Search Results (261)

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

In low-Earth-orbit (LEO) satellite networks, the need for intelligent parameter-adjustment strategies has become increasingly critical due to the presence of highly dynamic channel conditions, limited spectrum resources, and complex interference environments. In this paper, a method for optimizing LEO satellite communication links based on deep reinforcement learning (DRL) is proposed. Through the optimization of the transmit power, the modulation and coding scheme (MCS), the beamforming parameters, and the retransmission mechanisms, adaptive link control is achieved in dynamic operational scenarios. A multidimensional state space is constructed, within which the channel state information, the interference environment, and the historical performance metrics are integrated. The spatio-temporal characteristics of the channel are extracted by means of a hybrid neural architecture that incorporates a convolutional neural network (CNN) and a long short-term memory (LSTM) network. To effectively accommodate both continuous and discrete action spaces, a hybrid DRL framework that combines proximal policy optimization (PPO) with a deep Q-network (DQN) is employed, thereby enabling cross-layer optimization of the physical-layer and link-layer parameters. The results demonstrate that substantial improvements in throughput, bit error rate (BER), and transmit-power efficiency are achieved under severely time-varying channel conditions, which provides a new idea for resource management and dynamic-environment adaptation in satellite communication systems. Full article

To address the rigorous requirements of ultra-reliable low-latency communication (URLLC) in beyond 5G/6G networks, we propose an innovative architecture combining automatic repeat request (ARQ) protocol with a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) to enhance short-packet non-orthogonal multiple access (NOMA) communications. Specifically, retransmission mechanism provided by ARQ is utilized to mitigate packet errors stemming from practical system imperfections, i.e., imperfect channel state information (ipCSI), imperfect successive interference cancellation (ipSIC), and hardware impairments. Using the analytical foundation provided by finite blocklength (FBL) theory, expressions for two key performance metrics, i.e., the average block error rate (BLER) and effective throughput, are derived for two NOMA users. Simulation results validate the analytical derivations and demonstrate that the ARQ scheme provides significant reliability gains for each user and achieves synergistic gain with STAR-RIS technology. In addition, the effective throughput exhibits a peak at an optimal blocklength, balancing the reliability gain from a longer blocklength against the spectral efficiency loss from a lower coding rate. This optimal blocklength decreases with more STAR-RIS elements, as improved channel conditions reduce the need for long blocklengths. Full article

The integration of Wireless Sensor Networks (WSNs) in Industrial Internet of Things (IIoT) applications presents significant challenges in terms of energy efficiency and network reliability, especially in dynamic industrial environments. The Routing Protocol for Low-Power and High-Loss Networks for Indoor Environments (RPLIE), while designed for low-power lossy networks (LLNs), lacks mechanisms to adequately balance energy consumption, a critical requirement for industrial sustainability. This research introduces an enhancement called Energy-Conscious Routing Protocol for Industrial Environments (EC-RPLIE), which incorporates the Expected Breakage Cost (EBC) metric to optimize energy distribution and network stability by managing medium-term jitter. Through extensive simulations in the Cooja environment, the performance of EC-RPLIE was evaluated against the state-of-the-art RPLIE across topologies of 11, 21, and 31 nodes. Quantitative results demonstrate that EC-RPLIE significantly reduces unnecessary retransmissions by maintaining a superior Packet Delivery Ratio (PDR) and optimizing parent selection. The protocol achieved energy savings of 9.6% in 11-node networks, which increased to 36.8% in high-density 31-node scenarios, effectively doubling the network persistence compared to RPLIE. Additionally, EC-RPLIE improved average latency by 12.68% in dense configurations, confirming its robustness in handling industrial traffic. These findings confirm that EC-RPLIE is particularly effective in high-density networks, where the EBC metric successfully mitigates the ‘retransmission storms’ typical of standard protocols. This proposal provides a robust framework for enhancing the sustainability and resilience of WSNs in Industry 4.0, offering a scalable solution that addresses the energy–reliability trade-off. The results lay the groundwork for future large-scale implementations in real-world industrial environments. Full article

