Routing Capabilities Research Articles

Enhancing network tunnels anonymity through increasing combined traffic in a clustered structure

Given the visibility of the tunnel creation phase within tunnel-based anonymity structures, an entity’s traffic can be segregated based on the relay selection mechanism employed. Hence, the global attacker’s capability to detect communications and undermine the anonymity of entities is heightened. Another factor that can aid the attacker in identifying the tunnels is the improper combination of created tunnels and variations in the positioning of a combined relay within those tunnels. One potential solution to address these issues is to combine tunnels traffic by restricting the list of selectable relays. This can be accomplished by taking into account the choices made by tunnel owners and the network structure, as well as ensuring that the common selected relays occupy the same positions within the tunnels. We propose a clustering structure with routing capabilities to establish an infrastructure for creating combined tunnels. Our method has two key pillars. Firstly, both the tunnel creation packets and data packets follow the same pattern, making it difficult for the attacker to differentiate tunnel creation traffic from regular network traffic. Secondly, by allowing entities to join different clusters and maintaining a high ratio of entities to the number of interfaces within each cluster, the probability of combining traffic from senders within the same cluster is significantly increased. These interfaces within the proposed structure are referred to as permanent relays. Given the hierarchical nature of the proposed structure, the positions of relays within the tunnels of a cluster remain consistent. To assess the effectiveness of the proposed structure, we employ the average degree of anonymity metric, which relies on the Shannon entropy concept. Simulation results demonstrate a substantial increase in the degree of anonymity achieved by the proposed structure in comparison to previous approaches.

Enhancing Efficiency and Safety in Self-Guided Logistics Vehicles: A Comprehensive Analysis and Integration of Hardware and Control Systems

Self-guided logistics vehicles have become increasingly popular in various industries and warehouses for material lifting and transportation purposes. These automated systems have proven to be valuable in both small-scale production lines and large areas. However, challenges persist in the field of logistics, where industries and factories face difficulties in efficiently transporting materials. In response to these challenges, self-guided vehicles have been employed, but issues such as collisions with obstacles, material falls, and rigid path constraints have been observed. This project aims to address these challenges by implementing hardware safety precautions and incorporating routing and scheduling capabilities to enhance system automation. The article presents a comprehensive analysis of hardware safety precautions and highlights key features including sensors, actuators, and control systems, which contribute to the overall efficiency and smooth operation of the self-guided logistics vehicle system. By integrating these elements, the system can overcome obstacles, ensure material safety, and adapt to changing traffic conditions, thereby optimizing logistics operations in industries and warehouses.

Tunable targeted single-photon routing in a waveguide-QED structure containing a time-modulated two-level atom

Single-photon routing between two one-dimensional waveguides mediated by a single-mode cavity embedded with a time-modulated two-level atom is investigated. Two configurations, where the single photon is incident from an infinite or semi-infinite waveguide, are considered. Using the analytical expressions of the single-photon scattering amplitudes, the transmission behaviors in the two waveguides are discussed. The results show that the time modulation of the atomic frequency enables a dynamically tunable quantum router. A single photon with different frequencies can be routed dynamically from the incident waveguide to the other by properly manipulating the amplitude-to-frequency ratio of the atom. The routing efficiency can be improved to approach 100% by terminating the incident waveguide. In the semi-waveguide configuration, the routing behaviors controlled by the quantum coherent feedback are also investigated. The influence of the phase shifts introduced by the terminated waveguide on the routing capability and the conditions for perfect single-photon routing are discussed in detail. A frequency tunable targeted single-photon router can even be realized with the help of chiral coupling. These results may be beneficial to the photon control in a quantum network based on time-modulated quantum nodes.

