Physical Network Topology Research Articles

Automotive ethernet: High-speed in-vehicle networking for next-generation electronics

This research paper explores the adoption and implementation of Automotive Ethernet as a high-speed, low-latency in-vehicle networking solution that addresses the increasing demands of next-generation automotive electronics. The growing complexity of modern vehicles, driven by advancements in connectivity, autonomous driving technologies, and electric powertrains, has outpaced the capabilities of traditional automotive networks such as CAN, LIN, and MOST. Automotive Ethernet emerges as a scalable and flexible alternative, offering higher bandwidth, reduced wiring complexity, and improved interoperability with existing systems. The study begins by examining the evolution of in-vehicle networks, highlighting the limitations of legacy architectures and the need for more advanced networking solutions. It delves into the technical architecture of Automotive Ethernet, covering the physical layer, network topology, and key standards like 100BASE-T1 and 1000BASE-T1. Moreover, the paper discusses the integration of Time-Sensitive Networking (TSN) to ensure deterministic communication for critical automotive applications, such as Advanced Driver Assistance Systems (ADAS) and vehicle-to-everything (V2X) connectivity. Beyond technical details, the paper evaluates the diverse applications of Automotive Ethernet, ranging from infotainment systems to diagnostic and prognostic capabilities, and the role it plays in supporting domain controllers and zonal architectures in modern vehicle platforms. Performance factors, including signal integrity, electromagnetic compatibility (EMC), and quality of service (QoS), are analyzed to underscore the robustness of Automotive Ethernet in real-world environments. Finally, the paper addresses key integration challenges, such as coexistence with legacy networks, software and middleware development, and cybersecurity concerns. Looking ahead, it explores future trends and opportunities, including multi-gigabit Ethernet, energy-efficient designs for electric vehicles, and integration with 5G and V2X technologies. By synthesizing current research, industry standards, and future prospects, this paper provides a comprehensive outlook on the pivotal role of Automotive Ethernet in enabling the next generation of connected and autonomous vehicles.

Handling Missing Sensors in Topology-Aware IoT Applications with Gated Graph Neural Network

Reliable data collection, transmission, and delivery on Internet of Things (IoT) systems is crucial in order to provide high-quality intelligent services. However, sensor data delivery can be interrupted for various reasons, such as sensor malfunction, network failures, and external attacks. Thus, only data from a partial set of sensors may be available. We call it the missing sensor problem. This problem can lead to severe performance degradation at inference time by neural-network-based recognition models trained on the complete sensor set. This paper enhances the robustness of neural network models to the missing sensor problem by introducing a novel feature reconstruction module, named the graph recovery module, that handles missing sensors directly inside the network. Specifically, we consider topology-aware IoT applications, where sensors are placed on a physically interconnected network. We design a novel neural message passing mechanism that logically mimics physical network topology, based on recent advances in graph neural networks (GNNs). We rely on a spatial locality assumption, where only correlations between physically connected sensors are explicitly explored. When encountering missing sensors, information is passed from available sensors to missing sensors to be used to reconstruct their features. Moreover, at each message passing step, we utilize a gating mechanism inspired by Gated Recurrent Units (GRUs) to automatically control information flow between available sensors and missing sensors. We empirically evaluate the reconstruction performance of the graph recovery module with two representative IoT applications; human activity recognition (HAR) and electroencephalogram (EEG)-based motor-imagery classification, on three public datasets. Two different backbone networks are utilized for the tasks. Our design is shown to effectively maintain model performance, suffering only 7% to 18% accuracy loss when as much as 90% of sensors are removed, compared to a drop of 15% to 47% in the accuracy of competing state-of-the-art algorithms under the same conditions. The accuracy gap is largest when more sensors are missing.

Application-Aware SDN-Based Iterative Reconfigurable Routing Protocol for Internet of Things (IoT).

