Packet Reception Ratio Research Articles

Dynamic separate resource pool algorithm for vehicle-to-infrastructure communication

In conventional C-V2X, the resource pool used by all users for transmission is unified, regardless of the user identity. Moreover, NR-V2X sidelink feature allows users to randomly select a resource and maintain it for several periods, resulting in interference. In this paper, a separate resource pooling algorithm is introduced, in which the number of resources used by different users is dynamically changed with a fuzzy inference system, considering the current resource status and number of neighbours. Numerical results demonstrate the superiority of the proposed mechanism, in terms of Packet Reception Ratio (PRR) as well as the data Update Delay.

Adaptive Speed Control Scheme Based on Congestion Level and Inter-Vehicle Distance

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 (Disinter). 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 Disinter. First, the given scenario is divided into several zones. Subsequently, based on the congestion level of a zone and the Disinter 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.

Evaluating the Performance of ETSI-ITS Multi-Stack Protocols for V2V Communication in VANETs: A Simulation Study

The research evaluates various multi-stack protocols for Vehicular Ad-hoc Networks (VANETs), focusing on Vehicle-to-Vehicle (V2V) communication scenarios with Emergency Vehicle (EV) simulations. The study uses the ns-3 network and SUMO (Simulation of Urban MObility) traffic simulators to test these protocols in diverse scenarios, including fluctuating data rates and dense network conditions. By implementing the IEEE 802.11p protocol alongside vehicular message dissemination stacks compliant with ETSI (European Telecommunications Standards Institute) ITS (Intelligent Transport Systems) standards, the study performs simulation experiments with varying vehicle counts, ranging from 20 to 35. It employs two distinct data rate configurations while maintaining a constant transmission power of 23 dBm. The results indicate a decline in the average Packet Reception Ratio (PRR) as vehicle density increases, indicating heightened contention and interference. At the same time, there is an observed increase in average latency, contributing to increased message transmission and reception delays. The quantitative analysis demonstrates an inverse relationship between the average PRR and the total vehicle count when the SEND_CAM message is enabled. On the other hand, disabling SEND_CAM maintains a relatively consistent average PRR across scenarios. Additionally, a positive correlation between vehicle count and average latency underlines the impact of network congestion and interference on communication efficacy within VANETs. Despite suboptimal PRR values falling between 41% and 47%, latency performance remains satisfactory, with average latency durations ranging from 0.154 s to 0.187 s. Notably, the SEND_CAM parameter status shows negligible impact on protocol performance, suggesting that network density plays a more pivotal role. Finally, this study offers valuable insights into the trade-offs and challenges of multi-stack protocols in V2V communication within VANETs. Further optimization efforts are recommended to improve packet reception ratios, especially in high-vehicle-density environments, while maintaining acceptable latency levels. These findings contribute to the ongoing efforts to enhance the reliability and efficiency of communication protocols in VANETs, thus advancing the development of intelligent transportation systems. The study's quantitative protocol performance analysis under varying network conditions provides valuable guidance for optimizing V2V communication deployments in VANETs.

INVISIBLE 2.0: An enhanced interference-based handover technique for visible light communications in vehicular networks

Since the last decade, Visible Light Communication (VLC) has represented a huge capacity to complement conventional Radio Frequency (RF) technologies in plenty of applications. The credibility of VLC in terms of power consumption has always been compared to RF. In this work, in addition to addressing this issue through a detailed power model, the co-existence of VLC and the Long-Term Evolution (LTE) cellular network in a vehicular environment has been empirically investigated. The coordination of data transmission among different technologies is managed through a novel vertical handover mechanism, namely INVISIBLE 2.0. This technique is triggered by the error probability to meet the 3GPP requirements on packet reception ratio while endorsing VLC as the preferred technology bearer. We demonstrate that INVISIBLE 2.0 achieves a more stable latency, and energy efficiency based on extensive simulation results.

Reuse Distance-Aided Resource Selection Mechanisms for NR-V2X Sidelink Communication.

