Flying Ad Hoc Networks Research Articles

Learning to Routing in UAV Swarm Network: A Multi-Agent Reinforcement Learning Approach

The past few years have witnessed an exponential growth of compelling UAV swarm applications ranging from agricultural production, and intelligent transport, to disaster rescue. The high-speed mobility of the UAV swarm belongs to a new clan of networks, termed flying ad-hoc networks (FANETs). How to design an effective routing mechanism in such a dynamic network is challenging. Traditional flooding searching algorithms (e.g., OLSR, AODV) lead to huge communication overheads, while greedy searching algorithms (e.g., Geolocation-Based Routing protocol) pose low routing efficiency. In this paper, we propose an adaptive communication-based UAV swarm routing algorithm. In our algorithm, we design the Multilayer Perceptron algorithm to learn when routing flooding is required among different UAVs, and design the Gate Recurrent Unit algorithm to greatly compress the volume of communication data. Besides, we further adopt the multi-agent actor-critic algorithm to learn how to integrate shared information for cooperative routing decision-making. Extensive simulation results validate that our algorithms achieve efficient and effective routing under a partially observable distributed environment for large-scale UAV swarm cooperation.

Energy-aware message distribution algorithm for enhance FANET pipeline surveillance reliability

Features such as the communication scheme, energy awareness, and task distribution amongst others are the key component that characterizes the Flying Ad-hoc Network (FANET). The operational efficiency in FANET surveying a specific region is affected by the nature of the UAVs' node placement, routing protocol, energy-aware task distribution, and node interaction amongst others. In this paper, Drone 1 (D1), Master Drone (DM), and Drone 2 (D2) were used to survey a pipeline of length 12.2 m. This paper aims at minimising energy use by drones during surveillance using energy-aware node exchange technique, task interaction and distribution scheme for each UAV. Due to fast energy depletion of DM due to packets aggregation, its election is based on the UAV with the highest energy before take-off. For two different simulations, 14,697.0 J and 14,836.6 J were obtained for DM. To avoid system failure due to fast energy loss of DM, the drones swapped positions and status. First swapping command comes up when DM loses 50% of its energy, while the second command occurs when it further loses 15%. Return to base threshold energy is computed for the three UAVs to avoid crash due to insufficient energy during surveillance. DM returns to base threshold energy for both single and double swapping simulation were 658.105 J and 652.456 J respectively. From the results obtained the algorithms were able to exchange nodes to maximize energy usage and perform an interaction-based task distribution for cooperative task sharing during surveillance. This translates into longer surveillance time and effective telemetry data aggregation.

A Hybrid Optimization Solution for UAV Network Routing

An Unmanned Aerial Vehicle (UAV) network specifies a novel type of Mobile Ad hoc Network (MANET) in which drones serve as nodes and facilitate the retransmission of messages to their final destinations. Aside from its military application, it has recently begun to seep into the civilian sector. Similar to MANET and vehicular ad hoc networks, Flying Ad hoc Networks (FANET) are a subset of ad hoc networks. An FANET is different because it is founded on UAVs. Due to the characteristics of this sort of network, which is defined by a highly changing topology in a 3D environment, we must employ an adjusted configuration method to ensure good routing performance. Therefore, to deal with this problem, a technique that responds to any change in topology by always finding the best route is required. In this work, we propose a new protocol based on the hybrid optimization of the 2-opt heuristic and Honey Badger Algorithm (HBA), called HB-AODV. In order to locate its prey, a badger must move slowly and continuously while using scent markers and mouse-digging skills to catch it. In other words, the most efficient routes in terms of the number of hops are identified. Several simulations were conducted via the 3D version of Network Simulator (NS-2) on different deployment strategies. In comparison to AODV, DSDV, and AntHocNet, the obtained results demonstrated the proposed scheme’s good performance in terms of quality of service metrics.

