Search Results (62)

Cooperative Intelligent Transportation Systems (C-ITSs) are emerging as transformative technologies, paving the way for safe and fully automated driving solutions. As the demand for autonomous vehicles accelerates, the development of advanced Radio Access Technologies capable of delivering reliable, low-latency vehicular communications has become paramount. Standardized approaches for Vehicular-to-Everything (V2X) communication often fall short in addressing the dynamic and diverse requirements of multi-service, multi-priority systems. Conventional vehicular networks employ static parameters such as Access Category (AC) in IEEE 802.11p-based ITS-G5 and Resource Reservation Interval (RRI) in C-V2X PC5 for prioritizing different V2X services. This static parameter assignment performs unsatisfactorily in dynamic and diverse requirements. To bridge this gap, we propose intelligent Multi-Attribute Decision-Making algorithms for adaptive AC selection in ITS-G5 and RRI adjustment in C-V2X PC5, tailored to the varying priorities of active V2X services. These adaptations are integrated with a priority-aware rate-control mechanism to enhance congestion management. Through extensive simulations conducted using NS3, our proposed strategies demonstrate superior performance compared to standardized methods, achieving improvements in one-way end-to-end latency, Packet Reception Ratio (PRR) and overall communication reliability. Full article

Underwater acoustic sensor networks (UASNs) play an increasingly crucial role in both civilian and military fields. However, existing routing protocols primarily rely on node position information for forwarding decisions, neglecting link quality and energy efficiency. To address these limitations, we propose a fuzzy logic reasoning adaptive forwarding (FLRAF) routing protocol for three-dimensional (3D) UASNs. First, the FLRAF method redefines a conical forwarding region to prioritize nodes with greater effective advance distance, thereby reducing path deviations and minimizing the total number of hops. Unlike traditional approaches based on pipeline or hemispherical forwarding regions, this design ensures directional consistency in multihop forwarding, which improves transmission efficiency and energy utilization. Second, we design a nested fuzzy inference system for forwarding node selection. The inner inference system evaluates link quality by integrating the signal-to-noise ratio and some metrics related to the packet reception rate. This approach enhances robustness against transient fluctuations and provides a more stable estimation of link quality trends in dynamic underwater environments. The outer inference system incorporates link quality index, residual energy, and effective advance distance to rank candidate nodes. This multimetric decision model achieves a balanced trade-off between transmission reliability and energy efficiency. Simulation results confirm that the FLRAF method outperforms existing protocols under varying node densities and mobility conditions. It achieves a higher packet delivery rate, extended network lifetime, and lower energy consumption. These results demonstrate that the FLRAF method effectively addresses the challenges of energy constraints and unreliable links in 3D UASNs, making it a promising solution for adaptive and energy-efficient underwater communication. Full article

This study aims to explore the optimal power allocation problem under Distributed Denial of Service (DDoS) attack in wireless communication networks. The Starkberg Equilibrium (SE) framework is employed to analyze the strategic interactions between defenders and attacker under conditions of incomplete information. Considering the energy constraints of both sensors and attacker, this paper also proposes an Intrusion Detection System (IDS) based on remote estimation to achieve an optimal defense strategy, with Packet Reception Rate (PPR) serving as a criterion for intrusion detection. Targeting leaders and followers, the optimal power allocation solution is derived with Signal-to-Interference-Noise Ratio (SINR) and transmission cost as the objective functions. By combining the Adaptive Penalty Function (APF) method with the Differential Evolution (DE) algorithm, the study effectively addresses related non-linear and non-convex optimization problems. Finally, the effectiveness of the proposed method is verified through case studies. Full article