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.

A Hybrid Power-Rate Management Strategy in Distributed Congestion Control for 5G-NR-V2X Sidelink Communications.

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.

Eliminating Mapping Error of Link Quality Prediction for Low-Power Wireless Networks

The size of time windows used by low power wireless protocols to compute packet reception ratio (PRR) directly affects the accuracy and agility of link quality prediction. Using small time windows would make the prediction more agile, but its accuracy decreases seriously. To improve the accuracy while maintaining agility, many existing methods generally predict the physical layer parameters within small time windows first and then calculate PRR using mapping models of such parameters and PRR. However, due to the propagation characteristics of wireless signal, these mapping models usually introduce large errors. The mapping error will superimpose on the prediction error of physical layer parameters, which would inevitably degrade the accuracy and reliability of PRR prediction. This paper proposes to eliminate the step of mapping from physical layer parameters to PRR and directly use the historical series of PRR computed within small time windows as input parameters to build prediction model. Then, the PRR within large time windows could be predicted. This effectively avoids the errors introduced by using mapping models while not sacrificing the agility. To verify the proposed method, several machine learning algorithms were chosen to implement the prediction model. Compared with similar methods based on mapping models, the proposed one could achieve higher accuracy under different size of time windows. Specifically, under large time windows which are commonly used to describe PRR in practice, its accuracy is much higher. More importantly, agility of the proposed method is basically equivalent or even superior to existing ones.

A novel multiple access communication protocol for LoRa networks without LoraWAN

Current <span>studies predict there will be approximately 75 billion internet of things (IoT) devices connected to the internet by 2025. Such enormous IoT networks will require efficient communications systems to support those networks running well. We consider one of the emerging communications technologies that is widely used for wireless wide-area network, i.e., long range (LoRa). By default, LoRa wide area networking (LoRaWAN) has been equipped with a medium access control (MAC) protocol, however, several studies showed that LoRaWAN might not be the most excellent choice for certain applications of low-power wide area network, for example, peer-to-peer and mesh networks. In this work, we propose an application layer LoRa multi-communication (LMC) protocol to resolve multiple access problems in LoRa networks without employing LoRaWAN. The algorithm was embedded and tested on energy-constraint devices as building blocks of most IoT systems. Our examination in a controlled environment showed that the proposed algorithm achieved 94% averaged packet reception ratio (PRR) and 33.3 hours longer averaged battery lifetime compared to the default operation (without LMC algorithm) of LoRa networks. The proposed algorithm also introduced longer averaged time on air (ToA) compared to the default LoRa network mainly due to its best-effort service scenario applied to the proposed algorithm.</span>

Millimeter-wave channel modeling in a VANETs using coding techniques.

The Vehicular ad-Hoc Network (VANET) is envisioned to ensure wireless transmission with ultra-high reliability. In the presence of fading and mobility of vehicles, error-free information between Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) requires extensive investigation. The current literature lacks in designing an ultra-reliable comprehensive tractable model for VANET using millimeter wave. Ultra-reliable communication is needed to support autonomous vehicular communication. This article aims to provide a comprehensive tractable model for VANET over millimeter waves using Space-Time-Block-Coding (STBC) concatenated with Reed Solomon (RS) coding. The designed model provides the fastest way of designing and analyzing VANET networks on 60 GHz. By using the derived BER expressions and Reed Solomon coded doppler expression ultra-reliable vehicular networks can be build meeting the demands of massive growing volume of traffic. The performance of the model is compared with previous BER computational techniques and existing VANET communication systems, i.e., IEEE 802.11bd and 3rd generation partnership project vehicle to everything (3GPP V2X). The findings show that our proposed approach outperforms IEEE 802.11bd and the results are comparable with V2X NR. Packet Error Rate (PER), Packet Reception Ratio (PRR) and throughput are used as performance metrics. We have also evaluated the model on higher velocities of vehicles. Further, the simulation and numerical findings show that the proposed system surpass the existing BER results comprising of various modulation and coding techniques. The simulation results are verified by the numerical results there-by, showing the accuracy of our derived expressions.

