31 research outputs found
Towards a Novel Air–Ground Intelligent Platform for Vehicular Networks: Technologies, Scenarios, and Challenges
No full textModern cities require a tighter integration with Information and Communication Technologies (ICT) for bringing new services to the citizens. The Smart City is the revolutionary paradigm aiming at integrating the ICT with the citizen life; among several urban services, transports are one of the most important in modern cities, introducing several challenges to the Smart City paradigm. In order to satisfy the stringent requirements of new vehicular applications and services, Edge Computing (EC) is one of the most promising technologies when integrated into the Vehicular Networks (VNs). EC-enabled VNs can facilitate new latency-critical and data-intensive applications and services. However, ground-based EC platforms (i.e., Road Side Units—RSUs, 5G Base Stations—5G BS) can only serve a reduced number of Vehicular Users (VUs), due to short coverage ranges and resource shortage. In the recent past, several new aerial platforms with integrated EC facilities have been deployed for achieving global connectivity. Such air-based EC platforms can complement the ground-based EC facilities for creating a futuristic VN able to deploy several new applications and services. The goal of this work is to explore the possibility of creating a novel joint air-ground EC platform within a VN architecture for helping VUs with new intelligent applications and services. By exploiting most modern technologies, with particular attention towards network softwarization, vehicular edge computing, and machine learning, we propose here three possible layered air-ground EC-enabled VN scenarios. For each of the discussed scenarios, a list of the possible challenges is considered, as well possible solutions allowing to overcome all or some of the considered challenges. A proper comparison is also done, through the use of tables, where all the proposed scenarios, and the proposed solutions, are discussed
A Markov Decision Process Solution for Energy-Saving Network Selection and Computation Offloading in Vehicular Networks
No full textVehicular Edge Computing (VEC) enables the integration of edge computing facilities in vehicular networks (VNs), allowing data-intensive and latency-critical applications and services to end-users. Though VEC brings several benefits in terms of reduced task computation time, energy consumption, backhaul link congestion, and data security risks, VEC servers are often resource-constrained. Therefore, the selection of proper edge nodes and the amount of data to be offloaded becomes important for having VEC process benefits. However, with the involvement of high mobility vehicles and dynamically changing vehicular environments, proper VEC node selection and data offloading can be challenging. In this work, we consider a joint network selection and computation offloading problem over a VEC environment for minimizing the overall latency and energy consumption during vehicular task processing, considering both user and infrastructure side energy-saving mechanisms. We have modeled the problem as a sequential decision-making problem and incorporated it in a Markov Decision Process (MDP). Numerous vehicular scenarios are considered based upon the users' positions, the states of the surrounding environment, and the available resources for creating a better environment model for the MDP analysis. We use a value iteration algorithm for finding an optimal policy of the MDPs over an uncertain vehicular environment. Simulation results show that the proposed approaches improve the network performance in terms of latency and consumed energy
Joint Air-Ground Distributed Federated Learning for Intelligent Transportation Systems
No full textSupported by some of the major revolutionary technologies, such as Internet of Vehicles (IoVs), Edge Computing, and Machine Learning (ML), the traditional Vehicular Networks (VNs) are changing drastically and converging rapidly into one of the most complex, highly intelligent, and advanced networking systems, mostly known as Intelligent Transportation System (ITS). Recently, distributed ML techniques, such as Federated Learning (FL) have gained huge popularity mainly for their advantages in terms of intelligence sharing and privacy concerns. VNs are a natural contender for exploiting FL for solving challenging problems; however, their limited resources, dynamic nature, high speed, and reduced latency requirements often become the bottleneck. V2X communication technologies allow vehicular terminals (VTs) to share their valuable local environment parameters and become aware of their surroundings. Such information can be utilized to build a more sustainable and affordable FL platform for serving VTs. Gaining from recently introduced 3D architectures, integrating terrestrial and aerial edge computing layers, we present here a distributed FL platform able to distribute the FL process on a 3D fashion while reducing the overall communication cost for providing vehicular services. The framework is defined as a constrained optimization problem for reducing the overall FL process cost through a proper network selection between various nodes. We have modeled the FL network selection problem as a sequential decision-making process through a Markov Decision Process (MDP) with time-dependent state transition probabilities. A computation-efficient value iteration algorithm is adapted for solving the MDP. Comparison with various benchmark methods shows the overall improvement in terms of latency, energy, and FL performance
