Along with the recent rise in popularity of Electric Vehicles (EVs), Electric Vehicle Supply Equipment (EVSE) has emerged as a new target for cyber attacks. Therefore, ensuring the security and integrity of network communication between EVSE components and vehicular clients is a significant challenge that must be addressed. To this end, this paper proposes a Flow-based Analysis and Labeling for COnnected vehicular Network Cybersecurity (FALCON-C) framework. The FALCON-C framework leverages an autoencoder for anomaly detection and is trained on a small number of benign flows from the CICEVSE2024 dataset. The model's objective is to model benign flow behavior and identify malicious flows by detecting statistically different reconstruction error profiles. The results demonstrate that the model can successfully identify malicious flows, achieving 100
Customer Premises Equipment (CPE) plays a fundamental role in enabling Software-Defined Wide Area Networks (SD-WANs) to support emerging applications such as Metaverse environments. These environments demand agile, secure, and high-performance network infrastructure to ensure seamless connectivity, low latency, and dynamic resource allocation. As the Metaverse continues to evolve, the need for flexible, efficient, and open CPE solutions becomes increasingly important. This paper presents an open-source SD-WAN CPE software designed to enhance network agility, scalability, and performance, specifically addressing the stringent requirements of next-generation Internet applications. The proposed solution supports deployment on both virtual machines and bare-metal systems, enabling seamless integration into diverse infrastructures. To achieve high-speed packet processing, it leverages the Data Plane Development Kit (DPDK), ensuring low-latency and high-throughput communication. Furthermore, it incorporates different overlay tunneling mechanisms, supporting both site-to-site and hub-and-spoke topologies via WireGuard tunneling technology for secure and efficient connectivity. A key feature of this implementation is its rapid failover capability, powered by Bidirectional Forwarding Detection (BFD), alongside real-time performance monitoring, enabling proactive network optimization and fault tolerance. By combining these advanced capabilities with an open-source framework, this SD-WAN CPE software provides a robust and cost-effective solution for next-generation network deployments.
How to design switches (and routers) that provide quality of service (QoS) guarantees, such as low transit (through the switch) delay for packets, has been a long-standing research problem. Output-queued (OQ) switch architecture is perfect for minimizing this transit delay, but is prohibitively expensive to implement "as is" in hardware. In theory, combined input- and output-queued (CIOQ) switch architecture can fully emulate OQ while requiring only a 2x speedup on the crossbar. In practice, however, one last hurdle remains for CIOQ to be practically implementable despite more than a decade of research: This emulation requires using stable marriages (matchings) as crossbar schedules, and each stable marriage takes Omega(N-2) parallel iterations (N is the number of input/output ports) to compute in the worst case. The contribution of this work is SW-EDF, a novel parallel iterative switching algorithm (PISA) that overcomes this last hurdle: SW-EDF can compute, in a single low-complexity iteration, an "approximate stable marriage" that allows for near-perfect OQ emulation, in terms of tardy (compared to OQ) rates and distribution of tardiness.
In elastic optical networks (EONs), spectrum fragmentation occurs when available spectrum resources become split into small, non-contiguous blocks, resulting in inefficient spectrum usage. Traditional methods, which allocate separate, dedicated partitions for each bandwidth demand type, often lead to an underutilized spectrum. Dedicated partitioning limits available slots per demand type, significantly increasing the likelihood of blocking requests, particularly those with higher bandwidth demands—even if sufficient spectrum slots are free in other partitions. To address this challenge, this paper proposes DGSP, a bandwidth demand grouping and spectrum partitioning scheme designed to reduce connection blocking. DGSP utilizes an integer linear programming (ILP) model to optimally group demands based on their slot requirements and determine equitable partition sizes. Within each partition, spectrum allocation is done using the first-last fit (FLF) policy. Simulation studies conducted on two networks confirm that DGSP effectively reduces the blocking probability, achieving improvements of up to 97.51% compared to conventional dedicated partitioning approaches.
