High Mobility Network Research Articles

Machine Learning-assisted Distance Based Residual Energy Aware Clustering Algorithm for Energy Efficient Information Dissemination in Urban VANETs

A Vehicular Ad-hoc Network (VANET) is an essential component of intelligent transportation systems in the building of smart cities. A VANET is a self-configure high mobile and dynamic potential wireless ad-hoc network that joins all vehicle nodes in a smart city to provide in-vehicle infotainment services to city administrators and residents. In the smart city, the On-board Unit (OBU) of each vehicle has multiple onboard sensors that are used for data collection from the surrounding environment. One of the main issues in VANET is energy efficiency and balance because the small onboard sensors can’t be quickly recharged once installed on On-board Units (OBUs). Moreover, conserving energy stands out as a crucial challenge in VANET which is primarily contingent on the selection of Cluster Heads (CH) and the adopted packet routing strategy. To address this issue, this paper proposes distance and energy-aware clustering algorithms named SOMNNDP, which use a Self-Organizing Map Neural Network (SOMNN) machine learning technique to perform faster multi-hop data dissemination. Individual Euclidean distances and residual node energy are considered as mobility parameters throughout the cluster routing process to improve and balance the energy consumption among the participating vehicle nodes. This maximizes the lifetime of VANET by ensuring that all intermediate vehicle nodes use energy at approximately the same rate. Simulation findings demonstrate that SOMNNDP improves Quality of Service (QoS) better and consumes 17% and 14% less energy during cluster routing than distance and energy-aware variation of K-Means (KM) and Fuzzy C-Means (FCM) called KMDP and FCMDP respectively.

Estimating coverage and capacity of high frequency mobile networks in ultradense urban areas

High frequency communications (mmWave and TeraHz) in urban areas require a higher density of base stations compared to pre-5G mobile networks, but open the way to a quantum leap in increased throughput and reduced latency. However, we currently have no indication of how much we need to densify the deployment, and on the trade-off between the density of base stations and the performance improvement. This paper studies the problem of base stations placement to guarantee coverage to vehicles and pedestrians in urban areas when using high frequency communications. Our novel methodology takes advantage of vehicular traffic simulations and precise urban maps to generate a realistic demand model for vehicles and pedestrians in urban areas. We use a bounded error heuristic to find the maximal coverage that can be achieved with a given density of base stations, primarily using line-of-sight communications. We implemented the heuristic using CUDA libraries on Nvidia GPUs and evaluated the coverage in an urban area in the city of Luxembourg, for which vehicular traffic patterns are available. We focus on coverage and capacity analysis for the mmWave frequency, but the results are easily extended to TeraHz communications. Our results are the first to show that a reasonably low density (15 base stations per km2) is sufficient to provide coverage for vehicles in urban environments. However, optimizing on vehicles or on pedestrians are competing objectives: the operator needs to choose which one to target based on its business model when designing the network infrastructure. Our algorithms, code and open data can be used to perform this task and reproduce our results in different settings.

Integrated Sensing and Communication-Assisted User State Refinement for OTFS Systems

Orthogonal time frequency space (OTFS) modulation has been considered as one of the most promising candidates to support reliable data transmission especially in high-mobility networks, wherein the performance of communications strongly relies on timely and accurate tracking of the relevant user state parameters. In this context, the problem of integrated sensing and communication (ISAC) assisted user state refinement is addressed in the framework of OTFS systems. In particular, by exploiting the initial yet coarse angle estimate provided by the typical codebook-based user state sensing algorithm, we judiciously design a hybrid digital-analog architecture to output the nested array structured low dimensional observations. In this way, the corresponding nested array based technique is employed to perform angle refinement by fully utilizing the degrees of freedom provided by the measurements. Next, based on the refined angle estimate, we develop a two-stage joint delay and Doppler shifts estimation scheme to update the corresponding coarse estimates. Numerical results validate the effectiveness of the proposed algorithm in various scenarios, showing that our well designed user state refinement scheme is able to improve the performance of the considered ISAC-assisted OTFS systems in term of both radar and communication metrics.

