Unmanned Aerial Vehicle Ad Hoc Networks Research Articles

Secure UAV adhoc network with blockchain technology.

Recent advances in aerial robotics and wireless transceivers have generated an enormous interest in networks constituted by multiple compact unmanned aerial vehicles (UAVs). UAV adhoc networks, i.e., aerial networks with dynamic topology and no centralized control, are found suitable for a unique set of applications, yet their operation is vulnerable to cyberattacks. In many applications, such as IoT networks or emergency failover networks, UAVs augment and provide support to the sensor nodes or mobile nodes in the ground network in data acquisition and also improve the overall network performance. In this situation, ensuring the security of the adhoc UAV network and the integrity of data is paramount to accomplishing network mission objectives. In this paper, we propose a novel approach to secure UAV adhoc networks, referred to as the blockchain-assisted security framework (BCSF). We demonstrate that the proposed system provides security without sacrificing the performance of the network through blockchain technology adopted to the priority of the message to be communicated over the adhoc UAV network. Theoretical analysis for computing average latency is performed based on queuing theory models followed by an evaluation of the proposed BCSF approach through simulations that establish the superior performance of the proposed methodology in terms of transaction delay, data secrecy, data recovery, and energy efficiency.

Distributed MA‐IDDPG‐OLSR based stable routing protocol for unmanned aerial vehicle ad‐hoc network

AbstractIn unmanned aerial vehicle ad‐hoc network (UANET), the node speed of unmanned aerial vehicles (UAVs) may reach up to 400 km/h. The fast or slow movement of UAV nodes leads to different speeds of topology change of the nodes. Traditional optimized link state routing (OLSR) protocol cannot adaptively adjust the routing update period when the network topology changes, which may lead to the nodes calculating incorrect routing tables. This increases the average end‐to‐end delay and packet loss rate for packet transmission. To enhance the adaptability of OLSR routing protocol to network topology changes, this paper proposes a multi‐agent independent deep deterministic policy gradient‐OLSR (MA‐IDDPG‐OLSR) routing protocol based on distributed multi‐agent reinforcement learning. The protocol deploys DDPG algorithm on each UAV node, and each UAV node adaptively adjusts the Hello and TC message sending intervals, according to the one‐hop neighbouring nodes as well as its own state. Simulation results show that the proposed protocol is able to improve the throughput and reduce the packet loss rate as compared to traditional AODV, GRP, OLSR, and distributed multiple‐agent independent proximal policy optimization‐OLSR (MA‐IPPO‐OLSR), distributed multiple‐agent independent twin delayed deep deterministic policy gradient‐OLSR (MA‐ITD3‐OLSR) routing protocols. Since MA‐IDDPG‐OLSR relies only on local information, there is a minor performance degradation in MA‐IDDPG‐OLSR compared to centralized single‐agent DQN‐OLSR routing protocol. But it is more suitable to a completely distributed UAV network without a centralized node.

A dual-cluster-head based medium access control for large-scale UAV ad-hoc networks

A dual-cluster-head based medium access control for large-scale UAV ad-hoc networks

Centroid-Guided Target-Driven Topology Control Method for UAV Ad-hoc Networks Based On Tiny Deep Reinforcement Learning Algorithm

Centroid-Guided Target-Driven Topology Control Method for UAV Ad-hoc Networks Based On Tiny Deep Reinforcement Learning Algorithm

K-means online-learning routing protocol (K-MORP) for unmanned aerial vehicles (UAV) adhoc networks

Unmanned Aerial Vehicles (UAVs) have become a hot topic due to their flexible architecture adopted in many wireless technologies. In UAV ad hoc networks, traditional routing protocols with a fixed topology are ineffective due to dynamic mobility and unstable paths. Therefore, the mobility patterns of UAVs challenge efficient and reliable routing in UAV networks. Traditional routing algorithms are often based on assumptions of static nodes and predetermined network topologies. Which are not suitable for the dynamic and unpredictable nature of UAV mobility patterns. To address this problem, this paper introduces a K-means online learning routing protocol (KMORP) scheme employing a Markov mobility model for UAV ad hoc networks. Initially, the proposed method utilizes a 3D Gauss Markov mobility model to accurately estimate UAV positions, while K-means online learning is adopted for dynamic clustering and load balancing. Designed for real-time data processing, KMORP is well suited for UAV ad hoc networks, quickly adapting to network environmental changes such as UAV mobility, interference, and signal degradation to ensure efficient data transmission and communication. This is achieved while reducing the overall communication overhead and increasing the packet delivery ratio(PDR%). In the routing phase, the proposed scheme employs inter-cluster forwarding nodes to transmit messages among different clusters. Extensive simulations demonstrate the performance of the proposed KMORP, showing a 38% better PDR compared to OLSR and over 50% less end-to-end(E2E) delay compared to typical K-Means. Furthermore, the proposed KMORP exhibited an average throughput of 955 kbps, showing a substantial improvement in network performance. The results underscore that the proposed KMORP outperforms existing techniques in terms of PDR, E2E delay, and throughput.

