Search Results (275)

We propose optimal power allocation for a transmitter that operates with either the ARQ-I or CC-HARQ protocol in an interference-limited environment where both the desired and interfering signals experience Rayleigh fading, forming an analytically tractable non-line-of-sight scenario, to achieve ultra-reliable communication with minimal power. We formulate the power allocation problem as a minimization of the average retransmit power under a given target outage probability for both retransmission schemes. Using the Karush–Kuhn–Tucker method, we derive protocol-specific equations solved numerically for retransmit powers. Unlike prior interference-limited numerical studies and noise-limited closed-form results, we derive closed-form analytical solutions for optimal retransmit power expressions for ARQ-I and CC-HARQ in a Rayleigh–Rayleigh interference-limited environment for two retransmissions, which act as computationally efficient benchmarks for real-time ultra-reliable applications and establish a baseline for extending to more general fading environments. This constitutes the primary contribution of this work. We show through simulations that the proposed schemes achieve significant power savings compared to conventional open-loop transmission, particularly in the ultra-reliable region. Benchmarking against prior Rician–Rayleigh results shows that, as expected, the Rayleigh–Rayleigh case requires higher power because it lacks the line-of-sight component that improves reliability, while confirming that CC-HARQ consistently outperforms ARQ-I in power efficiency. Full article

This article presents an experimental evaluation of the data-transmission performance of an IoT device communicating with an MQTT broker over a local IEEE 802.11n Wi-Fi network in the 2.4 GHz band, coexisting with modern Wi-Fi networks based on the IEEE 802.11ax (Wi-Fi 6) and IEEE 802.11be (Wi-Fi 7) standards. The effect of sharing the radio medium on the communication quality parameters of the IoT device, such as end-to-end delay, jitter, the packet delivery ratio (PDR), and application throughput (goodput), was assessed. Unlike previous work, which focuses primarily on PHY/MAC metrics or on simulation-based analysis, this study targets the application layer (MQTT) from the perspective of a legacy IoT end-device. The results presented and discussed herein show that the presence of 802.11ax and 802.11be networks in the 2.4 GHz band can significantly degrade the temporal parameters of MQTT transmissions performed by IoT devices operating in the older 802.11n standard. The greatest impact is observed in jitter and in the extreme delay values (for instance, in the worst-case coexistence scenario—with simultaneous Wi-Fi 6 and Wi-Fi 7 interference, the P95 delay increased from 38 to 869 ms), whereas the PDR remains relatively high because retransmissions compensate for packet losses at the expense of delay. Full article

Smart meters are the core terminals for distribution network data acquisition in smart cities, yet their collected data commonly suffer from quality issues caused by harsh operating environments, communication failures, hardware degradation, and human factors. This paper presents a systematic review of anomaly detection and data repair methods for smart meter data based on a critical analysis of many publications. First, we characterize five typical anomalies—sudden jumps, reading stagnation, reverse readings, pulse spikes, and gradual drifts—from physical root causes to data manifestations and provide unified mathematical definitions with explicit traceability to the existing literature. Additional anomaly types including meter replacement jumps, data duplication from retransmission, complete missing segments, and timestamp errors are also discussed to present a more complete picture of operational data quality challenges. Second, existing anomaly detection methods are systematically reviewed and classified into four categories—statistical, machine learning, deep learning, and dedicated time-series methods—with representative studies, quantitative performance metrics, and scenario-specific applicability examined for each. Third, data repair approaches are reviewed across four categories—traditional interpolation, matrix completion, generative models, and time-series prediction—with systematic comparison of their accuracy and limitations across different anomaly types and durations. Based on the synthesized evidence, we identify three cross-cutting structural limitations that persist across method categories: the performance ceiling of data-only detection without physical constraint embedding, the open-loop architecture that separates detection from repair and allows error propagation, and the exclusive reliance on statistical error metrics that fails to distinguish physically plausible repairs from those violating conservation laws. To address these gaps, we discuss a physics-guided integrated framework incorporating physical constraint embedding, joint anomaly diagnosis, scenario-adaptive repair, and posterior verification as a promising forward-looking direction. Finally, open challenges and future research directions are outlined, including parameter adaptation in unlabeled scenarios, multi-source data fusion for physical disambiguation, new power system extensions, explainable AI integration, edge-computing deployment, and standardized benchmark development. This review provides a comprehensive theoretical reference and technical roadmap for smart meter data quality research in the context of smart city energy systems. Full article

