Recently, unmanned aerial vehicles (UAVs), also known as drones, have come in a great diversity of several applications such as military, construction, image and video mapping, medical, search and rescue, parcel delivery, hidden area exploration, oil rigs and power line monitoring, precision farming, wireless communication and aerial surveillance. The drone industry has been getting significant attention as a model of manufacturing, service and delivery convergence, introducing synergy with the coexistence of different emerging domains. UAVs offer implicit peculiarities such as increased airborne time and payload capabilities, swift mobility, and access to remote and disaster areas. Despite these potential features, including extensive variety of usage, high maneuverability, and cost-efficiency, drones are still limited in terms of battery endurance, flight autonomy and constrained flight time to perform persistent missions. Other critical concerns are battery endurance and the weight of drones, which must be kept low. Intuitively it is not suggested to load them with heavy batteries. This study highlights the importance of drones, goals and functionality problems. In this review, a comprehensive study on UAVs, swarms, types, classification, charging, and standardization is presented. In particular, UAV applications, challenges, and security issues are explored in the light of recent research studies and development. Finally, this review identifies the research gap and presents future research directions regarding UAVs.
Recent studies have shown that robust diets recommended to patients by Dietician or an Artificial Intelligent automated medical diet based cloud system can increase longevity, protect against further disease, and improve the overall quality of life. However, medical personnel are yet to fully understand patient-dietician's rationale of recommender system. This paper proposes a deep learning solution for health base medical dataset that automatically detects which food should be given to which patient base on the disease and other features like age, gender, weight, calories, protein, fat, sodium, fiber, cholesterol. This research framework is focused on implementing both machine and deep learning algorithms like, logistic regression, naive bayes, Recurrent Neural Network (RNN), Multilayer Perceptron (MLP), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM). The medical dataset collected through the internet and hospitals consists of 30 patient's data with 13 features of different diseases and 1000 products. Product section has 8 features set. The features of these IoMT data were analyzed and further encoded before applying deep and machine and learning-based protocols. The performance of various machine learning and deep learning techniques was carried and the result proves that LSTM technique performs better than other scheme with respect to forecasting accuracy, recall, precision, and F1-measures. We achieved 97.74% accuracy using LSTM deep learning model. Similarly 98% precision, 99% recall and 99% F1-measure for allowed class is achieved, and for not-allowed class precision is 89%, recall score is 73% and F1 Measure score is 80%.
A Flying Ad-hoc Network (FANET) consists of Unmanned Aerial Vehicles (UAVs) tasked to handle the communication jobs in a multi-hop ad-hoc fashion. Unlike its predecessors, i.e. Mobile Ad-hoc Networks (MANETs) and Vehicular Ad-hoc Networks (VANETs), a FANET promises uninterrupted connectivity, especially during events that are temporary and stipulate a massive audience reach. However, usually, the participating UAVs in a FANET environment are resource-constrained and are, therefore, prone to cyber-attacks. In order to resolve the issue and to enable a secure communication between the UAVs and the Base Station (BS), we propose a Certificateless Key-Encapsulated Signcryption (CL-KESC) scheme. The scheme is based on the concept of Certificateless Public Key Cryptography (CL-PKC). Since CL-PKC is immune to key escrow problems and thus one of the major drawbacks of the Identity-based Public Key Cryptography (ID-PKC) is addressed. Unfortunately, the existing construction models of CL-KESC rely on elliptic curve-based operations, which are computationally expensive for small UAVs. To counter the issue, in this paper, we present a new construction model of CL-KESC based on Hyperelliptic Curve Cryptography (HECC). HECC is an advanced version of the elliptic curve and is characterized by smaller parameter and key size. The key size stretches to a maximum of 80-bits, as opposed to the elliptic curve that demands a 160-bits key size. The proposed scheme proved to be superior, chiefly in terms of security and performance, as demonstrated by the results obtained from the security verification and by carrying out comparative analysis with the existing counterparts.
Unmanned aerial vehicles (UAVs), also known as drones, once centric to military applications, are presently finding their way in many civilian and commercial applications. If national legislations permit UAVs to operate autonomously, one will see the skies become populated with many small UAVs, each one performing various tasks such as mail and package delivery, traffic monitoring, event filming, surveillance, search and rescue, and other applications. Thus, advancing to multiple small UAVs from a single large UAV has resulted in a new clan of networks known as flying ad-hoc networks (FANETs). Such networks provide reliability, ease of deployment, and relatively low operating costs by offering a robust communication network among the UAVs and base stations (BS). Although FANETs offer many benefits, there also exist a number of challenges that need to be addressed; the most significant of these being the communication one. Therefore, the article aims to provide insights into the key enabling communication technologies through the investigation of data rate, spectrum type, coverage, and latency. Moreover, application scenarios along with the feasibility of key enabling technologies are also examined. Finally, challenges and open research topics are discussed to further hone the research work.
Unmanned aerial vehicles (UAVs), when interconnected in a multi-hop ad-hoc fashion, or as a flying ad-hoc network (FANET), can efficiently accomplish mission-critical tasks. However, UAVs usually suffer from the issues of shorter lifespan and limited computational resources. Therefore, the existing security approaches, being fragile, are not capable of countering the attacks, whether known or unknown. Such a security lapse can result in a debilitated FANET system. In order to cope up with such attacks, various efficient signature schemes have been proposed. Unfortunately, none of the solutions work effectively because of incurred computational and communication costs. We aimed to resolve such issues by proposing a blind signature scheme in a certificateless setting. The scheme does not require public-key certificates, nor does it suffer from the key escrow problem. Moreover, the data that are aggregated from the platform that monitors the UAVs might be too huge to be processed by the same UAVs engaged in the monitoring task. Due to being latency-sensitive, it demands high computational capability. Luckily, the envisioned fifth generation (5G) mobile communication introduces multi-access edge computing (MEC) in its architecture. MEC, when incorporated in a UAV environment, in our proposed model, divides the workload between UAVs and the on-board microcomputer. Thus, our proposed model extends FANET to the 5G mobile network and enables a secure communication between UAVs and the base station (BS).
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