UAV Ad Hoc Network Routing Algorithms in Space–Air–Ground Integrated Networks: Challenges and Directions

With the rapid development of 5G and 6G communications in recent years, there has been significant interest in space–air–ground integrated networks (SAGINs), which aim to achieve seamless all-area, all-time coverage. As a key component of SAGINs, flying ad hoc networks (FANETs) have been widely used in the agriculture and transportation sectors in recent years. Reliable communication in SAGINs requires efficient routing algorithms to support them. In this study, we analyze the unique communication architecture of FANETs in SAGINs. At the same time, existing routing protocols are presented and clustered. In addition, we review the latest research advances in routing algorithms over the last five years. Finally, we clarify the future research trends of FANET routing algorithms in SAGINs by discussing the algorithms and comparing the routing experiments with the characteristics of unmanned aerial vehicles.

Performance Evaluation of Standard and Modified OLSR Protocols for Uncoordinated UAV Ad-Hoc Networks in Search and Rescue Environments

Widespread usage of unmanned aerial vehicles (UAVs) in new and emerging applications needs dynamic and adaptive networking. The development of routing protocols for UAV ad hoc networks faces numerous issues because of the unique characteristics of UAVs, such as rapid mobility, frequent changes in network topology, and limited energy consumption. The Optimized Link State Routing (OLSR) protocol seems to be a promising solution as it offers improved delay performance. It is expected that OLSR will satisfy the strict demands of real-time UAV applications such as “search and rescue” (SAR) missions as it involves the most recent update of routing information. The classical OLSR routing protocol and its enhanced versions, D-OLSR, ML-OLSR, and P-OLSR, use different techniques to make an appropriate decision for routing packets. These routing techniques consider the quality of a wireless link, type of antenna, load, and mobility-aware mechanism to select the best UAV to send the message to the destination. This study evaluates and examines the performance of the original and modified OLSR routing protocols in UAV ad hoc networks for three SAR scenarios: (1) increasing mobility, (2) increasing scalability, and (3) increasing the allowed space of UAVs. It analyzes and validates the performance of the four OLSR-based routing protocols. It determines the best OSLR routing protocol by taking into account the packet delivery ratio, latency, energy consumption, and throughput. The four routing protocols and the SAR scenarios were simulated using NS-3.32. Based on the simulation results, ML-OLSR outperforms OLSR, D-OLSR, and P-OLSR in the considered measures.

ICRA: An Intelligent Clustering Routing Approach for UAV Ad Hoc Networks

As an important means of obtaining information of marine situation, the marine monitoring system relying on UAV has been paid more and more attention by all countries in the world, and the demand for tasks is growing continually. In UAV ad hoc networks, routing protocols with immutable routing policies that lack flexibility are generally incapable of maintaining effective performance due to the complicated and rapidly changing environmental situation and application requirements. In this paper, we propose an intelligent clustering routing approach (ICRA) for UANETs. The ICRA is composed of three components: the clustering module, the clustering strategy adjustment module and the routing module. In the clustering process, each node needs to calculate its utility. In order to maintain high topology stability and long network lifetime in different network states, the reinforcement learning based clustering strategy adjustment module needs continuous learning the benefits brought by adopting different strategies to calculate the nodes utility in a specific network state. With the learned knowledge, clustering strategy adjustment module could determine the optimal clustering strategy according to the current network state. In the routing phase, the proposed scheme can reduce the end-to-end delay and improve the packet delivery rate by introducing inter-cluster forwarding nodes to forward messages among different clusters. Extensive experiments have been conducted to verify ICRA’s robustness and superiority over existing schemes. The results demonstrate that ICRA could achieve better performance than its state-of-the-art counterparts with regard to the clustering efficiency, topology stability, energy efficiency and quality of service.

