to identify the key nodes of power communication system. Finally, the power communication backbone network of Qingdao and Jinan in China are taken as the simulation objects to verify the correctness of the method.

Synchronization Control for Network Systems With Communication Constraints.

A novel control strategy for synchronization control for network systems with communication constraints is presented in this paper. The dynamics of nodes in the network are nonidentical. The communication topology of network is weakly connected with communication constraints. The designed distributed controller for each node has two parts: reference generator (RG) and regulator. All RGs adopt the communication channels to exchange local information and track the target trajectory. Meanwhile, regulator can ensure that nonidentical node achieves synchronization with its RG. In order to reduce the communication frequency between node and its regulator, a sampled-date control strategy is utilized. The upper bound of the aperiodic sampling instants is calculated through the small-gain theorem, where the closed-loop system is equivalently formulated as the feedback interconnection of a linear time-invariant system and an integral sampled-data operator. Finally, some simulation results are given to demonstrate the effectiveness of the controller design strategy.

Message-Passing Neural Networks Learn Little’s Law

The paper presents a solution to the problem of universal representation of graphs exemplifying communication network topologies with the help of neural networks. The proposed approach is based on message-passing neural networks (MPNN). The approach enables us to represent topologies and operational aspects of networks. The usefulness of the solution is illustrated with a case study of delay prediction in queuing networks. This shows that performance evaluation can be provided without having to apply complex modeling. In consequence, the proposed solution makes it possible to effectively apply methods elaborated in the field of machine learning in communications.

Topology-aware multi-objective virtual machine dynamic consolidation for cloud datacenter

With help of virtualization technology, cloud datacenters provide affordable computation power to numerous enterprises and business users. Virtual machine dynamic consolidation (VMDC) plays an important role in the energy consumption management of datacenters. Previous solutions on VMDC either fail to consider impact resulting from the interaction between datacenter network topology and communication topology, or handle this issue in coarse granular level. In this paper, we propose a new approach to coordinate datacenter network topology and communication topology. The proposed VMDC approach adopts a multi-objective genetic algorithm to simultaneously minimize energy consumption and communication traffic causing performance bottlenecks, while meeting SLA constraint on migration time. In this approach, both SLA constraint and objective function on communication traffic are formulated with topology awareness in switch link level. A comprehensive simulation study shows that the proposed approach can significantly save energy consumption of datacenters and provide SLA assurance for different types of datacenter topologies. Simulation results demonstrate that the proposed approach outperforms prior approaches in terms of reducing energy consumption, bottleneck ratio and SLA violation ratio.

Impact of changing cell-cell communication network in models of epithelial pattern formation

When modelling multi-cellular systems, one has to account for cell-cell signalling in addition to the molecular networks driving cell behaviours. Here, we aim at exploring how the topology of the cell-cell communication network impacts the behaviour of the whole multicellular system. More precisely, we focus on epithelial pattern formation, on which our question can be rephrased in terms of cell sizes and shapes. Relying on a logical modelling framework, and using a simple lateral inhibition model over a population of epithelial cells, we assess the model behaviours considering a variety of communication networks. This study suggests that reasonable deviations from a fixed grid (with regular hexagonal shaped cells) do not change much the resulting patterns. We further explore the impact of cell shapes and show that characteristics such as network regularity and number of shared neighbours of contacting cells are relevant to qualify such deviations.

Impacts of human communication network topology on group optimism bias in Capital Project Planning: a human-subject experiment

Capital projects are critical to the world’s economy. Despite the advancement of prediction techniques, capital projects still suffer from overly optimistic plans, i.e. tight budgets and schedules. This article focuses on understanding this issue from the perspective of optimism bias, i.e. a psychological bias toward the inclination to be overly optimistic about the chance of success. We hypothesize that human communication network topologies are strong predictors of harmful optimism bias at the group level in project planning. A human-subject experiment was performed to test group optimism bias levels under different communication network topologies. We recruited 103 subjects to estimate the cost of an artificial power plant project. The communication networks were manipulated to reflect different topologies. The subjects’ estimates were compared to a Monte Carlo simulation result based on real historical data to quantify the level of optimism bias at the group level. Preliminary results find that certain human communication network topology leads to more realistic estimates, possibly due to a process of updating individual judgement based on peers’ judgements. The findings of this study are expected to urge further theoretical investigations into the development of simple yet effective decision support systems to reduce decision-making bias in capital project planning.

