Throughput optimization for flying ad hoc network based on position control using genetic algorithm

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.

Unmanned Aerial Vehicle (UAV) technology is being used increasingly for military and civilian purposes. The primary reason for this increase is that UAVs eliminate the risk to human life in difficult and dangerous missions, are cost effective, and easily are deployed. Developments in UAV technology and decreasing costs have increased UAV usage. However, when multiple UAVs are deployed, inter UAV communication becomes complicated. For this reason, communication in multi-UAV systems is the most important problem that needs to be solved. To enable communication among UAVs without infrastructure support, a Flying Ad Hoc Network (FANET) is used. A FANET provides UAVs to fly in tandem without colliding. To ensure coordinated flight, UAVs require the location information of other UAVs. In this study, we developed a common channel multi-token circulation protocol to share location information in multi-UAV systems that communicate using a FANET. The proposed method ensures that UAVs in multi-UAV systems know each other's coordinate information with minimum error.DOI: 10.5755/j01.eie.25.1.22738

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

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.

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.

The advancement of automated systems and their capabilities encourage the use of unmanned aerial vehicles (UAVs) to form ad hoc networks. Multi-UAV ad hoc networks are efficient and promising to complete the task, especially for perilous missions. A multi-UAV ad hoc network is also known as Flying Ad hoc Networks (FANETs). There are various issues required to be addressed before the effective use of UAVs to form a stable and reliable network. As UAVs collaborate and coordinate with each other to complete the mission, trust among the nodes (UAVs) is a crucial aspect. In this paper, a novel trust model has been proposed where the genetic algorithm is used to optimize the weights of various parameters to evaluate the direct trust values. Direct trust is aggregated with are commendation to compute final trust value of a node. Further, nodes are classified into various clusters and trust values of uncertain nodes undergo risk assessment. Based on risk assessment, nodes are included in the recommendation or discard list. Hence, malicious nodes are segregated from the network using the discard list. The experimental results show that the proposed model has better adaptability and accuracy to be considered for FANETs. More importantly, the proposed model achieves significant performance improvement too.

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.

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.

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.

The advent of flying ad hoc networks (FANETs) has opened an opportunity to create new added-value services. Even though it is clear that these networks share common features with its predecessors, e.g., with mobile ad hoc networks and with vehicular ad hoc networks, there are several unique characteristics that make FANETs different. These distinctive features impose a series of guidelines to be considered for its successful deployment. Particularly, the use of FANETs for telecommunication services presents demanding challenges in terms of quality of service, energy efficiency, scalability, and adaptability. The proper use of models in research activities will undoubtedly assist to solve those challenges. Therefore, in this paper, we review mobility, positioning, and propagation models proposed for FANETs in the related scientific literature. A common limitation that affects these three topics is the lack of studies evaluating the influence that the unmanned aerial vehicles (UAV) may have in the on-board/embedded communication devices, usually just assuming isotropic or omnidirectional radiation patterns. For this reason, we also investigate in this work the radiation pattern of an 802.11 n/ac (WiFi) device embedded in a UAV working on both the 2.4 and 5 GHz bands. Our findings show that the impact of the UAV is not negligible, representing up to a 10 dB drop for some angles of the communication links.

Unmanned Aerial Vehicles (UAVs) are the area of growing interest for the researchers and its different applications and facilities are in use. As for the automation of UAVs, or as the solution to form the ad hoc connected occasions when finishing complex work, suggests it appears to be effective in shaping these lines with a flying ad hoc network (FANET). Even if like Mobile Ad hoc Network (MANET) and Vehicular Ad hoc Network (VANET), FANETs has its own properties. One of the contradictions in principle is that the UAV is directed at all, but especially when sorted - they are mission-oriented, and their mobility models are often guided by their central goal and due to the idea of assignments received. We proposed relative investigations in view of various measures and explain about the points of interest and hindrances of every protocol. Further, new difficulties for later study are displayed, which will recommend the new kind of coordination between existing UAVs and VANETS. This overview of innovation is that it caters to the existing reviews on the same topic and gives more subtle elements on specific viewpoints communicated just in other bibliographic studies.

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.

The flying ad-hoc network (FANET) is a decentralized communication network for the unmanned aerial vehicles (UAVs). Because of the wireless nature and the unique network properties, FANET remains vulnerable to jamming attack with additional challenges. First, a decision from a centralized knowledge base is unsuitable because of the communication and power constraints in FANET. Second, the high mobility and the low density of the UAVs in FANET require constant adaptation to newly explored spatial environments containing unbalanced data; rendering a distributed jamming detection mechanism inadequate. Third, taking model-based jamming defense actions in a newly explored environment, without a precise estimation of the transitional probabilities, is challenging. Therefore, we propose an adaptive federated reinforcement learning-based jamming attack defense strategy. We developed a model-free Q-learning mechanism with an adaptive exploration-exploitation epsilon-greedy policy, directed by an ondevice federated jamming detection mechanism. The simulation results revealed that the proposed adaptive federated reinforcement learning-based defense strategy outperformed the baseline methods by significantly reducing the number of en route jammer location hop counts. The results also showed that the average accuracy of the federated jamming detection mechanism, leveraged in the defense strategy, was 39.9% higher than that of the distributed mechanism verified with the standard CRAWDAD jamming attack dataset and the ns-3 simulated FANET jamming attack dataset.