The demand for the quick transmission of data at any point and at any location motivates researchers from the industry and academics to work for the enhancement of ad hoc networks. With time, various forms of ad hoc networks are evolved. These are MANET, VANET, FANET, AANET, WSN, SPAN, etc. The initial objective of VANET is to provide safety applications by combining them with ITS. But later, the applications of VANET are extended to commercial, convenience, entertainment, and productive applications. Similarly, connections among multiple unmanned aerial vehicles (UAV) through wireless links, architectural simplicity, autonomous behaviour of UAV, etc. motivate the researchers to use FANET in various sectors like military, agriculture, and transportation for numerous applications. Search and rescue operations, forest fire detection and monitoring, crop management monitoring, area mapping, and road traffic monitoring are some of the applications of FANET. The authors mentioned some applications in the chapter using VANET, FANET, and the combination of VANET and FANET.
Drones equipped with visual sensors are extensively utilized in various area coverage applications such as area mapping, monitoring of crops and road traffic, rescue operations, and so on. To enhance the coverage process, a suitable mobility model and an effective way of data communication are required. In literature, many works have been performed by focusing on the above‐mentioned aspects individually. However, unwanted information processing and a less packet delivery ratio with high message flooding are some shortcomings that have been seen in existing works on mobility model and routing protocol respectively. The above‐mentioned issues motivate us to propose a three‐level working process of ground area coverage, which includes the design of a mobility model as well as a routing mechanism incorporating the above‐mentioned mobility pattern. A set of waypoints for the movement of a drone is generated by an optimized aerial decomposition process in order to maximize and minimize the actual and exterior ground area coverage respectively. The decomposition is performed by incorporating the position and orientation of the camera footprint. While moving through the waypoints, a drone finds a stable and energy‐enable path to transmit the collected information to other drones. To avoid the frequent link loss due to the high mobility and limited energy of drones, threshold values for the velocity and energy with an estimated link stability time are considered as three parametric for the proposed routing mechanism. The result shows that the proposed work outperforms the existing result in many folds, and is suitable for the area coverage applications.
Evolution of wireless access technology, availability of smart sensors, and reduction in the size of the set up of the communication system have engrossed many researchers toward vehicular ad hoc network (VANET).Vehicle-to-vehicle and vehicle-to-access-point communication in a vehicular environment facilitates the deployment of VANET for many different purposes. The success of any application implemented in a VANET relies on timely and accurate data dissemination across the nodes of the network. Implementation of any application is not going to be fruitful if the communication unit transmits incorrect sensor data due to the presence of a fault. This article focuses on the automatic detection of hard and soft faults for vehicular sensors and the classification of faults into permanent, intermittent, and transient faults using cloud-based VANET. For the cloud service, ThingSpeak cloud is used. At the RSU of the VANET, hard fault detection is performed, and for this purpose, a time-out strategy is proposed. The observation center, after receiving sensor status data over a vehicular cloud, does soft failure detection. The soft fault is identified by utilizing a comparative-based technique during soft fault diagnosis. Soft faults are categorized using two machine learning algorithms: Support vector machine and logistic regression. The effectiveness of the suggested work is assessed using performance metrics like fault detection accuracy, false alarm rate, false positive rate, precision, accuracy, recall, and F1 score.
2D ground area coverage is essential for various applications like rescue operation, ground target detection, agricultural inspection, etc. In order to successfully implement the above-mentioned applications, researchers have used unmanned aerial vehicles (UAVs) for the ground area coverage. UAVs equipped with visual sensors are extensively used in various coverage applications. The performance of the coverage applications is dependent on maximum target area inspection, which leads to an optimization problem. For any optimization problem, researchers have used various metaheuristic approaches. For the proposed work, authors have considered the maximization of a 2D target area coverage using a single UAV system through the optimization approaches as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Cuckoo Optimization Algorithm (COA). The effectiveness of the proposed work is measured by the proportion of both interior and exterior space that is covered.
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