The central intelligence offered by Software Defined Networking (SDN) promise the smart and reliable reconfiguration which enables the scalability of dynamic enterprise networks. The decoupled forwarding plane and control plane of SDN infrastructure is a key feature that supports the SDN controller to extract the physical network topology information at runtime to formulate network reconfigurations. This SDN-based network reconfiguration enables application-aware routing capability for Internet of Thing (IoT). However, these IoT enabled SDN-based routing protocols face some performance limitations in iterative reconfiguration process due to complete centralized path selection mechanism To this end, in this paper, we propose SDN-Based Application-aware Distributed adaptive Flow Iterative Reconfiguring (SADFIR) routing protocol. The proposed routing protocol enables the distributed SDN iterative solver controller to maintain the load-balancing between flow reconfiguration and flow allocation cost. In particular, the proposed routing protocol of SADFIR implements multiple SDN controllers that collaborate with network devices at forwarding plane to develop appropriate clustering strategy for routing the sensed information. This distributed SDN controllers are assisted to clustering topology that successfully map the residual network resources and also enable unique multi-hop application-aware data transmission. In addition, the proposed SADFIR monitor the iterative reconfiguration settings according to the network traffic of heterogeneity-aware network devices. The simulation experiments are conducted in comparison with the state-of-the-art routing protocols which demonstrates that SADFIR is heterogeneity-aware which is able to adopt the different scales of network with maximum network lifetime.

Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting

Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks. To address this challenge, we learn the traffic network as a graph and propose a novel deep learning framework, Traffic Graph Convolutional Long Short-Term Memory Neural Network (TGC-LSTM), to learn the interactions between roadways in the traffic network and forecast the network-wide traffic state. We define the traffic graph convolution based on the physical network topology. The relationship between the proposed traffic graph convolution and the spectral graph convolution is also discussed. An L1-norm on graph convolution weights and an L2-norm on graph convolution features are added to the model’s loss function to enhance the interpretability of the proposed model. Experimental results show that the proposed model outperforms baseline methods on two real-world traffic state datasets. The visualization of the graph convolution weights indicates that the proposed framework can recognize the most influential road segments in real-world traffic networks.

Optical Network Resource Management Supporting Physical Layer Reconfiguration

To cope with the capacity increase and diversification of information services, enhancing efficiency, flexibility, and agility in optical transport networks is becoming increasingly important. Quick reconfiguration of the optical physical layer, such as changing optical node architectures or optical physical network topologies, provides additional adaptability to requirement or environment changes. This paper proposes a new topology description scheme to support optical physical layer resource management at the granularity of optical functionality blocks in a machine-processible manner. The proposed scheme specifies intra-node structures as well as inter-node fiber connections (links). Furthermore, different switching functionalities of individual optical components are specified in a common format using integer linear programming (ILP) formulas. The ILP formulas are described in a machine-readable GNU MathProg modeling language so as to be directly introduced to the path computation mechanisms. Based on the proposed topology description scheme, a path computation engine named PathFinder is prototyped and demonstrated for optical networks consisting of various optical components with different switching functionalities. The computational feasibility of PathFinder is evaluated through numerical experiments in terms of elapsed time for path computation and the proposed scheme is successful while retaining reasonable time ranges. Operating topology and node architecture changes based on the proposed scheme are also successfully demonstrated over a multi-granular hierarchical optical network testbed with real hardware.

A Framework of Network Connectivity Management in Multi-Clouds Infrastructure

The NFV transformation is gaining incredible momentum from mobile operators as one of the significant solutions to improve resource allocation and system scalability in 5G networks. However, the ultra-dense deployments in 5G create high volumes of traffic that pushes the physical and virtual resources of cloud-based networks to edge limits. Consider a distributed cloud, replacing the core network with virtual entities in the form of VNFs still requires efficient integration with various underlying hardware technologies. Therefore, orchestrating the distribution of load between cloud geo-data centers starts by instantiating virtual and physical network topologies that connect involved fronthaul with relevant VNFs. In this article, we provide a framework to manage calls within 5G network clusters for efficient utilization of computational resources and also to prevent unnecessary signaling. We also propose a new scheme to instantiate virtual tunnels for call forwarding between network clusters leading to new logic networks that combine geo-data centers and fronthaul. To facilitate service chaining in cloud, we propose a new enhanced network functions virtualization management and orchestration (E-MANO) that extends telemetry services to the radio access network for instant monitoring of available radio resources. We provide analysis and testbed results in support of our proposals.