Civilian communication systems often fail during armed conflicts, political unrest, and large-scale Internet disruptions—precisely when reliable communication is most needed. This paper presents HERMES, a resilient hybrid communication architecture that integrates HTTP/IP networking, Bluetooth Low Energy (BLE) mesh communication, and Delay-Tolerant Networking (DTN) within a unified adaptive routing framework. Unlike conventional approaches that treat alternative transports as backup solutions, HERMES dynamically selects the most efficient transport path based on current network conditions using a transport-aware forwarding policy whose cost function combines round-trip time, transport preference, and observed link risk. The architecture is built on distributed microservices that support topology discovery, shortest-path routing, and fault-tolerant message delivery. Reliability is enhanced through acknowledgments, bounded retransmissions, duplicate suppression, and graceful degradation mechanisms, while end-to-end authenticated encryption (Noise XX with a Double Ratchet) ensures secure communication across transport changes. A prototype implementation developed in C# on .NET 9 was evaluated on a five-node testbed, and a custom Network Simulator 3 (NS-3) module was used to extend the evaluation to networks of up to 500 nodes, under multiple failure scenarios, including node crashes, network partitioning, and complete Internet outages. Experimental results show that HERMES maintains perfect or near-perfect delivery in static topologies, including during a complete Internet blackout that disables IP-only messaging. Compared with the published Delay-Tolerant Networking protocols Epidemic and PRoPHET at one hundred nodes, HERMES exceeds their delivery ratio in static and failure scenarios and remains within 0.06 of them under pedestrian mobility during blackout, while transmitting roughly 35× fewer bytes– and about 21× fewer even relative to the more bandwidth-efficient MaxProp baseline. Under coordinated drop attacks by adversarial relays, HERMES degrades gracefully where flooding-based baselines collapse. This approach demonstrates that resilient civilian communication can be effectively achieved through metric-driven adaptive multi-transport routing, making it suitable for disaster recovery, contested environments, and connectivity-limited regions. Full article

Reliable data collection is essential for disaster-oriented Internet of Things (IoT) systems, where damaged terrestrial communication infrastructure often leaves sensed data buffered at disconnected end devices. In Unmanned Aerial Vehicle (UAV)-Internet of Things device (IoTD) collaborative data collection, random UAV faults and limited energy and buffer resources further complicate mission execution, making fault-tolerant scheduling crucial for robust data recovery. To address these issues, a unified framework is developed by integrating dynamic UAV reliability modeling, Maximum Distance Separable (MDS)-coded fault-tolerant backup, and collaborative scheduling optimization. Within this framework, a data fault-tolerance mechanism, termed MFTB, and a bilevel collaborative scheduling algorithm, termed LP-DCFS, are proposed. Simulation results indicate that, in the evaluated scenarios, the proposed methods achieve better overall performance than the considered baselines. In a representative high-load, high-failure scenario, MFTB reduces data loss by 4.8% and 37.5% compared with Buffer-Limited Retransmission (BLR) and Replication, respectively, while LP-DCFS increases the amount of recovered data by 33.9%, 32.3%, and 53.1% compared with ACEPSO, ADE-DMRM, and DQN, respectively. Under the modeled independent random crash and non-return faults and the evaluated simulation settings, these results suggest that coordinating failure-risk characterization, data-protection mechanisms, and task-scheduling strategies can improve the robustness and data-recovery capability of disaster-oriented UAV-assisted data collection. Full article