Topology construction and topology adjustment in flying Ad hoc networks for relay transmission

A flying ad hoc network (FANET) is formed by multiple unmanned aerial vehicles (UAVs) in an ad hoc manner in which UAVs can cooperate more effectively to complete complex tasks. In this paper, we mainly investigate the topology construction and topology adjustment for FANETs in which relay UAVs are used for relay transmission between mission UAVs and a ground control station (GCS), so that each mission UAV can communicate with the GCS through a multi-hop communication connection. Firstly, we propose an alternating optimization algorithm to optimize the routing paths and the positions of relay UAVs alternately, so as to construct a feasible FANET topology using the minimal number of relay UAVs. Then, we propose a greedy algorithm to deploy new relay UAVs to the FANET topology constructed by the alternating optimization algorithm, so as to construct a high-performance FANET topology using the given number of relay UAVs. Both the alternating optimization algorithm and the greedy algorithm need to be executed in a centralized manner, so these two algorithms can only be used for FANET topology construction. Furthermore, we propose a distributed algorithm to adjust the existing FANET topology when the positions of mission UAVs change, so as to improve the FANET network performance. This distributed algorithm can be executed in a distributed manner, so this algorithm can be used for FANET topology adjustment. The simulation results show that the algorithms proposed in this paper are superior to other baselines.

Firefly swarm intelligence based cooperative localization and automatic clustering for indoor FANETs.

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.

Resource allocation for dataflow applications in FANETs using anypath routing

Management of network resources in advanced IoT applications is a challenging topic due to their distributed nature from the Edge to the Cloud, and the heavy demand of real-time data from many sources to take action in the deployment. FANETs (Flying Ad-hoc Networks) are a clear example of heterogeneous multi-modal use cases, which require strict quality in the network communications, as well as the coordination of the computing capabilities, in order to operate correctly the final service. In this paper, we present a Virtual Network Embedding (VNE) framework designed for the allocation of dataflow applications, composed of nano-services that produce or consume data, in a wireless infrastructure, such as an airborne network. To address the problem, an anypath-based heuristic algorithm that considers the quality demand of the communication between nano-services is proposed, coined as Quality-Revenue Paired Anypath Dataflow VNE (QRPAD-VNE). We also provide a simulation environment for the evaluation of its performance according to the virtual network (VN) request load in the system. Finally, we show the suitability of a multi-parameter framework in conjunction with anypath routing in order to have better performance results that guarantee minimum quality in the wireless communications.

MWCRSF: Mobility-based weighted cluster routing scheme for FANETs

Flying Ad-Hoc Network (FANET) formed by a swarm of Unmanned Aerial Vehicles (UAVs) is currently a trending research topic because of its capability in performing complex tasks more rapidly and efficiently. However, the high-speed mobility of UAVs, limited energy, and dynamically changing topology have led to critical challenges for the reliability of communication between UAVs, especially when designing routing schemes in FANET. In this paper, we introduce a Mobility-based Weighted Cluster Routing Scheme (MWCRSF) for FANET performance enhancement within the constraints of network resources. MWCRSF utilizes the high-level search efficiency of the Sparrow Search Algorithm (SSA) and a new clustering algorithm based on SSA is first proposed to set up the initial UAV clusters. A weighted sum-based algorithm is then proposed for Cluster Head (CH) selection, where the residual energy, node correlation degree, relative mobility, distance to the Base Station (BS), and average distance are adopted as the selection criteria. To keep the UAV clusters updated, an efficient cluster maintenance algorithm is proposed as well. In order to provide timely and reliable data transmission to the BS, the optimal next-hop route selection is performed considering the link lifetime, residual energy, and distance between nodes and BS. We exploit the power of skyline operators in multi-criteria decision-making to reduce the search space and improve the efficiency of CH selection and next-hop routing. The simulation results reveal that the proposed MWCRSF scheme outperforms the existing methods under different metrics including cluster lifetime, energy efficiency, throughput, and network lifetime.