Design and Simulation of a Secured Enterprise Network for Faculty of Engineering, Rivers State University

Institutions generally seek for a network infrastructure solution that intelligently combines voice and data networks. To compete globally, It has become necessary for the Faculty of Engineering to build and setup a secured enterprise network solution to drive the rapid engineering and technological advancement of the University. The aim of this study is to develop a secured, scalable, available, and manageable enterprise network for the Faculty of Engineering, Rivers State University, Port Harcourt, Nigeria. In this article the various services that comprise the enterprise network as a unit have been put together using the Hierarchical Network Model. The physical and logical network topology was designed for the Faculty of Engineering infrastructures and the results from the simulation showed that any user who tried to connect to the network and initiated http traffic were redirected to the authentication server for verification of credentials, before being allowed on the network. The result also shows that the Cisco Adaptive Security Appliance, the Core Router, the distribution switches and the integrated service routers were properly configured. The design reduced network device load and the time to identify network issues to resolve them. The configured network security provided availability, integrity, and confidentiality. This design also enhanced rapid connectivity, and the inclusion of new devices did not affect the transfer of packets. Finally, the specifications and commands used in this study is a model that could be modified and deployed for other Faculties or Universities. Keywords : Configuration,design, enterprise network, topology, security, Cisco ASA. DOI : 10.7176/CEIS/10-5-04 Publication date :June 30 th 2019

Computation Offloading Optimization Based on Probabilistic SFC for Mobile Online Gaming in Heterogeneous Network

To support new services targeted by 5G, great efforts have been taken not only on the research work of new waveform design and air interface but also on cloudification and softwarization for future heterogeneous network. As one of the most popular services toward 5G, cloud gaming offloads computation-intensive tasks to the cloud in order to alleviate the computation burden of mobile devices, but it introduces latency which deteriorates user experience especially for the delay-sensitive online game. In order to solve the optimization problem of resource allocation with the quality of experience guarantees and reduce the operational expenditures and capital expenditures of mobile operators for deploying online game, fog computing and network function virtualization are deemed as promising solutions. In this paper, a component-based approach is proposed to model online game based on the probabilistic service function chain. In order to obtain the optimal virtual function placement in the fog-enabled heterogeneous radio access network, the cost minimization of computation offloading on the data plane is formulated as an integer linear programming problem considering the constraints of application maximum tolerable latency, resource limitation, and user behavior. The optimization problem is NP-hard. To solve the problem with low complexity, the heuristic algorithm is proposed called Probabilistic Service function chain Embedding based on Cost Optimization(PSECO). The performances of the two algorithms are evaluated. The simulation results show that the costs are affected mainly by the number of components, the arrival rate of user requests, mobile user behavior, as well as the physical network topology and the number of users. The heuristic algorithm PSECO has optimal results with low complexity and it is suitable for large scale networks.

RELAY DEPLOYMENT ALGORITHMS IN WIRELESS SENSOR NETWORKS: A SURVEY

Wireless Sensor Networks (WSNs) are subject to failures. Even though reliable routing protocols for WSNs exist and are well-understood, the physical network topology must ensure that alternate routes with an acceptable length to the sinksare in fact available when failures occur. This requires a sensor network deployment to be planned with an objective of ensuring some measure of robustness in the topology, so that when failures do occur the protocols can continue to offer reliable delivery. To ensure that sensor nodes have sufficient paths, it may be necessary to add a number of additional relay nodes, which do not sense, but only forward data from other nodes. In this paper, we review a range of existing algorithms to deploy relay nodes for fault tolerance. We classify the state-of-the-art relay placement algorithms based on routing structures, connectivity requirements, deployment locations, and fault-tolerant requirements.