Digital twin-assisted resource allocation framework based on edge collaboration for vehicular edge computing

Vehicular Edge Computing (VEC) supports latency-sensitive and computation-intensive vehicular applications by providing caching and computing services in vehicle proximity. This reduces congestion and transmission latency. However, VEC faces implementation challenges due to high vehicle mobility and unpredictable network dynamics. These challenges pose difficulties to network resource allocation. Most existing VEC network resource management solutions consider edge–cloud collaboration and ignore collaborative computing between edge nodes. A reasonable collaboration between Roadside Units (RSUs) or small cells eNodeB can improve VEC network performance. Our proposed framework aims to improve VEC network performance by integrating Digital Twin (DT) technology which creates virtual replicas of network nodes to estimate, predict, and evaluate their real-time conditions. A DT is constructed centrally to maintain and simulate VEC network, thus enabling edge nodes collaboration and real-time resources information availability. We employ channel state information (CSI) for RSUs selection, and vehicles communicate with RSUs through a non-orthogonal multiple access (NOMA) protocol. We aim to maximize the VEC system computation rate and minimize task completion delay by jointly optimizing offloading decisions, subchannel allocation, and RSU association. In view of the resulting optimization problem complexity (NP-hard), we model it as a Markov Decision Process (MDP) and apply Advantage Actor–Critic (A2C) algorithm to solve it. Validated via simulations, our scheme shows superiority to the benchmarks in reducing task completion delay and improving VEC system computation rates.

Awareness routing algorithm in vehicular ad-hoc networks (VANETs)

AbstractThe behavior of a Vehicle Ad hoc Network (VANETs) is extremely unpredictable due to the high mobility and random network topology inherent to the nature of VANETs. Several problems, including frequent connection failures, scalability, multi-hop data transfer, and data loss, impact the performance of Transmission Control Protocols (TCP) in such wireless ad hoc networks. This study proposes using zone-based routing with consideration for mobility in VANETs as a means of avoiding this issue. A hybrid optimization approach is introduced and used to the routing process. Both Ant Colony Optimization (ACO) and Artificial Bee Colony Optimization (ABCO) are components of the hybrid algorithm (ABC). Link stability and Residual energy provide the basis of the fitness function. Several measures, including delivery ratio, time, and overhead, are used to evaluate the effectiveness of the suggested method. A comparison of the suggested method's efficiency with that of other algorithms.

Design and Simulation of a 3.5 GHz Pyramidal Horn Antenna for IoT and High Speed 5G Wireless Technology Mobile Networks

Design and Simulation of a 3.5 GHz Pyramidal Horn Antenna for IoT and High Speed 5G Wireless Technology Mobile Networks

Innovating Multi-Objective Optimal Message Routing for Unified High Mobility Networks

Futuristic applications of intelligent transportation systems (ITS) highly rely on ad-hoc connectivity between the stochastic vehicular networks (ground and airborne). Message routing in ITS faces several challenges in the dynamic environment of highly mobile networks in the form of obstacles, propagation, and stability of the connection. Unlike vehicular ad-hoc networks (VANETs), flying ad-hoc networks (FANETs) have a flexible and robust configuration with line-of-sight links, and can provide message relaying services to VANETs. However, constant FANET usage depletes residual energy, leading to a shorter network lifetime. In this study, we propose UniFaVa, a routing scheme that considers a combined and unified paradigm of FANETs and VANETs, where a routing decision prefers a higher transmission rate of unmanned aerial vehicles (UAVs) with ample residual energy as a relay. The proposed scheme guarantees the reliability of a complete transmission by analytically predicting the connectivity time. Moreover, an analytical approach is formulated for the proposed multi-objective optimal routing using hypervolume. Our extensive simulation results using real traffic traces of Seoul and New York City from OpenStreetMap and simulation of urban mobility (SUMO) indicate significant achievements over the three existing contemporary routing methods. The proposed UniFaVa multi-objective optimal routing achieves a packet delivery ratio of 90% and a connectivity time of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$3\sim 6$</tex-math></inline-formula> sec for established multi-hop routes, and short hop distances for a reliable propagation with only one extra hop on average.

