Sharing Location Information in Multi-UAV Systems by Common Channel Multi-Token Circulation Method in FANETs

In the last few years, a considerable amount of research effort has been put into creating networks of Unmanned Aerial Vehicles (UAVs). Flying Ad Hoc Networks (FANETs) and Internet of Drones (IoDs) are the result of such efforts. FANETs assume that the member UAV nodes belong to different users. However, in UAV networks that would find their applications in areas such as agriculture, environment monitoring, crowd surveillance and natural disaster situations, all UAVs would belong to the same organization, creating a private UAV network. The number of UAVs used in private UAV networks is relatively small compared to that of FANETs due to the cost. If the packet routing approaches designed for FANETs are used in such networks, the quality of video transmitted through the network will suffer. This is because, while there can be many neighbors for each UAV in a FANET, the movements of UAVs in a private UAV network may cause some UAVs not to have any neighbors. To overcome this problem, a UAV in such a network needs to be controlled differently and an appropriate method of selecting the next hop node is required. The new approach presented in this paper uses mathematical analysis to find the optimum next hop node in a private UAV network by reducing a global optimization problem to a local optimization problem at each UAV node along the path from the source to the destination. Once the best next hop node is found, the UAVs can be controlled accordingly.

In some complex applications, Flying Ad Hoc Network (FANET) can provide important support for multi UAV (Unmanned Aerial Vehicle) cooperation. In FANET each UAV is equivalent to a router, and the wireless link between them forms a network to achieve the purpose of relay communication. Throughput is an important network performance, and the position of UAV nodes affects it. In this paper, we analyze the influencing factors of FANET throughput with UAV position and terminator selection in first; Secondly we construct the mathematical model of throughput optimization of FANET; Thirdly we propose an algorithm based on genetic algorithm to optimize the position of UAV, and then maximize the throughput. Preparing for using genetic algorithm, we design the related details: Numbering Area, Determining the adjacency matrix and correlation matrix, determining the range of UAV node position movement. The key points of the genetic algorithm for FANET is proposed include the following aspects: coding and population initialization, fitness function, and chromosome replication/crossover/mutation and termination criteria. At last, Matlab is used to simulate the proposed algorithm from three aspects: performance, effect of Radius of Position Constraint (RPC) and effect of Radius of Particle Size (RPS). The results show that the throughput can reach the expected goal by controlling the UAV position, and the optimization speed is related to the RPC and RPS.

Flying Ad-Hoc Network (FANET) is a special type of Mobile Ad-Hoc Network (MANET) which consists of multiple networked Unmanned Aerial Vehicles (UAVs). Compared to traditional target search using a single UAV, multiple cooperative UAVs within a FANET have the potential to accomplish this work more efficiently, and multi-UAV cooperative target search has attracted increasing research attention in recent years. In this paper, we conduct a comprehensive investigation on efficient and accurate multi-UAV cooperative target search techniques. More specifically, we propose several adaptive mobility models and an innovative information fusion mechanism to improve the search efficiency. Meanwhile, to improve the search accuracy, we introduce a low-overhead decision-making mechanism to reduce chances for false target identifications. The simulation results show that the framework outperforms the baseline framework significantly in terms of both efficiency and accuracy. Our experiments also exhibit how individual mechanisms introduced in our work contribute to the improvements on search efficiency or accuracy, therefore providing more guidance for their adoption in practice.

The rapid development of aviation technology has made the application of unmanned aerial vehicles (UAVs) more popular in recent years. Due to the inadequate capability of a single UAV, flying ad hoc networks (FANETs), which consist of multiple different UAVs, not only break the limits of single UAV by introducing the cooperation between UAVs, but also complete more complex missions by extending the communication range at infrastructure-less areas. One of the most important design issues for FANETs is the communication and cooperation between UAVs, and the number of UAV relays has been proved to be closely related to the improvement of system performance. However, few FANET protocols or algorithms have been proposed from the perspective of scheduling the number of relays. Inspired by the idea, this paper proposes an optimal relay number selection algorithm based on a more realistic network model which includes a novel cooperative ( $n+2$ )-node system model considering the distance metrics between relays and a Nakagami- $m$ short-term static fading channel model more suitable for UAV operation environments. The system outage probability is calculated by introducing the Meijer-G function, and a three-dimensional discrete time Markov chain (DTMC) model is established and analyzed to derive the closed-form binary expressions of throughput, energy efficiency and average transmission delay. In order to balance the three performance to achieve a comprehensive system performance, a trade-off factor named EDT is further proposed and maximized to evaluate the optimal number of relays. Finally, simulation results assess the impact of network parameters on system performance, and verify the superiority of the proposed algorithm over its fixed relay number counterpart.

