Wireless Sensor Networks are increasingly deployed in mission-critical scenarios where resilience and energy efficiency are paramount. This paper presents a hybrid architecture combining Bluetooth Low Energy (BLE) and Long Range (LoRa) technologies to enhance both robustness and energy-aware performance in adversarial environments characterized by reactive jamming attacks. A comprehensive experimental testbed was developed, integrating BLE and LoRa nodes, a dual-protocol gateway, and a reactive jammer emulator. We introduce an adaptive clustering algorithm that performs energy-aware role assignment and jamming mitigation based on signal anomaly detection and multi-metric routing. To validate its effectiveness, we conducted an extensive time-series analysis on energy consumption, retransmission rates, and signal resilience under both mitigated and non-mitigated conditions across BLE-only, LoRa-only, and hybrid BLE-LoRa networks. The results show protocol-dependent performance trade-offs under the proposed mitigation algorithm. While LoRa-only and hybrid BLE–LoRa networks exhibit consistent reductions in energy consumption, retransmissions, and variability, the BLE-only configuration demonstrates improved resilience at the cost of a moderate increase in energy usage and retransmission activity due to clustering and control overhead. Notably, the BLE-LoRa architecture balances delivery assurance and energy efficiency while maintaining communication even under interference. Furthermore, we provide statistical modeling and hypothesis testing confirming the mitigation algorithm’s significant impact. These findings offer a critical empirical contribution to the design of resilient and energy-aware heterogeneous WSNs and demonstrate the viability of real-time adaptive mitigation strategies for emerging smart environments.

Control algorithms must deal with model uncertainties and disturbances, making them perfect for real-world applications. In addition, increased computational power in the industrial field allows the implementation of advanced control algorithms such as nonlinear model predictive control (NMPC), which is an optimal control scheme that includes system and control constraints imposed by robot dynamics and the environment. Nevertheless, modeling the robot and its environment is a complicated task due to high nonlinearities, such as model uncertainties in the form of complex unmodeled dynamics, varying payloads, and parameter mismatch, leading to fast degradation of NMPC; therefore, online adaptation laws that improve the performance even in unknown environments are needed. Due to the facts presented before, this work combines the universal approximation of RBFNN and the optimality offered by NMPC in a unified adaptive framework that guarantees good performance even under uncertainties and unmodeled dynamics. The proposed framework is tested in simulation using a 2-link planar robotic arm (SCARA Bosch SR-800), where optimization techniques were used to identify the robot's dynamics. Finally, a comparison of the proposed architecture with a baseline nominal NMPC is made with particular attention to trajectory tracking performance, demonstrating the reduction of the tracking error over non-adaptive NMPC.

Virtual Reality (VR) has the capacity to be used in cognitive rehabilitation interventions for diagnostic and training purposes. This technology allows the development of proposals that traditionally have been only implemented using physical elements that imply greater resources and a lesser degree of automation. This work presents an immersive virtual reality (IVR) application (the Cupboard task) for the evaluation of memory in a more ecological way and based on an activity of daily living (ADL). To appraise its construct validity, we have carried out a comparative study with a traditional method of memory assessment (method of loci). To check for any association between performance and age, performance with years of education, and reaction time with age, the Pearson's correlation was used. One-way ANOVA was used to check for differences in performance by gender. We also performed a reliability analysis with a two way mixed effects model where people effects are random and measures effects are fixed. Therefore, intra-class correlation coefficient with absolute agreement was reckoned to assess the consistency or concordance of the measures made by both the method of loci and the cupboard IVR task. Both tasks were evaluated on a sample of 22 healthy participants who voluntarily took part in the experiment. The results obtained showed a high degree of concordance between both memory performance measures, which assumes good clinical relevance. In addition, other age-related effects were found, common to memory assessment tasks. This work showed that it is possible to use an IVR application to successfully assess everyday memory. We have also demonstrated the potential of IVR to develop valid tests that assess memory functions reliably and efficiently and within ecologically valid contexts. The results obtained open the door to its use in clinical settings for cognitive training (and promoting cognitive health) of patients with mild cognitive impairment (MCI), severe cognitive impairment (SCI) such as Alzheimer or Dementia, etc., with full guarantees of application, although it must first be validated through a randomized control trial (RCT). The degree of usability of the Cupboard task was very high according to the test carried out by the participants.