With the development of Internet of Vehicles (IoV) technology, the need for real-time data processing and communication in vehicles is increasing. Traditional request-based methods face challenges in terms of latency and bandwidth limitations. Mode 4 in cellular vehicle-to-everything (C-V2X), also known as autonomous resource selection, aims to address latency and overhead issues by dynamically selecting communication resources based on real-time conditions. However, semi-persistent scheduling (SPS), which relies on distributed sensing, may lead to a high number of collisions due to the lack of centralized coordination in resource allocation. On the other hand, non-orthogonal multiple access (NOMA) can alleviate the problem of reduced packet reception probability due to collisions. Age of Information (AoI) includes the time a message spends in both local waiting and transmission processes and thus is a comprehensive metric for reliability and latency performance. To address these issues, in C-V2X, the waiting process can be extended to the queuing process, influenced by packet generation rate and resource reservation interval (RRI), while the transmission process is mainly affected by transmission delay and success rate. In fact, a smaller selection window (SW) limits the number of available resources for vehicles, resulting in higher collisions when the number of vehicles is increasing rapidly. SW is generally equal to RRI, which not only affects the AoI part in the queuing process but also the AoI part in the transmission process. Therefore, this paper proposes an AoI estimation method based on multi-priority data type queues and considers the influence of NOMA on the AoI generated in both processes in C-V2X system under different RRI conditions. Our experiments show that using multiple priority queues can reduce the AoI of urgent messages in the queue, thereby providing better service about the urgent message in the whole vehicular network. Additionally, applying NOMA can further reduce the AoI of the messages received by the vehicle. Full article

The accurate and continuous location monitoring of athletes helps in meeting health and safety requirements and supporting the infotainment needs of large marathon events with thousands of participants. Currently, the tracking of individuals and groups of athletes at mass sports events is only possible to a limited extent, due to the weight, size, and cost constraints of the necessary devices. At marathon events, the usual infrastructure for timekeeping is Radio Frequency Identification (RFID) technology, which allows only precise tracking at huge intervals, with heuristic and interpolative algorithms to estimate runner positions in between the measuring points. Setting up RFID tracking stations on site is also material- and labor-intensive. We instead propose a continuous, real-time tracking solution, relying on Long-Range Wide-Area Network (LoRaWAN) GPS trackers. Due to the large geographical area and urban space in which marathon events take place, the positioning of static gateways cannot provide complete and continuous coverage. This research article presents an implementation with multiple LoRa trackers and mobile LoRa gateways installed on vehicle escorts to assess coverage quality. The tracking data collected by a receiving LoRaWAN Network Server (LNS) are stored in a database. Three experiments were conducted at three different official running events: a 10 km race, a half marathon, and a marathon. The backdrop for the 42.195 km event was the official Vienna City Marathon 2024 with more than 35,000 participants. The experimental results under these realistic conditions show the reception quality of this approach; e.g., during the marathon, the received packets from LoRa gateways were at an average distance of about 136 m (σ 157 m) from the tracker with a median update rate of 31 s across all trackers, using DR3/SF9. At greater distances, the quality decreased, although some outliers were received up to a distance of two kilometers. A possible prospect is that the low-power wide-area network (LPWAN) may repeat the history of RFID by entering the mass sports market from the industry domain. Full article

Nowadays, M-Quadrature Amplitude Modulation (M-QAM) techniques are widely used to modulate information by bit packets due to their ability to increase transfer rates. These techniques require more power when increasing the modulation index M to avoid interference between symbols. This article proposes a technique that does not suffer from interference between symbols, but instead uses memory elements to store the modulation symbols. In addition, the aim of this paper is to implement a four-dimensional reconfigurable chaotic oscillator that generates 16-Chaotic Phase Shift Keying (16-CPSK) modulation–demodulation carriers. An encryption and modulation transmitter module, a reception module, and a master–slave Hamiltonian synchronization module make up the system. A 16-CPSK modulation scheme implemented in Field Programmable Gate Array (FPGA) and applied to a red-green-blue (RGB) and grayscale image encryption system are the main contributions of this work. Matlab and Vivado were used to verify the modulation–demodulation scheme and synchronization. This proposal achieved excellent correlation coefficients according to various investigations, the lowest being $-15.9\times {10}^{-6}$ and $0.13\times {10}^{-3}$ for RGB and grayscale format images, respectively. The FPGA implementation of the 16-CPSK modulation–demodulation system was carried out using a manufacturer’s card, Xilinx’s Artix-7 AC701 (XC7A200TFBG676-2). Full article

