Demand for Unmanned Aerial Vehicles (UAV) to fly Beyond the Visual Line of Sight has been significant for the past few years. To do so, the UAV must have a reliable long-range Command and Control (C2) link. The UAV is also expected to avoid any obstacle autonomously with a reliable Detect and Avoid (DAA) mechanism and the ability to detect nearby aircraft since the sky is occupied by manned aircraft. Lastly, the UAV should not endanger others’ life or property. Therefore, this article proposes a hardware and software configuration that utilises the cellular network and DAA system with the deployment of a parachute system to achieve a safe BVLOS UAV operation. The quality of cellular networks at multiple altitudes, the C2 link performance, the ability to perform long-range flights, and the parachute system’s reliability were investigated. The results indicate the effectiveness of the developed systems as the starting point to allow BVLOS drone operations.

This paper proposes an effective global path planning technique for cellular-connected UAVs to enhance the reliability of unmanned aerial vehicles’ (UAVs) flights operating beyond the visual line of sight (BVLOS). Cellular networks are considered one of the leading enabler technologies to provide a ubiquitous and reliable communication link for UAVs. First, this paper investigates a reliable aerial zone based on an extensive aerial drive test in a 4G network within a suburban environment. Then, the path planning problem for the cellular-connected UAVs is formulated under communication link reliability and power consumption constraints. To provide a realistic optimization solution, all constraints of the optimization problem are defined based on real-world scenarios; in addition, the presence of static obstacles and no-fly zones is considered in the path planning problem. Two powerful intelligent optimization algorithms, the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, are used to solve the defined optimization problem. Moreover, a combination of both algorithms, referred to as PSO-GA, is used to overcome the inherent shortcomings of the algorithms. The performances of the algorithms are compared under different scenarios in simulation environments. According to the statistical analysis of the aerial drive test, existing 4G base stations are able to provide reliable aerial coverage up to a radius of 500 m and a height of 85 m. The statistical analysis of the optimization results shows that PSO-GA is a more stable and effective algorithm to rapidly converge to a feasible solution for UAV path planning problems, with a far faster execution time compared with PSO and GA, about two times. To validate the performance of the proposed solution, the simulation results are compared with the real-world aerial drive test results. The results comparison proves the effectiveness of the proposed path planning method in suburban environments with 4G coverage. The proposed method can be extended by identifying the aerial link reliability of 5G networks to solve the UAV global path planning problem in the current 5G deployment.

The unmanned aerial vehicle (UAV) industry is moving toward beyond visual line of sight (BVLOS) operations to unlock future internet of drones applications, including unmanned environmental monitoring and long-range delivery services. A reliable and ubiquitous mobile communication link plays a vital role in ensuring flight safety. Cellular networks are considered one of the main enablers of BVLOS operations. However, the existing cellular networks are designed and optimized for terrestrial use cases. To investigate the reliability of provided aerial coverage by the terrestrial cellular base stations (BSs), this article proposes six machine learning-based models to predict reference signal received power (RSRP) and reference signal received quality (RSRQ) based on the multiple linear regression, polynomial, and logarithmic methods. In this regard, first, a UAV-to-BS measurement campaign was conducted in a 4G LTE network within a suburban environment. Then, the aerial coverage was statistically analyzed and the prediction methods were developed as a function of distance and elevation angle. The results reveal the capability of terrestrial BSs in providing aerial coverage under some circumstances, which mainly depends on the distance between the UAV and BS and flight height. The performance evaluation shows that the proposed RSRP and RSRQ models achieved RMSE of 4.37 dBm and 2.71 dB for testing samples, respectively.

Personalized remote monitoring healthcare devices have begun emerging in the industry over the years, slowly setting a new standard for long term monitoring services. In this study, the researchers are addressing epilepsy. This neurological disorder hinders mobility freedom and may affect humans of any age, often starting in childhood or people over 60 years old. Diagnosing epileptic patients still stands as a challenge due to similar symptoms shown by other medical conditions such as migraines, fainting and panic attacks, often unable to be ruled as epilepsy without detecting seizure. Electroencephalogram (EEG) has proven to be the most helpful procedure for diagnosis of epilepsy. Interictal epileptiform discharges (IED), detected in EEG aids in differentiating epileptic and other nonepileptic episodes. Currently, available EEG devices are often bulky and restricted to be in use of clinical environments, limiting treatment process among epilepsy patients. The aim of this research is to present a personalized mobile EEG device for epilepsy monitoring and management. A customizable dry electrode EEG headset with 16-channel was assembled and configured. A server and an Android based mobile application were also developed to aid in remote monitoring regardless of location and available network. The device was tested and validated for signal reliability by a neurologist at the Neurology Lab of Canselor Tuanku Muhriz Hospital. The proposed device has potential to be solution for numerous limitations in current epilepsy treatment decision and may even be vital in addressing the drawback of recent pandemic. The outcome of the study is expected to boost and improve neurological research and clinical diagnosis in patient monitoring.