Advancements in PCB Components Recognition Using WaferCaps: A Data Fusion and Deep Learning Approach

Microelectronics and electronic products are integral to our increasingly connected world, facing constant challenges in terms of quality, security, and provenance. As technology advances and becomes more complex, the demand for automated solutions to verify the quality and origin of components assembled on printed circuit boards (PCBs) is skyrocketing. This paper proposes an innovative approach to detecting and classifying microelectronic components with impressive accuracy and reliability, paving the way for a more efficient and safer electronics industry. Our approach introduces significant advancements by integrating optical and X-ray imaging, overcoming the limitations of traditional methods that rely on a single imaging modality. This method uses a novel data fusion technique that enhances feature visibility and detectability across various component types, crucial for densely packed PCBs. By leveraging the WaferCaps capsule network, our system improves spatial hierarchy and dynamic routing capabilities, leading to robust and accurate classifications. We employ decision-level fusion across multiple classifiers trained on different representations—optical, X-ray, and fused images—enhancing accuracy by synergistically combining their predictive strengths. This comprehensive method directly addresses challenges surrounding concurrency, reliability, availability, and resolution in component identification. Through extensive experiments, we demonstrate that our approach not only significantly improves classification metrics but also enhances the learning and identification processes of PCB components, achieving a remarkable total accuracy of 95.2%. Our findings offer a substantial contribution to the ongoing development of reliable and accurate automatic inspection solutions in the electronics manufacturing sector.

A hybrid algorithm based on the proposed square strategy and NSGA-II for ship pipe route design

Ship pipe route design (SPRD) is a difficult and time-consuming job due to the complexity of the routing space and the large number of pipes. This paper introduces a hybrid optimization algorithm called SQNSGA-II based on the proposed Square strategy and NSGA-II to efficiently solve SPRD problems in complex situations. The details of the hybrid algorithm are introduced, including the principle of the Square strategy, genetic operators and pipe repair operators et al. Additionally, a new method called Intercept Search strategy is proposed for optimizing branch pipe design in complex scenarios. Finally, we conducted three cases of simulation experiments to demonstrate that the proposed algorithm and strategy can efficiently and accurately optimize the design of pipes, highlighting their pipe routing capabilities in complex engineering projects.

A Method for 5G–ICN Seamless Mobility Support Based on Router Buffered Data

The 5G core network adopts a Control and User Plane Separation (CUPS) architecture to meet the challenges of low-latency business requirements. In this architecture, a balance between management costs and User Experience (UE) is achieved by moving User Plane Function (UPF) to the edge of the network. However, cross-UPF handover during communication between the UE and the remote server will cause TCP/IP session interruption and affect continuity of delay-sensitive real-time communication continuity. Information-Centric Networks (ICNs) separate identity and location, and their ability to route based on identity can effectively handle mobility. Therefore, based on the 5G-ICN architecture, we propose a seamless mobility support method based on router buffered data (BDMM), making full use of the ICN’s identity-based routing capabilities to solve the problem of UE cross-UPF handover affecting business continuity. BDMM also uses the ICN router data buffering capabilities to reduce packet loss during handovers. We design a dynamic buffer resource allocation strategy (DBRAS) that can adjust the buffer resource allocation results in time according to network traffic changes and business types to solve the problem of unreasonable buffer resource allocation. Finally, experimental results show that our method outperforms other methods in terms of average packet delay, weighted average packet loss rate, and network overhead. In addition, our method also has good performance in average handover delay.

Thermal Control of Quasi-2-Level Super-Switch by Power Routing

The requirement of power converters for Medium Voltage (MV) and High Voltage (HV) applications accelerates faster than the development of semiconductor switches capable of handling such voltage levels. For that reason, it is common to use the series connection of semiconductors with auxiliary snubbers. In contrast, an increased number of semiconductors is more likely to cause reliability problems due to increased redundancy, unequal thermal conditions, or different remaining lifetime. On the other hand, the quasi-2-level super-switch (Q2L SS) yields increased flexibility, enabling precise readjustments of the voltage and switching losses of individual devices of the converter due to the utilization of small capacitors. The power routing capability of the Q2L SS to redistribute the switching losses from the overheated semiconductors is presented in this paper. The influence of power routing on performance of Q2L SS is investigated with the Double Fourier analysis and experimental results.