LoRa (long range) is a communication technology that employs chirp spread spectrum modulation. Among various low-power wide area network (LPWAN) technologies, LoRa offers unique advantages, including low power consumption, long transmission distance, strong anti-interference capability, and high network capacity. Addressing the issue of power-constrained end devices in IoT application scenarios, this paper proposes an adaptive data rate (ADR) algorithm for LoRa networks designed for power-constrained end devices (EDs). The algorithm evaluates the uplink communication link state between the EDs and the gateway (GW) by using a combined weighting method to comprehensively assess the signal-to-noise ratio (SNR), received signal strength indication (RSSI), and packet reception rate (PRR), and calculates a list of transmission power and data rates that ensure stable and reliable communication between the EDs and the GW. By using ED power consumption models, network throughput models, and ED latency models to evaluate network performance, the Zebra optimization algorithm is employed to find the optimal data rate for each ED under power-constrained conditions while maximizing network performance. Test results show that, in a single ED scenario, the average PRR achieved by the proposed ADR algorithm for power-constrained EDs in LoRa networks is 14% higher than that of the standard LoRaWAN ADR algorithm. In a multi-ED link scenario (50 end devices), the proposed method reduces the average power consumption of EDs by 10% compared to LoRaWAN ADR, achieves a network throughput of 6683 bps, and an average latency of 2.10 s, demonstrating superior performance overall. The proposed method shows unique advantages in LoRa networks with power-constrained EDs and a large number of EDs, as it not only reduces the average power consumption of the EDs but also optimizes network throughput and average latency. Full article

To address the issues of high attenuation, weak reception signal, and channel blockage in the current electric field communication of underwater robots, research on autonomous underwater vehicle (AUV) multi-node networking communication based on underwater electric field Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) channel was conducted. This article, first through simulation, finds that the Optimized Link State Routing (OLSR) protocol has a smaller routing packet delay time and higher reliability compared to the Ad Hoc On-Demand Distance Vector (AODV) protocol on underwater electric field CSMA/CA channels. Then, a 2FSK underwater electric field communication system was established, and dynamic communication experiments were carried out between two AUV nodes. The experimental results showed that within a range of 0 to 3.5 m, this system can achieve underwater dynamic electric field communication with a bit error rate of 0 to 0.628%. Finally, to avoid channel blockage during underwater AUV multi-node communication, this article proposes a dynamic backoff method for AUV multi-node communication based on CSMA/CA. This system can achieve dynamic multi-node communication of underwater electric fields with an error rate ranging from 0 to 0.96%. The research results have engineering application prospects for underwater cluster operations. Full article

Low-power wide area network (LPWAN) technologies as part of IoT are gaining a lot of attention as they provide affordable communication over large areas. LoRa and Sigfox as part of LPWAN have emerged as highly effective and promising non-3GPP unlicensed band IoT technologies while challenging the supremacy of cellular technologies for machine-to-machine-(M2M)-based use cases. This paper presents the design goals of LoRa and Sigfox while throwing light on their suitability in congested environments. A practical traffic generator of both LoRa and Sigfox is introduced and further interpolated for understanding simultaneous operation of 100 to 10,000 such nodes in close vicinity while establishing deep understanding on effects of collision, re-transmissions, and link behaviour. Previous work in this field have overlooked simultaneous deployment, collision issues, effects of re-transmission, and propagation profile while arriving at a number of successful receptions. This work uses packet error rate (PER) and delivery ratio, which are correct metrics to calculate successful transmissions. The obtained results show that a maximum of 100 LoRa and 200 Sigfox nodes can be deployed in a fixed transmission use case over an area of up to 1 km. As part of the future scope, solutions have been suggested to increase the effectiveness of LoRa and Sigfox networks. Full article