Integrated Aerial-Ground Computation Offloading for Dependency-Aware IoV Multitask Services
No full textThe Internet of Vehicles (IoV) is a fundamental paradigm for enabling intelligent transportation systems and promoting high-quality services and applications that require a tremendous amount of data processing resources. In this paper, we consider a computational offloading problem on an Integrated Aerial-Ground (IAG) Edge Computing (EC) architecture, where each task is modeled as a chain of dependent subtasks. To solve the problem, a V2X-based Computation and Communication-efficient Multitask Offloading Approach (CCMTOA) is proposed in which mutual information is exchanged between users, allowing one to effectively solve the multitask multilayer network selection problem. The parameters of vehicle mobility are estimated using a realistic intelligent mobility model. The numerical results with varying VU density show the effectiveness of the proposed method over the benchmark approaches
Collaborative Reinforcement Learning for Multi-Service Internet of Vehicles
No full textInternet of Vehicles (IoV) is a recently introduced paradigm aiming at extending the Internet of Things (IoT) toward the vehicular scenario in order to cope with its specific requirements. Nowadays, there are several types of vehicles, with different characteristics, requested services, and delivered data types. In order to efficiently manage such heterogeneity, Edge Computing facilities are often deployed in the urban environment, usually co-located with the Roadside Units (RSUs), for creating what is referenced as Vehicular Edge Computing (VEC). In this paper, we consider a joint network selection and computation offloading optimization problem in multi-service VEC environments, aiming at minimizing the overall latency and the consumed energy in an IoV scenario. Two novel collaborative Q-learning based approaches are proposed, where Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication paradigms are exploited, respectively. In the first approach, we define a collaborative Q-learning method in which, through V2I communications, several vehicles participate in the training process of a centralized Q-agent. In the second approach, by exploiting the V2V communications, each vehicle is made aware of the surrounding environment and the potential offloading neighbors, leading to better decisions in terms of network selection and offloading. In addition to the tabular method, an advanced deep learning-based approach is also used for the action value estimation, allowing to handle more complex vehicular scenarios. Simulation results show that the proposed approaches improve the network performance in terms of latency and consumed energy with respect to some benchmark solutions
Network Selection and Computation Offloading in Non-Terrestrial Network Edge Computing Environments for Vehicular Applications
No full textThe Edge Computing (EC) paradigm and the Internet of Things (IoT) have transformed the conventional vehicular network (VN) into a highly reliable, intelligent, and complex networking system serving users with heterogeneous services. However, the traditional terrestrial network-based EC facilities, usually referred to as Vehicular Edge Computing (VEC), enabled through the Road Side Units (RSU) deployments, have limited resources, higher deployment costs, limited coverage, and can rapidly become a bottleneck for the VNs performance. On the other hand, various Non-terrestrial Networking (NTN) platforms from air and space networks have gained a lot of attention in 5G and beyond studies and are expected to play a key role in the upcoming days. Integration of NTN-based EC facilities into the current VEC system can be useful for serving vehicular users with different service types. In this work, we first design a multi-EC enabled vehicular networking platform for serving vehicular users (VUs) with a heterogeneous set of services. We model the various latency and energy requirements for processing the VUs task requests through partial computation offloading operations. We further aim at minimizing the overall latency and energy requirements for processing the VUs data by selecting the proper ENs and the offloading amounts over a multi-EC enabled VN. The problem is modeled as a constrained optimization problem and an evolutionary search-based metaheuristic approach is used to solve it. The results are compared with various benchmark methods for showing the performance gain
Hierarchical Reinforcement Learning for Multi-layer Multi-Service Non-Terrestrial Vehicular Edge Computing
