Where to Decide? Centralized vs. Distributed Vehicle Assignment for Platoon Formation

Platooning is a promising cooperative driving application for future intelligent transportation systems. In order to assign vehicles to platoons, some algorithm for platoon formation is required. Such vehicle-to-platoon assignments have to be computed on-demand, e.g., when vehicles join or leave the freeways. In order to get best results from platooning, individual properties of involved vehicles have to be considered during the assignment computation. In this paper, we explore the computation of vehicle-to-platoon assignments as an optimization problem based on similarity between vehicles. We define the similarity and, vice versa, the deviation among vehicles based on the desired driving speed of vehicles and their position on the road. We create three approaches to solve this assignment problem: centralized solver, centralized greedy, and distributed greedy, using a Mixed Integer Programming solver and greedy heuristics, respectively. Conceptually, the approaches differ in both knowledge about vehicles as well as methodology. We perform a large-scale simulation study using PlaFoSim to compare all approaches. While the distributed greedy approach seems to have disadvantages due to the limited local knowledge, it performs as good as the centralized solver approach across most metrics. Both outperform the centralized greedy approach, which suffers from synchronization and greedy selection effects.Since the centralized solver approach assumes global knowledge and requires a complex Mixed Integer Programming solver to compute vehicle-to-platoon assignments, we consider the distributed greedy approach to have the best performance among all presented approaches

Resource Allocation for UAV-Assisted Industrial IoT User with Finite Blocklength

We consider a relay system empowered by an unmanned aerial vehicle (UAV) that facilitates downlink information delivery while adhering to finite blocklength requirements. The setup involves a remote controller transmitting information to both a UAV and an industrial Internet of Things (IIoT) or remote device, employing the non-orthogonal multiple access (NOMA) technique in the first phase. Subsequently, the UAV decodes and forwards this information to the remote device in the second phase. Our primary objective is to minimize the decoding error probability (DEP) at the remote device, which is influenced by the DEP at the UAV. To achieve this goal, we optimize the blocklength, transmission power, and location of the UAV. However, the underlying problem is highly non-convex and generally intractable to be solved directly. To overcome this challenge, we adopt an alternative optimization (AO) approach and decompose the original problem into three sub-problems. This approach leads to a sub-optimal solution, which effectively mitigates the non-convexity issue. In our simulations, we compare the performance of our proposed algorithm with baseline schemes. The results reveal that the proposed framework outperforms the baseline schemes, demonstrating its superiority in achieving lower DEP at the remote device. Furthermore, the simulation results illustrate the rapid convergence of our proposed algorithm, indicating its efficiency and effectiveness in solving the optimization problem.Comment: This paper is accepted by IEEE VTC 2023-Fall, Hong Kong, Chin

A Computational Approach for the Characterization of Airborne Pathogen Transmission in Turbulent Molecular Communication Channels

Airborne pathogen transmission mechanisms play a key role in the spread of infectious diseases such as COVID-19. In this work, we propose a computational fluid dynamics (CFD) approach to model and statistically characterize airborne pathogen transmission via pathogen-laden particles in turbulent channels from a molecular communication viewpoint. To this end, turbulent flows induced by coughing and the turbulent dispersion of droplets and aerosols are modeled by using the Reynolds-averaged Navier-Stokes equations coupled with the realizable $k-\epsilon$ model and the discrete random walk model, respectively. Via simulations realized by a CFD simulator, statistical data for the number of received particles are obtained. These data are post-processed to obtain the statistical characterization of the turbulent effect in the reception and to derive the probability of infection. Our results reveal that the turbulence has an irregular effect on the probability of infection, which shows itself by the multi-modal distribution as a weighted sum of normal and Weibull distributions. Furthermore, it is shown that the turbulent MC channel is characterized via multi-modal, i.e., sum of weighted normal distributions, or stable distributions, depending on the air velocity.Comment: Accepted to be published in IEEE Transactions on Molecular, Biological and Multi-Scale Communication

220502

Energy-harvesting-powered sensors are increasingly deployed beyond the reach of terrestrial gateways, where there is often no persistent power supply. Making use of the internet of drones (IoD) for data aggregation in such environments is a promising paradigm to enhance network scalability and connectivity. The flexibility of IoD and favorable line-of-sight connections between the drones and ground nodes are exploited to improve data reception at the drones. In this article, we discuss the challenges of online flight control of IoD, where data-driven neural networks can be tailored to design the trajectories and patrol speeds of the drones and their communication schedules, preventing buffer overflows at the ground nodes. In a small-scale IoD, a multi-agent deep reinforcement learning can be developed with long short-term memory to train the continuous flight control of IoD and data aggregation scheduling, where a joint action is generated for IoD via sharing the flight control decisions among the drones. In a large-scale IoD, sharing the flight control decisions in real-time can result in communication overheads and interference. In this case, deep reinforcement learning can be trained with the second-hand visiting experiences, where the drones learn the actions of each other based on historical scheduling records maintained at the ground nodes.This work was supported in part by the National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit under Grant UIDP/UIDB/04234/2020, and in part by the National Funds through FCT, under CMU Portugal Partnership under Project CMU/TIC/0022/2019 (CRUAV).info:eu-repo/semantics/publishedVersio

220102

In wireless powered sensor networks (WPSN), data of ground sensors can be collected or relayed by an unmanned aerial vehicle (UAV) while the battery of the ground sensor can be charged via wireless power transfer. A key challenge of resource allocation in UAV-aided WPSN is to prevent battery drainage and buffer overflow of the ground sensors in the presence of highly dynamic lossy airborne channels which can result in packet reception errors. Moreover, state and action spaces of the resource allocation problem are large, which is hardly explored online. To address the challenges, a new data-driven deep reinforcement learning framework, DDRL-RA, is proposed to train flight resource allocation online so that the data packet loss is minimized. Due to time-varying airborne channels, DDRL-RA firstly leverages long short-term memory (LSTM) with pre-collected offline datasets for channel randomness predictions. Then, Deep Deterministic Policy Gradient (DDPG) is studied to control the flight trajectory of the UAV, and schedule the ground sensor to transmit data and harvest energy. To evaluate the performance of DDRL-RA, a UAV-ground sensor testbed is built, where real-world datasets of channel gains are collected. DDRL-RA is implemented on Tensorflow, and numerical results show that DDRL-RA achieves 19\% lower packet loss than other learning-based frameworks.This work was partially supported by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit (UIDP/UIDB/04234/2020); also by national funds through the FCT, under CMU Portugal partnership, within project CMU/TIC/0022/2019 (CRUAV). This work was in part supported by the Federal Ministry of Education and Research (BMBF, Germany) as part of the 6G Research and Innovation Cluster 6G-RIC under Grant 16KISK020K.info:eu-repo/semantics/publishedVersio

10402 Abstracts Collection and Executive Summary -- Inter-Vehicular Communication

From October 3 to October 6, 2010, the Dagstuhl Seminar 10402 ``Inter-Vehicular Communication\u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar are put together in this paper. The first section describes the seminar topics and goals in general