The development of new technologies has significantly enhanced the monitoring and analysis of network traffic. Modern solutions like the Extended Berkeley Packet Filter (eBPF) demonstrate a clear advancement over traditional techniques, allowing for more customized and efficient filtering. These technologies are crucial for influencing system performance as they operate at the lowest layer of the operating system, such as the kernel. Network-based Intrusion Detection/Prevention Systems (IDPS), including Snort, Suricata, and Bro, passively monitor network traffic from terminal access points. However, most IDPS are signature-based and face challenges on large networks, where the drop rate increases due to limitations in capturing and processing packets. High throughput leads to overheads, causing IDPS buffers to drop packets, which can pose serious threats to network security. Typically, IDPS are targeted by volumetric and multi-vector attacks that overload the network beyond the reception and processing capacity of IDPS, resulting in packet loss due to buffer overflows. To address this issue, the proposed solution, iKern, utilizes eBPF and Virtual Network Functions (VNF) to examine and filter packets at the kernel level before forwarding them to user space. Packet stream inspection is performed within the iKern Engine at the kernel level to detect and mitigate volumetric floods and multi-vector attacks. The iKern detection engine, operating within the Linux kernel, is powered by eBPF bytecode injected from user space. This system effectively handles volumetric Distributed Denial of Service (DDoS) attacks. Real-time implementation of this scheme has been tested on a 1Gbps network and shows significant detection and reduction capabilities against volumetric and multi-vector floods. Full article

In this paper, a network comprising wireless devices equipped with buffers transmitting deadline-constrained data packets over a slotted-ALOHA random-access channel is studied. Although communication protocols facilitating retransmissions increase reliability, a packet awaiting transmission from the queue experiences delays. Thus, packets with time constraints might be dropped before being successfully transmitted, while at the same time causing the queue size of the buffer to increase. To understand the trade-off between reliability and delays that might lead to packet drops due to deadline-constrained bursty traffic with retransmissions, the scenario of a wireless network utilizing a slotted-ALOHA random-access channel is investigated. The main focus is to reveal the trade-off between the number of retransmissions and the packet deadline as a function of the arrival rate. Towards this end, analysis of the system is performed by means of discrete-time Markov chains. Two scenarios are studied: (i) the collision channel model (in which a receiver can decode only when a single packet is transmitted), and (ii) the case for which receivers have multi-packet reception capabilities. A performance evaluation for a user with different transmit probabilities and number of retransmissions is conducted. We are able to determine numerically the optimal probability of transmissions and the number of retransmissions, given the packet arrival rate and the packet deadline. Furthermore, we highlight the impact of transmit probability and the number of retransmissions on the average drop rate and throughput. Full article

In recent years, with the development of Internet of Things technology, the demand for low-power wireless communication technology has been growing, giving rise to LoRa technology. A LoRa network mainly consists of terminal nodes, gateways, and LoRa network servers. As LoRa networks often deploy many terminal node devices for environmental sensing, the limited resources of LoRa technology, the explosive growth in the number of nodes, and the ever-changing complex environment pose unprecedented challenges for the performance of the LoRa network. Although some research has already addressed the challenges by allocating channels to the LoRa network, the impact of complex and changing environmental factors on the LoRa network has yet to be considered. Reasonable channel allocation should be tailored to the situation and should face different environments and network distribution conditions through continuous adaptive learning to obtain the corresponding allocation strategy. Secondly, most of the current research only focuses on the channel adjustment of the LoRa node itself. Still, it does not consider the indirect impact of the node’s allocation on the entire network. The Inner External DQN SF allocation method (IEDQN) proposed in this paper improves the packet reception rate of the whole system by using reinforcement learning methods for adaptive learning of the environment. It considers the impact on the entire network of the current node parameter configuration through nested reinforcement learning for further optimization to optimize the whole network’s performance. Finally, this paper evaluates the performance of IEDQN through simulation. The experimental results show that the IEDQN method optimizes network performance. Full article

