Search Results (186)

The traditional TCP sender-driven approach to data communication in transport protocols can lead to ambiguity between the sender and receiver regarding packet delivery status. This issue stems primarily from the sender relying on explicit feedback from the receiver in the form of cumulative acknowledgments. While optimizations such as SACK can mitigate this issue to some extent, ambiguity may still arise due to receiver reneging or under high-loss environments, including retransmission loss. Recent research in transport protocols and new architectures has highlighted the advantages of using a receiver-driven approach over a sender-driven one for Internet communication. This shifts the traditional push-based data retrieval paradigm to a pull-based paradigm, allowing the creation of new transport services such as transparent caching and multicasting. This paper builds on these efforts to abstract and formalize a receiver-driven ARQ (RDQ) that follows established end-to-end principles in transport protocols, providing in-order reliability from the perspective of the receiver. We present the design of RDQ, layered on top of UDP, leveraging sender-driven and receiver-driven protocol elements. RDQ is implemented in the ns-3 simulator and evaluated against TCP- and SACK-style sender-driven ARQ under high-loss conditions. The preliminary results indicate the feasibility of incorporating a receiver-driven ARQ with a classic retransmission strategy in transport protocols, offering positive gains in reduced recovery delay and transmission efficiency relative to TCP/SACK-style sender-driven ARQ. Full article

Connecting distributed applications across multiple cloud-native domains is growing in complexity. Applications have become containerized and fragmented across heterogeneous infrastructures, such as public clouds, edge nodes, and private data centers, including emerging IoT-driven environments. Existing networking solutions like CNI plugins and service meshes have proven insufficient for providing isolated, low-latency and secure multi-cluster communication. By combining SDN control with Kubernetes abstractions, we present L2S-CES, a Kubernetes-native solution for multi-cluster layer-2 network slicing that offers flexible isolated connectivity for microservices while maintaining performance and automation. In this work, we detail the design and implementation of L2S-CES, outlining its architecture and operational workflow. We experimentally validate against state-of-the-art alternatives and show superior isolation, reduced setup time, native support for broadcast and multicast, and minimal performance overhead. By addressing the current lack of native link-layer networking capabilities across multiple Kubernetes domains, L2S-CES provides a unified and practical foundation for deploying scalable, multi-tenant, and latency-sensitive cloud-native applications. Full article

Neuromorphic computing has emerged as a promising paradigm for edge AI systems owing to its event-driven operation and high energy efficiency. However, conventional spiking neural network (SNN) architectures often suffer from redundant computation and inefficient power control, particularly during on-chip learning. This paper proposes a network-on-chip (NoC) architecture featuring a unified refractory-enabled neuron (UREN)-based router that globally coordinates spike-driven computation across multiple neuron cores. The router applies a unified refractory time to all neurons following a winner spike event, effectively enabling clock gating and suppressing redundant activity. The proposed design adopts a star-routing topology with multicasting support and integrates nearest-neighbor spike-timing-dependent plasticity (STDP) for local online learning. FPGA-based experiments demonstrate a 30% reduction in computation and 86.1% online classification accuracy on the MNIST dataset compared with baseline SNN implementations. These results confirm that the UREN-based router provides a scalable and power-efficient neuromorphic processor architecture, well suited for energy-constrained edge AI applications. Full article

To address the high computational and communication overheads and the limited edge security found in many existing batch verification methods for Mobile Edge Computing (MEC), this paper presents a blockchain-based batch authentication and symmetric group key agreement protocol. A core feature of this protocol is the establishment of a shared symmetric key among all authenticated participants. This symmetry in key distribution is fundamental for enabling secure and efficient broadcast or multicast communication within the MEC group. The protocol introduces a chameleon hash function built on elliptic curves, allowing smart mobile devices (SMDs) to generate lightweight signatures. The edge server (ES) then performs efficient large-scale batch authentication using an aggregate signature technique. Considering the need for secure and independent communication between SMDs and ES, the protocol further establishes a one-to-one session key agreement mechanism and uses a Merkle tree to verify session key correctness. Formal verification with ProVerif2.05 tool confirms the protocol’s security and multiple protection properties. Experimental results show that, compared with the CPPBA, ECCAS, and LBVP schemes, the protocol improves computational efficiency of batch authentication by 0.94%, 67.20%, and 49.53%, respectively. For group key agreement, the protocol achieves a 35.26% improvement in computational efficiency over existing schemes. Full article