Characterization of Low-Power Wireless Links in UAV-Assisted Wireless-Sensor Network

The introduction of unmanned aerial vehicle (UAV) makes low-power links face many new challenges in UAV-assisted wireless-sensor network (WSN). In this article, the spatial characteristics of these low-power links are analyzed comprehensively, and the applicability of traditional link quality metrics and models is evaluated. In particular, the differences between link characteristics of UAV-assisted WSN and those of traditional WSN are discussed, and possible reasons for these differences are analyzed. To achieve this goal, two UAVs working on different bands are used and three different flight modes are designed. Experimental results show that link characteristics of UAV-assisted WSN become more complicated due to the 3-D movement of UAV. Link quality fluctuates significantly when the altitude or horizontal distance changes, and it is difficult to demarcate a clear bound between the connected and transitional region. Traditional models for spatial characteristic are only applicable when there is no UAV communication interference and the direction between the air node and ground node remains unchanged. Meanwhile, existing models between physical-layer metrics and packet reception ratio are only applicable when there is no UAV communication interference. For links with the same quality, stability of UAV-assisted WSN is not significantly different from that of traditional WSN. Finally, there is more obvious overall asymmetry between uplinks and downlinks in UAV-assisted WSN, and the quality of downlinks is significantly better than that of uplinks. The discovery of these new features would have great significance on the design of UAV-assisted WSN.

Multi-Agent Deep Reinforcement Learning for Enhancement of Distributed Resource Allocation in Vehicular Network

To solve a decentralized radio resource management problem in a 5G vehicular network, we propose a novel resource allocation algorithm based on a multiagent deep reinforcement learning (MARL). We let each vehicle act as an individual agent that can select a unique combination of transport block (TB) and transmission power to broadcast periodic packets. Agent explores the environment and collects observations that later will be used to find the best combination of TB and transmission power. We apply an actor-critic reinforcement learning technique to choose optimal TB for each agent. To eliminate a nonstationarity in a multiagent setting, we utilize a centralized training that allows all agents to share their observations over critic networks. The information shared through critic network can assist each agent to learn the policies of other agents. In a decentralized execution, each agent may only use its actor network and local observation to find the most appropriate TB in the given level of transmission power. While training, the actions taken by actor are evaluated by corresponding critic that maps Q-value for all feasible actions in the given state. Our method results in 18% higher packet reception ratio than a spectrum allocation scheme based on a double DQN. The proposed method achieves 33% higher reward than the previous state-of-the-art that is also based on MARL.

Application of internet of things-based efficient security solution for industrial

Recent years have seen a rise in demonstrations of the Industrial Internet of Things (IIoT). Creating secure Internet of Things (IIoT) applications has become a major challenge. Because of the constant demand for additional functionality, such as foresight, the number of connected devices in the enterprise facility is continually growing. Certifying a smaller number of signatories provides stronger authentication methods and prohibits trustworthy third parties from being publicly certified among the available encryption instruments. Certain network criteria must be met by the virtual network function (VNFs) used in this arrangement. As a result of this output, the final service may be delivered at the lowest possible cost, including processing time and network delay. According to the results of this study, our solution is better suited to simplifying trust management in IIoT devices. These findings reveal that our proposed confidence model may withstand behavioural shifts in hostile IIoT environments. The experimental results show our proposed method BCEPP has a high computational cost of 95.3%, a low latency ratio of 28.5%, and increased data transmitting rate of 94.1%, enhanced security rate of 98.6%, packet reception ratio of 96.1%, user satisfaction index of 94.5%, probability ratio of 33.8% when compared to other methods.