Adaptive information distribution for dynamic sets using multicast push and pull

When developing applications in the area of ubiquitous computing, the developer often does not know at design time which and how many devices will be available at runtime when the user deploys the application in his personal environment. To tackle this problem, we proposed a new programming abstraction called dynamic sets in previous work. A dynamic set allows an application to interact transparently with a set of remote objects in the same way it interacts with a single object. The developer programs against a proxy that replicates the calls to the objects and aggregates the return values. While dynamic sets make application development easier and more flexible, efficiency problems can arise when an application is calling proxy methods with high frequency as if the object was local (since the calls are replicated to all members of the dynamic set). In this paper, we propose adaptive heuristics that tackle this problem for getter methods returning information about the objects' state. The heuristics use unicast and multicast communication and dynamically switch between pull (application requests update from object) and push (object sends update to application) depending on the getter rates of applications and the setter rates of objects to optimize communication costs. After describing the basic interaction patterns and presenting the details of our heuristics, we present the results of a comprehensive evaluation based on discrete event simulations. The results show that our heuristics effectively reduce communication costs although they operate on the application layer and, thus, have no detailed knowledge about the physical network topology.

Optimizing location of variable message signs using GPS probe vehicle data.

A multi-objective optimization model is proposed to allocate the location of VMSs by maximizing the average traffic guidance utility of VMSs and the number of benefited links, while minimizing information redundancy. The traffic guidance utility is defined to quantitatively measure the value of an installed VMS, which is calculated from passively collected GPS data and the physical topology of road network. The number of benefited links is to measure how many links are covered by upstream VMS to disseminate information. Information redundancy is introduced to quantify the mutual impairing between any two VMSs. A heuristic search algorithm is developed to solve the optimization model, which can calculate the saturated number of VMS for a road network and optimize the project schedule of VMS installation process based on the proposed objectives. A real-world case study is conducted in Beijing to illustrate the validity of the proposed approach, where taxis are used as probe vehicles to provide GPS data. The results show the effectiveness of the proposed multi-objective optimization model and it is promising to use the emerging GPS data to help agencies to allocate the locations of VMSs on both urban roads and highway networks, instead of relying on the subjective judgment from practitioners.

Energy-Efficient Ant Colony-Based k-Hop Clustering and Transmission Range Assignment Protocol for Connectivity Construction in Dense Wireless Sensor Networks

Connectivity construction is the main phase of a communication-oriented topology control process. It consists of improving the current network physical topology while preserving important properties such as connectivity and symmetry. In this paper, we address the problem of combining two of the techniques commonly used for this purpose in networks composed of a large number of energy constrained wireless sensor-nodes namely, clustering and power control. We propose an ant colony-based asynchronous and localized protocol that helps to significantly reduce energy losses by simultaneously eliminating redundant and poor quality links, always keeping the Cluster Head-to-member distance up to k-hops (k≥1) and minimizing signalization. Simulation results show that our protocol outperforms some state-of-the-art solutions in terms of Quality of the Topology (QoT) and network lifetime prolongation.

GreenTouch GreenMeter Core Network Energy-Efficiency Improvement Measures and Optimization

In this paper, we discuss energy-efficiency improvements in core networks obtained as a result of work carried out by the GreenTouch consortium over a five-year period. A number of techniques that yield substantial energy savings in core networks were introduced, including (i) the use of improved network components with lower power consumption, (ii) putting idle components into sleep mode, (iii) optically bypassing intermediate routers, (iv) the use of mixed line rates, (v) placing resources for protection into a low power state when idle, (vi) optimization of the network physical topology, and (vii) the optimization of distributed clouds for content distribution and network equipment virtualization. These techniques are recommended as the main energy-efficiency improvement measures for 2020 core networks. A mixed integer linear programming optimization model combining all the aforementioned techniques was built to minimize energy consumption in the core network. We consider group 1 nations' traffic and place this traffic on a US continental network represented by the AT&T network topology. The projections of the 2020 equipment power consumption are based on two scenarios: a business as usual (BAU) scenario and a GreenTouch (GT) (i.e., BAU + GT) scenario. The results show that the 2020 BAU scenario improves the network energy efficiency by a factor of 4.23 x compared with the 2010 network as a result of the reduction in the network equipment power consumption. Considering the 2020 BAU + GT network, the network equipment improvements alone reduce network power by a factor of 20 x compared with the 2010 network. Including of all the BAU + GT energy-efficiency techniques yields a total energy efficiency improvement of 315×. We have also implemented an experimental demonstration that illustrates the feasibility of energy-efficient content distribution in IP/WDM networks.