A covert communication scheme is designed for pinching antenna (PA)-enabled integrated sensing and communication (ISAC) systems. The base station (BS) emits sensing signals to detect the potential eavesdropper while opportunistically performing covert multicast transmissions. To enhance covertness, the inherent power uncertainty of the sensing signals is exploited to confuse eavesdroppers, thereby creating protective coverage for the legitimate transmission. For the considered systems, we design an alternating optimization framework to iteratively optimize the baseband, beamforming, and PA positionson the two waveguides, in which successive convex approximation and particle swarm optimization methods are introduced. Simulated results confirm that the proposed scheme achieves the highest covert communication rates with different numbers of multicast users compared to benchmark methods. Furthermore, increasing the transmit power and the number of PAs can further improve the covertness performance. Full article

Dynamic Spectrum Sharing (DSS) enables spectrum sharing between Long-Term Evolution (LTE) and New Radio (NR) systems, addressing spectrum scarcity in NR. To avoid interference when supporting NR traffic within LTE spectrum, key factors must be compatible. Effective DSS techniques are essential for coexistence. This paper discusses these issues in two parts. Part I covers LTE and NR coexistence using DSS, introducing resource grids, control signals, and channels, and explores DSS approaches for NR data traffic, including NR Synchronization Signal/Physical Broadcast Channels (SSB) transmission via LTE Multicast-Broadcast Single-Frequency Network (MBSFN) and non-MBSFN subframes with associated challenges and standardization efforts for DSS improvement. Part II presents a DSS technique using MBSFN subframes in a heterogeneous network with a macrocell and picocells running on LTE, and in-building small cells running on NR, sharing LTE spectrum via DSS. An optimization problem is formulated to manage traffic through MBSFN allocation, determining the optimal number of MBSFN subframes per LTE frame. System simulations indicate DSS improves Spectral and Energy Efficiency in small cells. The paper concludes with key lessons for LTE and NR coexistence. Full article

We consider the problem of identifying the source of a rumor in a network, given only a snapshot observation of infected nodes after the rumor has spread. Classical approaches, such as the maximum likelihood (ML) and joint maximum likelihood (JML) estimators based on the conventional Susceptible–Infectious (SI) model, exhibit degeneracy, failing to uniquely identify the source even in simple network structures. To address these limitations, we propose a generalized estimator that incorporates independent random observation times. To capture the structure of information flow beyond graphs, our formulations consider rate constraints on the rumor and the multicast capacities for cyclic polylinking networks. Furthermore, we develop forward elimination and backward search algorithms for rate-constrained source detection and validate their effectiveness and scalability through comprehensive simulations. Our study establishes a rigorous and scalable foundation on the infodemic source detection. Full article

This study presents a reconfigurable optical convolutional neural network (CNN) architecture that integrates a crossbar switch network into a smart-pixel-based optical CNN (SPOCNN) framework. The SPOCNN leverages smart pixel light modulators (SPLMs), enabling high-speed and massively parallel optical computation. To address the challenge of data rearrangement between CNN layers—especially in multi-channel and deep-layer processing—a crossbar switch network is introduced to perform dynamic spatial permutation and multicast operations efficiently. This integration significantly reduces the number of processing steps required for core operations such as convolution, max pooling, and local response normalization, enhancing throughput and scalability. The architecture also supports bidirectional data flow and modular expansion, allowing the simulation of deeper networks within limited hardware layers. Performance analysis based on an AlexNet-style CNN indicates that the proposed system can complete inference in fewer than 100 instruction cycles, achieving processing speeds of over 1 million frames per second. The proposed architecture offers a promising solution for real-time optical AI applications. The further development of hardware prototypes and co-optimization strategies between algorithms and optical hardware is suggested to fully harness its capabilities. Full article

