Automated Uncrewed Aerial Vehicle (UAV) landing is crucial for autonomous UAV services such as monitoring, surveying, and package delivery. It involves detecting landing targets, perceiving obstacles, planning collision-free paths, and controlling UAV movements for safe landing. Failures can lead to significant losses, necessitating rigorous simulation-based testing for safety. Traditional offline testing methods, limited to static environments and predefined trajectories, may miss violation cases caused by dynamic objects like people and animals. Conversely, online testing methods require extensive training time, which is impractical with limited budgets. To address these issues, we introduce GARL, a framework combining a genetic algorithm (GA) and reinforcement learning (RL) for efficient generation of diverse and real landing system failures within a practical budget. GARL employs GA for exploring various environment setups offline, reducing the complexity of RL's online testing in simulating challenging landing scenarios. Our approach outperforms existing methods by up to 18.35% in violation rate and 58% in diversity metric. We validate most discovered violation types with real-world UAV tests, pioneering the integration of offline and online testing strategies for autonomous systems. This method opens new research directions for online testing, with our code and supplementary material available at https://github.com/lfeng0722/drone_testig/.

Fifth-generation (5G) wireless networks are likely to offer high data rates, increased reliability, and low delay for mobile, personal, and local area networks. Along with the rapid growth of smart wireless sensing and communication technologies, data traffic has increased significantly and existing 5G networks are not able to fully support future massive data traffic for services, storage, and processing. To meet the challenges that are ahead, both research communities and industry are exploring the sixth generation (6G) Terahertz-based wireless network that is expected to be offered to industrial users in just ten years. Gaining knowledge and understanding of the different challenges and facets of 6G is crucial in meeting the requirements of future communication and addressing evolving quality of service (QoS) demands. This survey provides a comprehensive examination of specifications, requirements, applications, and enabling technologies related to 6G. It covers disruptive and innovative, integration of 6G with advanced architectures and networks such as software-defined networks (SDN), network functions virtualization (NFV), Cloud/Fog computing, and Artificial Intelligence (AI) oriented technologies. The survey also addresses privacy and security concerns and provides potential futuristic use cases such as virtual reality, smart healthcare , and Industry 5.0 . Furthermore, it identifies the current challenges and outlines future research directions to facilitate the deployment of 6G networks.

This paper proposes an Emergency Battery Service (EBS) for drones in which an EBS drone flies to a drone in the field with a depleted battery and transfers a fresh battery to the exhausted drone. The authors present a unique battery transfer mechanism and drone localization that uses the Cross Marker Position (CMP) method. The main challenges include a stable and balanced transfer that precisely localizes the receiver drone. The proposed EBS drone mitigates the effects of downwash due to the vertical proximity between the drones by implementing diagonal alignment with the receiver, reducing the distance to 0.5 m between the two drones. CFD analysis shows that diagonal instead of perpendicular alignment minimizes turbulence, and the authors verify the actual system for change in output airflow and thrust measurements. The CMP marker-based localization method enables position lock for the EBS drone with up to 0.9 cm accuracy. The performance of the transfer mechanism is validated experimentally by successful mid-air transfer in 5 seconds, where the EBS drone is within 0.5 m vertical distance from the receiver drone, wherein 4m/s turbulence does not affect the transfer process.

Cooperative UAV transport operations in GPS-denied environments pose significant challenges in localization, coordination, and payload stability. This paper introduces a vision-based Leader-Follower drone system using MAVROS and depth cameras for real-time pose estimation and control. The leader transmits pose and velocity updates to the follower, ensuring synchronized movements. The system maintained a 50 Hz update rate, achieving 28 FPS, 12 ms latency, and 1.2 cm position error on a straight path. The 3-DEE system effectively managed payload-induced attitude variations with low vibration levels and improved speed accuracy. These results confirm the system's robustness for precise localization and stable cooperative UAV transport.

Medical (health) informatics broadly encompasses the cognitive, information processing, and communication tasks inherent in medical practice, education, and research, with a particular emphasis on the development of computer-based patient records, decision support systems, information standards, data aggregation systems, communication systems, and educational programs for patients and health providers. In addition, this rapidly growing area is confronted with developing technological solutions sensitive to special populations' specific requirements, i.e.,Preventive, Assistive, and Medical Children Health Informatics. First, children have distinct physiology, come from diverse backgrounds, and are disproportionately affected by illnesses. Thus, children are not little adults, as a famous adage among child health experts. These distinctions have been extensively discussed and are frequently called the four D's. Second, children depend on their parents and extended relatives to access necessary health care. Thus, plans must include gathering and distributing information to many patients. Third, childhood is defined by a developmental trajectory marked by fast change and the emergence of capacities for health information utilization. Fourth, children's health is defined by distinct epidemiology characterized by fewer significant chronic diseases, a high prevalence of acute illnesses, and reliance on preventative interventions. Finally, since children are the poorest and most varied in our society, they exhibit distinct demographic trends.