An adaptive and multi-path greedy perimeter stateless routing protocol in flying ad hoc networks

At present, the applications of multiple unmanned aerial vehicles (UAVs) are becoming more and more widespread, covering many civil and military fields. When performing tasks, UAVs will form a flying ad hoc network (FANET) to communicate to each other. However, subject to high mobility, dynamic topology, and limited energy of FANETs, maintaining stable communication performance is a challenging task. As a potential solution, the clustering routing algorithm divides the entire network into multiple clusters to achieve strong network performance. Meanwhile, the accurate localization of UAV is also strongly required when FANETs are applied in the indoor scenario. In this paper, we propose a firefly swarm intelligence based cooperative localization (FSICL) and automatic clustering (FSIAC) for FANETs. Firstly, we combine the firefly algorithm (FA) and Chan algorithm to better cooperative locate the UAVs. Secondly, we propose the fitness function consisting of link survival probability, node degree-difference, average distance, and residual energy, and take it as the light intensity of the firefly. Thirdly, the FA is put forward for cluster-head (CH) selection and cluster formation. Simulation results indicate that the proposed FSICL algorithm achieves the higher localization accuracy faster, and the FSIAC algorithm achieves the higher stability of clusters, longer link expiration time (LET), and longer node lifetime, all of which improve the communication performance for indoor FANETs.

Flying Ad-hoc Networks (FANETs) have been employed in modern warfare for monitoring and reconnaissance to produce a healthy living environment for wildlife through multiple Unmanned Aerial Vehicles (UAVs). FANETs allow multiple UAVs to communicate in 3D space to establish an ad-hoc network. FANETs applications, among others, can deliver costeffective services to help future wildlife. However, adopting FANET's technology in wildlife monitoring is difficult due to its challenges in mobility, data routing, energy, and security considerations. Therefore, this paper aims to look at FANET's possible applications in wildlife Monitoring and the implications and issues that come with them.

As there has been an advancement in avionic systems in recent years, the enactment of unmanned aerial vehicles (UAV) has upgraded. As compared to a single UAV system, multiple UAV systems can perform operations more inexpensively and efficiently. As a result, new technologies between user/control station and UAVs have been developed. FANET (Flying Ad-Hoc Network) is a subset of the MANET (Mobile Ad-Hoc Network) that includes UAVs. UAVs, simply called drones, are used for collecting sensitive data in real time. The security and privacy of these data are of priority importance. Therefore, to overcome the privacy and security threats problem and to make communication between the UAV and the user effective, a competent anonymous mutual authentication scheme is proposed in this work. There are several methodologies addressed in this work such as anonymous batch authentication in FANET which helps to authenticate a large group of drones at the same time, thus reducing the computational overhead. In addition, the integrity preservation technique helps to avoid message alteration during transmission. Moreover, the security investigation section discusses the resistance of the proposed work against different types of possible attacks. Finally, the proposed work is related to the prevailing schemes in terms of communication and computational cost and proves to be more efficient.

An unmanned aerial vehicle (UAV) is an aircraft without a human pilot that is operated remotely. When multiple UAVs are connected together for performing specific task, the arrangement is called a flying adhoc network (FANET). In a multi-UAV system, communication and coordination among the flying nodes are essential to carry out the mission properly. As the flying nodes are highly dynamic in nature, an efficient routing strategy is important. The intelligent routing decisions in this scenario can be taken by applying bio-inspired optimisation algorithms. This chapter focuses on bio-inspired optimisation techniques for the FANET.

In this study, the trend of unmanned aerial vehicle (UAV) technology and its applications in the mining industry were reviewed. To understand the trend of the UAV technology, the definition and classification of the UAVs, their major applications in each field, the characteristics of the sensors of the UAV were investigated. Some examples that used the UAV technology for planning, work management, facility management, safety management, and other works in the mining industry were reviewed. By introducing the UAV technology in the mining site, it is expected that the work efficiency related to mining operations would be increased. Therefore, the application of the UAV technology in the mining industry would be expanded further.

SummaryMuch progress can be expected in the domain of unmanned aerial vehicle (UAV) communication by the next decade. The cooperation between multiple UAVs in the air exchanging data among themselves can naturally form a flying ad hoc network (FANET). Such networks can be the key support to accomplish several kinds of missions while providing the required assistance to terrestrial networks. However, they are confronted with many challenges and difficulties, which are due to the high mobility of UAVs, the frequent packet losses, and the weak links between UAVs, all affecting the reliability of the data delivery. Furthermore, the unbalanced energy consumption may result in earlier UAV failure and consequently accelerate the decrease of the network lifetime, thus disrupting the overall network. This paper supports the use of the movement information and the residual energy level of each UAV to guarantee a high level of communication stability while predicting a sudden link breakage prior to its occurrence. A robust route discovery process is used to explore routing paths where the balanced energy consumption, the link breakage prediction, and the connectivity degree of the discovered paths are all considered. The performance of the scheme is evaluated through a series of simulations. The outcomes demonstrate the benefits of the proposed scheme in terms of increasing the lifetime of the network, minimizing the number of path failures, and decreasing the packet losses.