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

Migrating to Post-Quantum Cryptography (PQC) is critical for securing resource-constrained Internet of Things (IoT) devices against the “harvest-now, decrypt-later” threat. While ML-KEM (CRYSTALS-Kyber) has been standardized under FIPS 203 for general encryption, these devices often operate on unreliable networks suffering from high latency and packet loss. Our recent systematic review identified a critical gap that existing research overwhelmingly focuses on Transport Layer Security (TLS). This leaves the resilience of lightweight protocols like MQTT and CoAP under challenging network conditions largely unexplored. This paper introduces PQC-IoTNet, a novel Software-in-the-Loop (SITL) framework to address this gap. Our three-tier architecture integrates a Python-based IoT client with kernel-level emulation to test the full protocol stack. Validation results comparing Kyber and ECC demonstrate the framework’s ability to capture critical performance cliffs caused by TCP retransmissions. Notably, the framework revealed that while Kyber maintained an 18% speed advantage over ECC at 5% packet loss, both protocols experienced nonlinear latency spikes. This work provides a reproducible blueprint to identify operational boundaries and select resilient protocols for secure IoT systems. Full article

Marine scientific observation missions operate over disrupted, high-loss links and must keep heterogeneous sensor, image, and log data confidential and verifiable under fragmented, out-of-order delivery. This paper proposes an end-to-end encryption–verification co-design that integrates HMR integrity structuring with EMR hybrid encapsulation. By externalizing block boundaries and maintaining a minimal receiver-side verification state, the framework supports block-level integrity/provenance verification and selective recovery without continuous sessions, enabling multi-hop and intermittent connectivity. Experiments on a synthetic multimodal ocean dataset show reduced storage/encapsulation overhead (10.4% vs. 12.8% for SHA-256 + RSA + AES), lower hashing latency (6.8 ms vs. 12.5 ms), and 80.1 ms end-to-end encryption–decryption latency (21.2% lower than RSA + AES). Under fragmentation, verification latency scales near-linearly with block count (R² = 0.998) while throughput drops only slightly (11.8 → 11.3 KB/ms). With 100 KB blocks, transmission latency stays below 1.024 s in extreme channels and around 0.08–0.10 s in typical ranges, with expected retransmissions < 0.25. On Raspberry Pi 4, runtime slowdown remains stable at ~3.40× versus a PC baseline, supporting deployability on resource-constrained nodes. Full article

With the increasing deployment of grid-forming static var generators (GFM-SVG) in modern power systems, the reliability of the valve hall that houses the core power modules has become a critical concern. To overcome the limitations of conventional wired monitoring systems—complex cabling, poor scalability, and incomplete state perception—this paper proposes and implements a multi-source fusion wireless data acquisition system specifically designed for GFM-SVG valve halls. The system integrates acoustic, visual, and infrared sensing nodes into a wireless sensor network (WSN) to cooperatively capture thermoacoustic visual multi-physics information of key components. A dual-mode communication scheme, using Wireless Fidelity (Wi-Fi) as the primary link and Fourth-Generation Mobile Communication Network (4G) as a backup channel, is adopted together with data encryption, automatic reconnection, and retransmission-checking mechanisms to ensure reliable operation in strong electromagnetic interference environments. The main innovation lies in a multi-source information fusion algorithm based on an improved Dempster–Shafer (D–S) evidence theory, which is combined with the object detection capability of the You Only Look Once, Version 8 (YOLOv8) model to effectively handle the uncertainty and conflict of heterogeneous data sources. This enables accurate identification and early warning of multiple types of faults, including local overheating, abnormal acoustic signatures, and coolant leakage. Experimental results demonstrate that the proposed system achieves a fault-diagnosis accuracy of 98.5%, significantly outperforming single-sensor approaches, and thus provides an efficient and intelligent operation-and-maintenance solution for ensuring the safe and stable operation of GFM-SVG equipment. Full article

