Résumé

Vehicle-as-a-Service (VaaS) refers to on-demand rides and the sharing/offering of various kinds of intelligent transportation facilities (e.g., smart buses, electric vehicles, autonomous cars) to move from a source to a destination across one or several regions. Coupled with smart transportation systems—which are critical for addressing road network issues such as traffic congestion, parking shortages, and safety concerns—VaaS is increasingly being adopted in smart cities.
For example, a user may specify their needs in terms of source and destination stations, time and cost constraints, as well as preferred transportation modes and services (e.g., only connected buses and cars). Such a user profile is evaluated against available VaaS options under current and anticipated urban network conditions. However, current solutions do not support the customization of VaaS compositions and often treat user requests as traditional vehicle routing problems. In a smart mobility context, however, processing VaaS requests involves not only finding the optimal transportation path that meets user constraints (e.g., time, cost, transfer stations) but also selecting the top-rated combination of available VaaSs (e.g., a sequence of smart buses) with respect to the user profile (e.g., connectivity needs, specific facilities) and the quality of smart urban services.
To address these challenges, the goal of this paper is to develop a multi-modal recommender system that enables the personalized selection, composition, and scheduling of VaaS services while optimizing trip constraints (e.g., minimizing trip time and cost, and maximizing VaaS availability and reputation).
As a multi-population technique, Composite Particle Swarm Optimization (CPSO) is applied to aggregate the optimal set of high-quality and high-coverage VaaSs with respect to the requested trip. The regions composing the trip are explored using a modified A* search algorithm to find the optimal local (partial) path in each traversed region of the smart urban network. Comparative experiments involving two metaheuristics, a greedy algorithm, and a fuzzy clustering technique demonstrate the efficiency and superiority of our CPSO-based approach, achieving approximately a 28% improvement over its closest competitors.

Résumé

As drones continue to see widespread adoption across commercial, private, healthcare, and education sectors, their commercial use is experiencing rapid growth. To enhance drone-based package delivery efficiency and improve customer experience, the vast amount of flight and recharging data collected from drones and stations offers valuable opportunities for predicting both resource availability within the sky network and drones’ capacity to complete delivery missions. However, the variations in regional regulations and privacy restrictions enforced by drone service providers lead to data heterogeneity, making centralized processing of flight and charging history impractical. Running machine learning models locally at the service provider level (drones and stations) addresses privacy concerns, yet processing the large volume and diversity of raw data remains a significant challenge. To deal with these issues, a collaborative learning approach based on historical delivery data presents an elegant solution. Aiming to offer predictive scheduling of drone delivery missions, while taking into consideration the complexity, heterogeneity, and dynamic nature of their flight environment, we propose a predictive and federated approach for the resilient selection and composition of drone delivery services, leveraging the strengths of federated learning (FL) to handle data privacy and heterogeneity. Our method utilizes a federated Recurrent Neural Network (FL-RNN) model that combines predictive capabilities with federated behavior, enabling collaborative forecasting and efficient mission scheduling in low-congestion regions based on the most reliable drone services. Additionally, an enhanced A* search algorithm is defined to identify the optimal delivery path by factoring in station overload probabilities. Besides computational efficiency, experimental results demonstrate the effectiveness of our approach, achieving a 16.66% improvement in prediction accuracy and an 8.89% reduction in delivery costs compared to non-federated and non-predictive solutions.

Résumé

Résumé

As the adoption of unmanned aerial vehicles by both consumers and companies is growing rapidly, the use of drones is nowadays leading the way various types of packages (e.g., food, medication, supplies) are delivered. Drone services have increased companies’ benefits and saved money on their shipping costs, which resulted in a reduced delivery time and cost for consumers. However, the delivery tasks either achieved by single or swarming drones are facing several challenges, which are mainly related to the modeling of drones’ skyway network, the uncertainty of flight conditions and available resources, the consumers’ trust and quality of experience, the privacy concerns caused by shared user information (e.g., GPS, camera). Among these issues, we focus on the modeling and resource availability issues. To cope with complex delivery requests, this paper takes advantage of the service computing paradigm to propose a service composition approach, in which multiple drone services can participate in the delivery plan. We first propose a graph-based modeling of the skyway network and flights history. This latter, is fed into a proposed frequent subgraph mining algorithm, and is processed to extract relevant patterns from the previously generated delivery paths, based on consumers’ positive feedback. We also adopt Long Short-Term Memory to propose a model that forecasts the overload of charging stations, with the goal of tuning the mined (i.e., frequently traversed) paths with the low-congestion stations. The prediction and mining results are, finally, exploited, in the selection of the appropriate drone formation that will achieve the delivery mission. Experimental studies based on real-world datasets have confirmed the efficiency of our approach, compared to three graph-based approaches.

Résumé

Recently, drones have emerged as an effective and rapid mean to deliver various packages as services. Exhibiting the same behavior and principles of the service paradigm, drones’ delivery tasks can be abstracted as services, also called DaaS (Drone-as-a-Service). However, drones operate in a dynamic and uncertain environment (flight regulations, weather conditions, charging resources). In addition, the drones’ limited capacity (e.g., flying range, battery capacity, payload) and the delivery constraints (e.g., cost, delivery time) make the delivery missions beyond one drone’s capacity. To cope with the above challenges, and to meet the customers’ and providers’ delivery constraints, composing drone services is a natural solution. We propose a DaaS composition approach that takes advantage of a recent and powerful technology, called knowledge graph. First, we model the drones’ environment (charging stations, skyway segments, drone services) as a heterogeneous information network. Then, we combine knowledge graph embedding with subgraph-aware proximity measure, to arrange drones and charging stations in a low-dimentional vector space. This pre-processing step helped select high-flight capacity drone services, and generate low-congestion delivery paths. Experimental results have proven the approach’s performance and the quality of DaaS compositions, compared to recent state-of-the-art approaches.