Green Edge Intelligence for Smart Management of a FANET in Disaster-Recovery Scenarios

Disruption of the ground communication infrastructure in emergency scenarios makes post-disaster rescue operations very complicate. This paper proposes to use edge intelligence to support rescue operators in managing these emergency scenarios. A set of Unmanned Aerial Vehicles (UAV), organized as Flying Ad-Hoc network (FANET), autonomously takeoff and land to provide emergency operators with edge computing services. A charging station for batteries is supplied by a renewable-energy generator. The FANET Controller applies model-based Reinforcement Learning to decide how many UAVs have to take off, according to the current edge-computing service requests and the power availability, and a forecast of them. The optimal management policy has to provide the necessary level of edge-computing avoiding wide use of satellite channels in a short-time horizon during low green-energy generation and high service request periods. Results highlight that the optimal policy is an efficient modification of the greedy one, i.e., the policy enabling the takeoff of all the necessary UAVs without being care of challenging events in the future. A deep analysis has revealed that the level of modification depends on the combination of the edge-computing service request and the green power availability.

Maintaining Links in the Highly Dynamic FANET Using Deep Reinforcement Learning

Routing protocols do not respond quickly to environmental changes due to the high mobility of nodes in the Flying Ad Hoc Network (FANET), to obtain reliable transmission links. This paper proposes an adaptive link maintenance method based on deep reinforcement learning (DRL-MLsA), which can dynamically adjust the time interval of broadcasting Hello packets. This method can cope with the highly dynamic network environment, and adapt to both active routing and table-driven routing protocols. The method considers the channel model of the signal and investigates the impact of UAV communication range on link maintenance. We can get an agent by perceiving the degree of changes in the number of neighbors in a dynamic environment. The optimal broadcast cycle was obtained to maximize the energy of the node to send and receive task data. We substituted the single-output network model with a competitive network to overcome the reward overestimation problem, which also improves the convergence speed of the algorithm. Simulation results showed that DRL-MLsA can reduce the communication overhead for link maintenance, while at the same time increase the throughput of the network and decrease the packet loss of transmission.

Анализ особенностей и методов определения маршрутов доставки данных в беспроводных самоорганизующихся сетях на основе беспилотных летательных аппаратов

Currently, one of the promising areas in the development of network technologies is the wireless self-organizing networks based on the unmanned aerial vehicles, i.e., FANET (Flying Ad-Hoc Networks), which most important task in the course of their operation lies in organizing the effective data exchange. The distinctive properties of wireless self-organizing networks with altering topology are leading to the fact that technical solutions and methods of determination of the data delivery routes used in telecommunication networks with the traditional fixed architecture turn out to be inefficient in the special FANET networks and are not able to provide the required performance. At the same time, wireless self-organizing networks based on the unmanned aerial vehicles possess their own techniques used in data routing, which are subject to the requirements that take into account characteristic differences inherent in the networks of this type, including high mobility and low density of the nodes, dynamic and frequent topology alterations. Features and methods for determining data delivery routes were analyzed in the wireless self-organizing networks, which basis (nodes) were the unmanned aerial vehicles

Robust Data-Aware Sensing Based on Half Quadratic Hashing for the Visual Internet of Things

In the Visual Internet of Things (VIoT) such as drones over flying ad-hoc networks (FANETs), terminal nodes need to preliminarily identify sensing data. Thus, data can be rapidly dispatched to dedicated edge nodes for further processing. Nonetheless, terminals may collect partially observed visual data, which cause biased labeling. In view of such, this study proposes half quadratic supervised discrete hashing (HQSDH) that can conquer the biased similarity estimation for partially observed images, especially when large continuous occlusion, missing values, or sample-specific outliers exist. The proposed method can automatically and adaptively adjust itself via HQ auxiliary variables to avoid oversensitivity to large errors caused by partially observed data. In this study, to solve HQ discrete optimization problems in HQSDH, HQ discrete cyclic coordinate descent (HQDCCD) is developed. Second, partially observed images may cause inconsistent binary codes and increase the Hamming distances. Thus, this study devises HQ balancing decorrelation constraints along with normalization regularization to accommodate the problems. Third, a systematic and generic solution that allows various HQ functions to jointly work in the same framework is modeled to provide versatility. Experiments on open image data sets were carried out for evaluation. The results showed that the proposed method provided more resilience against continuous occlusion, missing values, and sample-specific outliers than the baselines when dealing with partially observed images.