The need to develop a low-power, low-cost nitrate, phosphate, and pH sensor and sensing system is essential for monitoring water quality in real time. A novel interdigital sensor has been fabricated and characterized for temperature, nitrate, phosphate, and pH detection in water. The sensor is fabricated using the 3-D printing technique, where the electrodes are formed using multiwalled carbon nanotubes, and the substrate is developed using polydimethylsiloxane. The sensor is characterized by electrochemical impedance spectroscopy to determine various temperatures, pH levels, nitrate, and phosphate concentrations. Experimental outcomes prove that the developed sensor can distinguish nitrate and phosphate concentrations ranging from 0.1 to 30 ppm, pH values from 1.71 to 12.59, temperature from 0 to 45 °C. The sensitivity for temperature, nitrate, phosphate, and pH level of the sensor are 1.1974$\Omega /^{\circ }\text{C}$, 1.9396$\Omega /$ppm, 0.8839$\Omega /$ppm, and 1.0295$\Omega $, respectively. A location-independent portable smart sensing system with LoRa connectivity is also developed to surveil water quality and get feedback from the experts. A machine learning algorithm trains the Arduino-based system and determines temperature, nitrate and phosphate concentrations, and pH level in real water samples. All the outcomes are compared with the standard method for validation. The sensor and the sensing system’s performances are highly stable, reliable, and repeatable to be a part of a smart sensing network for continuous water quality monitoring.

Node scheduling provides an effective way to prolong the network lifetime of Internet-of-Things (IoT) networks comprising of energy-constrained sensor nodes. Barrier scheduling is a special type of node scheduling scheme that targets IoT-based surveillance applications. An efficient barrier scheduling scheme must address the key Quality-of-Service (QoS) requirements of smart surveillance applications, such as coverage, connectivity, and energy efficiency. Moreover, such a scheme must be capable of dynamically adapting its execution strategy in the event of node failures caused due to faults arising out of unexpected battery depletion. This article proposes a fault-tolerant barrier scheduling scheme that satisfies the key QoS requirements of surveillance applications in the event of such faults. The approach is based on a novel fully weighted dynamic graph model. This article suggests two novel heuristics to guarantee fault tolerance and recovery. Extensive simulation studies are conducted to evaluate and compare the performance and effectiveness of this scheme with other such approaches.

Recognizing movements during sleep is crucial for the monitoring of patients with sleep disorders, and the utilization of ultra-wideband (UWB) radar for the classification of human sleep postures has not been explored widely. This study investigates the performance of an off-the-shelf single antenna UWB in a novel application of sleep postural transition (SPT) recognition. The proposed Multi-View Learning, entitled SleepPoseNet or SPN, with time series data augmentation aims to classify four standard SPTs. SPN exhibits an ability to capture both time and frequency features, including the movement and direction of sleeping positions. The data recorded from 38 volunteers displayed that SPN with a mean accuracy of 73.7 ±0.8 % significantly outperformed the mean accuracy of 59.9 ±0.7 % obtained from deep convolution neural network (DCNN) in recent state-of-the-art work on human activity recognition using UWB. Apart from UWB system, SPN with the data augmentation can ultimately be adopted to learn and classify time series data in various applications.

Due to the variation in factors surrounding humans, the physiological impact of stress is reported to be different for each individual. Thus, an efficient stress monitoring system needs to assess both the physiological and psychological impact of stress on individual basis and translate these assessments into an accurate quantitative metric that is of value to the individual. Therefore, this study proposed a logistic regression based model that integrates data from psychological Stress Response Inventory, biochemical (salivary cortisol), and physiological (HRV measures) domains via a principle of triangulation for achieving high reliability and consistency during stress assessment. With the proposed model, a mental stress index (MSI) based on the correlation between salivary cortisol and HRV time-/frequency-domain features were established. A total of 30 college students were recruited to verify the feasibility of proposed method by identifying targeted stressful event. The obtained results reveal that MSI values were sensitive to acute stress, and could predict the association level of normal individual to a stress group with approximately 97% accuracy. Findings from this study could provide potential insight on self-tracking and training of individual's stress with adoption of wearable sensor system in a dynamic setting.

