This study addresses the challenge of robust human pose recognition in complex indoor environments by proposing a system that generalizes to unseen user locations using only Angle of Arrival (AoA) data from Bluetooth Low Energy (BLE) 5.1 devices. A comparative evaluation was performed, assessing a Random Forest model alongside three neural network architectures— CNN, LSTM, and hybrid models—for identifying four physical states: standing, sitting, lying down, and walking. Data were collected in a real indoor environment using smart Personal Protective Equipment (PPE) equipped with BLE transmitters placed on a helmet, upper and lower shirt regions, and a boot. The preprocessing pipeline included filtering, interpolation, feature extraction, and application of the Fast Fourier Transform (FFT). Models were evaluated under two scenarios: (i) known positions (used in training) and (ii) unknown positions (never seen before). The best overall performance on unknown positions was achieved by theAttention-based BiLSTM–CNN,reaching an accuracy ofA= 94.89% and a macro averaged F1-score ofF1= 95.59%, demonstrating strong generalization capabilities. In the scenario with known positions, the highest performance was obtained by theRandom Forest, withA= 99.95% andF1= 99.95%, followed by theDeep CNN,withA= 97.27% andF1= 97.28%, closely followed by theAttention-based BiLSTM–CNN,which also showed robust classification accuracy across all posture classes. The analysis confirmed that using four wearable devices led to the highest performance, and sensor combinations significantly affected accuracy. The study also provides a publicly available dataset to support reproducibility and benchmarking.

This work aims at improving indoor positioning systems (IPS) by integrating multiple radio frequency techniques, namely received signal strength indiction (RSSI), Angle of Arrival (AoA), and a combination of both, within the bluetooth low energy (BLE) 5.1 framework. While AoA stands out for its precision, low energy consumption, and cost-effectiveness, RSSI is characterized by its simplicity and widespread availability. By resorting to a database of real RSSI and AoA measurements from a BLE 5.1 target node in a$14\times 8$-m environment, our work employs the Kalman filter (KF) to improve the accuracy of multilateration, AoA combined with RSSI, and AoA-only algorithms. Moreover, we consider one more step in our IPS where the aforementioned KF-filtered outputs are then fused through a track fusion model. Results demonstrate that the proposed scheme, which we refer to as angle-RSSI fusion localization (ARFL), significantly improves localization accuracy compared to other techniques. In particular, it reduces up to 81.61% in the average position error when compared to multilateration with KF. This advanced IPS offers a cost-effective and precise solution suitable for various applications in industries, such as healthcare, commerce, and logistics.

Age of Information (AoI) has turned to be an outright metric to evaluate information freshness in Internet of Things (IoT) networks. In this work, we evaluate the average AoI of an uplink IoT monitoring network where multiple end devices have independent status updates to transmit to a common access point (AP). More specifically, we propose the so-called successive interference cancellation (SIC)-aided age-independent random access (AIRA-SIC) scheme, where the AP performs SIC aiming at recovering collisions of multiple packets that are transmitted simultaneously by different devices in a slotted ALOHA fashion. Our results show that the proposed scheme not only achieves considerably lower AoI levels than the standard AIRA but also outperforms a recently proposed threshold-based age-dependent random access (ADRA) scheme, where the channel access probability (CAP) of each device is dynamically adapted based on each devices’ AoI. Finally, we provide some insights on the optimal CAP that minimizes the network average AoI, as well on the influence of imperfect channel state information (CSI) in the performance of the proposed scheme.

IoT applications are changing our daily lives. These innovative applications are supported by new communication technologies and protocols. Particularly, the information-centric network (ICN) paradigm is well suited for many IoT application scenarios that involve large-scale wireless sensor networks (WSNs). Even though the ICN approach can significantly reduce the network traffic by optimizing the process of information recovery from network nodes, it is also possible to apply data aggregation strategies. This paper proposes an unsupervised machine learning-based data aggregation strategy for multi-hop information-centric WSNs. The results show that the proposed algorithm can significantly reduce the ICN data traffic while having reduced information degradation.

The performance and complexity of turbo decoding using rate k/n constituent codes are investigated. The conventional, minimal and sectionalized trellis modules of the constituent convolutional codes are utilized. The performance metric is the bit error rate (BER), while complexity is analyzed based on the number of multiplications, summations and comparisons required by the max-log-MAP decoding algorithm. Our results show that the performance depends on how the systematic bits are grouped in a trellis module. The best performance is achieved when the k systematic bits are grouped together in the same section of the module, so that the log-likelihood ratio (LLR) of the k-bit vector is calculated at once. This is a characteristic of the conventional trellis module and of some of the sectionalizations of the minimal trellis module. Moreover, we show that it is possible to considerably reduce the decoding complexity with respect to the conventional trellis if a particular sectionalization of the minimal trellis module is utilized. In some cases, this sectionalization is found within the best performing group, while in some other cases a small performance loss can be traded off for a large complexity reduction.

This paper describes the design and implementation of a hardware for parallel pairwise alignment, implemented in a FPGA device. This system is aimed at aligning pairs of proteins, using a dynamic programming algorithm. The alignment is done in parallel thanks to a pipelined approach. All functional blocks are described in detail. Experiments to evaluate the performance of the system were done for pairs of proteins with up to 2000 amino acids-long. Hardalign was compared with a similar algorithm implemented in software and running in a PC, resulting in a 1:5 speed-up ratio