An Energy-Efficient Clustering and Fuzzy-Based Path Selection for Flying Ad-Hoc Networks

Flying Ad-hoc Networks (FANET) allow for an ad-hoc networking among Unmanned Aerial Vehicles (UAV), have recently gained popularity in a variety of military and non-militant applications. The existing work used the Glowworm Swarm Optimization (GSO) algorithm to create a self-organization depending on clustering technique for FANET. Owing to UAV increased mobility, network topology might vary over time, providing route discovery and maintenance is one of the most difficult tasks. And also, the network throughput is still more worsened by the network congestion. To solve this problem, the proposed work designed an energy efficient clustering and fuzzy-based path selection for FANET. In this work, initially, the clustering is performed using the UAV distance. For efficient communication and energy consumption, optimal selection of Cluster Head (CH) is performed by using Adaptive Mutation with Teaching-Learning-Based Optimization (AMTLBO) algorithm. To improve the optimal selection of CH nodes, the best fitness values are calculated. The fitness function depends on Link capacity, remaining energy and neighbor UAV distance. Next to that, nodes begin communications as well as transmit their information to their CH. Improved Fuzzy-based Routing (IFR) is introduced for improving the route discovery process. The goal is to find routes that have a high level of flying autonomy, minimal mobility, and a higher Received Signal Strength Indicator (RSSI). As a result, the energy usage of network is decreased, as well as the cluster’s lifespan is extended. Finally, an adaptive and reliable congestion detection mechanism is introduced to transmit the packets with congestion free path. The experimental result shows that the proposed AMTLBO system attains higher performance compared to the existing system in terms of energy usage, throughput, delay, overhead and packet delivery ratio.

FANET Routing Protocol Analysis for Multi-UAV-Based Reconnaissance Mobility Models

Different from mobile ad hoc networks (MANETs) and vehicular ad hoc networks (VANETs), a flying ad hoc network (FANET) is a very low-density network where node topology changes rapidly and irregularly. These characteristics, the density, mobility, and speed of flight nodes, affect the performance of FANET. Furthermore, application scenarios and environmental settings could affect the performance of FANETs. In this paper, we analyzed the representative FANET protocols, AODV, DSDV, and OLSR, according to mobility models, SRWP, MP, RDPZ, EGM, and DPR, under the multi-UAV-based reconnaissance scenario. We evaluated them in terms of the number of nodes, network connectivity, mobility model’s reconnaissance rate, speed of nodes, and ground control station (GCS) location. As a result, we found that AODV showed the highest PDR performance (81%) with SRWP in multiple UAV-based reconnaissance scenarios. As for a mobility model under the consideration of reconnaissance rate, SRWP was excellent at 76%, and RDPZ and EGM mobility models were reasonable at 62% and 60%, respectively. We also made several interesting observations such as how when the number of nodes increases, the connectivity of the network increases, but the performance of the routing protocol decreases, and how the GCS location affects the PDR performance of the combination of routing protocols and mobility models.

