The use of Unmanned Aerial Vehicles (UAVs) has been popular area of research and more in demand especially with the emergence of new technologies that support it, such as the 5G networks. However, with the increasing numbers of UAV in the air as well as their different types of services. It gets more difficult to control and secure their network. In this paper, we discuss the up-to-date research that has been done where the blockchain technology has been used to support multiple applications of the UAV network. We also conclude that a single blockchain ledger is still limited in providing full services to the UAV network. Thus, we address the open issues found in use of a single blockchain scheme and introduce a new scheme and research field that uses the technology of cross-blockchain to support the application of UAV networks as well as the challenges that arise with it.
Despite the growing interest in the field of security, there is yet to be developed like a system that can inspect insider intrusion intent before it happens. However, several systems have been developed to protect critical places from outsider intrusion attacks by applying various authentication mechanisms or restrictive technologies to detect any attack attempts. All this great focus led to overlook the knowledge or access privileges that an employee has which in turn can maliciously abuse their role to harm the place or damage the system. Hence, this research paper aims to produce a reliable and accurate system to recognize any intention to cause damage or investigate any previous incidents. It be achieved through detecting concealed information stored in the brain by measuring Event related-potential signals, analyzing them using continues wavelets transform, and training convolutional neural network.
Nowadays, smartphones and smart devices have become essential for everyday activities including business, communication and entertainment. The spread of these devices along with the rich set of sensors they are equipped with has led to the emergence of Mobile Crowdsensing. Although a large number of participants is essential to make the sensing effective, some obstacles may still prevent the task requester from obtaining reliable information such as having malicious workers who either try to sabotage a task or try to attain multiple tasks completion for increased monetary rewards. This work tackles the problem of malicious nodes by profiling workers over time using data obtained from their continuous interactions with their mobile devices. We can leverage such information to uniquely distinguish each user's interaction from the rest of the users.
A promising solution to the current spectrum crunch is the proposal of visible light communications (VLC), which explores the unregulated visible light spectrum to enable high-speed short range communications, in addition to providing efficient lighting. Although VLC is inherently secure and able to overcome the shortcomings of current RF wireless systems, it suffers from several limitations, including the limited modulation bandwidth of light-emitting diodes. In this respect, several interesting solutions have been proposed in the recent literature to overcome this limitation. In this article, we consider the integration of the newly emerged multiple access scheme rate splitting multiple access (RSMA) with VLC systems. Our results illustrate the flexibility of RSMA in generalizing other multiple access techniques, namely NOMA and SDMA, as well as its superiority in terms of weighted sum rate.
In this paper, an adaptive bit loading algorithm for multicarrier non-orthogonal multiple access (NOMA) systems is proposed. The obtained results show that the bit loading provides NOMA with an additional degree of freedom that allows non uniform spectrum sharing among the users. It is shown that NOMA can outperform orthogonal multiple access (OMA) by 100% in terms of spectral efficiency, for the two-user scenario.
Future wireless networks are evolving towards enabling reliable communications for miniature-sized and resource constrained Internet of things (IoT) devices, imposing stringent requirements on the future sixth-generation (6G) mobile networks. These requirements include low cost, ultra-low latency, improved spectral and energy efficiencies, higher reliability, and significantly enhanced data rate. Emphasizing on the fact that these devices have limited capabilities and might be in inaccessible places, which make battery replacement or recharging a challenging task, energy-efficient solutions should be developed to ensure uninterrupted and seamless wireless communications for power-limited IoT devices. In this paper, we propose a framework for Long Range (LoRa)-enhanced backscatter communications (BackCom) and present the error-rate performance analysis of the system in Additive white Gaussian Noise (AWGN) channels.
With the recent proliferation of CubeSats missions, there is a critical need for space-ready antennas with a desired set of characteristics. In this paper, an S-band wideband, circularly polarized (CP), slot antenna with a co-planar waveguide (CPW) feed is proposed for CubeSat applications. The antenna geometry is presented, and its performance is validated through extensive numerical electromagnetic simulation. The proposed antenna is shown to achieve fractional impedance bandwidth of 28.6?ntered around 3.2 GHz, axial ratio bandwidth (ARBW) of 13.7%, and 6-6.5 dBi gain all while being lower profile and constructed space-proven materials (i.e., Rogers RT/duriod 5880).
The COVID-19 pandemic has caused an increase in healthcare waste (HCW) generation. Mismanagement of waste, a result of archaic, paper-based systems, can pose environmental threats. In this paper, we propose a blockchain based system that tracks and records all steps of HCW management. Our solution records generation of HCW, transportation, and treatment and landfilling.
By 2050, the global population is estimated to increase to about 9.8 billion with over 70% living in the cities. The associated rise in the number of buildings has been considered to be one of the major contributors to energy consumption with its demands being mainly covered by burning fossil fuels. In the fight against climate change, buildings play an essential role as they account for around 40% of global energy consumption. Therefore, researching, designing, and adopting PV systems is essential to help transition towards more sustainable building and infrastructure sectors. A limited number of studies have evaluated the impact of uncertainty in operation patterns on the performance of RE systems. The goal of this research is to present a comprehensive framework to quantify the impact of uncertainty in building operation patterns on the techno-economic performance of PV systems.
In this complex world and rapidly shifting market environment, new product development (NPD) is a significant concern for companies regardless of industry. Notably, because of the continuous change in the demand and customers' needs and technology, the marketplace is considered to be dynamic. For this reason, companies are required to maintain their product and services by keeping them up-to-date and in line with the consumer desire. Even so, nowadays, organizations' willingness to deeply understand their customers is not enough the primary focus must be to address customer needs and requirements and achieve those requirements and exceed them. Consequently, this study aims to contribute and propose an advanced framework for developing a new product by Integrating the Theory of Inventive Problem Solving (TRIZ) into the existing Design for Six Sigma (DFSS) methodology.