IMPLEMENTATION OF RT / RW NET HOTSPOT NETWORK WITH MIKROTIK-BASED VOUCHER SYSTEM AT UNITEX BOGOR HOUSING

Technological developments are currently growing very rapidly, especially the use of computer networks. The use of the internet is increasing because it makes it easier for everyone to dig up existing information from the internet world, internet access has become a basic need for society. One effective way to provide internet access to the public is through the RT/RW Net network. However, there are still several obstacles in managing the RT/RW Net network in the Unitex housing complex in Bogor City, such as use that is not well controlled, many users who are not interested and do not have the right to use hotspot facilities by knowing the password for the Wi-Fi network in the housing complex. Unitex. To overcome these obstacles, the author proposes implementing an RT/RW Net network with a MikroTik-based voucher system. MikroTik is a network device that has reliable routing, switching and wireless capabilities. By utilizing MikroTik, RT/RW Net network managers can better manage and control internet access for users. By implementing the hotspot voucher login web page system provided by the Hotspot Server on the Mikrotik Router, to provide authentication features for users who will use the Hotspot/Wi-Fi network. So registered users are required to enter their username and password on the web login page provided by Mikrotik to be able to access the internet via the RT/RW Net Hotspot network. The research methods used in this research are observation, questionnaires, and literature studies, as well as the Network Development Life Cycle (NDLC) Method which is used to analyze needs in implementing the Rt/Rw Net Hotspot Network in the Unitex housing complex in the city of Bogor

Trickle-down strategies: integrating simulations with control loops of autonomous vessels on lab scale

This study integrates strategic decisions and operational control systems in autonomous shipping. By providing ships with situational information and adding a virtual operator, we show that vessels can make informed choices regarding their route and engine settings. To demonstrate this integration, we developed new components and put these to the test in three lab experiments.The green routing capability experiment showed the bridge between the control system of the autonomous vessel, operated via Robot Operating System (ROS), to the simulation environment of OpenCLSim. We developed a real-time variant of OpenCLSim and a communication component that could expose the state of the OpenCLSim simulation with the ROS system. This experiment showed that an autonomous vessel could follow a path provided by the simulation.The green steaming capability experiment showed that the ship could also adapt its speed based on information from the simulations. We developed an additional communication component capable of advising the vessel about its velocity. Together with the green-routing capability, this forms the basis for more complex experiments.The port layout experiment showed a potential use case of the green-routing and green-steaming capabilities. We created a waypoint layout similar to the port. While a ship is sailing, twelve simulations are computed every five seconds. The scenarios vary in engine order, route choices, resulting in varying emissions, fuel, and cost. We evaluated the impact of different tactics such as green-routing, green-steaming, and full-speed sailing on operational behavior like steering and engine order. Our approach, using a real-time version of a Vessel in the OpenCLSim simulation software, enabled predictive simulations to facilitate the chosen tactic based on a given strategy. Integrating simulations to evaluate the options with the control systems can develop into a valuable tool for optimizing vessel performance and reducing environmental impact in autonomous shipping operations.

Disjoint Paths Construction and Fault-Tolerant Routing in BCube of Data Center Networks

BCube is a promising structure of data center network, as it can significantly improve the performance of typical applications. With the expansion of network scale and increasement of complexity, reliability and stability of networks have become more essential. In this paper, we study the fault-tolerant routings in BCube. Firstly, we design a fault-tolerant routing algorithm based on node disjoint multi-paths. The proposed multi-path routing has stronger fault tolerance, since each path has no other common nodes except the source node and the destination node. Secondly, we investigate an effective fault-tolerant routing based on routing capabilities algorithm for BCube. The proposed algorithm has higher fault tolerance and success rate of finding feasible routes, since it does not limit the faults number. Thirdly, we present an adaptive path finding algorithm for establishing virtual links between any two nodes in BCube, which can shorten the diameter of BCube. Extensive simulation results show that the proposed routing scheme outperforms the existing popular algorithms. Compared with the state-of-the-art fault-tolerant routing algorithms, the proposed algorithm has a 21.5% to 25.3% improvement on both throughput and packet arrival rate. Meanwhile, it reduces the average latency of 18.6% and the maximum latency of 23.7% in networks.