Resilient distributed state estimation with mobile agents: overcoming Byzantine adversaries, communication losses, and intermittent measurements

Applications in environmental monitoring, surveillance and patrolling typically require a network of mobile agents to collectively gain information regarding the state of a static or dynamical process evolving over a region. However, these networks of mobile agents also introduce various challenges, including intermittent observations of the dynamical process, loss of communication links due to mobility and packet drops, and the potential for malicious or faulty behavior by some of the agents. The main contribution of this paper is the development of resilient, fully-distributed, and provably correct state estimation algorithms that simultaneously account for each of the above considerations, and in turn, offer a general framework for reasoning about state estimation problems in dynamic, failure-prone and adversarial environments. Specifically, we develop a simple switched linear observer for dealing with the issue of time-varying measurement models, and resilient filtering techniques for dealing with worst-case adversarial behavior subject to time-varying communication patterns among the agents. Our approach considers both communication patterns that recur in a deterministic manner, and patterns that are induced by random packet drops. For each scenario, we identify conditions on the dynamical system, the patrols, the nominal communication network topology, and the failure models that guarantee applicability of our proposed techniques. Finally, we complement our theoretical results with detailed simulations that illustrate the efficacy of our algorithms in the presence of the technical challenges described above.

Evolution of the Internet AS-level topology: From nodes and edges to components** Project supported by the National Natural Science Foundation of China (Grant No. 61671142).

Studying the topology of infrastructure communication networks (e.g., the Internet) has become a means to understand and develop complex systems. Therefore, investigating the evolution of Internet network topology might elucidate disciplines governing the dynamic process of complex systems. It may also contribute to a more intelligent communication network framework based on its autonomous behavior. In this paper, the Internet Autonomous Systems (ASes) topology from 1998 to 2013 was studied by deconstructing and analysing topological entities on three different scales (i.e., nodes, edges and 3 network components: single-edge component M1, binary component M2 and triangle component M3). The results indicate that: a) 95% of the Internet edges are internal edges (as opposed to external and boundary edges); b) the Internet network consists mainly of internal components, particularly M2 internal components; c) in most cases, a node initially connects with multiple nodes to form an M2 component to take part in the network; d) the Internet network evolves to lower entropy. Furthermore, we find that, as a complex system, the evolution of the Internet exhibits a behavioral series, which is similar to the biological phenomena concerned with the study on metabolism and replication. To the best of our knowledge, this is the first study of the evolution of the Internet network through analysis of dynamic features of its nodes, edges and components, and therefore our study represents an innovative approach to the subject.

Multiparametric Monitoring in Equatorian Tomato Greenhouses (II): Energy Consumption Dynamics.

Tomato greenhouses are a crucial element in the Equadorian economy. Wireless sensor networks (WSNs) have received much attention in recent years in specialized applications such as precision farming. The energy consumption in WSNs is relevant nowadays for their adequate operation, and attention is being paid to analyzing the affecting factors, energy optimization techniques working on the network hardware or software, and characterizing the consumption in the nodes (especially in the ZigBee standard). However, limited information exists on the analysis of the consumption dynamics in each node, across different network technologies and communication topologies, or on the incidence of data transmission speed. The present study aims to provide a detailed analysis of the dynamics of the energy consumption for tomato greenhouse monitoring in Ecuador, in three types of WSNs, namely, ZigBee with star topology, ZigBee with mesh topology (referred to here as DigiMesh), and WiFi with access point topology. The networks were installed and maintained in operation with a line of sight between nodes and a 2-m length, whereas the energy consumption measurements of each node were acquired and stored in the laboratory. Each experiment was repeated ten times, and consumption measurements were taken every ten milliseconds at a rate of fifty thousand samples for each realization. The dynamics were scrutinized by analyzing the recorded time series using stochastic-process analysis methods, including amplitude probability functions and temporal autocorrelation, as well as bootstrap resampling techniques and representations of various embodiments with the so-called M-mode plots. Our results show that the energy consumption of each network strongly depends on the type of sensors installed in the nodes and on the network topology. Specifically, the CO sensor has the highest power consumption because its chemical composition requires preheating to start logging measurements. The ZigBee network is more efficient in energy saving independently of the transmission rate, since the communication modules have lower average consumption in data transmission, in contrast to the DigiMesh network, whose consumption is high due to its topology. Results also show that the average energy consumption in WiFi networks is the highest, given that the coordinator node is a Meshlium™ router with larger energy demand. The transmission duration in the ZigBee network is lower than in the other two networks. In conclusion, the ZigBee network with star topology is the most energy-suitable one when designing wireless monitoring systems in greenhouses. The proposed methodology for consumption dynamics analysis in tomato greenhouse WSNs can be applied to other scenarios where the practical choice of an energy-efficient network is necessary due to energy constrains in the sensor and coordinator nodes.

