Search Results (13)

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

The use of a static modulation coding scheme (MCS), such as 7, and resource keep probability ($P_{rk}$) value, such as 0.8, was proven to be insufficient to achieve the best packet reception ratio (PRR) performance. Various adaptation techniques have been used in the following years. This work introduces a novel optimization algorithm approach called the fuzzy inference reinforcement learning (FIRL) sequence for adaptive parameter configuration in cellular vehicle-to-everything (C-V2X) mode-4 communication networks. This innovative method combines a Sugeno-type fuzzy inference system (FIS) control system with a Q-learning reinforcement learning algorithm to optimize the PRR as the key metric for overall network performance. The FIRL sequence generates adaptive configuration parameters for $P_{rk}$ and MCS index values each time the Long-Term Evolution (LTE) packet is generated. Simulation results demonstrate the effectiveness of this optimization algorithm approach, achieving up to a 169.83% improvement in performance compared to static baseline parameters. 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

Cellular vehicle-to-everything (C-V2X) enables short-distance communication between vehicles and other users to improve road safety through data sharing. Conventional research on C-V2X typically assumes that vehicles travel at the same speed with a fixed inter-vehicle distance (${Dis}_{inter}$). However, this assumption does not reflect the real driving environment or promote road traffic efficiency. Conversely, assigning different speeds to vehicles without a structured approach inevitably increases the collision risk. Therefore, determining appropriate speeds for each vehicle in the C-V2X framework is crucial. To this end, considering the road environment and mobility, this study introduces an adaptive speed mechanism based on the congestion level of a zone and ${Dis}_{inter}$. First, the given scenario is divided into several zones. Subsequently, based on the congestion level of a zone and the ${Dis}_{inter}$ level, an adaptive speed is defined for each vehicle. This approach ensured that vehicles adopt lower speeds in congested situations to reduce the collision probability and higher speeds in sparse traffic cases to improve traffic efficiency. The performance of the proposed adaptive speed scheme is compared with that of conventional fixed-speed settings. The results show that the adaptive speed control scheme outperforms conventional fixed-speed schemes in terms of the packet reception ratio (PRR) and collision ratio (CR). Specifically, the proposed mechanism can reduce the CR to 0 and ensure that the PRR is higher than 0.98 in low-density scenarios. Full article

Cellular vehicle-to-everything (C-V2X) facilitates direct communication between vehicles and other user equipment (UE) to improve the efficiency of the Internet of vehicles communication through sidelink. In addition, in the new radio vehicle-to-everything (NR-V2X) Mode 2, users can automatically select resources using the conventional sensing-based semi-persistent scheduling (SB-SPS) resource selection algorithm. This mechanism allows users to generate a list of available resources after a sensing window, after which the users can randomly select resources, and the resource can be used continuously over multiple periods before reselection. However, during the sensing window, neighbors may generate a similar list of available resources, and random selection may lead to resource conflicts. This phenomenon may lead to deteriorated communication performance and increased latency due to incorrect reception. Therefore, this paper proposes a reuse distance-aided resource selection (RD-RS) method which integrates resource reuse distance judgement with SB-SPS to mitigate resource conflicts and interference caused by random selection. Moreover, the reuse distance judgement is performed before the final resource selection, and whether the user will select the current resource depends on the reuse distance between that user and other occupiers. Furthermore, the performance of the proposed scheme is compared with other algorithms. Simulation results show that the proposed RD-RS not only achieves a higher packet reception ratio (PRR) but also effectively reduces the inter-packet gap (IPG). Moreover, in specific scenarios, the proposed method outperforms conventional schemes by 9% in terms of PRR and 70% in terms of Range. Full article