Software-Defined Wide Area Networking (SD-WAN) has emerged as a popular solution for today’s enterprise networks, where Traffic Engineering (TE) plays a crucial role in optimizing traffic distribution across different overlay tunnels. During network congestion, traditional SD-WAN approaches often switch traffic to backup Multiprotocol Label Switching (MPLS) tunnels with high economic costs. To address this issue, we introduce Dynamic Path Switching (DPS), a novel SD-WAN TE solution that leverages underlay path diversity within an Internet overlay tunnel to maintain high Quality of Service (QoS) while substantially reducing economic costs. DPS operates on a Virtual extensible Local Area Network (VXLAN) overlay and controls the 5-tuple flow ID in the outer encapsulation header of traffic flows at SD-WAN gateways, which enables Internet Service Providers (ISPs) to dynamically switch traffic flows across multiple underlay paths based on the hashing results of their flow IDs. Moreover, we leverage extended Berkeley Packet Filter (eBPF) to implement DPS with high efficiency. Our prototype implementation, evaluated on a real Internet testbed, demonstrates that DPS can provide 99.974% service availability for enterprise traffic while reducing economic costs by 64.26% compared to traditional MPLS-based SD-WAN TE solutions.
In an Optical Packet Switching (OPS) network environment, the absence of practical optical buffers results in frequent packet collisions at intermediate nodes, as multiple packets may arrive simultaneously at the same output port of a certain intermediate node. This leads to substantial packet loss even under non-congested conditions. Consequently, the conventional Transmission Control Protocol (TCP) does not fully utilize the bandwidth of the bottleneck links. Moreover, highly bursty data transmission can impose the above impact excessively on intermediate nodes in OPS networks, making it undesirable. To address this issue, we have presented a control mechanism consisting of two disciplines: microburst transmission and congestion window updating. However, the performance of microburst transmission control itself, which corresponds to TCP pacing, has not been quantitatively clarified. In this paper, we quantitatively evaluate the performance of microburst transmission control. Specifically, we investigate the impact of different microburst transmission intervals on throughput and clarify the potential fairness of throughput among optical paths with different route lengths.
Effective bandwidth management in switches is critical to ensuring fair resource allocation among diverse application flows in data center networks. Modern switches often adopt shared memory architectures to improve resource efficiency. While fair queuing mechanisms are designed to ensure fair bandwidth allocation, existing approaches in shared memory switches frequently suffer from excessive packet drops, resulting in suboptimal network utilization. In this paper, we propose DFQ+, a dynamic fair queuing mechanism that leverages a limited number of priority queues to achieve both high network utilization and fair bandwidth allocation. DFQ+ is designed based on three key novel ideas. First, it dynamically adjusts the sliding window size according to flow arrival rates and queue occupancy, adapting the observation horizon to track traffic dynamics. Second, it introduces dynamic admission thresholds, which manage packet enqueuing by monitoring both the accumulated packet bits and the buffer occupancy in real time. Third, DFQ+ reclaims unused space from the head queue and reallocates it to the tail queue, improving buffer utilization while preserving fairness. Simulation experiments and a P4-based emulated prototype validate the functionality of DFQ+ and show that it can significantly improve fairness and overall network utilization, while reducing flow completion time by up to 83.96%.
Optical circuit switching (OCS) is being used in some data center networks due to its low power consumption, low latency, and high bandwidth. Previous research introduced a design model for a twisted and folded Clos network (TF-Clos) as a data center network to maximize the switching network size, i.e., the number of connected terminals, while guaranteeing the admissible blocking probability. The previous model assumes that the request active rates from all the terminals are identical. However, it is an overly conservative design when the active rates differ, resulting in a smaller switching network size than desired. This paper proposes a terminal-shuffling (TS) scheme for designing an OCS TF-Clos network with an admissible blocking probability guarantee, which supports different active rates. Each terminal can arbitrarily choose any leaf switch to connect, enhancing the flexibility of the network design to accommodate heterogeneous active rates across different terminals. A patch panel or direct termination by operators can wire optical fibers between the terminals and the leaf switches. We formulate a TS-based TF-Clos design problem to maximize the switching network size. We develop an approximation approach to find a feasible solution to the optimization problem. Numerical results demonstrate that the switching network size of the proposed TS scheme is larger than that of baseline schemes.