TAGKA: Threshold Authenticated Group Key Agreement Protocol Against Member Disconnect for UANET

The development of unmanned aerial vehicle (UAV) technology has been advancing rapidly and is widely applied in various domains. Compared to a single UAV, the multi-UAV system, known as the UAV Ad Hoc Network (UANET), can collaborate to accomplish complex tasks more efficiently. Due to the UAVs communicating through open wireless channels, they are vulnerable to various attacks. Group key agreement protocols are often used to guarantee the security of UANET communication. However, the high dynamics of UAVs and the instability of the wireless links often result in certain UAVs temporarily losing connection with the group, rendering them incapable of participating in the group key agreement process. Regrettably, existing literature on group key agreement does not consider this issue, which makes them not applicable to UANET. To address this issue, we propose a threshold authenticated group key agreement protocol (TAGKA) that can resist the disconnection of group members. Then, considering that the temporarily disconnected UAVs need to obtain the latest group key after the communication links are recovered, we design a group key recovery protocol. The security of the scheme is proved under the random oracle model. Moreover, formal security verification using the Tamarin and rigorous informal security analysis is also performed. Finally, we use NS-3 to simulate the protocol, and the experimental results show that our protocol can be effectively applied to UANET.

Stable routing protocol for unmanned aerial vehicle ad‐hoc networks based on DQN‐OLSR

In unmanned aerial vehicle ad-hoc network (UANET), the network topology changes with time due to the movement of the unmanned aerial vehicles (UAVs), which brings great challenges to the design of the routing protocol. In traditional routing protocols, when UANET topology changes, nodes cannot dynamically update neighbour nodes and topology information, and routing table calculation cannot accurately reflect the actual transmission path. As a result, network cannot meet the quality of service (QoS) requirements such as low end-to-end delay, high throughput and low packet loss rate. This paper proposes a dynamically optimized link state routing (OLSR) protocol based on Deep Q-Network algorithm (DQN-OLSR). In this protocol, each node first adjusts the sending interval of Hello messages adaptively in real time, according to the position and speed information of its neighbour nodes. Then the protocol uses the DQN algorithm to dynamically adjust the flooding interval of topology control (TC) messages to improve the routing update capability of nodes. The simulation verifies that the UANETs under this protocol have higher throughput and less packet loss rate than ad-hoc on-demand distance vector (AODV), grid routing protocol (GRP) and OLSR protocols, at different movement speeds in random waypoint (RWP) and random walk mobile models. Under nomadic as well as pursue mobile models, DQN-OLSR performs consistently with OLSR QoS performance, with the best performance among all four protocols. By further adding positioning errors to the nodes, it shows that the proposed protocol has good robustness, and the QoS performance degradation keeps within a low level.

Joint Resource Allocation on Slot, Space and Power Towards Concurrent Transmissions in UAV Ad Hoc Networks

With innovative applications of unmanned aerial vehicle (UAV) ad hoc networks in various areas, their demands on broad bandwidth, large capacity and low latency become prominent. The combination of millimeter wave, directional antenna and time division multiple access techniques, which enables concurrent transmissions, is promising to deal with it. In this paper, we study the resource allocation problem in UAV ad hoc networks. Specifically, the slot assignment, antenna boresight and transmit power are jointly optimized to promote the network capacity. First, we formulate the optimization problem as the maximization of the fairness-weighted network capacity, subject to the constraint on priority guarantee. Then, because the formulated problem is a mixed integer non-linear programming problem (MINLP), which is NP-hard, two algorithms called dual-based iterative search algorithm (DISA) and sequential exhausted allocation algorithm (SEAA) are respectively proposed to efficiently solve it with acceptable complexity. DISA slacks the MINLP into a continuous-variable optimization problem and solves it with the Lagrangian dual method in an iterative manner. As a heuristic method, SEAA schedules links sequentially, i.e., from high-priority to low-priority ones. Numerical results demonstrate that both DISA and SEAA can efficiently allocate resources for UAVs, while guaranteeing the fairness and priority of links.

DDQN with Prioritized Experience Replay-Based Optimized Geographical Routing Protocol of Considering Link Stability and Energy Prediction for UANET.