Intelligent Caching with Graph Neural Network-Based Deep Reinforcement Learning on SDN-Based ICN

Information-centric networking (ICN) has gained significant attention due to its in-network caching and named-based routing capabilities. Caching plays a crucial role in managing the increasing network traffic and improving the content delivery efficiency. However, caching faces challenges as routers have limited cache space while the network hosts tens of thousands of items. This paper focuses on enhancing the cache performance by maximizing the cache hit ratio in the context of software-defined networking–ICN (SDN-ICN). We propose a statistical model that generates users’ content preferences, incorporating key elements observed in real-world scenarios. Furthermore, we introduce a graph neural network–double deep Q-network (GNN-DDQN) agent to make caching decisions for each node based on the user request history. Simulation results demonstrate that our caching strategy achieves a cache hit ratio 34.42% higher than the state-of-the-art policy. We also establish the robustness of our approach, consistently outperforming various benchmark strategies.

Broadband 32 × 32 Strictly‐Nonblocking Optical Switch on a Multi‐Layer Si3N4‐on‐SOI Platform

AbstractLarge‐scale silicon optical switches are essential to support the ever‐increasing data traffic. Among the various switching architectures, the well‐known switch‐and‐select (S&S) architecture has the advantages of low crosstalk and strict non‐blocking. However, it suffers from high path‐dependent losses as the waveguide crossings increase dramatically with the number of ports. In this paper, a large‐scale 32 × 32 S&S optical switch on a multi‐layer Si3N4‐on‐SOI platform is reported. The optical switch chip incorporates 1984 broadband thermo‐optic Mach‐Zehnder interferometer (MZI)‐based switch elements, 246 016 three‐dimensional (3D) waveguide crossings, and 2048 interlayer couplers. Both ≈1‐mdB‐loss 3D waveguide crossings and ≈0.3‐dB‐loss interlayer couplers are realized, significantly reducing the overall insertion loss and footprint of the switch chip. The measured average fiber‐to‐fiber insertion loss is 12.88 dB at the 1580 nm wavelength. In addition, the crosstalk is less than −20.7 dB over the 110‐nm wavelength range. The power consumption of the entire switch chip is only ≈0.98 W due to the air trenches and substrate undercut. High‐fidelity optical transmission of a 50 Gb s−1 quadrature phase‐shift keying signal verifies the high‐performance routing capability of this chip. These results indicate that the large‐scale optical switch with broadband, low crosstalk, and high‐power efficiency is promising for datacenter optical network applications.

Real-time health monitoring in WBANs using hybrid Metaheuristic-Driven Machine Learning Routing Protocol (MDML-RP)

Wireless body area networks (WBANs) are helpful for remote health monitoring, especially during the COVID-19 pandemic. Due to the limited batteries of bio-sensors, energy-efficient routing is vital to achieve load-balancing and prolong the network's lifetime. Although many routing techniques have been presented for WBANs, they were designed for an application, and their performance may be degraded in other applications. In this paper, an ensemble Metaheuristic-Driven Machine Learning Routing Protocol (MDML-RP) is introduced as an adaptive real-time remote health monitoring in WBANs. The motivation behind this technique is to utilize the superior route optimization solutions offered by metaheuristics and to integrate them with the real-time routing capability of machine learning. The proposed method involves two phases: offline model tuning and online routing. During the offline pre-processing step, a metaheuristic algorithm based on the whale optimization algorithm (WOA) is used to optimize routes across various WBAN configurations. By applying WOA for multiple WBANs, a comprehensive dataset is generated. This dataset is then used to train and test a machine learning regressor that is based on support vector regression (SVR). Next, the optimized MDML-RP model is applied as an adaptive real-time protocol, which can efficiently respond to just-in-time requests in new, previously unseen WBANs. Simulation results in various WBANs demonstrate the superiority of the MDML-RP model in terms of application-specific performance measures when compared with the existing heuristic, metaheuristic, and machine learning protocols. The findings indicate that the proposed MDML-RP model achieves noteworthy improvement rates across various performance metrics when compared to the existing techniques, with an average improvement of 42.3% for the network lifetime, 15.4% for reliability, 31.3% for path loss, and 31.7% for hot-spot temperature.