3D printing technology has breakthrough many long pending medical challenges. In this study the researchers are addressing epilepsy, a disability that limits mobility freedom, that can appear at any age but usually start in childhood or people over 60 years old. Diagnosing epilepsy quickly can be challenging due to the fact other conditions such as migraines, panic attacks and fainting possess similar symptoms. Regularly, it cannot be confirmed until seizure is detected. Electroencephalogram (EEG) is the most common test used to diagnose epilepsy. Epileptiform brain activity presence is used as a change seen on an EEG recording among epilepsy patients. The availability of EEG device for epilepsy diagnosis is currently limited to clinical settings which restricts the treatment process. The objective of this study is to offer an option for personalized home-based EEG device for epilepsy diagnosis and monitoring. A customized 3D printed EEG headset with 8 channel dry electrodes device is assembled and configured. The customization is managed by offering three different printable headset sizes with material selection options. The device is supported with an OpenBCI application connected through Bluetooth for recording and further processing options. The proposed device has potential to address number of limitations including the recent pandemic’s challenge where hospitalization option is restricted. The outcome of the research is expected to bring a new breakthrough in brain activity related research and clinical diagnosis in patient monitoring. The customization option of this device is also expected to offer a new trend in managing treatment compliance and adherence in clinical practice.

Unmanned aerial vehicles (UAVs) are used for commercial, medical, public safety, and scientific research purposes in various countries.

Telecommunications among unmanned aerial vehicles (UAVs) have emerged recently due to rapid improvements in wireless technology, low-cost equipment, advancement in networking communication techniques, and demand from various industries that seek to leverage aerial data to improve their business and operations. As such, UAVs have started to become extremely prevalent for a variety of civilian, commercial, and military uses over the past few years. UAVs form a flying ad hoc network (FANET) as they communicate and collaborate wirelessly. FANETs may be utilized to quickly complete complex operations. FANETs are frequently deployed in three dimensions, with a mobility model determined by the work they are to do, and hence differ between vehicular ad hoc networks (VANETs) and mobile ad hoc networks (MANETs) in terms of features and attributes. Furthermore, different flight constraints and the high dynamic topology of FANETs make the design of routing protocols difficult. This paper presents a comprehensive review covering the UAV network, the several communication links, the routing protocols, the mobility models, the important research issues, and simulation software dedicated to FANETs. A topology-based routing protocol specialized to FANETs is discussed in-depth, with detailed categorization, descriptions, and qualitatively compared analyses. In addition, the paper demonstrates open research topics and future challenge issues that need to be resolved by the researchers, before UAVs communications are expected to become a reality and practical in the industry.

Efficient data collection in wireless sensor networks (WSNs) is crucial. While traditional approaches rely on stationary data sinks, the use of mobile sinks, like unmanned aerial vehicles (UAVs), has shown promise in enhancing data-gathering capabilities. However, existing methods often overlook the importance of trust, leaving the network susceptible to malicious or faulty nodes. To address this issue, we propose a novel trust-aware data-gathering algorithm, Variable Length Multi-Objective Whale Optimization Data Gathering (VLMOWO-DG). Our algorithm simultaneously optimizes energy consumption, delay, and trust while employing mobile sinks to collect data from sensor nodes. By considering trust as a key performance metric, we improve data reliability and security. The VLMOWO-DG algorithm introduces flexibility through variable-length solution representation and application-oriented operators, allowing for efficient exploration of the solution space. Simulation results demonstrate that our proposed algorithm significantly outperforms established benchmarks, NSGA-II and NSGA-III, in terms of domination and hypervolume. This leads to a remarkable improvement of 200% in data gathering performance.