This paper explores the design method of integrating Wireless Rechargeable Sensor Network (WRSN) technology into the comprehensive experiment of the Signals and Systems course. By analyzing current of the Signals and Systems course and its experimental teaching, and combining the characteristics of WRSN technology, we design an innovative experimental design of bridge safety monitoring system based on WRSN. This experimental design aims to help students grasp core concepts of Signals and Systems through practical application scenarios, while fostering problem-solving skills. Results demonstrate that the comprehensive experiment based on WRSN can effectively improve students' practical ability, innovative thinking, and teamwork spirit, supporting the cultivation of future-oriented engineers.
RFC 6437 introduces the 20-bit flow label field in the IPv6 header and recommends it to be used alongside source and destination IP addresses for efficient flow classification. A recent paper from Google authors explains how they have begun to use this field in their IPv6 network to select an alternative path, thereby preventing congestion or outages. However, there is no prior research investigating whether connections make use of this field on the Internet. In this paper, we present a comprehensive analysis of the 9-year MAWI dataset (2016-2024), the Internet traces gathered from a large backbone link in Japan, and examine whether the IPv6 flow label is indeed employed on the Internet. Our findings reveal an increasing upward trend in the adoption of the flow label field. In particular, this trend correlates with the use of QUIC, whose specification—different from TCP—explicitly repeats the recommendation to utilize the flow label.
With the rapid advancement of digital communication technologies, there is an increasing demand for low-latency services, especially Ultra-Reliable Low-Latency Communication (URLLC) required in 5G and future networks. However, despite significant progress, several technical challenges remain unaddressed. To help overcome these issues, we have previously proposed an innovative connectionless (CL) IP router architecture suitable for both edge nodes and core transit nodes. Achieving ultra-low switching delay with minimal delay variation requires novel switching mechanisms. These mechanisms are crucial for mitigating delay fluctuations caused by variations in outgoing traffic volume, and for maintaining the simplicity of connectionless packet-based communication protocols. In this paper, we elucidate the essential mechanisms needed to implement the proposed CL IP switch architecture, aiming to support URLLC services for long-distance real-time communication. Recent progress in CMOS and optoelectronic device integration further enhances the feasibility of implementing a wide range of URLLC network services for 5G and 6G. Assuming a Poisson arrival distribution offers a fundamental basis for analyzing the impact of data traffic on IoT devices, particularly in the development of safety-critical design criteria for switch architecture and simulation. We evaluated the performance of a 4 x 4 switch prototype implemented using commercially available FPGA and System Verilog, incorporating parallel processing at an input/output line transmission speed of 100 Mbps. The measured switching delays were 2:56 mu s for short packets (32 bytes) and 5:12 mu s for long packets (64 bytes). The priority packet loss probability was approximately 1:44%, which meets the requirements for URLLC even under high line utilization (0:85) and priority packet ratios of up to 0:9, assuming Poisson-distributed packet arrivals. These results demonstrate that the proposed switch is well-suited for URLLC services, even under heavy traffic conditions. To further enhance data switching efficiency, this study explores the feasibility of incorporating wide range of adaptive asynchronous timeslot allocation into the switch design.
In rural areas such as national parks, forests, and mountains, recognizing and classifying wildlife is crucial for monitoring animals that may pose risks to crops and human safety. Deep learning provides the most accurate approach due to its dynamic adaptability. However, it is also highly energy intensive. Given the constraints of rural environments, including the use of extreme edge devices and limited power availability, this study employs distributed computing to maximize battery life while enabling the execution of DL tasks. The core concept is Split Computing, specifically applied to the YOLOv8m and YOLOv10 models, which have been provided by researchers as a highly effective solution for wildlife classification. This approach dynamically reallocates different model components across available boards, specifically the NVIDIA Jetson Orin Nano, to optimize energy consumption. This research investigates various model distribution configurations across the head, backbone, and neck components, assessing whether video stream compression between the camera and the computing boards affects energy consumption and network load. Our findings suggest that the choice of model splitting configuration significantly impacts energy efficiency, bandwidth consumption, and computational load distribution.