Controller Placement in Vehicular Networks: A Novel Algorithm Utilizing Elite Opposition-Based Salp Swarm and an Adaptable Approach

The rapid advancement of networking technology has enabled small devices to have communication capabilities, but the current decentralized communication system is not ideal for heterogeneous networks like vehicular networks. The integration of routing, switching, and decision-making capabilities in the same network device limits innovation and impedes performance in decentralized networks, especially in vehicular networks where network topologies change frequently. To address the demands of such networks, Software-Defined Networking (SDN) provides a promising solution that supports innovation. However, SDN's single-controller-based system may restrict the network's operational capabilities, despite being programmable and flexible. This paper suggests two methods to tackle the complex problem of controller placement in SDN: an adaptable approach based on OpenFlow protocol in OpenNet and an evolutionary algorithm called Elite Opposition-Based Salp Swarm Algorithm (EO-SSA) to minimize propagation latency, load imbalance, and network resilience. Multiple controllers increase the network's capabilities and provide fault tolerance, but their placement requires a trade-off among various objectives. The proposed methods have been evaluated and analyzed to confirm their effectiveness. The current decentralized network system is not adequate for vehicular networks, and SDN offers a promising solution that supports innovation and can meet the current demands of such networks.

Artificial Gauge Field Enabled Low‐Crosstalk, Broadband, Half‐Wavelength Pitched Waveguide Arrays

AbstractDense waveguide arrays with half‐wavelength pitch, low‐crosstalk, broadband, and flexible routing capability are essential for integrated photonics. However, achieving such performance is challenging due to the relatively weaker confinement of dielectric waveguides and the increased interactions among densely packed waveguides. Here, leveraging the artificial gauge field mechanism, half‐wavelength‐pitched dense waveguide arrays, consisting of 64 waveguides, in silicon with −30 dB crosstalk suppression from 1480 to 1550 nm are demonstrated. The waveguide array features negligible insertion loss for 90° bending. This approach enables flexibly routing a large‐scale dense waveguide array that significantly reduces on‐chip estate, leading to a high‐density photonic integrated circuit, and may open up opportunities for important device performance improvement, such as half‐wavelength‐pitch optical phased array and ultra‐dense space‐division multiplexing.

Rethinking LEO Mega-Constellation Routing to Provide Fast Internet Access Services

In the realm of providing space-based internet access services, utilizing large-scale low Earth orbit (LEO) satellite networks have emerged as a promising solution for bridging the digital divide and connecting previously unconnected regions. The deployment of LEO satellites can augment terrestrial networks, with increased efficiency and reduced costs. However, as the size of LEO constellations continues to grow, the routing algorithm design of such networks faces numerous challenges. In this study, we present a novel routing algorithm, designated as Internet Fast Access Routing (IFAR), aimed at facilitating faster internet access for users. The algorithm consists of two main components. Firstly, we develop a formal model that calculates the minimum number of hops between any two satellites in the Walker-Delta constellation, along with the corresponding forwarding direction from source to destination. Then, a linear programming is formulated, to match each satellite to the visible satellite on the ground. Upon receipt of user data, each satellite then forwards the data only to the set of visible satellites that correspond to its own satellite. To validate the efficacy of IFAR, we conduct extensive simulation work, and the experimental results showcase the potential of IFAR to enhance the routing capabilities of LEO satellite networks and improve the overall quality of space-based internet access services.