A three-stage stochastic optimization model integrating 5G technology and UAVs for disaster management

In this paper, we develop a three-stage stochastic network-based optimization model for the provision of 5G services with Unmanned Aerial Vehicles (UAVs) in the disaster management phases of: preparedness, response and recover/reconstruction. Users or devices on the ground request services of a fleet of controller UAVs in flight and the requested services are executed by a fleet of UAVs organized as a Flying Ad-Hoc Network and interconnected via 5G technology. A disaster scenario can create difficulties for the provision of services by service providers. For this reason, in the first stage, service providers make predictions about possible scenarios in the second stage. Therefore, the first stage represents the preparedness phase, the second stage represents the response phase, followed by the recovery/reconstruction phase, represented by the third stage. In each of the three stages, service providers seek to maximize the amount of services to be performed, assigning each service a priority. They also aim to, simultaneously, minimize the total management costs of requests, the transmission and execution costs of services, the costs to increase the resources of the pre-existing network and, if need be, to reduce them in the recovery/reconstruction phase. For the proposed multi-stage stochastic optimization model, we provide variational formulations for which we investigate the existence and uniqueness of the solution. Finally, a detailed numerical example is solved in order underline some of the key aspects of the model. This paper adds to the literature on the rigorous mathematical modeling of advanced technologies for disaster management.

LoRa Technology in Flying Ad Hoc Networks: A Survey of Challenges and Open Issues.

The Internet of Things (IoT) and Flying Ad Hoc Networks (FANETs) have become hot topics among researchers because of the increased availability of Unmanned Aerial Vehicles (UAVs) and the electronic components required to control and connect them (e.g., microcontrollers, single board computers, and radios). LoRa is a wireless technology, intended for the IoT, that requires low power and provides long-range communications, which can be useful for ground and aerial applications. This paper explores the role that LoRa plays in FANET design by presenting a technical overview of both, and by performing a systematic literature review based on a breakdown of the communications, mobility and energy topics involved in a FANET implementation. Furthermore, open issues in protocol design are discussed, as well as other challenges associated with the use of LoRa in the deployment of FANETs.

Mission-based PTR triangle for multi-UAV systems flight planning

The Unmanned Aerial Vehicle (UAV) domain, especially the design of UAV cooperative systems, is one of the most important research and applied fields nowadays. The multi-UAV system design is a wealth of research points under the flight planning umbrella, including the design of communication protocols for the Flying Ad-hoc Network (FANET) connecting UAVs, and the inevitable flight design aspects which are the mission planning and path planning. With the fact that topology and routing are the communication design parts most affected by FANET's highly dynamic 3D movement, this paper introduces a design concept for multi-UAV system flight plan that has been named mission-based PTR triangle to indicate the importance of joint optimization of the three design pillars: Path planning, Topology control, and Routing strategy based on mission requirements. These aspects are very interconnected; they determine the UAV positions and relative placement and how the UAVs are connected; any of them can influence or be influenced by the other. They can be regarded as a single process and the trade-off between their parameters controls their influence. In addition, most of the flight planning operations are confined to a PTR triangle its edges represent cross-layer or joint optimization associations, results in different optimization cases to support adaptive optimality suitable to the dynamic nature of FANET, design constraints, and variety in mission scenarios. To this end, this paper sheds light on and reviews the work done on each process and on the mission analysis, including classifications, approaches, and examples of separate and cross-layered optimization. The paper drew a roadmap for flight planning employing the mission-based PTR triangle design approach with a proof-of-concept to the PTR joint optimization ideas. The paper serves as an entry point to the interested researchers in the field of UAV systems design.

Traffic Flow Optimization for UAVs in Multi-Layer Information-Centric Software-Defined FANET