Artificial intelligence support for 5G/6G-enabled Internet of Vehicles networks: An overview

Improving transportation efficiency and on-road safety using Intelligent Transportation Systems (ITSs) has become crucial as road congestion and vehicle complexity increase coupled with ongoing rapid development and deployment of electric vehicles across the globe. Recent advances in computer systems and wireless communications have ushered in more possibilities for smart solutions to road traffic safety, congestion reduction, convenience, and overall efficiency. The evolution and deployment of 5G have opened up new technologies and features that can provide the much needed high-mobility wireless networks for the emerging Internet of Vehicles (IoV). The application of AI consisting of Deep Learning (DL), Machine Learning (ML) and Swarm Intelligence (SI) techniques have emerged in both conventional and vehicular wireless networks with strong promises towards enhancing traditional data-centric methods. Particularly, in the application domains of IoV, big data is frequently generated from various sources within the vehicular communication environment. The collected big data is usually processed and used for both safety and infotainment services including routing, broadening drivers' awareness, traffic mobility prediction for hazardous situation avoidance to improve overall safety and passenger comfort, and general quality of road experience. Applying data-driven methods enables AI to address high mobility and dynamic vehicular communications and network issues facing traditional solutions and approaches like network optimization techniques and conventional control loop design. This study provides a concise review of DL, ML and SI techniques and applications that are currently being explored by different research efforts within the application area of vehicular networks. The paper further discusses the strengths and weaknesses of the proposed AI-based solutions for the IoV networks.

STALB: A Spatio-Temporal Domain Autonomous Load Balancing Routing Protocol

Due to vehicle mobility, the topology of Vehicle Ad-hoc Networks (VANETs) may change dynamically. High mobility, limited bandwidth, and dynamic network topology pose challenges for communication in the Internet of Vehicles (IoVs). Literature works have attempted to promote efficient (e.g., lower end-to-end latency) message forwarding. However, due to the uncertain direction of message forwarding and vehicle mobility, they suffer from unreachable destinations and unstable connections. This paper explores the efficient method of message forwarding to alleviate network congestion in IoVs. We propose a Spatio-Temporal domain Autonomous Load Balancing (STALB) routing protocol. Specifically, STALB is a trajectory-based method for controlling the direction of message forwarding. STALB can significantly reduce the end-to-end latency and overload ratio, since it considers the local status of network relay devices (i.e., buffer score, congestion status) from the spatio-temporal domain. Then, we present a path reconstruction mechanism, which ensures that messages are forwarded to destinations within limited Time-To-live (TTL). Extensive simulation results show that STALB significantly outperforms other baseline methods (BSaW, TDOR, and TBHGR) regarding overhead ratio, average delivery latency, and average buffer time. Especially, the delivery rate of STALB can reach 99.9% under the sparse network scenario (4,500 messages), at least 0.7% higher than other baseline methods. Similarly, the average delivery delay of STALB is at least 84.31% lower than that of other baseline methods under the dense network scenario (18,000 messages).

MetaLearn: Optimizing routing heuristics with a hybrid meta-learning approach in vehicular ad-hoc networks

Routing protocols in vehicular ad-hoc networks (VANETs) are typically challenged by high vehicular mobility and changing network topology. It becomes more apparent as the inherently dispersed nature of VANETs affects the Quality-of-Service (QoS), which makes it challenging to find a routing algorithm that maximizes the network throughput. Integrating Reinforcement Learning (RL) with Meta-Heuristic (MH) techniques allow for solving constrained, high dimensional problems such as routing optimization. Motivated by this fact, we introduce MetaLearn, a technique akin to global search, which employs a parameterized approach to remove future rewards uncertainty as well as vehicular state exploration to optimize the multilevel network structure. The proposed technique searches for the optimum solution that may be sped up by balancing global exploration using Grey Wolf Optimization (GWO) and exploitation through Temporal Difference Learning (particularly Q(λ)). MetaLearn approach enables cluster heads to learn how to adjust route request forwarding according to QoS parameters. The input received by a vehicle from previous evaluations is used to learn and adapt the subsequent actions accordingly. Furthermore, a customized reward function is developed to select the cluster head and identify stable clusters through GWO. An in-depth experimental demonstration of the proposed protocol addresses applicability and solution challenges for hybrid MH-RL algorithms in VANETs.