Unmanned aerial vehicles (UAVs) are important equipment for efficiently executing search and rescue missions in disaster or air-crash scenarios. Each node can communicate with the others by a routing protocol in UAV ad hoc networks (UANETs). However, UAV routing protocols are faced with the challenges of high mobility and limited node energy, which hugely lead to unstable link and sparse network topology due to premature node death. Eventually, this severely affects network performance. In order to solve these problems, we proposed the deep-reinforcement-learning-based geographical routing protocol of considering link stability and energy prediction (DSEGR) for UANETs. First of all, we came up with the link stability evaluation indicator and utilized the autoregressive integrated moving average (ARIMA) model to predict the residual energy of neighbor nodes. Then, the packet forward process was modeled as a Markov Decision Process, and according to a deep double Q network with prioritized experience replay to learn the routing-decision process. Meanwhile, a reward function was designed to obtain a better convergence rate, and the analytic hierarchy process (AHP) was used to analyze the weights of the considered factors in the reward function. Finally, to verify the effectiveness of DSEGR, we conducted simulation experiments to analyze network performance. The simulation results demonstrate that our proposed routing protocol remarkably outperforms others in packet delivery ratio and has a faster convergence rate.

Dynamic value iteration networks for the planning of rapidly changing UAV swarms

In an unmanned aerial vehicle ad-hoc network (UANET), sparse and rapidly mobile unmanned aerial vehicles (UAVs)/nodes can dynamically change the UANET topology. This may lead to UANET service performance issues. In this study, for planning rapidly changing UAV swarms, we propose a dynamic value iteration network (DVIN) model trained using the episodic Q-learning method with the connection information of UANETs to generate a state value spread function, which enables UAVs/nodes to adapt to novel physical locations. We then evaluate the performance of the DVIN model and compare it with the non-dominated sorting genetic algorithm II and the exhaustive method. Simulation results demonstrate that the proposed model significantly reduces the decision-making time for UAV/node path planning with a high average success rate.

Blockchain-Empowered Trusted Networking for Unmanned Aerial Vehicles in the B5G Era

An unmanned aerial vehicle ad hoc network (UAANET) is an information sensing, analyzing, and transmitting network formed by multiple coordinated and collaborating UAVs. It is an emerging networking technology with a broad market prospect, but faces the problems of high dynamic topology and lack of trust. To address these issues, this article proposes a blockchain-em-powered trusted networking framework for UAANET, as well as the corresponding network architecture, protocol stack, key control signaling, and algorithms. To be more specific, blockchain is designed as a layer of distributed peer-to-peer security running on top of the physical UAANET. Three types of control signaling are designed, namely HELLO packet, topology control packet, and consensus packet. A max-min stable routing algorithm with three-dimensional link duration and a multipoint-relay-driven delegated Byzantine fault tolerance consensus mechanism are proposed for trusted networking of UAANET. The involved technical background and challenges are also detailed. These works together provide good references for scale deployment of UAANET in the near future.

Towards Trust Model in Unmanned Aerial Vehicle Ad Hoc Networks

Unmanned Aerial Vehicle ad hoc networks (UAANETs) are originally designed to work in a cooperative environment. These networks are vulnerable to a wide range of attacks due to the lack of predefined infrastructure and the dynamic topology. Security in ad hoc networks, in general, is handled through authentication and encryption. This can be considered as a heavy way to protect the network due to the lack of resources in the nodes. However, trust can be introduced to address a light weight solution for some security issues. In this paper, we focus on the concept of decentralized trust to design an efficient and trustful routing protocol, and ensure stable routes between nodes in spite of the rapidly changing topology, and to provide a mechanism for detecting malicious incorrect packet forwarding attacks. The proposed light-weight trust-quality routing protocol (TQAODV) provides two main functionalities: monitoring the behavior of the neighboring nodes and computing the trust value based on the historical information in the network. Moreover, the new proposed model reduces routing overhead and route discovery frequency. The simulations we used in NS-2 show that the proposed routing scheme gives better performance against attacks compared to the traditional Ad-hoc On-demand Distance Vector (AODV), and improves the packets delivery ratio with about 15%, routing packets overhead and average delay with about 20%, compared to trust AODV.

