Intelligent Hello Dissemination Model for FANET Routing Protocols

Ayub, Muhammad Shoaib; Adasme, Pablo; Carrillo, Dick; Rosa, Renata Lopes; Rodríguez, Demóstenes Zegarra

doi:10.1109/access.2022.3170066

Cited by 23 publications

(7 citation statements)

References 58 publications

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“…However, this method is an OLSR routing protocol parameter‐seeking approach and does not allow adaptive adjustment of parameters in time with network interaction. Literature [13] proposes a method for adaptive adjustment of the sending period of Hello messages based on centralized deep deterministic policy gradient (DDPG) algorithm. The method takes network traffic matrix as the input of DDPG algorithm and outputs the sending period of Hello messages through interactive learning.…”

Section: Related Workmentioning

confidence: 99%

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

Zeng,

Zhou,

Liu

et al. 2024

IET Communications

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In 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.

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Section: Related Workmentioning

confidence: 99%

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

Zeng,

Zhou,

Liu

et al. 2024

IET Communications

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“…have recently been proposed for designing intelligent routing systems. The motivation behind such approach lies in the ability and high potential of these methods to solve various challenges in ad hoc networks ( MANET, VANET, FANET, ...) such as end-to-end delay, packet delivery rate, route stability, energy consumption and routing overhead [15]- [17]. With all this in mind, paper [18] presents a detailed survey of the main advantages and areas of use of the above techniques in VANET.…”

Section: A Intelligent Routingmentioning

confidence: 99%

An Intelligent Machine Learning Based Routing Scheme for VANET

et al. 2022

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Today, Vehicular Ad-hoc Networks (VANET) have become an interesting research topic for developing Intelligent Transport Systems. In urban environments, vehicles move continuously and at different speeds, which leads to frequent changes in the network topology. The main issue faced in an urban scenario is the performance of routing protocols when delivering data from one vehicle to another. This paper introduces ECRDP, an Efficient Clustering Routing approach using a new clustering algorithm based on Density Peaks Clustering (DPC) and Particle Swarm Optimization (PSO). First, the PSO algorithm is applied to determine the cluster heads, or a new fitness function for finding the best solutions is formulated using the DPC algorithm. Next, clustering is performed based on the reliability of links parameter between vehicles. Then, a maintenance phase is proposed to update the cluster heads and redistribute the vehicles in the clusters. Finally, the effectiveness of the suggested scheme is evaluated by a simulation operated by MATLAB on a real urban scenario. The results achieved show an overall increase in stability, proven by a reduction in change rate by 74%, and an improvement in performance indicated by an increase in intracluster throughput by 34% and inter-cluster by 47%, as well as an overall reduction of average delay by 16%.

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“…Then the positions of node i and neighbour node j after t move time can be calculated. The calculation equation is shown in the system of Equations (10). (10) where…”

Section: Adaptive Adjustment Of Hello Message Interval Based On Node ...mentioning

confidence: 99%

“…This protocol dynamically adjusts the broadcast period of Hello messages according to the topology change of the network, immediately decreasing the Hello broadcast period to the optimal value when a node joins in, and setting the broadcast period to the protocol default value when the topology no longer changes, thus improving the QoS of the network. Literature [10] proposes an intelligent Hello propagation model based on reinforcement learning algorithms (AI‐Hello). This protocol can adaptively adjust the sending interval of Hello messages to avoid energy consumption.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Intelligent Hello Dissemination Model for FANET Routing Protocols

Cited by 23 publications

References 58 publications

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

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

An Intelligent Machine Learning Based Routing Scheme for VANET

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

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