In this paper, we propose deliverable partitioning in prompt design to assist Large Language Models (LLMs) in improving response correctness for network design and configuration. While recent research has explored the use of LLMs to enhance network management efficiency, their responses often remain inconsistent, incomplete, or inaccurate. Often, LLM-generated configurations contain missing or erroneous configuration commands, which can lead to operational failures. Our proposed partitioning methodology aims to mitigate these issues by decomposing complex network configuration tasks into simplified and focused tasks. To evaluate the effectiveness of this approach, we introduce a scoring policy and conduct extensive experiments across three levels of network complexity and varying degrees of design choice ambiguity. We also compare the performance of leading LLMs, including ChatGPT, Copilot, and DeepSeek. Our findings indicate that partitioning the inquiry process leads to more accurate and consistent responses than non-partitioned approaches, especially in scenarios where design parameters are explicitly defined and leave some but small room, as ambiguity, for inference.
With the rapid expansion of IoT, there is a growing demand for efficient data processing and network load optimization. Service Function Chaining (SFC) has emerged as a key technology for distributed processing, particularly in edge computing environments. While SFC is traditionally implemented over IP networks, recent research has explored Named Data Networking (NDN) as a more flexible alternative, leveraging its content caching and name-based forwarding. However, NDN's inherent request aggregation and multicast mechanisms introduce unique challenges in applying conventional distributed tracing techniques, which are crucial for SFC applications to understand potential root causes of problems. This paper proposes a distributed tracing method for NDN-based SFC (NDN SFC) that enables packet flow visualization and network latency analysis. Our method introduces logging agents on NDN routers to capture request flows and inter-node delays, which are then formatted using OpenTelemetry for seamless integration with visualization tools such as Grafana((R)) and Jaeger. Our tracing method helps to optimize service chains, to enhance network performance evaluation, and to streamline debugging, making NDN SFC deployment more practical. By offering a more efficient alternative to traditional IP-based SFC, our method supports the practical adoption of NDN SFC in IoT and edge computing environments.
In modern dynamic and heterogeneous network environments, the limitations of traditional routing algorithms have become increasingly apparent. While these classical methods have provided robust solutions over decades, they fall short in meeting the current demands for scalability, adaptability, and simplicity. In contrast, Layer-2 networking offers a decentralized, plug-and-play paradigm that is well suited for rapid deployment across diverse scenarios, including data center networks or softwarized services for edge computing networks. However, existing solutions suffer from prolonged convergence times and high control overhead, which hampers their efficiency in dynamic topologies and deviates from the autoconfiguration and plug-and- play essence of a pure Layer-2 approach. In this paper, we present BareFlow, a novel pure switching mechanism that obviates the need for explicit control messages by operating without modifying packet headers. We evaluated BareFlow using the NS-3 simulator in a Spine-Leaf topology (usual in data center networks) under various traffic distributions. These preliminary results demonstrate that BareFlow achieves near-maximum throughput for large flows and efficiently manages flow completion times across different flow sizes. These findings highlight the potential of BareFlow as a scalable, self-configuring solution, offering efficient and adaptive forwarding with significantly reduced overhead, which is particularly suited for edge computing environments, in which devices are usually constrained in capacity.
With the advancement of technology, the volume of data can be huge, but the transportation process may introduce uncertainties due to possible data loss. Addressing such uncertain data has become a primary research trend. Group skyline (G-Skyline) query, a variant of the skyline query, is used in multi-criteria decision-making and environmental monitoring to identify group of members that cannot be dominated by any other groups. This paper considers group skyline query processing on the uncertain data. The uncertainty of data complicates the computations and manipulating the groups instead of the individual data points makes the computation even harder. The Top-m Probabilistic Point Combination algorithm (TmPPC) is proposed to reduce the space and time required for computing the G-Skyline that may need all group combinations. By preprocessing data with DSG and selecting the top m groups with the highest probability, the proposed algorithm resolves the issues and significantly reduces the computation time and the space requirement as the experimental results demonstrated.