This paper introduces a novel methodology that integrates 6G wireless Federated Edge Learning (FEEL) frameworks with MAC to PHY cross layer optimization strategies. In the context of mobile edge computing, typically ensuring robust channel estimation within the 6G network use cases presents critical challenges, particularly in managing data retransmissions. Inaccurate updates from distributed 6G devices can undermine the reliability of federated learning, affecting its overall performance. To address this, rather than relying on direct evaluations of the objective function, we propose an AI/ML-assisted algorithm for global optimization based on radial basis functions (RBFs) decision-making process to assess learned preference options. Full article

Reliable Internet connectivity is essential for latency-sensitive services such as video conferencing, media streaming, and online gaming. Round-trip time (RTT) is a key indicator of network performance and is central to setting retransmission timeout (RTO); inaccurate RTT estimates may trigger unnecessary retransmissions or slow loss recovery. This paper proposes an Enhanced Regularized Extreme Learning Machine (RELM) for RTT estimation that improves generalization and efficiency by interleaving a bidirectional log-step heuristic to select the regularization constant C. Unlike manual tuning or fixed-range grid search, the proposed heuristic explores C on a logarithmic scale in both directions ($\times 10$ and /10) within a single loop and terminates using a tolerance–patience criterion, reducing redundant evaluations without requiring predefined bounds. A custom RTT dataset is generated using Mininet with a dumbbell topology under controlled delay injections (1–1000 ms), yielding 1000 supervised samples derived from 100,000 raw RTT measurements. Experiments follow a strict train/validation/test split (6:1:3) with training-only standardization/normalization and validation-only hyperparameter selection. On the controlled Mininet dataset, the best configuration (ReLU, 150 hidden neurons, $C={10}^2$) achieves $R^2=0.9999$, $MAPE=0.0018$, $MAE=966.04$, and $RMSE=1589.64$ on the test set, while maintaining millisecond-level runtime. Under the same evaluation pipeline, the proposed method demonstrates competitive performance compared to common regression baselines (SVR, GAM, Decision Tree, KNN, Random Forest, GBDT, and ELM), while maintaining lower computational overhead within the controlled simulation setting. To assess practical robustness, an additional evaluation on a public real-world WiFi RSS–RTT dataset shows near-meter accuracy in LOS and mixed LOS/NLOS scenarios, while performance degrades markedly under dominant NLOS conditions, reflecting physical-channel limitations rather than model instability. These results demonstrate the feasibility of the Enhanced RELM and motivate further validation on operational networks with packet loss, jitter, and path variability. Full article

A low-power IPv6 mesh standard, Thread, is gaining traction in smart-home, building-automation, and industrial IoT deployments. It extends mesh connectivity with the help of Router-Eligible End Devices (REEDs), which can be promoted to, or demoted from, the router status. Promotion and demotion hinge on two tunable parameters, the router upgrade and the router downgrade thresholds. Yet the OpenThread reference stack ships with fixed values (16/23) for these thresholds. This paper presents a systematic study of how these thresholds shape router-election dynamics across diverse traffic loads and network topologies. Leveraging an extended OpenThread Network Simulator, a sweep through both router upgrade and router downgrade thresholds with different gaps was performed. Results reveal that the default settings may over-provision routing capacity and may result in increased frame retransmissions, wasting airtime and reducing energy efficiency. Full article