The accelerated growth of 5G technology has facilitated substantial progress in the realm of vehicle-to-everything (V2X) communications. Consequently, achieving optimal network performance and addressing congestion-related challenges have become paramount. This research proposes a unique hybrid power and rate control management strategy for distributed congestion control (HPR-DCC) focusing on 5G-NR-V2X sidelink communications. The primary objective of this strategy is to enhance network performance while simultaneously preventing congestion. By implementing the HPR-DCC strategy, a more fine-grained and adaptive control over the transmit power and transmission rate can be achieved. This enables efficient control by dynamically adjusting transmission parameters based on the network conditions. This study outlines the system model and methodology used to develop the HPR-DCC algorithm and investigates its characteristics of stability and convergence. Simulation results indicate that the proposed method effectively controls the maximum CBR value at 64% during high congestion scenarios, which leads to a 6% performance improvement over the conventional DCC approach. Furthermore, this approach enhances the signal reception range by 20 m, while maintaining the 90% packet reception ratio (PRR). The proposed HPR-DCC contributes to optimizing the quality and reliability of 5G-NR-V2X sidelink communication and holds great promise for advancing V2X applications in intelligent transportation systems. Full article

Multichannel communication has great potential in environments with unknown interference patterns. However, existing link quality metrics and models are generally established and verified under a single-channel scenario, which does not consider the impacts of radio interference and channel change. Therefore, it is hard to directly judge whether these metrics and models are still valid under a multichannel scenario. This paper empirically analyzes the applicability of popular link quality metrics and models in multiple channels with different interference levels. Results show that the link quality estimation (LQE) capability of traditional metrics will be affected by the interference level of the channel, which makes the conclusions obtained under a single-channel scenario no longer valid. Meanwhile, traditional LQE models are basically not adaptive to radio interference and channel change. They are only valid for channels with similar interference under which they are modeled. If these models are directly used under a multichannel scenario, the link quality will be overestimated inevitably. In other words, traditional LQE metrics and models cannot be directly used in the multichannel scenario. It is necessary to deeply analyze the statistical characteristics of popular link quality metrics in multiple typical channels and design channel and interference adaptive metrics and models to support effective multichannel communication. Full article

Optimization of the transmission power and rate allocation is a significant problem in wireless networks with mobile nodes. Due to mobility, the vehicles establishing wireless networks may exhibit severe fluctuations of their link quality, affecting their connection reliability and throughput. In Vehicular Ad-hoc Networks (VANETS), the IEEE 802.11p standard provides a practical metric for the Packet Reception Ratio (PRR), which is related with the transmission power and rate. Finding a global strategy for optimizing PRR for all mobile nodes can be treated as a potential game where each vehicle is considered as a selfish player, aiming to maximise its transmission reliability while rate constraints are satisfied. To this end, we propose a game-theoretic approach that converges to a Nash equilibrium. The main contributions of this work include: (i) identification of the best case equilibrium, for two cases of interference: diminished or kept stable, and (ii) verification of the equilibrium optimality, by showing that the value of stability is 1. Moreover, numerical results exhibiting the ease of the utility function calculation are provided, especially after an SINR level, whereby the utility function is concave and can be solved efficiently in polynomial time. Full article

One of the main methods for link quality prediction is to predict the physical layer parameters first, and then evaluate the link quality based on the mapping models between such parameters and packet reception ratio (PRR). However, existing methods often ignore the temporal correlations of physical layer parameter series and rarely consider the influence of link fluctuations, which lead to more errors under moderate and sudden changed links with larger fluctuations. In view of these problems, this paper proposes a more effective link quality prediction method RNN-LQI, which adopts Recurrent Neural Network (RNN) to predict the Link Quality Indicator (LQI) series, and then evaluates the link quality according to the fitting model of LQI and PRR. This method accurately mines the inner relationship among LQI series with the help of short-term memory characteristics of RNN and effectively deals with link fluctuations by taking advantage of the higher resolution of LQI in the transitional region. Compared with similar methods, RNN-LQI proves to be better under different link qualities. Especially under moderate and sudden changed links with larger fluctuations, the prediction error reduces at least by 14.51% and 13.37%, respectively. Therefore, the proposed method is more suitable for low power wireless links with more fluctuations. Full article