Unmanned Aerial Vehicles (UAVs) have received significant research interest from academia due to their on-demand content distribution capabilities using mobile edge computation and the next-generation Flying Ad-hoc Network (FANET). With the addition of Software-Defined Networking (SDN) and network virtualization, these UAVs have transformed into three-dimensional distributed heterogeneous networks. However, the softwarized UAV-based communication is prone to high latency, energy consumption, resource constraints, and link failures. Hence, content orchestration has become a significant challenge. Information-Centric Networking (ICN) uses content-based rapid data dissemination in the dynamic wireless scenario. However, ICN-based content discovery and distribution have not been explored extensively for UAV-assisted networks. In this work, we propose a UAV-assisted multi-layer IC-SDN solution to tackle the content distribution challenges using distributed controllers placed hierarchically in the edge and cloud tiers. Besides, we formulate the traffic optimization problem into a joint forwarding and flow scheduling problem using M/M/1 queueing allocation model and propose a heuristic edge-cloud traffic flow assignment solution that allocates requests based on the service type and device location. We evaluate the proposed solution in a simulation environment considering the mobility principle of FANET nodes. Besides, the effectiveness of the optimization solution and the performance gains are evaluated analytically. The simulation and numerical results show that the proposed optimization model is efficient as compared to other solutions, in maximizing throughput and minimizing computational latency, delay, and packet loss.

A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme

Clustering methods are promising tools for ensuring the network scalability and maintainability of large-scale flying ad hoc networks (FANETs). However, due to the high mobility and limited energy resources of unmanned aerial vehicles (UAVs), it is difficult to maintain the network reliability and extend the network life of FANETs. In this paper, a new K-means algorithm is developed, and a dynamic transmission power of the cluster heads based clustering (DTPCH-C) scheme is proposed. The goal of this scheme is presented for FANETs to improve the reliability and lifetime of FANETs. Firstly, the optimal number of clusters is calculated and the initial UAV clusters are set up by a K-means algorithm. Then, using a weighted clustering algorithm, the adaptive node degree, the node energy and the distance from the cluster head are weighted and summed for the cluster head election. In the process of inter-cluster communication, the cluster head adjusts its transmit power in real-time through meshing and mobile prediction, thus saving the energy consumption and improving the network lifetime. The proposed DTPCH-C simultaneously optimizes the cluster number, the cluster head energy consumption, the selected cluster head, and the cluster maintenance process. The simulation results show that compared with traditional clustering methods, the proposed DTPCH-C has obvious advantages in terms of the network reliability, network life, and energy consumption.

An energy-aware routing method using firefly algorithm for flying ad hoc networks

Flying ad-hoc networks (FANETs) include a large number of drones, which communicate with each other based on an ad hoc model. These networks provide new opportunities for various applications such as military, industrial, and civilian applications. However, FANETs have faced with many challenges like high-speed nodes, low density, and rapid changes in the topology. As a result, routing is a challenging issue in these networks. In this paper, we propose an energy-aware routing scheme in FANETs. This scheme is inspired by the optimized link state routing (OLSR). In the proposed routing scheme, we estimate the connection quality between two flying nodes using a new technique, which utilizes two parameters, including ratio of sent/received of hello packets and connection time. Also, our proposed method selects multipoint relays (MPRs) using the firefly algorithm. It chooses a node with high residual energy, high connection quality, more neighborhood degree, and higher willingness as MPR. Finally, our proposed scheme creates routes between different nodes based on energy and connection quality. Our proposed routing scheme is simulated using the network simulator version 3 (NS3). We compare its simulation results with the greedy optimized link state routing (G-OLSR) and the optimized link state routing (OLSR). These results show that our method outperforms G-OLSR and OLSR in terms of delay, packet delivery rate, throughput, and energy consumption. However, our proposed routing scheme increases slightly routing overhead compared to G-OLSR.

3D Hybrid Localization Algorithm for Mitigating NLOS Effects in Flying Ad Hoc Networks

Positions of unmanned aerial vehicles (UAVs) are typically obtained using the global positioning system (GPS). However, in GPS-denied or GPS-degraded environments, ad hoc networks with flying sensor nodes are used for UAV localization. In this study, we propose a novel three-dimensional (3D) localization algorithm for UAVs in flying ad hoc sensor networks. Interacting multiple model probability data association and finite impulse response filters are integrated in our hybrid localization algorithm. The non-line-of-sight condition can be overcome using the proposed algorithm, which is demonstrated through 3D localization simulations based on flying ad hoc networks.