Search

Unmanned Aerial Vehicles (UAVs) or drones introduced for military applications are gaining popularity in several other fields as well such as security and surveillance, due to their ability to perform repetitive and tedious tasks in hazardous environments. Their increased demand created the requirement for enabling the UAVs to traverse independently through the Three Dimensional (3D) flight environment consisting of various obstacles which have been efficiently addressed by metaheuristics in past literature. However, not a single optimization algorithms can solve all kind of optimization problem effectively. Therefore, there is dire need to integrate metaheuristic for general acceptability. To address this issue, in this paper, a novel reinforcement learning controlled Grey Wolf Optimisation-Archimedes Optimisation Algorithm (QGA) has been exhaustively introduced and exhaustively validated firstly on 22 benchmark functions and then, utilized to obtain the optimum flyable path without collision for UAVs in three dimensional environment. The performance of the developed QGA has been compared against the various metaheuristics. The simulation experimental results reveal that the QGA algorithm acquire a feasible and effective flyable path more efficiently in complicated environment. [ABSTRACT FROM AUTHOR]

Next-generation cellular networks are expected to provide users with innovative gigabits and terabits per second speeds and achieve ultra-high reliability, availability, and ultra-low latency. The requirements of such networks are the main challenges that can be handled using a range of recent technologies, including multi-access edge computing (MEC), artificial intelligence (AI), millimeterwave communications (mmWave), and software-defined networking. Many aspects and design challenges associated with the MEC-based 5G/6G networks should be solved to ensure the required quality of service (QoS). This article considers developing a complex MEC structure for fifth and sixth-generation (5G/6G) cellular networks. Furthermore, we propose a seamless migration technique for complex edge computing structures. The developed migration scheme enables services to adapt to the required load on the radio channels. The proposed algorithm is analyzed for various use cases, and a test bench has been developed to emulate the operator's infrastructure. The obtained results are introduced and discussed. [ABSTRACT FROM AUTHOR]

Next-generation networks, including the Internet of Things (IoT), fifthgeneration cellular systems (5G), and sixth-generation cellular systems (6G), suffer from the dramatic increase of the number of deployed devices. This puts high constraints and challenges on the design of such networks. Structural changing of the network is one of such challenges that affect the network performance, including the required quality of service (QoS). The fractal dimension (FD) is considered one of the main indicators used to represent the structure of the communication network. To this end, this work analyzes the FD of the network and its use for telecommunication networks investigation and planning. The cluster growing method for assessing the FD is introduced and analyzed. The article proposes a novel method for estimating the FD of a communication network, based on assessing the network's connectivity, by searching for the shortest routes. Unlike the cluster growing method, the proposed method does not require multiple iterations, which reduces the number of calculations, and increases the stability of the results obtained. Thus, the proposed method requires less computational cost than the cluster growing method and achieves higher stability. The method is quite simple to implement and can be used in the tasks of research and planning of modern and promising communication networks. The developed method is evaluated for two different network structures and compared with the cluster growing method. Results validate the developed method. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a recent technology, which implies the union of objects, “things”, into a single worldwide network. This promising paradigm faces many design challenges associated with the dramatic increase in the number of end-devices. Device identification is one of these challenges that becomes complicated with the increase of network devices. Despite this, there is still no universally accepted method of identifying things that would satisfy all requirements of the existing IoT devices and applications. In this regard, one of the most important problems is choosing an identification system for all IoT devices connected to the public communication networks. Many unique software and hardware solutions are used as a unique global identifier; however, such solutions have many limitations. This article proposes a novel solution, based on the Digital Object Architecture (DOA), that meets the requirements of identifying devices and applications of the IoT. This work analyzes the benefits of using the DOA as an identification platform in modern telecommunication networks. We propose a model of an identification system based on the architecture of digital objects, which differs from the well-known ones. The proposed model ensures an acceptable quality of service (QoS) in the common architecture of the existing public communication networks. A novel interaction architecture is developed by introducing a Middle Handle Register (MHR) between the global register, i.e., Global Handle Register (GHR), and local register, i.e., Local Handle Register (LHR). The aspects of the network interaction and the compatibility of IoT end-devices with the integrated DOA identifiers in heterogeneous communication networks are presented. The developed model is simulated for a wide-area network with allocated registers, and the results are introduced and discussed. [ABSTRACT FROM AUTHOR]

Since World Health Organization (WHO) has declared the Coronavirus disease (COVID-19) a global pandemic, the world has changed. All life’s fields and daily habits have moved to adapt to this new situation. According to WHO, the probability of such virus pandemics in the future is high, and recommends preparing for worse situations. To this end, this work provides a framework for monitoring, tracking, and fighting COVID-19 and future pandemics. The proposed framework deploys unmanned aerial vehicles (UAVs), e.g.; quadcopter and drone, integrated with artificial intelligence (AI) and Internet of Things (IoT) to monitor and fight COVID-19. It consists of two main systems; AI/IoT for COVID-19 monitoring and drone-based IoT system for sterilizing. The two systems are integrated with the IoT paradigm and the developed algorithms are implemented on distributed fog units connected to the IoT network and controlled by software-defined networking (SDN). The proposed work is built based on a thermal camera mounted in a face-shield, or on a helmet that can be used by people during pandemics. The detected images, thermal images, are processed by the developed AI algorithm that is built based on the convolutional neural network (CNN). The drone system can be called, by the IoT system connected to the helmet, once infected cases are detected. The drone is used for sterilizing the area that contains multiple infected people. The proposed framework employs a single centralized SDN controller to control the network operations. The developed system is experimentally evaluated, and the results are introduced. Results indicate that the developed framework provides a novel, efficient scheme for monitoring and fighting COVID-19 and other future pandemics. [ABSTRACT FROM AUTHOR]

Coronavirus disease (COVID-19) is a big problem that scares people all over the world. Life over the world has changed, new aspects for daily life have been introduced. A main problem with COVID-19 is the way it spreads. Covid-19 spreads, primarily, through contact with an infected person when they cough or sneeze, or with an infected surface. Thus, a novel way to make a protection against COVID-19 is to stay away or make yourself isolated from infected people and surfaces. To this end, this work, mainly, aims to design and develop a novel auto-sterilized suit embedded with some medical sensors and other Internet of Things (IoT) devices to provide the required level of isolation, safety, tracking and monitoring of COVID-19 and other pandemic diseases. The developed suit is an auto-sterilized suit for medical purposes and for daily life use. The sterilizing process of the suit is controlled by the IoT paradigm to provide the required control and interface in an automated way. According to the location of the user, wearing the suit, an appropriate sterilizing mode is activated automatically and the suit is sterilized via distributed nozzles over the suit. Furthermore, the distributed medical sensors represent a wireless body area network (WBAN) that is integrated with an IoT gateway to provide periodic measures of medical healthcare parameters such as body temperature, breathing rate, oxygen saturation level and pulse rate. These measures are used to identify the user’s health and the probability of being infected by COVID-19. All measures are transferred to the remote IoT cloud to analyze these data and monitor people around the day. In case of unusual measures, users are moved among three databases associated with health, infected and properly infected users. The suit is under prototyping and the work is mainly introduced to present the design stages. [ABSTRACT FROM AUTHOR]