Efficient data transmission using trusted third party in smart home environments

Currently, deployed Internet of Things (IoT) technology acts as a passive observer of the environment that sends data to a remote location. Developing and deploying future IoT applications will need re-tasking this one-way behaviour in a reliable manner. A novel computationally tractable optimization technique that can accept cross-layer resource configurations and focus on network enhancement with longevity should be created for the smart home, one of the heterogeneous IoT applications. This study shows different smart home architectures in static and mobile environments, taking into account some of the challenges like orchestration, mobility, and range in IoT. For network communication, routing protocol over 6LoWPAN (RPL) is used. The goal of the work is to optimize the communication network in both static and mobile environment. To attain the goal, this paper proposes an algorithm that improves the path selection by modifying the existing objective functions of RPL. The proposed smart home architectures are analysed and compared based on different parameters such as packet reception ratio, network overhead, throughput, average latency, and total energy consumption. Even when some of the devices in the smart home are mobile, the modified smart home-optimized path (MSHOP) is found to achieve a packet reception ratio of 99.93%, minimum latency of 0.9 s, and the highest total energy usage in the network of 3373 millijoules. In conclusion, proposed MSHOP outperforms all existing smart home architectures when considering network efficiency, time, and usability.

Exploiting Rateless Codes and Cross-layer Optimization for Low-power Wide-area Networks

Long communication range and low energy consumption are the two most important design goals of Low-power Wide-area Networks (LPWANs); however, many prior works have revealed that the performance of LPWAN in practical scenarios is not satisfactory. Although there are PHY-layer and link-layer approaches proposed to improve the performance of LPWAN, they either rely heavily on the hardware modifications or suffer from low data recovery capability, especially with bursty packet loss patterns. In this article, we propose a practical system, eLoRa, for COTS devices. eLoRa utilizes rateless codes and joint decoding with multiple gateways to extend the communication range and lifetime of LoRaWAN. To further improve the performance of LoRaWAN, eLoRa optimizes parameters of the PHY-layer (e.g., spreading factor) and the link layer (e.g, block length). We implement eLoRa on COTS LoRa devices and conduct extensive experiments on an outdoor testbed to evaluate the effectiveness of eLoRa. Results show that eLoRa can effectively improve the communication range of DaRe and LoRaWAN by 43.2% and 55.7% with a packet reception ratio higher than 60%, and increase the expected lifetime of DaRe and LoRaWAN by 18.3% and 46.6%.

Sparse Code Multiple Access Assisted Resource Allocation for 5G V2X Communications

In 5G vehicle-to-everything (V2X) systems, the scarcity of spectrum resources and the inefficiency of resource allocation make vehicle-to-vehicle (V2V) communications that require stringent latency and high reliability still a challenge. Existing relevant literatures either focus on vehicle-to-infrastructure (V2I) communications, or consider scenarios where the vehicle roles (transmitter or receiver) are fixed in V2V communications, or study the resource allocation within a single cell. What’s more, considering the high-speed movement of vehicles across the coverage regions of multiple cells, the above resource allocation problem becomes even more challenging. Toward this end, we consider in this paper a multi-cell 5G V2X system where V2V links and V2I links coexist and the vehicle roles are not fixed, and propose a sparse code multiple access-based centralized resource allocation scheme, so as to address the above challenges. In view of the fact that our formulated maximizing packet reception ratio problem is a combinatorial optimization problem and is NP-hard, we design a three-stage heuristic yet joint alternating optimization approach to obtain a suboptimal solution. Extensive numerical results demonstrate the superior performances of the proposed scheme in multiple perspectives.

PolarTracker: Attitude-Aware Channel Access for Floating Low Power Wide Area Networks

Low Power Wide Area Networks (LPWAN) such as Long Range (LoRa) show great potential in emerging aquatic IoT applications. However, our deployment experience shows that the floating LPWAN suffers significant performance degradation, compared to the static terrestrial deployments. Our measurement results reveal the reason behind this is the polarization and directivity of the antenna. The dynamic attitude of a floating node incurs varying signal strength losses, which is ignored by the attitude-oblivious link model adopted in most of the existing methods. When accessing the channel at a misaligned attitude, packet errors can happen. In this paper, we propose an attitude-aware link model that explicitly quantifies the impact of node attitude on link quality. Based on the new model, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PolarTracker</i> , a novel channel access method for floating LPWAN. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PolarTracker</i> tracks the node attitude alignment state and schedules the transmissions into the aligned periods with better link quality. To support concurrent access of multiple LoRa nodes, an attitude-based slotted-ALOHA protocol is proposed to reduce collision. We implement a prototype of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PolarTracker</i> on commercial LoRa platforms and extensively evaluate its performance in various real-world environments. The experimental results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PolarTracker</i> can efficiently improve the packet reception ratio by 50.6%, compared with ALOHA in LoRaWAN.