Node Embedding for Security-Aware Clustering of Mobile Information-Centric Sensor Networks

In cluster-based information-centric wireless sensor networks (ICWSNs), mobile sensor nodes are grouped into clusters in rounds. In each cluster, a cluster head (CH) is selected, which collects, aggregates, and forwards locally sensed data to a sink node. CHs further store a copy of data for the round period to act as cache nodes and deliver data to mobile users upon requests. Nevertheless, clustering and securing mobile ICWSNs are challenging. This is because, in addition to sensor nodes’ and users’ mobility, sensor nodes are often resource constrained. Therefore, clustering and security resource allocation in mobile ICWSNs should be carefully redesigned to ensure efficient ICWSN operation, data security, and timely data access to mobile users. This article proposes a node embedding with security resource allocation (NESRA) clustering algorithm for mobile ICWSNs in rounds. NESRA allocates security resources to sensor nodes based on the location, mobility, and energy resources available in the first step. An optimization problem is formulated to select CHs that maximize network coverage and minimize data delivery delay to mobile users in the second step. In the third step, NESRA utilizes network representation learning that embeds sensor nodes’ location, mobility, and expected energy expenditure features into a 2-D space to form well-separated clusters of sensing nodes. Compared to existing works, NESRA achieves lower energy consumption, nodes’ death rate, and latency and allows higher throughput and cache nodes’ utilization with stable data security. Still, NESRA has some challenges to overcome in high-mobility networks.

GaDQN-IDS: A Novel Self-Adaptive IDS for VANETs Based on Bayesian Game Theory and Deep Reinforcement Learning

Due to the nature of high mobility and dynamic network topology, Intrusion Detection Systems (IDSs) in Vehicular Ad-hoc Networks (VANETs) face lots of challenges, especially in balancing the accuracy and efficiency of detection. Current researches about the deployment of IDSs in VANETs mainly focus on a tradeoff between the effectiveness and efficiency, but few efforts have been done about the adaptability of the tradeoff in the changeable networks. Thus, we address two crucial problems: 1) how to perceive the environmental change in the perspective of an IDS? 2) how to make the IDS adaptive in different scenarios? In this paper, a Bayesian Game theory and Deep Q-learning Network-based IDS is proposed for VANETs, called GaDQN-IDS. The interactions between an IDS and attackers are formulated as a dynamic intrusion detection game, in which the IDS decides either to just adjust the tradeoff between the accuracy and efficiency or to be retrained completely when its detection capacity has declined. The Nash Equilibria (NE) of the game is derived to reveal how the optimal decision of the IDS depends on the detection performance and road conditions. Moreover, a Deep Q-learning Network (DQN)-Adjustment is proposed to realize the self-adaptation of the IDS in the dynamic game, while an Error Priority Learning (EPL) is further designed for IDS retraining in changing VANETs. Simulation results show that the GaDQN-IDS has better performance than other existing IDSs with higher detection rate as well as lower detection time and overhead.

BBRv2+: Towards balancing aggressiveness and fairness with delay-based bandwidth probing

BBRv2, proposed by Google, aims at addressing BBR’s shortcomings of unfairness against loss-based congestion control algorithms (CCAs) and excessive retransmissions in shallow-buffered networks. In this paper, we first comprehensively study BBRv2’s performance under various network conditions and show that BBRv2 mitigates the shortcomings of BBR. Nevertheless, BBRv2’s benefits come with several costs, including slow responsiveness to bandwidth dynamics and low resilience to random losses. We then propose BBRv2+ to address BBRv2’s performance issues without sacrificing its advantages over BBR. To this end, BBRv2+ uses delay information to cautiously guide the aggressiveness of its bandwidth probing. In doing so, it achieves fast responsiveness to bandwidth dynamics and fairness against loss-based CCAs at the same time. BBRv2+ also integrates mechanisms for improved resilience to random losses and network jitter. Extensive experiments demonstrate the effectiveness of BBRv2+. Specifically, it achieves 25% higher throughput and comparable queuing delay in comparison with BBRv2 in high-mobility network scenarios.