Mixed integer optimization for the combined capacitated facility location-routing problem

Given a set of potential facilities (with individual opening cost and finite capacity) and a set of client demands, the capacitated Facility Location Problem (cFLP) consists in selecting a set of facilities to open and performing an assignment of demands to open facilities that minimize the sum of the facility opening cost, supplying cost and transportation/connection cost. This problem shares many similarities with the one consisting in minimizing the cost for opening a set of servers or replicas for storing a set of data objects (files) and connecting clients to facility locations so as to satisfy their demands. The multi-source dimension of the problem translates the situation where the same data object may be available simultaneously at different facility locations. When various data objects with different properties are available at (possibly multiple) opened facility locations, the problem shares the characteristics of the multi-product or multi-commodity model. On the other hand, the combination of the cFLP with the network design problem, referred to as the Capacitated Facility Location-Network Design Problem (cFLNDP), has been subject to several studies since the early 2000s. This (class of) problem(s) was initially motivated by the observation that when the (physical) network topology is determined endogenously, it may be more effective to change the configuration of the underlying network physical topology instead of locating additional facilities in order to account for the spatio-temporal variability in clients demands. However, in the context of communication networks, one does not require to physically rewire the network to obtain a new logical topology. Hence, in this paper we combine the capacitated multi-source multi-product facility location with the traffic routing problem and propose a mixed-integer program formulation. As dynamic routing offers the flexibility to re-configure routing tables entries and adapt routing decisions, our combined model also enables to dynamically re-allocate demands upon facility failure.

Opnet Based Performance Analysis and Comparison Among Different Physical Network Topologies

Physical network topology is the configuration of computers, cables and other peripherals as a geometrical shape. There are many types of topologies each of them has many advantages and disadvantages. For high performance Local Area Networks (LANs), the builder of a network should select the best physical network topology. This paper analyzes three basic network topologies including bus, star and ring for different number of connected nodes (various network topology size) using OPNET simulator. Four scenarios are configured for each topology with number of connected nodes equal to 5, 10, 15 and 20 for senario1, 2, 3 and 4 respectively. In this analysis, four parameters investigated in Delay (Sec.) for the complete network, Load (Bits/Sec.), Traffic Received (Bits/Sec.) and the number of Collision for single node (server). Performance comparison is made in each topology separately based on various network size and another comparison is also made between these topologies for the same number of connected devices. The comparison results indicate that performance decreased when the network size increased and also show that bus topology is more effected than two other topologies.

Bandwidth Slicing for Socially-Aware D2D Caching in SDN-Enabled Networks

The explosive growth of mobile data traffic poses challenge to the network architecture design. Caching popular contents on device level instead of infrastructure level, termed as distributed caching, can potentially alleviate the traffic load. As an emerging technology to decouple the control plane from the physical network topology, software-defined networking (SDN) promises to build a flexible, scalable, and programmable framework by enabling centralized network control and management. In this paper, assuming that user can leverage social networking and share contents via device-to-device (D2D) communications, we propose a SDN-enabled socially aware D2D caching scheme, in which Internet service providers split bandwidth into distinct slices and dynamically allocate these slices to content providers (CPs) for controlling traffic flows. Then, the CPs can provide share contents with content requesters for further traffic offloading. To improve the spectrum efficiency, we conceive this caching scheme with the architecture of hierarchical matching game. In this way, the combinatorial problem can be solved in two-stage. In the first stage, we propose a dynamic bandwidth allocation algorithm to allocate distinct bandwidth slices for differentiated quality-of-service requirements. In the second stage, we formulate the content sharing problem as a many-to-many matching game with externalities. To solve this complex matching problem, we design a novel distributed algorithm with faster converge speed. A joint solution is thus provided to solve the content sharing and power allocation problems iteratively by exploiting non-orthogonal multiple access. We conduct extensive simulations to demonstrate that our proposed schemes provide a better tradeoff between performance and complexity than other benchmarks.