Résumé

In modern smart cities, user request handling has evolved beyond conventional software solutions and electronic services. Companies now leverage a diverse range of cutting-edge technologies, such as Artificial Intelligence, the Internet of Things (IoT), Edge/Fog computing, and Cloud computing, to deliver various smart services. These services cover domains such as smart healthcare, smart buildings, smart transportation, smart living, and smart administration. However, a significant challenge hindering the widespread adoption and effective deployment of these services is the limited awareness among citizens of their availability and features, including aspects like pricing models, security policies, and service-level agreements. This issue largely stems from the lack of centralized public repositories where companies can showcase their smart offerings. Consequently, citizens often rely on traditional Web search tools (e.g., search engines, social networks), which limits their ability to fully benefit from the advantages of smart services. In addition, users are often connected to multiple service providers, meaning that their related knowledge (e.g., profiles, service usage, ratings) is distributed across multiple information sources.
To bridge the gap between smart service providers and citizens, and to effectively utilize user and service information across multiple networks, we propose CrossRecSmart, a cross-network recommender system built upon a multiplex network of smart services. This system can be conceptualized as a large-scale, distributed, domain-specific marketplace for available smart services. Our approach integrates Graph Neural Networks (GNNs) with cross-network representation learning to construct this multiplex network. The complex and distributed nature of this task is addressed through a cross-network learning model that facilitates collaboration among multiple smart service providers and enables the aggregation of their knowledge. This is achieved by interconnecting multiple networks through bridge nodes, also referred to as anchor entities. Furthermore, we introduce an algorithm for the cross-network recommendation of top-rated smart services. Comparative analyses with existing recommender systems designed for smart cities demonstrate the superiority of our proposed approach, owing to the concept of anchor links.

Résumé

In recent years, smart mobility networks have experienced significant growth due to the integration of key technologies such as cloud computing, edge intelligence, and the Internet of Things (IoT) into transportation infrastructure. When combined with the principles of service-oriented computing (SOC), various transportation modes now feature intelligent capabilities, including eco-driving assistance, emergency service integration, V2X communication, environmental sensors, in-vehicle infotainment, Over-the-Air (OTA) updates, driver behavior monitoring, and AI-powered assistance. This has led to the emergence of Connected Vehicle as a Service (CVaaS) as a new paradigm for smart vehicles and transportation services.
However, with the increasing complexity of AI-driven features and integration with smart city infrastructure, traditional recommender systems can no longer meet user requirements such as personalized connectivity preferences and eco-friendly route optimization. CVaaS recommendations also inherit challenges from traditional transportation systems, including multi-modal integration (e.g., coordinating smart buses and autonomous vehicles), environmental considerations (e.g., smart parking and dedicated lanes for autonomous cars), uncertain demand, user trust, regulatory compliance, and data privacy concerns.
In this paper, we address the challenges of multi-modal transportation and environmental uncertainty, such as traffic congestion and VaaS demand fluctuations. By modeling Smart Urban Network (SUN) traffic and VaaS demand, we predict congestion patterns and VaaS availability using a Long Short-Term Memory (LSTM) model. Additionally, we apply Spectral hyper-graph Theory to cluster the SUN into closely connected regions, identifying traversed areas for trip requests. These preprocessing steps help eliminate high-congestion zones and low-demand VaaS services, improving trip efficiency.
Finally, inspired by the combinatorial nature of VaaS selection, we propose a Quantum-Inspired variant of the Gravitational Search Algorithm (Q-GSA) to explore and evaluate possible VaaS combinations, ultimately selecting an optimal set of smart transportation services. Experimental comparisons with four benchmark methods confirm the superiority of our approach in terms of efficiency and solution quality.

Résumé

Vehicle-as-a-Service (VaaS) refers to on-demand rides and the sharing/offering of various kinds of transportation facilities (e.g., smart buses, electric vehicles, business jets, autonomous cars), to move from a source to a destination across one or several regions. Coupled with smart transportation services, which are critical for addressing road network issues (e.g., traffic congestion, parking, safety), VaaS is increasingly adopted in smart cities. For example, a user may express his needs in terms of source/destination regions, time and cost constraints, in addition to preferred transportation modes and services (e.g., only connected buses and electric cars). This user profile is evaluated against each available VaaS, as well as the smart urban network (SUN) conditions. However, current solutions do not consider the customization of VaaS combinations and treat user requests as a traditional vehicle routing problem; while in a SUN context, processing VaaS requests not only consists of finding the optimal transportation path that meets user constraints (e.g., time, cost), but also selecting the top-rated combination of available VaaSs (e.g., sequence of smart buses) that meet the user profile (e.g., connectivity, specific facilities, transit restrictions) and trip constraints. To cope with these challenges, the goal of this paper is to develop an orchestration engine that allows the personalized selection and composition of VaaS services. We applied Fuzzy Relational Concept Analysis as an effective data analysis and clustering technique to solve the following issues: (i) representing VaaS-related knowledge and excluding low-quality VaaSs, (ii) clustering of VaaS services based on their common features and covered regions, (iii) clustering of smart urban regions based on their neighborhood and their frontier stations. At a second stage, a search method based on A* algorithm is proposed to find the optimal local paths in each traversed region of the smart urban network. Finally, a bottom-up lattice parsing method is defined to determine the VaaSs with high quality and high coverage capacity. Comparative experiments with state-of-the-art solutions proved the efficiency of our approach.