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Description
Publications
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2025Haithem Mezni, Mokhtar Sellami, Hela Elmannai, Reem Alkanhel
Federated resource prediction in UAV networks for efficient composition of drone delivery services
Computer Networks, 2025
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.
Haithem Mezni, Mokhtar Sellami, Hela Elmannai, Reem ElkanhelDaas composition: enhancing UAV delivery services via LSTM-based resource prediction and flight patterns mining
Computing, 2025
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.
Mokhtar Sellami, Haithem Mezni, Hela Elmannai, Reem AlkanhelDrone-as-a-Service: proximity-aware composition of UAV-based delivery services
Cluster Computing, 2025
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.
Haithem Mezni, Hiba Yahyaoui, Hela Elmannai, Reem AlkanhelPersonalized service recommendation in smart mobility networks
Cluster Computing, 2025
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.
Zaki Brahmi, Haithem Mezni, Hela Elmannai, Reem AlkanhelMulti-modal VaaS Selection in Smart Mobility Networks via Spectral Hyper-graph Clustering and Quantum-driven Optimization.
Concurrency and Computation: Practice and Experience, 2025
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.Haithem Mezni, Mokhtar Sellami, Abeer Algarni, Hela ElmannaiCrossRecSmart: A Cross-Network Anchor-Based Representation Learning for the Recommendation of Smart Services.
Journal of Supercomputing, 2025
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. -
2023Mouhamed Gaith Ayadi, Haithem Mezni, Rana Alnashwan, Hela Elmannai
Effective healthcare service recommendation with network representation learning: A recursive neural network approach
Data & Knowledge Engineering, 2023
Résumé
Recently, recommender systems have been combined with healthcare systems to recommend needed healthcare items for both patients and medical staff. By monitoring the patients’ states, healthcare services and their consumed smart medical objects can be recommended to a medical team according to the patient’s critical situation and requirements. However, a common drawback of the few existing solutions lies in the limited modeling of the healthcare information network. In addition, current solutions do not consider the typed nature of healthcare items. Moreover, existing healthcare recommender systems lack flexibility, and none of them offers re-configurable healthcare workflows to medical staff. In this paper, we take advantage of collaborative filtering and representation learning principles, by proposing a method for the recommendation of healthcare services. These latter follow a predefined execution pattern, i.e. treatment/medication workflow, that is determined by our framework depending on the patient’s state. To achieve this goal, we model the healthcare information network as a knowledge graph. This latter, based on an incremental learning method, is then transformed into a cuboid space to facilitate its processing. That is by learning latent representations of its content (e.g., smart objects, healthcare services, patients symptoms, etc.). Finally, a collaborative recommendation method is defined to select the high-quality healthcare services that will be composed and executed according to a determined workflow model. Experimental results have proven the efficiency of our solution in terms of recommended services’ quality.
Maha Drissi, Wadii Boulila, Haithem Mezni, Mokhtar Sellami, Safa Ben Atitallah, Nouf AlharbiAn Evidence Theory Based Embedding Model for the Management of Smart Water Environments
Sensors, 2023
Résumé
Having access to safe water and using it properly is crucial for human well-being, sustainable development, and environmental conservation. Nonetheless, the increasing disparity between human demands and natural freshwater resources is causing water scarcity, negatively impacting agricultural and industrial efficiency, and giving rise to numerous social and economic issues. Understanding and managing the causes of water scarcity and water quality degradation are essential steps toward more sustainable water management and use. In this context, continuous Internet of Things (IoT)-based water measurements are becoming increasingly crucial in environmental monitoring. However, these measurements are plagued by uncertainty issues that, if not handled correctly, can introduce bias and inaccuracy into our analysis, decision-making processes, and results. To cope with uncertainty issues related to sensed water data, we propose combining network representation learning with uncertainty handling methods to ensure rigorous and efficient modeling management of water resources. The proposed approach involves accounting for uncertainties in the water information system by leveraging probabilistic techniques and network representation learning. It creates a probabilistic embedding of the network, enabling the classification of uncertain representations of water information entities, and applies evidence theory to enable decision making that is aware of uncertainties, ultimately choosing appropriate management strategies for affected water areas.
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2022Haithem Mezni, Maha Drissi, Wadii Boulila, Safa Ben Atitallah, Mokhtar Sellami, Nouf Alharbi
SmartWater: A Service-Oriented and Sensor Cloud-Based Framework for Smart Monitoring of Water Environments
Sensor Networks, 2022
Résumé
Due to the sharp increase in global industrial production, as well as the over-exploitation of land and sea resources, the quality of drinking water has deteriorated considerably. Furthermore, nowadays, many water supply systems serving growing human populations suffer from shortages since many rivers, lakes, and aquifers are drying up because of global climate change. To cope with these serious threats, smart water management systems are in great demand to ensure vigorous control of the quality and quantity of drinking water. Indeed, water monitoring is essential today since it allows to ensure the real-time control of water quality indicators and the appropriate management of resources in cities to provide an adequate water supply to citizens. In this context, a novel IoT-based framework is proposed to support smart water monitoring and management. The proposed framework, named SmartWater, combines cutting-edge technologies in the field of sensor clouds, deep learning, knowledge reasoning, and data processing and analytics. First, knowledge graphs are exploited to model the water network in a semantic and multi-relational manner. Then, incremental network embedding is performed to learn rich representations of water entities, in particular the affected water zones. Finally, a decision mechanism is defined to generate a water management plan depending on the water zones’ current states. A real-world dataset has been used in this study to experimentally validate the major features of the proposed smart water monitoring framework.
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