Personalized and Context-aware Route Planning for Edge-assisted Vehicles

Conventional route planning services typically offer the same routes to all drivers, focusing primarily on a few standardized factors such as travel distance or time, overlooking individual driver preferences. With the inception of autonomous vehicles expected in the coming years, where vehicles will rely on routes decided by such planners, there arises a need to incorporate the specific preferences of each driver, ensuring personalized navigation experiences. In this work, we propose a novel approach based on graph neural networks (GNNs) and deep reinforcement learning (DRL), aimed at customizing routes to suit individual preferences. By analyzing the historical trajectories of individual drivers, we classify their driving behavior and associate it with relevant road attributes as indicators of driver preferences. The GNN is capable of representing the road network as graph-structured data effectively, while DRL is capable of making decisions utilizing reward mechanisms to optimize route selection with factors such as travel costs, congestion level, and driver satisfaction. We evaluate our proposed GNN-based DRL framework using a real-world road network and demonstrate its ability to accommodate driver preferences, offering a range of route options tailored to individual drivers. The results indicate that our framework can select routes that accommodate driver's preferences with up to a 17% improvement compared to a generic route planner, and reduce the travel time by 33% (afternoon) and 46% (evening) relatively to the shortest distance-based approach.

Leverage Variational Graph Representation For Model Poisoning on Federated Learning

This paper puts forth a new training data-untethered model poisoning (MP) attack on federated learning (FL). The new MP attack extends an adversarial variational graph autoencoder (VGAE) to create malicious local models based solely on the benign local models overheard without any access to the training data of FL. Such an advancement leads to the VGAE-MP attack that is not only efficacious but also remains elusive to detection. VGAE-MP attack extracts graph structural correlations among the benign local models and the training data features, adversarially regenerates the graph structure, and generates malicious local models using the adversarial graph structure and benign models' features. Moreover, a new attacking algorithm is presented to train the malicious local models using VGAE and sub-gradient descent, while enabling an optimal selection of the benign local models for training the VGAE. Experiments demonstrate a gradual drop in FL accuracy under the proposed VGAE-MP attack and the ineffectiveness of existing defense mechanisms in detecting the attack, posing a severe threat to FL.

Empowering the 6G Cellular Architecture with Open RAN

Innovation and standardization in 5G have brought advancements to every facet of the cellular architecture. This ranges from the introduction of new frequency bands and signaling technologies for the radio access network (RAN), to a core network underpinned by micro-services and network function virtualization (NFV). However, like any emerging technology, the pace of real-world deployments does not instantly match the pace of innovation. To address this discrepancy, one of the key aspects under continuous development is the RAN with the aim of making it more open, adaptive, functional, and easy to manage. In this paper, we highlight the transformative potential of embracing novel cellular architectures by transitioning from conventional systems to the progressive principles of Open RAN. This promises to make 6G networks more agile, cost-effective, energy-efficient, and resilient. It opens up a plethora of novel use cases, ranging from ubiquitous support for autonomous devices to cost-effective expansions in regions previously underserved. The principles of Open RAN encompass: (i) a disaggregated architecture with modular and standardized interfaces; (ii) cloudification, programmability and orchestration; and (iii) AI-enabled data-centric closed-loop control and automation. We first discuss the transformative role Open RAN principles have played in the 5G era. Then, we adopt a system-level approach and describe how these Open RAN principles will support 6G RAN and architecture innovation. We qualitatively discuss potential performance gains that Open RAN principles yield for specific 6G use cases. For each principle, we outline the steps that research, development and standardization communities ought to take to make Open RAN principles central to next-generation cellular network designs.

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.

Insights from the Design Space Exploration of Flow-Guided Nanoscale Localization

Nanodevices with Terahertz (THz)-based wireless communication capabilities are providing a primer for flow-guided localization within the human bloodstreams. Such localization is allowing for assigning the locations of sensed events with the events themselves, providing benefits in precision medicine along the lines of early and precise diagnostics, and reduced costs and invasiveness. Flow-guided localization is still in a rudimentary phase, with only a handful of works targeting the problem. Nonetheless, the performance assessments of the proposed solutions are already carried out in a non-standardized way, usually along a single performance metric, and ignoring various aspects that are relevant at such a scale (e.g., nanodevices' limited energy) and for such a challenging environment (e.g., extreme attenuation of in-body THz propagation). As such, these assessments feature low levels of realism and cannot be compared in an objective way. Toward addressing this issue, we account for the environmental and scale-related peculiarities of the scenario and assess the performance of two state-of-the-art flow-guided localization approaches along a set of heterogeneous performance metrics such as the accuracy and reliability of localization.

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.

Reducing Waiting Times at Charging Stations with Adaptive Electric Vehicle Route Planning

Electric vehicles are becoming more popular all over the world. With increasing battery capacities and a growing fast-charging infrastructure, they are becoming suitable for long distance travel. However, queues at charging stations could lead to long waiting times, making efficient route planning even more important. In general, optimal multi-objective route planning is extremely computationally expensive. We propose an adaptive charging and routing strategy, which considers driving, waiting, and charging time. For this, we developed a multi-criterion shortest-path search algorithm using contraction hierarchies. To further reduce the computational effort, we precompute shortest-path trees between the known locations of the charging stations. We propose a central charging station database (CSDB) that helps estimating waiting times at charging stations ahead of time. This enables our adaptive charging and routing strategy to reduce these waiting times. In an extensive set of simulation experiments, we demonstrate the advantages of our concept, which reduces average waiting times at charging stations by up to 97 %. Even if only a subset of the cars uses the CSDB approach, we can substantially reduce waiting times and thereby the total travel time of electric vehicles.