Data collection protocols for VANETs: a survey

We live in the era of Intelligent Transport Systems (ITS), which is an extension of Vehicular AdHoc Networks (VANETs). In VANETs, vehicles act as nodes connected with each other and sometimes with a public station. Vehicles continuously exchange and collect information to provide innovative transportation services; for example, traffic management, navigation, autonomous driving, and the generation of alerts. However, VANETs are extremely challenging for data collection, due to their high mobility and dynamic network topologies that cause frequent link disruptions and make path discovery difficult. In this survey, various state-of-the-art data collection protocols for VANETs are discussed, based on three broad categories, i.e., delay-tolerant, best-effort, and real-time protocols. A taxonomy is designed for data collection protocols for VANETs that is essential to add precision and ease of understandability. A detailed comparative analysis among various data collection protocols is provided to highlight their functionalities and features. Protocols are evaluated based on three parametric phases. First, protocols investigation based on six necessary parameters, including delivery and drop ratio, efficiency, and recovery strategy. Second, a 4-D functional framework is designed to fit most data collection protocols for quick classification and mobility model identification, thus eradicating the need to read extensive literature. In the last, in-depth categorical mapping is performed to deep dive for better and targeted interpretation. In addition, some open research challenges for ITS and VANETs are discussed to highlight research gaps. Our work can thus be employed as a quick guide for researchers to identify the technical relevance of data collection protocols of VANETs.

Ant-based Energy Efficient Routing Algorithm for Mobile Ad hoc Networks

In this paper, an Ant Colony Optimization (ACO) based Energy Efficient Shortest Path Routing (AESR) algorithm is developed for Mobile Ad hoc Network (MANET). The Mobile Ad hoc Network consists of a group of mobile nodes that can communicate with each other without any predefined infrastructure. The routing process is critical for this type of network due to its dynamic topology, limited resources and wireless channel. The technique incorporated in this paper for optimizing the routing in a Mobile ad hoc network is Ant Colony Optimization. The ACO algorithm is used to solve network problems related to routing, security, etc. in wired and wireless networks. This proposed work is used for selecting the energy efficient shortest route between the source and the destination of the network. This routing technique uses important parameters such as residual energy of the nodes, minimum residual energy of the path, the distance between the nodes, trip time and hop count to select the optimal route for communication to extend the lifetime of the network by reducing the energy consumption. The proposed routing protocol is compared with other conventional routing protocols. The experimental results demonstrate that the proposed AESR routing protocol outperforms the existing protocols in terms of performance metrics such as Packet Delivery Ratio, Average Path Length, Network Lifetime, Number of Dead nodes and Average Energy Consumption in high mobility networks.

VC 3: A Novel Vehicular Compatibility-Based Cooperative Communication in 5G Networks

This letter explores the challenges of high mobility networks in 5G communications and introduces a novel and efficient clustering scheme. We propose VC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> : vehicular compatibility-based cooperative communications to form clusters having longer and stable connections, which reduces coverage holes and extends the network access. Our exhaustive simulations in real OpenStreetMap traffic traces in the simulation for urban mobility (SUMO) and the network simulator (NS3) with LENA module demonstrate exceptional results of having more than 80% packet delivery ratio and 70%~90% success probability with only 1 s delay. Moreover, the proposed VC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> outperforms two existing solutions (MMZ and Two-Way clustering) and achieves 2~4 Mbps higher throughput.

An intelligent cluster optimization algorithm based on Whale Optimization Algorithm for VANETs (WOACNET).