Research on automatic network based on low voltage power line carrier

To improve the power line communication distance, shorten the networking time and the reliability of the system communication, a method based on the clustering algorithm to change the network time and based on the distance between nodes is proposed in this paper. Firstly, the physical structure and logical topology of the low-voltage power line network are described in details. Then, the clustering algorithm and the method of route reconstruction are described. Finally, the method is simulated and analyzed. The results show that the proposed method can not only reduce network time, but also improve the communication stability of nodes within the network.

Optimal Virtualized Inter-Tenant Resource Sharing for Device-to-Device Communications in 5G Networks

Device-to-Device (D2D) communication is expected to enable a number of new services and applications in future mobile networks and has attracted significant research interest over the last few years. Remarkably, little attention has been placed on the issue of D2D communication for users belonging to different operators. In this paper, we focus on this aspect for D2D users that belong to different tenants (virtual network operators), assuming virtualized and programmable future 5G wireless networks. Under the assumption of a cross-tenant orchestrator, we show that significant gains can be achieved in terms of network performance by optimizing resource sharing from the different tenants, i.e., slices of the substrate physical network topology. To this end, a sum-rate optimization framework is proposed for optimal sharing of the virtualized resources. Via a wide site of numerical investigations, we prove the efficacy of the proposed solution and the achievable gains compared to legacy approaches.

A hierarchical network model for network topology design using genetic algorithm

Network topology design has directly impact on network construction costs and network performance. Majority of current network topology design take the network physical topology parameters into consideration, such as the reliability and cost constraints, ignoring the actual traffic information on the logical network. Moreover, the network traffic exhibits self-similar feature over large time scales, which is complicated and is difficult to predict. In this paper, firstly, a hierarchical network model is proposed that consists of the upper logical topology and the lower physical topology. The logical topology describes the self-similar traffic information on the network and the ON/OFF model is adopted to model the self-similar traffic. The lower physical topology represents the connection relationship between the all kinds of network devices and links. Then, taking advantage of the hierarchical network model, a novel network topology design method based on the genetic algorithm is proposed, which aimed at obtaining the network with minimum delay under certain reliability and cost constraints. Finally, a practical example is presented to verify the effectiveness and the accuracy of our network topology design method. Results show that our method obtains better results than the other methods.

Design and Simulation of a Topology Aggregation Algorithm in Multi-Domain Optical Networks

The aggregate conversion from the complex physical network topology to the simple virtual topology reduces not only load overhead, but also the parameter distortion of links and nodes during the aggregation process, thereby increasing the accuracy of routing. To this end, focusing on topology aggregation of multi-domain optical networks, a new topology aggregation algorithm (ML-S) was proposed. ML-S upgrades linear segment fitting algorithms to multiline fitting algorithms on stair generation. It finds mutation points of stair to increase the number of fitting line segments and makes use of less redundancy, thus obtaining a significant improvement in the description of topology information. In addition, ML-S integrates stair fitting algorithm and effectively alleviates the contradiction between the complexity and accuracy of topology information. It dynamically chooses an algorithm that is more accurate and less redundant according to the specific topology information of each domain. The simulation results show that, under different topological conditions, ML-S maintains a low level of underestimation distortion, overestimation distortion, and redundancy, achieving an improved balance between aggregation degree and accuracy.