As the demand for sensing and monitoring the marine environment increases, the Ocean Mobile Internet of Things (OM-IoT) has gradually attracted the interest of researchers. However, the unreliability of communication links represents a significant challenge to data transmission in the OM-IoT, given the complex and dynamic nature of the marine environment, the mobility of nodes, and other factors. Consequently, it is necessary to enhance the reliability of underwater data transmission. To address this issue, this paper proposes a reinforcement learning-based adaptive network coding (RL-ANC) approach. Firstly, the channel conditions are estimated based on the reception acknowledgment, and a feedback-independent decoding state estimation method is proposed. Secondly, the sliding coding window is dynamically adjusted based on the estimates of the channel erasure probability and decoding probability, and the sliding rule is adaptively determined using a reinforcement learning algorithm and an enhanced greedy strategy. Subsequently, an adaptive optimization method for coding coefficients based on reinforcement learning is proposed to enhance the reliability of the underwater data transmission and underwater network coding while reducing the redundancy in the coding. Finally, the sampling period and time slot table are updated using the enhanced simulated annealing algorithm to optimize the accuracy and timeliness of the channel estimation. Simulation experiments demonstrate that the proposed method effectively enhances the data transmission reliability in unreliable communication links, improves the performance of underwater network coding in terms of the packet delivery rate, retransmission, and redundancy transmission ratios, and accelerates the convergence speed of the decoding probability. Full article

Agricultural environments are usually characterized by height differences and tree shading, which pose challenges for communication in smart agriculture. This study focuses on optimizing the packet loss rate and power consumption of LoRa’s practical communication quality. The research includes the investigation of the PHY anti-frame loss mechanism, encompassing PHY frame loss detection and the response mechanism between gateways and nodes. By implementing a closed loop for transmission and reception, the study enhances the communication network’s resistance to interference and security. Theoretical performance calculations for the SX1278 radio frequency chip were conducted under different parameters to determine the optimal energy efficiency, reducing unnecessary energy waste. An experimental assessment of the packet loss rate was conducted to validate the practical efficacy of the research findings. The results show that the LoRa communication with the anti-frame loss mechanism and the optimal energy ratio parameter exhibits an adequate performance. In the presence of strong and weak interferences, the reception rates are maximally improved by 37.8% and 53.4%, with effective distances of 250 m and 600 m, corresponding to enhancements of 100 m and 400 m, respectively. This research effectively reduces LoRa energy consumption, mitigates packet loss, and extends communication distances, providing insights for wireless transmission in agricultural contexts. Full article

The present manuscript introduces a method for evaluating and forecasting the quality of vehicle-to-infrastructure (V2I) communication channels in urban settings. This method precisely classifies and predicts channel quality levels in V2I scenarios based on long-range (LoRa) technology. This approach aims to accurately classify and predict channel quality levels in V2I scenarios. The concept of channel quality scoring was first introduced, offering a more precise description of channel quality compared to traditional packet reception rate (PRR) assessments. In the channel quality assessment model based on the gated recurrent unit (GRU) algorithm, the current channel quality score of the vehicular terminal and the spatial channel parameters (SCP) of its location are utilized as inputs to achieve the classification of channel quality levels with an accuracy of 97.5%. Regarding prediction, the focus lies in forecasting the channel quality score, combined with the calculation of SCP for the vehicle’s following temporal location, thereby achieving predictions of channel quality levels from spatial and temporal perspectives. The prediction model employs the Variational Mode Decomposition-Backoff-Bidirectional Long Short-Term Memory (VMD-BO-BiLSTM) algorithm, which, while maintaining an acceptable training time, exhibits higher accuracy than other prediction algorithms, with an R² value reaching 0.9945. This model contributes to assessing and predicting channel quality in V2I scenarios and holds significant implications for subsequent channel resource allocation. Full article