New Radio Vehicle-to-Everything (NR-V2X) safety scheduling is difficult because burst episodes increase urgent arrivals, lower transmission success, and create retransmissions that compete for future slots. A scheduler that waits for the visible queue can react late, whereas always reserving extra safety physical resource blocks (PRBs) consumes the best-effort (BE) capacity after the stress has passed. This study proposes Memory-Based Predictive Allocation (MPA), a finite-action PRB allocation rule for safety and BE coexistence. MPA combines the deadline queue and retry state with a decayed transient-deficit memory, online success calibration, and a recoverability-aware BE cost guard. At each slot, it tests feasible safety PRB increments and chooses the action that first limits urgent safety loss, then reduces next-slot carryover, and finally avoids unnecessary PRB use. The model uses an NR-V2X resource pool interpretation and a calibrated signal-to-interference-plus-noise-ratio (SINR)-to-success mapping with hybrid automatic repeat request (HARQ)-like combining. Monte Carlo results show that MPA lowers safety misses relative to queue-reactive scheduling while preserving more BE throughput than a maximum safety reservation. In dense non-line-of-sight (NLOS) stress, MPA keeps the 95th-percentile (p95) delivered packet delay within the three-millisecond budget and preserves 0.892 normalized BE throughput, versus 0.534 under fixed maximum reservation. Full article

The co-existence of event-driven critical traffic and time-driven periodic traffic inevitably exacerbates cross-layer network congestion in resource-constrained edge environments. Although hybrid protocol architectures integrating the Constrained Application Protocol (CoAP) at the edge and Quick UDP Internet Connections (QUIC) in the core network have emerged, existing gateways manage these protocols independently, failing to provide an organic congestion control mechanism. To overcome these limitations, this paper proposes an ultra-lightweight Edge Intelligence (EI)-assisted end-to-end (E2E) CoAP-QUIC cross-layer congestion control framework powered by Proximal Policy Optimization (PPO). The proposed scheme introduces an ultra-lightweight traffic classification mechanism that instantly distinguishes traffic classes by parsing the existing two-bit type field in the CoAP header, effectively bypassing the payload inspection overhead. On the basis of this, the PPO agent shapes its reward function in real time, actively shifting optimization weights between delay reduction and throughput optimization. This dual-action control directly mitigates congestion by dynamically tuning the QUIC congestion window and CoAP back-off timers to prevent edge buffer saturation. Extensive simulations using Network Simulator 3 (NS-3) demonstrate that the proposed framework significantly outperforms state-of-the-art baselines, bounding end-to-end latency for critical traffic under 100 ms and improving overall energy efficiency by 21.5% while achieving a 98.2% packet delivery ratio. Full article

The Constrained Application Protocol (CoAP) is widely adopted to ensure end-to-end reliability in resource-constrained Artificial Intelligence of Things (AIoT) and Wireless Sensor Networks (WSNs). However, CoAP’s default retransmission timeout (RTO) mechanism lacks algorithmic responsiveness under volatile channel conditions, and state-of-the-art benchmarks like CoCoA+ and FASOR often suffer from over-conservative backoff states or destabilizing retransmission storms. To overcome these operational bottlenecks, this paper proposes a novel dual-adaptive Dynamic RTO algorithm specifically engineered for heterogeneous IoT deployment scales. The proposed framework dynamically adjusts its parameter inspection cycle (N) based on instantaneous round-trip time (RTT) variance while simultaneously scaling its tuning coefficient (α) in response to real-time packet loss indicators. To rigorously validate the algorithmic resilience, performance evaluations were conducted within a highly volatile network environment governed by the Gilbert–Elliott dynamic loss model across multi-hop linear (1 × 6) and grid (3 × 6, 5 × 6) topologies. Experimental results demonstrate that the proposed Dynamic RTO consistently optimizes the throughput–latency trade-off, achieving a total communication time of 25.92 s in complex grids—outperforming CoCoA+ and FASOR by 14.28% and 8.89%, respectively. Furthermore, the proposed mechanism significantly curtails transmission overhead, restricting the cumulative retransmission footprint to just 59 counts under severe localized impairments, thereby establishing a scalable, resource-efficient, and empirically robust transport-layer solution for next-generation edge-computing infrastructures. Full article

