As urban logistics continue to evolve, there is a growing need for innovative delivery solutions that can meet rising demand while addressing sustainability and cost-effectiveness. To tackle these challenges, ecofriendly transportation methods must be adopted to minimize environmental impact, enhance safety and optimize costs. Drone delivery has emerged as a transformative approach that offers speed, adaptability and reduced emissions compared to conventional logistics. However, its large-scale deployment presents technical and operational challenges that require an interdisciplinary approach. This paper examines these challenges through the integration of control engineering and computer science, identifying how their synergies can enhance efficiency and scalability in drone logistics by developing complementary solutions that address various logistical challenges.

This paper explores the relationship between network data complexity and machine learning (ML) performance, focusing on distributed and real-time IoT systems. Using intrinsic dimensionality (ID) to measure structural complexity, we analyze 20 datasets ($\mathbf{1 0}$for network and IoT systems and$\mathbf{1 0}$for non-network systems) and show that network datasets have lower ID values, indicating simpler structures that correlate with improved ML performance. We identify optimal algorithms for different ID ranges, offering practical guidance for selecting ML models tailored to network data. Additionally, we find that Euclidean distance outperforms Hamming distance for complexity measurement across both categories of data, though its higher computational cost should be considered for real-time IoT applications. These findings provide valuable insights for selecting efficient ML algorithms and metrics, supporting scalable and time-sensitive IoT systems.

Recently the use of Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, has exploded in several domains, leading to potential security issues. As such, estimating the exact position of those eventual malicious drones has become of crucial interest. However, computing an accurate and precise geolocation of these drones, especially in outdoor environments, remains challenging. This paper focuses on outdoor 3-dimensional (3D) drone geolocation techniques based on Radio Frequency (RF) signals. We first present a RF-based 3D drone geolocation dataset, and then apply and compare various geolocation techniques, ranging from geometrical-based to machine learning-based methods. We further propose a new hybrid method blending the two above categories of geolocation techniques, that achieves an average 3D error of the order of 11.7 meters within a search volume of about 520×560×115 m3, significantly below the one achieved with geometrical-based techniques, and with a reduced computational complexity compared to the regular machine-learning based techniques.

In this paper, a new bi-objective model is proposed to deal with the Home Health Care Routing and Scheduling Problem. The considered problem combined the Vehicle Routing Problem with the Personnel scheduling Problem. Two well-known multi-objective Evolutionary algorithms are suggested to solved it with test instances taking from the literature. The obtained results show the effectiveness and the suitability of evolutionary algorithms to solve the problem.

Information system is vital for any company. However, the opening to the outside world makes the computer system more vulnerable to attack. It is essential to protect it. Intrusion Detection System (IDS) is an auditing mechanism that analyzes the traffic system and applications to identify normal use of the system and an intrusion attempt and also it prevent security managers. Despite the advantages of IDS, they suffer from a few problems. The major problem in the field of intrusion detection is the classification problem. Genetic Fuzzy System (GFS) are models capable of integrating accuracy and high comprehensibility in their results. They have been widely employed to solve classification problems. In this paper, we use a new GFS model called Genetic Programming Fuzzy Inference System for Classification (GPFIS-Class). It based on Multi-Gene Genetic Programming (MGGP). This model is not used in the intrusion detection area. We use an efficient feature selection method to eliminate data redundancy and irrelevant features in order to analyze the huge data namely the NSL-KDD data set.