Free-space optical (FSO) communications represent an attractive technology for future high-capacity wireless and satellite networks, offering multi-Gbps data rates, unlicensed spectrum, and built-in physical-layer security. However, their performance is severely affected by atmospheric turbulence, misalignment errors, and noise, which limit reliability and throughput. Hybrid automatic repeat request (HARQ) protocols provide a powerful mechanism to mitigate such impairments by combining forward error correction with retransmissions. In this paper, we investigate the fundamental performance limits of HARQ applied to FSO systems employing On–Off Keying (OOK) modulation. Using information-theoretic tools, we characterize the achievable rate and the finite-blocklength performance by resorting to channel dispersion, which plays a crucial role in quantifying rate–reliability tradeoffs. We further examine the interaction between HARQ retransmissions, turbulence-induced fading, and feedback delay, providing insights into the design of low-latency, high-reliability optical links. This analysis highlights how HARQ improves the robustness of OOK-based FSO systems and provides guidelines for parameter selection in next-generation space and terrestrial optical networks. Full article

This article addresses the lack of low-cost, secure image-transmission solutions for IoT systems in remote environments. The design and implementation of a complete LoRa-based transmission system using ESP32 microcontrollers and Ebyte E220 modules, featuring AES-CBC encryption, HMAC integrity protection, and a custom retransmission protocol, are presented. The system achieves 100% packet delivery ratio (PDR) for 20 kB images over distances exceeding 2 km under line-of-sight conditions, with functional transmission up to 4.1 km. Image transmission time ranges from 35 s (0.1 m) to 110 s (600 m), while energy consumption increases from 4.95 mWh to 15.18 mWh. Critically, encryption imposes less than 1% overhead on total energy consumption. Unlike prior work focusing on isolated components, this article provides a complete, deployable architecture combining (i) low-cost hardware (<USD 50 total), (ii) long-range LoRa communication, (iii) custom reliability mechanisms for fragmenting 20 kB images into 100 packets, and (iv) end-to-end cryptographic protection, all evaluated experimentally across multi-kilometer distances. These findings demonstrate that secure long-range image transmission using commodity hardware is feasible and scalable for smart home and industrial monitoring applications. Full article

The increasing use of medical applications using mobile devices has led to increased concerns about data security. Mobile Edge Computing (MEC) is an emerging technology that can be used to improve end-user services, especially in medical applications that require low latency and high bandwidth. MEC provides a promising solution that brings data processing closer to the end user to process requests faster. In doing so, security concerns about communication links and MEC server faults become a problem for MEC architectures and the concerned applications. In this paper, we propose a secure and fault-tolerant mechanism for the Internet of Medical Things (IoMT) based on an MEC architecture in a Software-Defined Network. Then, we present a secure scheme that consists of four main steps. (1) Establishment of secure virtual private network connection to secure network communication, (2) monitoring of network traffic to detect threats, (3) application of intrusion prevention measures, and (4) updating of security rules to prevent future attacks. The fault-tolerant aspect is developed based on the Kubernetes cluster to avoid service disruption in case of faults on a given MEC server. The simulation results depict substantial improvements, including reduced latency, better workload equilibrium, increased achievable throughput, and reduced retransmissions between servers. Our proposed scheme is effective in detecting distributed denial of service attacks. Moreover, in the event of an MEC server fault, the service is not interrupted and the architecture continues to render optimal services. Full article

This paper presents an approach to error correction in wireless communication systems, with a focus on the Bluetooth Low Energy standard. Our method uses the redundancy provided by the cyclic redundancy check and leaves the transmitter unchanged. The approach has two components: an error-detection algorithm that validates data packets and a neural network with reject option that classifies signals received from the channel and identifies bit errors for later correction. This design localizes and corrects errors and reduces transmission failures. Extensive simulations were conducted, and the results demonstrated promising performance. The method achieved correction rates of 94–98% for single-bit errors and 54–68% for double-bit errors, which reduced the need for packet retransmissions and lowered the risk of data loss. When applied to images, the approach enhanced visual quality compared with baseline methods. In particular, we observed improvements in visual quality for signal-to-noise ratios between 9 and 11 dB. In many cases, these enhancements were sufficient to restore the integrity of corrupted images. Full article