Ultra-reliable low-latency communication (URLLC) is a fundamental technology that plays a crucial role in enabling fifth-generation new radio (5G-NR) communication. URLLC aims to provide a highly reliable connection with strict block error probability requirements and extremely low latency for mission-critical and remote operations. Meanwhile, the advent of sixth-generation (6G) communication, marked by its novel, immersive, and high-stakes control applications, imposes notably more stringent demands on reliability and latency, alongside the added requirements of high data rates, scalability, precision, security, and real-time operation. This scenario introduces unparalleled challenges for both system architecture and the solutions it entails. Several previously proposed solutions, such as retransmission schemes, error correction techniques, and grant-free access, have been insufficient for emerging requirements, as most of these solutions primarily facilitate either low latency or high reliability, but not both. Latency and reliability are conflicting objectives of URLLC. Therefore, an in-depth understanding of the associated issues and careful mitigation of these challenges are essential. This article provides an extensive review of 5G URLLC, emphasizing its technical evolution from 3GPP Release 15 through 19, while also detailing its inherent shortcomings and the potential solutions required for 6G and beyond. We investigate the prerequisites and enabling technologies necessary for URLLC services, exploring related issues across various network components, including frame structure, propagation, processing, retransmission, scheduling, fading, and interference. An important discussion is provided on the fundamental trade-off between latency and reliability, particularly due to retransmission mechanisms. Furthermore, we examine the practical limitations of 5G URLLC when coexisting with other 5G application use cases, such as enhanced mobile broadband (eMBB) and massive machine-type communication (mMTC). Finally, we discuss the future trajectory of URLLC in 6G, identifying key research challenges and opportunities to meet the escalating demands of future mission-critical applications. Full article

The rapid growth of maritime communication networks introduces significant challenges to routing optimization, arising from large-scale network topologies, highly dynamic node mobility, and stringent real-time communication requirements. Conventional routing algorithms often exhibit limited scalability and poor adaptability when facing frequent topology variations. The routing problem is modeled as a multi-agent distributed decision-making process, where each node acts as an autonomous agent. In this paper, we propose a graph-partitioned hierarchical graph representation learning framework (GP-HGRL) for scalable and continual routing optimization in dynamic maritime networks. By explicitly modeling the network as a time-evolving graph, GP-HGRL first partitions the global topology into topology-aware subgraphs, enabling distributed learning and inference with reduced computational complexity. A hierarchical graph neural network architecture is then developed to jointly capture intra-subgraph local structures and inter-subgraph global dependencies, producing topology-aware embeddings for routing decision-making. Based on the learned representations, a deep reinforcement learning policy is employed to perform distributed next-hop routing decisions. To effectively handle topology dynamics induced by node mobility and link variations, we further introduce a continual graph learning mechanism that selectively updates representations and routing policies only within affected subgraphs, thereby avoiding costly global retraining and preserving routing stability. Extensive simulations demonstrate that GP-HGRL consistently outperforms shortest-path routing and existing reinforcement learning-based approaches in terms of packet delivery ratio, retransmission rate, packet loss, and training efficiency under various network loads and dynamic conditions. Full article

Edge Internet of Things (IoT) blockchain deployments increasingly rely on continuous transaction ingestion from resource-constrained IoT devices to nearby edge gateways over heterogeneous wireless links. In this setting, transient channel noise and packet corruption can inject invalid payloads into the edge processing pipeline and trigger unnecessary buffering, parsing, and, most critically, computationally expensive cryptographic operations such as digital signature verification. This leads to wasted computation, increased latency, and reduced energy efficiency at the edge, particularly under dense IoT traffic. This paper presents an energy-aware and fault-tolerant Quantum-Dot Cellular Automata (QCA)-based integrity pre-filter for IoT-to-edge blockchain transaction ingestion. At the circuit level, we adapt and modify a previously reported fault-tolerant five-input majority gate (MV5) structure and use it as a robust primitive for nanoscale integrity-screening circuits. Building on this modified MV5, we design a set of QCA integrity blocks, including a parity checker, a compact XNOR gate circuit, a parity-bit generation circuit, and a sender-to-channel/receiver nano-communication integrity workflow suitable for early screening of corrupted payloads. Compared with the best previously reported baseline considered in this study, the modified MV5 achieves 76.47% tolerance to single-cell omission defects, corresponding to a 17.47 percentage-point increase and an approximately 29.61% relative improvement over the prior 59% omission-tolerance result, while preserving 100% tolerance against extra-cell deposition defects. At the system level, the proposed circuit is discussed as a potential early screening stage for edge-IoT blockchain transaction ingestion. A bounded analytical model is used to estimate the possible reduction in unnecessary signature-verification workload under assumed corruption and detection conditions. This analysis is not intended as a deployment-level validation; full edge-node implementation, throughput measurement, queueing-delay evaluation, real traffic traces, retransmission behavior, and empirical signature-verification profiling remain future work. The proposed parity/chunk-parity pre-filter is designed for low-cost detection of random transmission-induced corruption and does not replace cryptographic authentication, hashing, digital signatures, CRC-based detection, or blockchain validation. All proposed designs are validated using QCADesigner tools. Full article