The Third Generation Partnership Project (3GPP) Release 16 defines the sensing-based semi-persistent scheduling (SPS) as the resource allocation scheme for Sidelink Mode 2 in New Radio (NR)-based vehicle-to-everything (V2X) communication. A well-known issue in Mode 2 is the persistent packet collision that results from two or more vehicles repeatedly using the same resource for transmission. It may create serious safety problems when the vehicles are in a situation where only the broadcast safety beacons can assist in driving. To resolve this issue, a solution that relies on the feedback from neighboring vehicles is proposed, through which the vehicles suffering from persistent packet collisions can quickly part and select other resources. Extensive simulations show that the proposed broadcast feedback scheme reduces persistent packet collisions by an order of magnitude compared to SPS, and it is achieved without sacrificing the average packet reception ratio (PRR). Namely, it is the quality aspect (i.e., burstiness) of the packet collisions that the proposed scheme addresses rather than the quantity (i.e., total number of collision losses). By preventing extended packet loss events, the proposed scheme is expected to serve NR V2X better, which requires stringent QoS in terms of the information update delay thereby helping to reduce the chances of vehicle crashes. Full article

IEEE 1609/802.11p standard obligates each vehicle to broadcast a periodic basic safety message (BSM). The BSM message comprises a positional and kinematic information of a transmitting vehicle. It also contains emergency information that is to be delivered to all the target receivers. In broadcast communication, however, existing carrier sense multiple access (CSMA) medium access control (MAC) protocol cannot guarantee a high reliability as it suffers from two chronic problems, namely, access collision and hidden terminal interference. To resolve these problems of CSMA MAC, we propose a novel enhancement algorithm called a neighbor association-based MAC (NA-MAC) protocol. NA-MAC utilizes a time division multiple access (TDMA) to distribute channel resource into short time-intervals called slots. Each slot is further divided into three parts to conduct channel sensing, slot acquisition, and data transmission. To avoid a duplicate slot allocation among multiple vehicles, NA-MAC introduces a three-way handshake process during slot acquisition. Our simulation results revealed that NA-MAC improved packet reception ratio (PRR) by 19% and successful transmission by 30% over the reference protocols. In addition, NA-MAC reduced the packet collisions by a factor of 4. Using the real on-board units (OBUs), we conducted an experiment where our protocol outperformed in terms of PRR and average transmission interval by 82% and 49%, respectively. Full article

Immediately after a disaster, such as a flood, wildfire or earthquake, networks might be congested or disrupted and not suitable for supporting the traffic generated by rescuers. In these situations, the use of a traditional fixed-gateway approach would not be effective due to the mobility of the rescuers. In the present work, a double-layer network system named LoRaUAV has been designed and evaluated with the purpose of finding a solution to the aforementioned issues. LoRaUAV is based on a WiFi ad hoc network of Unmanned Aerial Vehicle (UAV) gateways acting as relays for the traffic generated between mobile LoRaWAN nodes and a remote Base Station (BS). The core of the system is a completely distributed mobility algorithm based on virtual spring forces that periodically updates the UAV topology to adapt to the movement of ground nodes. LoRaUAV has been successfully implemented in ns-3 and its performance has been comparatively evaluated in wild area firefighting scenarios, using Packet Reception Ratio (PRR) and end-to-end delay as the main performance metrics. It is observed that the Connection Recovery and Maintenance (CRM) and Movement Prediction (MP) mechanisms implemented in LoRaUAV effectively help improve the PRR, with the only disadvantage of a higher delay affecting a small percentage of packets caused by buffer delays and disconnections. Full article

Wireless sensor networks are expected to automatically monitor the ecological evolution and wildlife habits in forests. Low-power links (transceivers) are often adopted in wireless sensor network applications, in order to save the precious sensor energy and then achieve long-term, unattended monitoring. Recent research has presented some performance characteristics of such low-power wireless links under laboratory or outdoor scenarios with less obstacles, and they have found that low-power wireless links are unreliable and prone to be affected by the target environment. However, there is still less understanding about how well the low-power wireless link performs in real-world forests and to what extent the complex in-forest surrounding environments affect the link performances. In this paper, we empirically evaluate the low-power links of wireless sensors in three typical different forest environments. Our experiment investigates the performance of the link layer compatible with the IEEE 802.15.4 standard and analyzes the variation patterns of the packet reception ratio (PRR), the received signal strength indicator (RSSI) and the link quality indicator (LQI) under diverse experimental settings. Some observations of this study are inconsistent with or even contradict prior results that are achieved in open fields or relatively clean environments and thus, provide new insights both into effectively evaluating the low-power wireless links and into efficiently deploying wireless sensor network systems in forest environments. Full article