This paper addresses the challenges of multicasting in Mobile Ad Hoc Networks (MANETs), where communication relies exclusively on direct interactions between mobile nodes without the support of fixed infrastructure. In such networks, efficient information dissemination is critical, particularly in scenarios where an event detected by one node must be reliably communicated to a designated subset of nodes. The highly dynamic nature of MANET, characterized by frequent topology changes and unpredictable connectivity, poses significant challenges to stable and efficient multicasting. To address these issues, we adopt a Publish/Subscribe (Pub/Sub) model that utilizes brokers as intermediaries for information dissemination. However, ensuring the robustness of broker-based multicasting in a highly mobile environment requires novel strategies to mitigate the effects of frequent disconnections and mobility-induced disruptions. To this end, we propose a framework based on three key principles: (1) leveraging the Disruption-Tolerant Networking Implementations of the Bundle Protocol 7 (DTN7) at the network layer to sustain message delivery even in the presence of intermittent connectivity and high node mobility; (2) dynamically generating broker replicas to ensure that broker functionality persists despite sudden node failures or disconnections; and (3) enabling brokers and their replicas to periodically broadcast advertisement packets to maintain communication paths and facilitate efficient data forwarding, drawing inspiration from Named Data Networking (NDN) techniques. To evaluate the effectiveness of our approach, we conduct extensive simulations using ns-3, examining its impact on message delivery reliability, latency, and overall network throughput. The results demonstrate that our method significantly reduces message delivery delays while improving delivery rates, particularly in high-mobility scenarios. Additionally, the integration of DTN7 at the bundle layer proves effective in mitigating performance degradation in environments where nodes frequently change their positions. Our findings highlight the potential of our approach in enhancing the resilience and efficiency of broker-assisted multicasting in MANET, making it a promising solution for real-world applications such as disaster response, military operations, and decentralized IoT networks. Full article

Extremely large-scale multi-input and multi-output (XL-MIMO) communication, compared to conventional massive multi-input multi-output communication, can support more users and higher data throughput, thereby significantly improving its spectral efficiency and spatial multiplexing capabilities. This paper investigates the optimization of resource allocation for device-to-device (D2D) multicast communication in XL-MIMO cellular networks. The “many-to-many” sharing model permits one subcarrier to be shared among multiple D2D groups (DGs) and each DG to reuse multiple subcarriers. The objective is to maximize the total multicast data rate of DGs while meeting the data rate requirements of cellular users. This optimization problem is formulated as a 0–1 mixed-integer nonlinear programming problem, with the challenge lying in the fact that adjusting the subcarriers and the power of the user equipment alters the network’s carrier occupation and interference relationships, thereby increasing computational complexity. To address this challenge, a phased strategy is proposed. Initially, subcarrier allocation and coarse power allocation are conducted for cellular users. Subsequently, an adversarial multi-player multi-armed bandit framework is employed, treating DGs as players and subcarrier and power combinations as arms, to maximize the total multicast data rate. An improved Exp3 algorithm is utilized for selecting the optimal combination of arms. Finally, precise power allocation for cellular users is conducted based on the allocation results of the DGs. A comparative analysis of various simulations confirms the superiority of our algorithm over the established heuristic subcarrier assignment and proposed power allocation (HSAPP) and the channel allocation scheme using full information of device locations (CAFIL) approaches. Full article

Blockchain technology, as a distributed ledger technology, is becoming increasingly popular in various fields. However, the performance limitations of blockchain networks hinder their further development. Existing research on optimizing blockchain communication mechanisms based on P2P networks is constrained by the end-to-end transmission principles of TCP/IP networks, which lead to network congestion and bandwidth wastage during large-scale blockchain content distribution. Meanwhile, studies on ICN-based blockchain systems primarily focus on blockchain communication protocol implementation and compatibility within ICN/NDN networks. However, research on blockchain communication mechanisms in hybrid IP/ICN networking environments remains limited, failing to fully leverage ICN’s advantages to enhance the communication efficiency of existing blockchain P2P networks. To address this issue, this paper proposes BLOCK-ICN, an ICN-based blockchain network communication architecture compatible with IP networks. BLOCK-ICN enables ICN nodes with computing and storage capabilities to deploy blockchain applications, while maintaining compatibility with P2P networks. By leveraging ICN multicast technology, the architecture provides relay acceleration services for blockchain data dissemination. Specifically, in terms of network topology, BLOCK-ICN classifies network domains based on delay information provided by an enhanced resolution system and establishes select domain gateways based on data flow forwarding dependencies, thereby constructing a hierarchical and structured relay network topology. Regarding the broadcast protocol, ICN nodes perform parallel broadcasting via ICN multicast, and upon receiving messages, they further disseminate them to P2P nodes, reducing the overall network broadcast latency and bandwidth consumption. We extended SimBlock to implement and evaluate BLOCK-ICN. Simulation results demonstrated that, in a Bitcoin network with 16,000 nodes and an ICN node ratio of 1%, the broadcast delays for propagating blockchain data to 90% and 50% of the network were reduced by 25% and 33.2%, respectively, compared to Bitcoin. Full article