Tunable single-photon nonreciprocal scattering and targeted router in a giant atom-waveguide system with chiral couplings

We investigate the single-photon scattering properties of a driven three-level giant atom chirally coupled to two waveguides simultaneously in both the Markovian and the non-Markovian regimes. It is shown that under the Markovian limit, the chiral photon-atom interactions enable nonreciprocal scattering in a single waveguide and targeted photon routing with a probability of 100% in two waveguides, while the presence of the driving field and the giant atom structure introduce a more tunable parameter to manipulate the single-photon scattering behaviors. We also examine how the non-reciprocity and routing capability are influenced by the imperfect chirality and the atomic dissipation. In the non-Markovian regime, we show that the scattering behaviors are more complicated. The non-Markovicity induced non-reciprocity and photon routing are demonstrated in this paper. We believe that those results have potential applications in quantum network engineering.

Traffic management in vehicular adhoc networks using hybrid deep neural networks and mobile agents

<p>The traffic congestion in vehicular adhoc networks (VANETs) is a vital problem due to its dynamic increase in traffic loads. VANETs undergo inefficient routing capability due to its increasing traffic demands. This has led to the need for intelligent transport system (ITS) to assist VANETs in enabling suitable traffic loads between vehicles and road side units (RSU). Most conventional systems offer distributed solution to manage traffic congestion but fail to regulate real-time traffic flows. In this paper, a dynamic traffic control in VANETs is offered by combining deep neural network (DNN) with mobile agents (MA). An experimental analysis is carried out to test the efficacy of the DNN-MA against conventional machine learning and a deep learning routing algorithm in VANETs. DNN-Mal is validated under various traffic congestion metrics like latency, percentage delivery ratio, packet error rate, and throughput. The results show that the proposed method offers reduced energy consumption and latency.</p>

Hotspots Reduction for GALS NoC Using a Low-Latency Multistage Packet Reordering Approach

Traffic splitting enabled by Globally Asynchronous Locally Synchronous (GALS) Network-on-chip (NoC) brings multipath routing capability, which significantly increases link bandwidth at the cost of out-of-order packet delivery. Solving the packet reordering problem is one of the keys to ensure the quality of service (QoS) for NoC. However, traditional packet reordering approaches rely on local reorder buffer, causing on-chip hotspots, which aggravates chip aging and even leads to interconnection failures. In this paper, we present a multistage packet reordering (MPR) approach, which cannot only reduce the transmission latency but also effectively reduces hotspots caused by local reordering. Specifically, we propose multistage reordering buffer (MRB) by reusing channel buffers for implementing MPR. Experimental results show that our proposed approach achieved improved thermal efficiency with reduced hardware resource consumption.

Energy-Efficient Routing Protocol with Multi-Hop Fuzzy Logic for Wireless Networks

A Wireless Sensor Network (WSN) becomes a newer type of real-time embedded device that can be utilized for a wide range of applications that make regular networking which appears impracticable. Concerning the energy production of the nodes, WSN has major issues that may influence the stability of the system. As a result, constructing WSN requires devising protocols and standards that make the most use of constrained capacity, especially the energy resources. WSN faces some issues with increased power utilization and an on going development due to the uneven energy usage between the nodes. Clustering has proven to be a more effective strategy in this series. In the proposed work, a hybrid method is used for reducing the energy consumption among CHs. A Fuzzy Logic-based clustering protocol FLUC (unequally clustered) and Fuzzy Clustering with Energy-Efficient Routing Protocol (FCERP) are used. A Fuzzy Clustering with Energy Efficient Routing Protocol (FCERP) reduces the WSN power usage and increases the lifespan of the network. FCERP has created a novel cluster-based fuzzy routing mechanism that uses a limit value to combine the clustering and multi-hop routing capabilities. The technique creates uneven groups by using fuzzy logic with a competitive range to choose the Cluster Head (CH). The input variables include the distance of the nodes from the ground station, concentrations, and remaining energy. The proposed FLUC-FCERP reduces the power usage and improves the lifetime of the network compared with the existing algorithms.