No full textVehicular Edge Computing (VEC) represents a novel advancement within the Internet of Vehicles (IoV). Despite its implementation through Road Side Units (RSUs), VEC frequently falls short of satisfying the escalating demands of Vehicle Users (VUs) for new services, necessitating supplementary computational and communication resources. Non-Terrestrial Networks (NTN) with onboard Edge Computing (EC) facilities are gaining a central place in the 6G vision, allowing one to extend future services also to uncovered areas. This scenario, composed of a multitude of VUs, terrestrial and non-terrestrial nodes, and characterized by mobility and stringent requirements, brings in a very high complexity. Machine Learning (ML) represents a perfect tool for solving these types of problems. Integrated Terrestrial and Non-terrestrial (T-NT) EC, supported by innovative intelligent solutions enabled through ML technology, can boost the VEC capacity, coverage range, and resource utilization. Therefore, by exploring the integrated T-NT EC platforms, we design a multi-EC-enabled vehicular networking platform with a heterogeneous set of services. Next, we model the latency and energy requirements for processing the VU tasks through partial computation offloading operations. We aim to optimize the overall latency and energy requirements for processing the VU data by selecting the appropriate edge nodes and the offloading amount. The problem is defined as a multi-layer sequential decision-making problem through the Markov Decision Processes (MDP). The Hierarchical Reinforcement Learning (HRL) method, implemented through a Deep Q network, is used to optimize the network selection and offloading policies. Simulation results are compared with different benchmark methods to show performance gains in terms of overall cost requirements and reliability
A Time-Continuous Federated Learning Framework for Enabling Intelligent Applications Over Latency-Critical Aerial Networks
No full textDistributed Machine Learning (DML) methods are expected to play a crucial role in the forthcoming 6G era, with the goal of enabling ubiquitous connected intelligence. Distributed intelligence enabled through distributed computing environments, 6G technology, and big data can be extremely supportive of achieving the goals of emerging intelligent IoT applications in the proximity of end users. With this in mind, we propose an advanced Federated Learning (FL) approach for efficiently enabling intelligent applications over latency-critical networks in Non-Terrestrial environments. In the proposed solution, the client and server nodes reduce idle time using a parallel processing approach with the help of a replica of the training model. Next, the proposed FL framework is tested in a Python environment to show its effectiveness with respect to the traditional FL approach
Multi-Time-Scale Markov Decision Process for Joint Service Placement, Network Selection, and Computation Offloading in Aerial IoV Scenarios
No full textVehicular Edge Computing (VEC) is considered a major enabler for multi-service vehicular 6G scenarios. However, limited computation, communication, and storage resources of terrestrial edge servers are becoming a bottleneck and hindering the performance of VEC-enabled Vehicular Networks (VNs). Aerial platforms are considered a viable solution allowing for extended coverage and expanding available resources. However, in such a dynamic scenario, it is important to perform a proper service placement based on the users' demands. Furthermore, with limited computing and communication resources, proper user-server assignments and offloading strategies need to be adopted. Considering their different time scales, a multi-time-scale optimization process is proposed here to address the joint service placement, network selection, and computation offloading problem effectively. With this scope in mind, we propose a multi-time-scale Markov Decision Process (MDP) based Reinforcement Learning (RL) to solve this problem and improve the latency and energy performance of VEC-enabled VNs. Given the complex nature of the joint optimization process, an advanced deep Q-learning method is considered. Comparison with various benchmark methods shows an overall improvement in latency and energy performance in different VN scenarios
Network Sliced Distributed Learning-as-a-Service for Internet of Vehicles Applications in 6G Non-Terrestrial Network Scenarios
No full textIn the rapidly evolving landscape of next-generation 6G systems, the integration of AI functions to orchestrate network resources and meet stringent user requirements is a key focus. Distributed Learning (DL), a promising set of techniques that shape the future of 6G communication systems, plays a pivotal role. Vehicular applications, representing various services, are likely to benefit significantly from the advances of 6G technologies, enabling dynamic management infused with inherent intelligence. However, the deployment of various DL methods in traditional vehicular settings with specific demands and resource constraints poses challenges. The emergence of distributed computing and communication resources, such as the edge-cloud continuum and integrated terrestrial and non-terrestrial networks (T/NTN), provides a solution. Efficiently harnessing these resources and simultaneously implementing diverse DL methods becomes crucial, and Network Slicing (NS) emerges as a valuable tool. This study delves into the analysis of DL methods suitable for vehicular environments alongside NS. Subsequently, we present a framework to facilitate DL-as-a-Service (DLaaS) on a distributed networking platform, empowering the proactive deployment of DL algorithms. This approach allows for the effective management of heterogeneous services with varying requirements. The proposed framework is exemplified through a detailed case study in a vehicular integrated T/NTN with diverse service demands from specific regions. Performance analysis highlights the advantages of the DLaaS approach, focusing on flexibility, performance enhancement, added intelligence, and increased user satisfaction in the considered T/NTN vehicular scenario
