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.

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.

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.

The evolution of Flying Ad-hoc Networks (FANETs) marks the paradigm shift from a single large drone to multiple small drones linked together in an ad-hoc fashion. To maintain the Quality of Service (QoS) in the multi-hop networking schema, FANETs utilize the available resources efficiently. However, due to the open wireless boundary and high mobility of the drones, FANETs are vulnerable to malicious nodes that can penetrate the network and, thus, pose serious security threats, particularly at the Medium Access Control (MAC) layer. This vulnerability could adversely affect the network security and privacy, resulting in colossal damages to the information exchange operations within the network. An attacker intending to damage the network has multiple options to carry on the malicious aims. For instance, the attacker can transmit plenty of reservation requests, eavesdrop the control messages and/or forge the information. The network, as a result, enervates and, for survival, calls for a mechanism that aggregates secure access control and key agreement. We contribute to the sum of knowledge by harnessing the technique of Hyperelliptic Curve Cryptography (HECC) to present a certificate-based access control and key agreement scheme. To assess the viability and performance of the proposed scheme, we analyze it using formal security analysis techniques, such as the Real-Or-Random (ROR) model and Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The scheme is also evaluated using the informal security analysis technique, or the non-mathematical approach. The results obtained from both the analyses affirm the superiority of our proposed scheme.

Today, farmers are suffering from the low yield of crops. Though right crop selection is the main boosting key to maximize crop yield by doing soil analysis and considering metrological factors, the lack of knowledge about soil fertility and crop selection is the main reason for low crop production. In the changed current climate, the farmers having primitive knowledge about conventional farming are facing challenges about making sagacious decisions on crop selection. The selection of the same crop in every seasonal cycle makes the low soil fertility. This study is aimed at making an efficient and accurate system using IoT devices and machine learning (ML) algorithms that can correctly select a crop for maximal yield. Such a system is reliable as compared to the old laboratory testing manual systems, which bear the chances of human errors. Correct selection of a crop is predominantly a priority in agricultural arena. As a contribution, we propose an ML-based model, Smart Crop Selection (SCS), which is based on data of metrological and soil factors. These factors include nitrogen, phosphorus, potassium, CO2, pH, EC, temperature, humidity of soil, and rainfall. Existing IoT-based systems are not efficient as compared to our proposed model due to limited consideration of these factors. In the proposed model, real-time sensory data is sent to Firebase cloud for analysis. Its results are also visualized on the Android app. SCS ensembles the following five ML algorithms to increase performance and accuracy: Decision tree, SVM, KNN, Random Forest, and Gaussian Naïve Bayes. For rainfall prediction, a dataset containing historical data of the last fifteen years is acquired from Bahawalpur Agricultural Department. This dataset and an ML algorithm, Multiple Linear Regression leverages prediction of the rainfall in future, a much-desired information for the health of any crop. The Root Mean Square Error of the rain fall prediction model is 0.3%, which is quite promising. The SCS model is trained for 11 crops’ prediction, while its accuracy is 97% to 98%.