An Optimized Routing OLSR Protocol with Low Control Overhead for UAV Ad Hoc Networks

OLSR (Optimized Link State Routing) protocol is a priori Routing protocol applied in MANET network. All nodes in the network have Routing information to other nodes locally and need to send a large number of control messages to maintain the topology information of the whole network. In UAV application scenarios, with the increase of node density, the control overhead will be too high and the problems of hidden terminals will be intensified. Secondly, due to the fast moving speed of UAVs, when the neighboring UAVs have moved out of the communication range of the local node, the local node still retains the routing information of the neighbor node, leading to the problem of packet loss when the link information is updated behind time. Aiming at the above problems, an OLSR protocol with low control overhead and optimal routing is proposed. The protocol uses the incremental HELLO message mechanism to ensure that only incremental information is exchanged when the network topology changes slowly, and the control overhead is greatly reduced. When calculating routing, in the range of n hops, the more stable link is selected under the same number of hops. Simulation results show that the proposed algorithm can significantly improve the success rate of packet transmission and end-to-end delay, and reduce the system control overhead.

A traffic-aware Q-network enhanced routing protocol based on GPSR for unmanned aerial vehicle ad-hoc networks

In dense traffic unmanned aerial vehicle (UAV) ad-hoc networks, traffic congestion can cause increased delay and packet loss, which limit the performance of the networks; therefore, a traffic balancing strategy is required to control the traffic. In this study, we propose TQNGPSR, a traffic-aware Q-network enhanced geographic routing protocol based on greedy perimeter stateless routing (GPSR), for UAV ad-hoc networks. The protocol enforces a traffic balancing strategy using the congestion information of neighbors, and evaluates the quality of a wireless link by the Q-network algorithm, which is a reinforcement learning algorithm. Based on the evaluation of each wireless link, the protocol makes routing decisions in multiple available choices to reduce delay and decrease packet loss. We simulate the performance of TQNGPSR and compare it with AODV, OLSR, GPSR, and QNGPSR. Simulation results show that TQNGPSR obtains higher packet delivery ratios and lower end-to-end delays than GPSR and QNGPSR. In high node density scenarios, it also outperforms AODV and OLSR in terms of the packet delivery ratio, end-to-end delay, and throughput.

The Research of Slot Adaptive 4D Network Clustering Algorithm Based on UAV Autonomous Formation and Reconfiguration

Being independent of any fixed equipment, Ad Hoc wireless sensor networks, a kind of acentric and self-organized wireless network, possesses some features such as easiness of deployment, strong invulnerability and flexibility of networking, which leads to a promising application prospect in terms of UAV military and civilian use. This paper proposes a new slot adaptive 4D network clustering algorithm based on UAV autonomous formation and reconfiguration to solve the problem of UAV Ad Hoc network such as networking confusion, poor network reconstruction performance, huge energy consumption and other issues. The algorithm can optimize the topology of UAVs network. We build the network topology and generate clustering network by the slot adaptive 4D network clustering algorithm in Matlab. According to the real combat of UAV, four states are simulated and analyzed. The simulation results validate the feasibility of the slot adaptive 4D network clustering algorithm. The clustering structure generated by the slot adaptive 4D network clustering algorithm is robust and the algorithm is suitable for the UAV group operation.

Research on Routing Algorithm of UAV AD Hoc Network Based on Decision Tree

Research on Routing Algorithm of UAV AD Hoc Network Based on Decision Tree

Securing ICN-Based UAV Ad Hoc Networks with Blockchain

Named data networking (NDN) enables fast and efficient content dissemination in mission-critical unmanned aerial vehicle ad hoc networks (UAANETs); however, its in-network caching mechanism brings a new security challenge: content poisoning. Poisoned content can contaminate the cache on the routers and isolate valid content from the network, leading to performance degradation or denial of service. To mitigate such attacks and enhance network-layer trust of NDN-based UAANETs, this article proposes a novel and systematic framework that integrates interest-key-content binding (IKCB), forwarding strategy, and on-demand verification to efficiently discover poisoned content. To further provide decentralized IKCB store and detect internal attackers, we introduce a lightweight permissioned blockchain system over NDN and develop a scalable adaptive delegate consensus algorithm. Our experimental results have demonstrated that our proposed framework can effectively purge poisoned content with low overhead, and our algorithms achieve great performance to fit UAANETs.