Flying Ad-hoc Network (FANET) is a special class of MANET that provides the communication among small flying drones called UAVs (Unmanned Aerial Vehicle) enable with camera, sensor and communication system. The FANET nodes communicate with each others in the air; transfer the data and signals between each other without any human experts and without any physical connectivity between the nodes. FANET has a wide area of applications like reconnaissance and surveillance for military and civil purposes. In this paper, flying ad-hoc network architecture is presented along with its usages, characteristics, advantages, routing protocols and mobility models. Along with delineates open research issues with dissecting openings and future work are also discussed.

This paper investigates the problem of data routing in flying ad hoc networks (FANETs) composed of multiple flying nodes, i.e., unmanned aerial vehicles (UAVs), supported by communication platforms. The objective is to exploit the mobility of UAVs in order to establish routing paths and transfer a message between two ground nodes at minimum transmission time. Assuming that the UAVs are already deployed to execute a given primary task, the cooperation of the UAVs in the data transfer process, considered as a secondary task, becomes subject to three conditions. First, the energy consumed by each UAV has to respect the allocated budget for the data routing process. Second, the UAVs cannot move out of the boundaries of a tolerated and well-defined region in order to maintain the operation of the primary task. Finally, the UAVs need to reduce their traveled distances in order to reduce the delay of the transfer. A mixed non-linear integer programming problem determining the routing path and the new locations of the UAVs participating in the data transfer process is formulated. Due to its nonconvexity, we proceed with a deterministic exploratory strategy inspired from the Hooke-Jeeves algorithm to meet the problem goals. Selected numerical results investigate the performance of the proposed solution for different scenarios and compare some of them to those of a metaheuristic approach based on swarm intelligence.

An unmanned aerial vehicle (UAV), is an aircraft that does not have a human pilot and is controlled either independently by the aircraft's computers or by remote control. Because of their high mobility, FANETs are often used in UAVs because they can disrupt communications as well as network stability due to frequent topology changes. In-depth exploration and implementation of UAV network development, concentrating on diverse topologies such as star, multi-star, and network configurations, we developed the Energy-Efficient Data Transmission Policy for UAV (EEDTP-UAV) model. It emphasizes mobility, energy efficiency, UAV distance control, and road availability in the context of Flying Ad-hoc Network (FANET) technology development. Special attention is given to path optimization techniques and different link types, which are crucial for effective UAV communication. By using a relay-based energy-efficient process, off-grid user connections are optimized according to energy consumption meters, and data is collected cooperatively between the UAV cluster and the mobile synchronous node, providing equal energy usage and efficient data transmission. Due to its flexibility and adaptive nature, the proposed algorithm is well-suited for real-time UAV swarm operations that tackle dynamic path planning, and energy efficient communication methods such as airborne navigation or crowd control. Compared with existing model EEDS, DSSPCA, EEUCH, and ESRD-PDCA models with our proposed EEDTP-UAV model. By the following parameters communication delay, energy efficiency, data success rate, throughput, and routing overhead have been calculated.

Flying ad hoc network (FANET) is a new technology, which creates a self-organized wireless network containing unmanned aerial vehicles (UAVs). In FANET, routing protocols deal with important challenges due to limited energy, frequent link failures, high mobility of UAVs, and limited communication range of UAVs. Thus, a suitable path is always essential to transmit data between UAVs. In this paper, a local filtering-based energy-aware routing scheme (LFEAR) is proposed for FANETs. LFEAR improves the template of the route request (RREQ) packet by adding three fields, namely the energy, reliable distance, and movement similarity of the relevant route to create stable and energy-efficient paths. In the routing process, LFEAR presents a local filtering construction technique to avoid the broadcasting storm issue. This filter limits the broadcasting range of RREQs in the network. Accordingly, only UAVs inside this local filtered area can rebroadcast RREQs and other UAVs must eliminate these packets. After ending the route discovery process, the destination begins the route selection phase and extracts information about each discovered route, including the number of hops, route energy, reliable distance, and movement similarity. Then, the destination node calculates a score for each path based on the extracted information, selects the route with the highest score, and sends a route reply (RREP) packet to the source node through this route. Finally, the simulation process of LFEAR is performed using the NS2 simulator, and two simulation scenarios, namely change in network density and change in the speed of UAVs, are defined to evaluate network performance. In the first scenario, LFEAR improves energy consumption, packet delivery rate, network lifespan, and delay by 1.33%, 1.77%, 6.74%, and 1.71%, while its routing overhead is about 16.51% more than EARVRT. In the second scenario, LFEAR optimizes energy consumption and network lifetime by 5.55% and 5.67%, respectively. However, its performance in terms of routing overhead, packet delivery rate, and delay is 23%, 2.29%, and 6.67% weaker than EARVRT, respectively.