The integration of blockchain with Information-Centric Networking (ICN) enhances content distribution efficiency in areas such as the Internet of Things (IoT) and 5G/6G communications. This integration implies that the network state information of ICN can significantly impact consensus efficiency. However, the Ethereum Casper FFG consensus algorithm overlooks the network heterogeneity among consensus nodes, leading to a potential bottleneck in consensus efficiency, especially when nodes with inferior network quality participate. To address this issue, this paper proposes a multidimensional reputation model based on an ICN-enabled blockchain architecture. The model combines on-chain stake and network contributions to evaluate the reputation of ICN consensus nodes. Furthermore, a reputation-based hybrid consensus mechanism, RepuICN, is introduced, which enhances the network layer of the Casper FFG algorithm. This mechanism selects higher-reputation ICN consensus nodes as proposers for checkpoint blocks, mitigating the impact of network latency fluctuations on block propagation. Additionally, RepuICN improves block propagation efficiency through ICN multicast and caching techniques. Simulation results show that, under identical conditions with a network of 5000 nodes and 2% ICN nodes, RepuICN reduces broadcast latency by 17% for regular blocks and 61.4% for checkpoint blocks and achieves 3.4 times higher transaction throughput than Casper FFG. Full article

As the concept of the Metaverse evolves, virtual reality (VR) plays a pivotal role in creating immersive, socially interactive environments that form the backbone of this interconnected digital universe. However, VR technology often faces significant challenges, including hardware limitations, platform incompatibilities, and difficulties supporting seamless multiplayer experiences. VR streaming offers a potential solution by offloading computational tasks to remote servers, enabling high-quality VR experiences on lower-end devices and enhancing accessibility to a broader audience. In this paper, we present Loka, a versatile and extensible VR streaming framework designed to address these challenges, providing the necessary infrastructure to support social interactions and real-time collaboration in virtual environments—the key components of the Metaverse. Loka is built on Unity Engine and WebRTC, enabling seamless cross-platform VR experiences without the need for device-specific SDKs. It also supports real-time integration of custom sensory data streams, such as motion capture and physiological signals from IoT devices, which can enhance user interaction and personalization in virtual environments, as well as provide a more convenient accessible platform for research. Furthermore, Loka’s native multiplayer and multicasting capabilities facilitate collaborative and interactive social experiences, aligning with the core goals of the Metaverse. By leveraging cloud-based rendering with low-latency streaming, Loka allows users to engage in immersive VR environments on a wide range of devices, without requiring high-end hardware. Its modular architecture ensures extensibility, allowing researchers and developers to integrate new data types and experimental setups more easily. With its ability to set up immersive VR scenes to support social interaction and handle complex virtual environments, we believe the proposed work can be leveraged to foster the development and research of the Metaverse. Full article

Disaster backup, which occurs over long distances and involves large data volumes, often leads to huge energy consumption and the long-term occupation of network resources. However, existing work in this area lacks adequate optimization of the trade-off between energy consumption and latency. We consider the one-to-many characteristic in disaster backup and propose a novel algorithm based on multicast and reinforcement learning to optimize the data transmission process. We aim to jointly reduce network energy consumption and latency while meeting the requirements of network performance and Quality of Service. We leverage hybrid-step Q-Learning, which can more accurately estimate the long-term reward of each path. We enhance the utilization of shared nodes and links by introducing the node sharing degree in the reward value. We perform path selection through three different levels to improve algorithm efficiency and robustness. To simplify weight selection among multiple objectives, we leverage the Chebyshev scalarization function based on roulette to evaluate the action reward. We implement comprehensive performance evaluation with different network settings and demand sets and provide an implementation prototype to verify algorithm applicability in a real-world network structure. The simulation results show that compared with existing representative algorithms, our algorithm can effectively reduce network energy consumption and latency during the data transmission of disaster backup while obtaining good convergence and robustness. Full article

Since MANET consists of only nodes and is in wireless communication with limited resources, cooperation between nodes is very important. Multicasting technology supports various applications that allow data to be immediately transmitted to target nodes. In environments such as MANET, where nodes are constantly moving, finding an efficient path from the source to the destination is a critical challenge. Providing integrity for the data transmitted from the source to the target node set is also an important part. Maintaining the state of neighbor nodes not only increases the communication and processing overhead but also requires more memory. In this paper, we propose a zone-based secure routing algorithm to improve the performance of routing protocols. We will also prove efficient management of node groups in an area and better scalability, performance, and security despite frequent topology changes by using group keys. To evaluate the proposed technique, detailed and extensive simulation performance with PAST-DM and MSZRP are evaluated. The simulation results show that the proposed technique, compared to other routing protocols, can achieve scalability by maintaining the routing load even if the speed of nodes, the number of sources, the number of group members, and the size of the network increase. Full article