Formation control with collision avoidance for uncertain networked Lagrangian systems via adaptive gain techniques

This study addresses the problem of formation control with collision avoidance for networked Lagrangian systems with uncertain parameters interacting on directed network communication topologies. Two adaptive formation control strategies with collision avoidance are proposed by making use of adaptive gain techniques for both cases of with and without a dynamic leader. The main objective of the proposed control strategies is to dispatch a group of agents to maintain a desired geometric pattern, while still guarantee collision avoidance at any time, and eventually to achieve velocity matching. A distinctive feature of the developed adaptive gain is to adapt itself duly based on both the network communication topology and collision avoidance constraints, so it is feasible to be implemented in practice. Some general criteria are derived to guarantee that the desired formation with collision avoidance for the networked Lagrangian systems can be achieved. Finally, numerical simulations are given to show the performance of the proposed control methodologies.

Network Topology Independent Multi-Agent Dynamic Optimal Power Flow for Microgrids With Distributed Energy Storage Systems

This paper proposes a multi-agent dynamic optimal power flow (DOPF) strategy for microgrids with distributed energy storage systems. The proposed control strategy uses a convex formulation of the ac DOPF problem developed from a d-q reference frame voltage–current model and linear power flow approximations. The convex DOPF problem is divided between autonomous agents and solved based on local information and neighbour-to-neighbour communication over a sparse communication network, using a distributed primal subgradient algorithm. Each agent is only required to solve convex quadratic sub-problems, for which robust and efficient solvers exist, making the control strategy suitable for receding horizon model predictive control. Also, the agent sub-problems require limited power network information and include only a subset of the centralised optimisation problem decision variables and constraints, providing scalability and data privacy. Unlike existing distributed optimal power flow methods, such as alternating direction method of multipliers, under the proposed control strategy the information required by each agent is independent of the communication network topology, providing increased flexibility and robustness. The performance of the proposed control strategy was verified for an ac microgrid with distributed lead-acid batteries and intermittent photovoltaic generation, using an RTDS Technologies real-time digital simulator.

A Wireless Sensor Network-Based Defence Model against Selective Forwarding Attack

Due to low communication costs and convenient deployment, wireless sensor network has been widely applied in various fields. However, it still has some problems in the defence against selective forwarding attacks. To address these problems, this paper proposes a model against selective forwarding attacks, which is built on the threshold secret sharing mechanism and adopts the individualized path routing protocol. Through simulation test, this paper studies the effects of attack intensity on the successful transmission rate and communication load under the same network deployment and communication topology but at different node densities and average neighbourhood degrees. The results show that this model can effectively defend against forwarding attacks, also saves communication resource, offering a technical reference for similar studies.

Interference-Free Graph Based TDMA Protocol for Underwater Acoustic Sensor Networks

Underwater acoustic sensor networks (UW-ASNs) have been attracting more and more research interests recently due to their various promising applications. In this paper, we focus on the time division multiple access (TDMA) based media access control (MAC) protocol design for UW-ASNs in order to improve the network performance. Different from existing work, we consider a more general underwater communication scenario, where the communication network topology is modeled as a three-dimensional (3-D) scenario and the mobility of sensor nodes is also taken into consideration. We introduce a dynamic and flexible spatial reuse strategy for the TDMA protocol design, and formulate the interference scenario as a dynamic interference-free graph according to the nodes' current position distribution and a preset interference-free threshold. To achieve high spatial reuse efficiency, we propose two interference-free graph (IG) clustering algorithms, which lead to the optimal IG-TDMA protocol and the heuristic IG-TDMA protocol, respectively. The former can achieve the optimal network throughput but may not be feasible for high traffic networks due to its high computational complexity. The latter can achieve near-optimal network performance with much lower computational complexity and thus is more practical. Simulations verify the benefits of our proposed protocols in comparison with the CSMA/CA protocol and some existing TDMA protocols with and without spatial reuse.