Any proposal to provide security for implantable medical devices (IMDs), such as cardiac pacemakers and defibrillators, has to achieve a trade-off between security and accessibility for doctors to gain access to an IMD, especially in an emergency scenario. In this paper, we propose a finger-to-heart (F2H) IMD authentication scheme to address this trade-off between security and accessibility. This scheme utilizes a patient's fingerprint to perform authentication for gaining access to the IMD. Doctors can gain access to the IMD and perform emergency treatment by scanning the patient's finger tip instead of asking the patient for passwords/security tokens, thereby, achieving the necessary trade-off. In the scheme, an improved minutia-cylinder-code-based fingerprint authentication algorithm is proposed for the IMD by reducing the length of each feature vector and the number of query feature vectors. Experimental results show that the improved fingerprint authentication algorithm significantly reduces both the size of messages in transmission and computational overheads in the device, and thus, can be utilized to secure the IMD. Compared to existing electrocardiogram signal-based security schemes, the F2H scheme does not require the IMD to capture or process biometric traits in every access attempt since a fingerprint template is generated and stored in the IMD beforehand. As a result, the scarce resources in the IMD are conserved, making the scheme sustainable as well as energy efficient.

Monitoring the nitrate concentration in the field is an excellent ability for a water-monitoring study. We report an interdigital FR4-based capacitive sensor, which is characterized for nitrate concentration. The concentration range of nitrate is 0–40 ppm (mg/L). Different unknown samples were measured and validated with standard UV-Spectrometry. A smart sensing node has been developed which can collect water from a lake, stream, or river, measure the instantaneous nitrate concentration, and transfer the data through the gateway to a user-defined cloud server. The system is completely autonomous and solar powered, robust, and trialed in the field successfully. A simple moving-average algorithm is used to smooth the collected data in the cloud side. The LoRa protocol and WiFi protocol are compared in terms of power consumption. The proposed system is trialed in the field continuously and the result validated with standard UV-Spectrometry. The developed smart system can be easily deployable and friendly to use, and offers new possibilities for both spatial and temporal analysis for nitrate concentration.

This paper presents a new single inductor Dual Input-Dual Output dc-dc converter for a standalone hybrid energy system. The converter has two unidirectional ports, one bidirectional port for storage element and one output port. The converter operates in dual input-dual output mode, dual input-single output mode and single input-single output mode. Both the input sources can power up the load individually and simultaneously. A single inductor is used with five independently controlled switches which make the size compact and easy to control. The analytical results in Continuous Conduction mode and simulation results in MATLAB/Simulink environment for different operating conditions are shown and are experimentally validated.

One of the critical concerns in wireless sensor networks (WSNs) is the continuous maintenance of sensing coverage. Many particular applications, such as battlefield intrusion detection and object tracking, require a full-coverage at any time, which is typically resolved by adding redundant sensor nodes. With abundant energy, previous studies suggested that the network lifetime can be maximized while maintaining full coverage through organizing sensor nodes into a maximum number of disjoint sets and alternately turning them on. Since the power of sensor nodes is unevenly consumed over time, and early failure of sensor nodes leads to coverage loss, WSNs require dynamic coverage maintenance. Thus, the task of permanently sustaining full coverage is particularly formulated as a hybrid of disjoint set covers and dynamic-coverage-maintenance problems, and both have been proven to be nondeterministic polynomial-complete. In this paper, a hybrid memetic framework for coverage optimization (Hy-MFCO) is presented to cope with the hybrid problem using two major components: 1) a memetic algorithm (MA)-based scheduling strategy and 2) a heuristic recursive algorithm (HRA). First, the MA-based scheduling strategy adopts a dynamic chromosome structure to create disjoint sets, and then the HRA is utilized to compensate the loss of coverage by awaking some of the hibernated nodes in local regions when a disjoint set fails to maintain full coverage. The results obtained from real-world experiments using a WSN test-bed and computer simulations indicate that the proposed Hy-MFCO is able to maximize sensing coverage while achieving energy efficiency at the same time. Moreover, the results also show that the Hy-MFCO significantly outperforms the existing methods with respect to coverage preservation and energy efficiency.

In this study, we reported integration of Wireless Sensor Network (WSN) based systems for monitoring elderly health perception and daily activity behaviour recognition. The amalgamation of the systems helps in deducing the elderly wellness indices, thereby informing the health care providers about the tendency of unusual behaviour through telecare system. The developed sensing system along with the intelligent software is low cost, flexible, robust and easy to install and monitor elderly living alone.