Power Line Communication (PLC) utilizes the existing power line infrastructure for data transmission and offers the advantage of low deployment costs. However, the PLC channel is subject to a highly complex network topology, frequent load variations, and noise as well as impulsive interference introduced by the switching operations of various electrical devices. As a result, it exhibits pronounced frequency-selective fading and time-varying characteristics. Under such challenging channel conditions, existing PLC transmission schemes are no longer sufficient to meet increasing performance requirements. This paper introduces the Chase combining mechanism of Hybrid Automatic Repeat Request (HARQ) into the PLC physical-layer link. At the receiver, soft information from multiple transmissions is accumulated, thereby improving the transmission stability and resource utilization efficiency of PLC under complex channel environments. Simulation results show that Chase combining can significantly reduce the bit error rate in the low signal-to-noise ratio region and enhance link reliability in complex PLC noise environments. Hardware implementation results indicate that the main overhead of this mechanism is concentrated in buffering and accumulation logic, demonstrating its feasibility for Field-Programmable Gate Array (FPGA) implementation. Full article

We developed a hardware-in-the-loop simulation of a battery management system (BMS) using controller area network (CAN) as the communication backbone for electric-powered response boats in flood rescue. A LiFePO4 pack and discharge motor/charger were modeled in MATLAB/Simulink/Simscape, while an STM32 Nucleo-F446RE executed CAN messaging. The BMS monitored voltage, current, temperature, and state of charge. Results indicate CAN’s reliability under rescue-like disturbances: priority arbitration delivered over-temperature and over-current warnings ahead of routine telemetry; error detection and retransmission preserved data integrity; and bus-load analysis showed low latency for urgent frames without interrupting state-of-charge reporting, improving situational awareness and reducing operator risk. Full article

Energy harvesting wireless sensor networks (EH-WSNs) have been widely studied as a data collection infrastructure in the context of Artificial Intelligence of Things (AIoT). EH-WSNs face the challenge of achieving consistent data collection due to irregularly harvested environmental energy. Energy allocation and data allocation schemes have been proposed to balance energy consumption and data collection across the network; however, conventional error recovery techniques such as Automatic Repeat reQuest (ARQ) and Forward Error Correction (FEC) do not consider these allocation constraints, potentially leading to unintended energy depletion and data collection imbalance. In this paper, we propose a Cooperative ARQ (C-ARQ) scheme for EH-WSNs that incorporates energy allocation and data allocation. The proposed scheme computes the retransmittable data amount from the extra energy remaining after data allocation and performs retransmissions within that limit to recover errors, thereby preventing energy depletion and increasing the amount of data gathered at the sink node. Simulation results demonstrate that the proposed scheme improves the amount of data gathered at the sink node compared to other schemes, particularly in environments with longer hop paths, higher packet error rates, or more harvested energy. Full article

The fault tolerance of a swarm of small aerial vehicles (SAVs) is directly dependent on the reliability of data transmitted over communication channels. One of the key threats is the intentional distortion of signal sequences by an attacker, such as Barker codes or M-sequences, which are used for synchronization and control of the swarm. Such an attack can disable the entire swarm. The aim of this study is to develop a method for detecting such intrusions. The proposed algorithm analyzes mathematical expressions that describe the sidelobes’ levels of the autocorrelation function of the code. This approach not only detects unauthorized changes but also accurately identifies the location and magnitude of the distorted element. The conducted experiments confirm the high accuracy of the algorithm. The practical significance of the work lies in the possibility of integrating this method into the security subsystem of group interaction for small aerial vehicles. This creates a mechanism for active anomaly detection in communication channels: when a threat is detected, the swarm can respond promptly by switching to a backup channel, requesting data retransmission, or isolating the compromised channel, which in turn enhances the survivability and fault tolerance of the system’s functioning within the group. Full article