A method of network topology optimization design considering application process characteristic

Communication networks are designed to meet the usage requirements of users for various network applications. The current studies of network topology optimization design mainly considered network traffic, which is the result of network application operation, but not a design element of communication networks. A network application is a procedure of the usage of services by users with some demanded performance requirements, and has obvious process characteristic. In this paper, we first propose a method to optimize the design of communication network topology considering the application process characteristic. Taking the minimum network delay as objective, and the cost of network design and network connective reliability as constraints, an optimization model of network topology design is formulated, and the optimal solution of network topology design is searched by Genetic Algorithm (GA). Furthermore, we investigate the influence of network topology parameter on network delay under the background of multiple process-oriented applications, which can guide the generation of initial population and then improve the efficiency of GA. Numerical simulations show the effectiveness and validity of our proposed method. Network topology optimization design considering applications can improve the reliability of applications, and provide guidance for network builders in the early stage of network design, which is of great significance in engineering practices.

Exponential synchronization for second-order nonlinear systems in complex dynamical networks with time-varying inner coupling via distributed event-triggered transmission strategy

This paper focuses on the exponential synchronization problem of complex dynamical networks (CDNs) with time-varying inner coupling via distributed event-triggered transmission strategy. Information update is driven by properly defined event, which depends on the measurement error. Each node is described as a second-order nonlinear dynamic system and only exchanges information with its neighboring nodes over a directed network. Suppose that the network communication topology contains a directed spanning tree. A sufficient condition for achieving exponential synchronization of second-order nonlinear systems in CDNs with time-varying inner coupling is derived. Detailed theoretical analysis on exponential synchronization is performed by the virtues of algebraic graph theory, distributed event-triggered transmission strategy, matrix inequality and the special Lyapunov stability analysis method. Moreover, the Zeno behavior is excluded as well by the strictly positive sampling intervals based on the upper right-hand Dini derivative. It is noted that the amount of communication among network nodes and network congestion have been significantly reduced so as to avoid the waste of network resources. Finally, a simulation example is given to show the effectiveness of the proposed exponential synchronization criteria.

Cooperative Path Following of Autonomous Vehicles with Model Predictive Control and Event Triggered Communications

This paper presents a solution to the problem of multiple vehicle cooperative path following (CPF) that takes explicitly into account the constraints on the vehicles inputs and the topology of the inter-vehicle communications network. The solution involves decoupling the original constrained CPF problem into two sub-problems: i) single vehicle constrained path following and ii) multi-agent system (MAS) coordination. The first is solved by adopting a sampled-data model predictive control (MPC) scheme, whereas the latter is tackled by using a distributed control law with an event triggered communication (ETC) mechanism. We show that this design methodology yields a stable closed-loop CPF system: the path following error for each vehicle is globally asymptotically stable (GAS) and the coordination errors between the vehicles are bounded. A simulation example consisting of three autonomous vehicles following a given 2D- desired formation illustrates the efficacy of the CPF strategy proposed.

IEEE Access Special Section Editorial: High Mobility 5G LTE-V: Challenges and Solutions

The Internet of Vehicles, previously referred to as Vehicular Ad Hoc Networks (VANETs), will be a collection of mobile ad hoc networks for vehicular communications to improve driving safety and traffic efficiency. Major categories include vehicle-to-vehicle (V2V) communications and vehicle-to-infrastructure (V2I) communications. Collectively these (and future categories) can be termed V2X communications. These two categories entail a variety of communication channels, network topologies, vehicle densities and communication scenarios, and in response to this, IEEE has released the IEEE 802.11p and IEEE 1609 protocols for VANETs. With the global commercial application of Long Term Evolution (LTE) mobile communication systems, LTE based vehicular (LTE-V) networks have been shown to offer advantages in V2I communications, coverage, high mobility and complicated communication scenarios. Therefore, in the 5th generation’s (5G) mobile communication networks, the 3GPP is discussing V2V communications as a device-to-device (D2D) communication application. As is well known, in high mobility scenarios, the wireless channel is rapidly time- varying, Doppler shifts and spreads can be much larger than in cellular, network topology can change quickly, and switching among base stations and vehicles is more frequent. All this presents formidable challenges to realizing reliable communications with low latency in 5G LTE-V systems.

Representing network knowledge using provenance-aware formalisms for cyber-situational awareness

Due to the volume, variety, and veracity of network data available, information fusion and reasoning techniques are needed to support network analysts’ cyber-situational awareness. These techniques rely on formal knowledge representation to define the network semantics with data provenance at various levels of granularity. To this end, this paper proposes the Communication Network Topology and Forwarding Ontology, a state-of-the-art ontology that enables the formal, unified representation of complex network concepts regardless of the type of the data source. The implementation of this ontology allows network analysts to represent expert knowledge and query network data fused from disparate data sources.