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- Benchmarking a Wide Spectrum of Metaheuristic Techniques for the Radio Network Design ProblemPublication . Mendes, S.P.; Molina, G.; Vega-Rodriguez, M.A.; Gomez-Pulido, J.A.; Saez, Y.; Miranda, G.; Segura, C.; Alba, E.; Isasi, P.; Leon, C.; Sanchez-Perez, J.M.; Mendes, SilvioThe radio network design (RND) is an NP-hard optimization problem which consists of the maximization of the coverage of a given area while minimizing the base station deployment. Solving RND problems efficiently is relevant to many fields of application and has a direct impact in the engineering, telecommunication, scientific, and industrial areas. Numerous works can be found in the literature dealing with the RND problem, although they all suffer from the same shortfall: a noncomparable efficiency. Therefore, the aim of this paper is twofold: first, to offer a reliable RND comparison base reference in order to cover a wide algorithmic spectrum, and, second, to offer a comprehensible insight into accurate comparisons of efficiency, reliability, and swiftness of the different techniques applied to solve the RND problem. In order to achieve the first aim we propose a canonical RND problem formulation driven by two main directives: technology independence and a normalized comparison criterion. Following this, we have included an exhaustive behavior comparison between 14 different techniques. Finally, this paper indicates algorithmic trends and different patterns that can be observed through this analysis.
- The Radio Network Design Optimization ProblemPublication . Mendes, Silvio; Gómez-Pulido, Juan A.; Vega-Rodríguez, Miguel A.; Sánchez-Pérez, Juan M.; Sáez, Yago; Isasi, PedroThe fast growth and merging of communication infrastructures and services turned the planning and design of wireless networks into a very complex subject. The Radio Network Design (RND) is a NP-hard optimization problem which consists on the maximization of the coverage of a given area while minimizing the base station (BS) deployment. Solving such problems resourcefully is relevant for many fields of application and has direct impact in engineering, scientific and industrial areas. Its significance is growing due to cost dropping or profit increase allowance and can additionally be applied to several different business targets. Numerous works can be found in the literature dealing with the RND problem, although they all suffer from the same shortfall: a non-comparable efficiency. Therefore, the aim of this work is threefold: first, to offer a reliable RND benchmark reference covering a wide algorithmic spectrum, second, to offer a grand insight of accurately comparisons of efficiency, reliability and swiftness of the different employed algorithmic models and third, to disclose reproducibility details of the implemented models, including simulations of a hardware co-processing accelerator.
- Driving Behavior Classification Using a ConvLSTMPublication . Pingo, Alberto; Castro, João; Loureiro, Paulo; Mendes, Silvio; Bernardino, Anabela; Miragaia, Rolando; Husyeva, IrynaThis work explores the classification of driving behaviors using a hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks (ConvLSTM). Sensor data are collected from a smartphone application and undergo a preprocessing pipeline, including data normalization, labeling, and feature extraction, to enhance the model’s performance. By capturing temporal and spatial dependencies within driving patterns, the proposed ConvLSTM model effectively differentiates between normal and aggressive driving behaviors. The model is trained and evaluated against traditional stacked LSTM and Bidirectional LSTM (BiLSTM) architectures, demonstrating superior accuracy and robustness. Experimental results confirm that the preprocessing techniques improve classification performance, ensuring high reliability in driving behavior recognition. The novelty of this work lies in a simple data preprocessing methodology combined with the specific application scenario. By enhancing data quality before feeding it into the AI model, we improve classification accuracy and robustness. The proposed framework not only optimizes model performance but also demonstrates practical feasibility, making it a strong candidate for real-world deployment.