Vehicular Ad hoc Networks (VANETs) an important category in networking focuses on many applications, such as safety and intelligent traffic management systems. The high node mobility and sparse vehicle distribution (on the road) compromise VANETs network scalability and rapid topology, hence creating major challenges, such as network physical layout formation, unstable links to enable robust, reliable, and scalable vehicle communication, especially in a dense traffic network. This study discusses a novel optimization approach considering transmission range, node density, speed, direction, and grid size during clustering. Whale Optimization Algorithm for Clustering in Vehicular Ad hoc Networks (WOACNET) was introduced to select an optimum cluster head (CH) and was calculated and evaluated based on intelligence and capability. Initially, simulations were performed, Subsequently, rigorous experimentations were conducted on WOACNET. The model was compared and evaluated with state-of-the-art well-established other methods, such as Gray Wolf Optimization (GWO) and Ant Lion Optimization (ALO) employing various performance metrics. The results demonstrate that the developed method performance is well ahead compared to other methods in VANET in terms of cluster head, varying transmission ranges, grid size, and nodes. The developed method results in achieving an overall 46% enhancement in cluster optimization and an F-value of 31.64 compared to other established methods (11.95 and 22.50) consequently, increase in cluster lifetime.

АНАЛИЗ ПРОИЗВОДИТЕЛЬНОСТИ ПРОТОКОЛОВ РЕАКТИВНОЙ МАРШРУТИЗАЦИИ В VANET НА БАЗЕ NS3

Since importance of improving of Intelligent Transportation System (ITS) always follow modern trends by using new wireless communication technologies, the trend of latest research topics is focusing on Vehicular Ad Hoc Network (VANET). VANET networks play a vital role in ITS due to their increasing importance for the building of ITS. VANET is a subclass of mobile ad-hoc networks (MANET). VANET depends on wireless technologies to establish communication between moving vehicles (nodes). An appropriate and efficient routing protocol helps to successful exchange data between mobility nodes in vehicular ad-hoc networks. VANET has a lot of similar features to MANETs such as finite bandwidth, self-arrangement, self-administration, and unstable network topology. Except it has some important features of its characteristic such as very high node mobility, delay restrictions, and frequent network outages. For this reason, routing in VANET networks is much more complex than routing in MANET networks. The purpose of this study - to evaluate the performance of protocols AODV (Ad hoc On-Demand Distance Vector) and DSR (Dynamic Source Routing) and their impact on the performance of networks VANET. This paper differs in that it analyses the impact of network size at a large number of nodes, and different vehicle speeds on network performance metrics like packet delivery ratio, throughput, average delay, overhead and packet loss ratio and assessing the level of network performance at realistic mobility scenarios for the movement of vehicles in the street generated by Bonnmotion tool. Also, the simulation is carried out in NS-3 simulator to create VANET network topology and routing protocols.

Immigrant nationality and human capital formation in Brazil

This paper investigates the impact of the mass migration episode of the late nineteenth and early twentieth century (1888–1920) on human capital creation in Brazil. We argue that the presence of immigrants at the turn of the twentieth century has had a positive impact on current measures of human capital and that the effect is heterogeneous. Demand for education depends on an immigrants’ experiences with public education, on their religious background, and on their migration objectives and demographics. The supply of education is contingent on social capital and size of immigrant community. Further, we indicate that this effect persisted over time and remained localized due to high mobility costs and network effects. We find a positive relationship between German, Japanese and Italian presence in 1920 and current human capital, while Portuguese and Spanish presence are not associated with human capital increases.

Performance optimization of smartphones in dual-band high-efficiency and very high throughput mobile networks

By focusing on dense areas, 802.11ax high-efficiency wireless (HEW) standard offers some improvements over 802.11ac very high throughput (VHT). The HEW improvements are mainly based on several key factors and diversity of their values. While each particular value corresponds to a key factor can directly affect the network performance, there is a great deal of uncertainty regarding their practical effectiveness in dense areas. Thus, identifying the efficiency of each particular value of the key factors is of prime importance for determining the optimal values and thereby enhancing the network performance. However, identifying the optimal values in wireless dense areas where a large number of users share the same link is a challenging task and needs comprehensive comparative approaches. In this context, this work proposes a model for high-density HEW and VHT deployment with a special focus on their common key factors. The model includes 204 distinct simulation scenarios, categorized under six major classes, each corresponds to a particular key factor. The model is implemented and the results are obtained to determine the optimal values of the key factors. Moreover, to validate the accuracy of the simulation results, the analytical results are obtained and compared.