Multi‐layer neural network algorithm for vehicle‐to‐everything communication in 5G networks

SummaryInternet of Vehicles (IoVs), the emerging trend of Internet of Things (IoTs), has undoubtedly become a promising trend to improve communication among vehicles on the roads. Vehicle‐to‐everything (V2X) communication that is based on 5G technology enables vehicle users to communicate and collaborate with each other to enhance road traffic efficiency and safety. Owing to the increased traffic load and restricted resources of existing network substructure, a channel that responds to the latency and reliability needs of V2X communication must be designed. Thereby, several intelligent spectrum allocation techniques have been proposed to improve the system's overall effectiveness. In this paper, we discuss the spectrum sharing issue of V2X communication in Device‐to‐Device (D2D)‐based cellular networks. We propose a new multi‐layer neural network (MLNN)‐based Resource Allocation and sharing approach (MNNRA) for D2D‐based V2X communications. According to the main advantages of MLNN, the proposed algorithm takes several profits by improving system performance while reducing computational complexity. Numerical analysis is presented to approve the effectiveness of our proposed solution in terms of network sum rate, packet reception ratio, resource utilization ratio, and time complexity.

An Efficient Opportunistic Routing Based on Prediction for Nautical Wireless Ad Hoc Networks

Nautical wireless ad hoc networks are becoming increasingly popular in oceans due to their easy deployment and self-curing capability. They may alternate frequently between connected mobile ad hoc networks and partitioned opportunistic networks due to mobility in large spaces. Traditional mobile ad hoc network routing is used to find the shortest route for connected networks. However, for opportunistic networks, routing schemes with a broadcast nature mainly exploit the reduction in message duplication and the local relaying technologies described in the literature, which may lead to unnecessary resource waste and low packet delivery ratios. To solve the problem, we propose an efficient opportunistic routing scheme based on prediction for nautical wireless ad hoc networks. The scheme first develops an effective candidate intermediate region to recognize the unavailability of some apparently qualified intermediate nodes, and then takes into account the packet reception ratio between nodes and relay advancement prediction, to improve packet delivery. The proposed scheme achieves performance improvements regarding packet loss ratio and throughput with a tolerable latency increase, compared to other schemes.

LoRa Signal Synchronization and Detection at Extremely Low Signal-to-Noise Ratios

In recent years, LoRa has been deployed in countless Internet of Things (IoT) applications across the globe. However, as LoRa is a proprietary technology, research into its physical-layer performance has been challenging. Implementing LoRa on software-defined radio (SDR) platforms yields valuable insight into the physical layer of the LoRa standard and paves the way for improvements in packet reception capabilities for LoRa receivers. This article presents an independently developed packet reception algorithm, which drastically improves the physical performance of LoRa communication links. The advanced signal presence detection, synchronization, and symbol detection strategies are shown to significantly increase packet reception ratios in extremely adverse noise conditions. Multiple algorithm variations are presented and compared in terms of bit error rate (BER) performance and computational cost. In comparison to a theoretical system with perfect channel state information, the simulated BER performance of the best performing algorithm only requires an increase of 1.6 dB in signal-to-noise ratio (SNR) to exhibit the same performance. Finally, SDR implementations of the algorithms exhibit average SNR performance gains up to 4.7 dB when compared to commercially available hardware.

Game-Theoretic Power and Rate Control in IEEE 802.11p Wireless Networks

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.