In recent years, unmanned aerial vehicles (UAVs) equipped with wireless chargers have been utilized as mobile charging units to flexibly recharge energy-depleted sensor nodes in wireless sensor networks. However, the endurance of UAVs remains a critical challenge. By deploying wireless charging pads to provide energy replenishment, UAVs can sustain their operations and successfully complete their charging tasks. Therefore, a key problem is how to effectively deploy the minimum number of wireless charging pads while establishing at least one feasible charging path from the base station (BS). This ensures that the UAV can reach and recharge all sensor nodes from BS. When designing an optimization algorithm for wireless charging pad deployment, connectivity, coverage, and geometric properties must be considered simultaneously. However, previous approaches often employed greedy algorithms to solve the optimal deployment problem, treating coverage and connectivity as interdependent properties. This led to excessive constraints on the placement of wireless charging pads, as each newly added charging pad had to satisfy both properties at the same time. Additionally, previous works overlooked a critical issue, which is avoiding the occurrence of isolated sensor nodes in uncovered fragmented regions, in deployment. Failing to address this issue would require additional deployment costs to compensate for uncovered nodes. This study focuses on addressing these challenges by proposing a novel deployment strategy to enhance the energy replenishment efficiency and overall performance of wireless charging sensor networks. To overcome the limitations of previous methods, we apply computational geometry techniques to develop a more effective deployment algorithm. Our proposed onion-peeling like wireless charging pad deployment algorithm deploys charging pads layer by layer from the outermost region inward, prioritizing coverage before connectivity. Simulation results demonstrate that the proposed approach significantly reduces the number of required wireless charging pads compared to existing methods.
The rigid security and performance demands of sixth-generation (6G) networks necessitate advanced quantum communication solutions. This study integrates cadmium selenide (CdSe) quantum dots (QDs) as single-photon sources (SPS) for quantum key distribution (QKD), leveraging their size-dependent emission properties to meet telecom wavelength requirements. Using machine learning-driven optimization, we achieved a 40% reduction in optimization time and high prediction accuracy (R2 > 0.92) for emission wavelength and quantum yield. These results were validated by simulations, which demonstrated improved photon purity (g2(0) < 0.1) and extraction efficiency (65%), with a key generation rate of 20 Mbps and a quantum bit error rate (QBER) below 3%. By addressing challenges such as photon loss and multi-photon emissions through advanced core/shell engineering and photonic integration, this scalable framework paves the way for next-generation secure communication systems, including potential applications in quantum computing and beyond.
yPeer-to-peer networks, such as IPFS, adopt the distributed hash table (DHT) to find contents efficiently. IPFS uses Kademlia DHT as a routing scheme to efficiently lookup any content with CID (content ID). However, due to the completely matching nature of DHT, IPFS does not have any scheme to lookup content with one or more attributes and ranges. Thus, it cannot be applied to a situation where several contents can be retrieved in sensed data such as an IoT network. In this paper, we propose a multi-attribute and range query scheme for Kademlia DHT in IPFS. In our proposal, several responsible nodes are used for maintaining a specific attribute prefix and having CIDs for mapped contents. As a result, once a query arrives at a responsible node, any contents with mapped attributes are retrieved.
The need for real-time inference of large volumes of data led to the development of in-network machine learning. Programmable network switches can now execute various machine learning models in the data-plane at line rate. While a stream of data may require several prediction tasks, such as predicting bit rate, flow size, or traffic class, current solutions only support separate models for each task. This places a significant burden on the data-plane and leads to substantial resource consumption when deploying multiple tasks. To solve this problem, we introduce MUTA; a novel in-network multi-task learning solution. MUTA enables executing multiple inference tasks concurrently in the data-plane, without exhausting available resources. It introduces a data-plane mapping methodology to fit non-binarized multi-task neural networks within network switches. MUTA is deployed on P4-based hardware switches, and is shown to reduce memory requirements by x10.5 and improve accuracy by up to 9.14% using limited training data, compared with state-of-the-art single-task learning solutions.
