• Journal of Korea Multimedia Society
• /
• v.19 no.11
• /
• pp.1829-1836
• /
• 2016

Today, the application area and scope of FANET(Flying Ad Hoc Network) has been extended. As a result, FANET related research are actively conducted, but there is no decision yet as the routing protocol for FANET. In this paper, we propose the OLSR-Pds (Prediction with direction and speed) which is added a method to predict status of link for OLSR protocol. The mobility of nodes are modeled using Gauss-Markov algorithm, and relative speed between nodes were calculated by derive equation of movement, and thereby we can predict link status. An experiment for comparing AODV, OLSR and, OLSR-Pds was conducted by three factors such as packet delivery ratio, end to end delay, and routing overhead. In experiment result, we were confirm that OLSR-Pds performance are superior in these three factors. OLSR-Pds has the disadvantage that requires time-consuming calculations for link state and required for computing resources, but we were confirm that OLSR-Pds is suitable for routing to the FANET environment because it has all the characteristics of proactive protocol and reactive protocol.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.11
• /
• pp.4244-4274
• /
• 2021

The utilization of UAVs in various fields has led to the development of flying ad hoc network (FANET) technology. In a network environment with highly dynamic topology and frequent link changes, the traditional routing technology of FANET cannot satisfy the new communication demands. Traditional routing algorithm, based on geographic location, can "fall" into a routing hole. In view of this problem, we propose a geolocation routing protocol based on multi-agent reinforcement learning, which decreases the packet loss rate and routing cost of the routing protocol. The protocol views each node as an intelligent agent and evaluates the value of its neighbor nodes through the local information. In the value function, nodes consider information such as link quality, residual energy and queue length, which reduces the possibility of a routing hole. The protocol uses global rewards to enable individual nodes to collaborate in transmitting data. The performance of the protocol is experimentally analyzed for UAVs under extreme conditions such as topology changes and energy constraints. Simulation results show that our proposed QLGR-S protocol has advantages in performance parameters such as throughput, end-to-end delay, and energy consumption compared with the traditional GPSR protocol. QLGR-S provides more reliable connectivity for UAV networking technology, safeguards the communication requirements between UAVs, and further promotes the development of UAV technology.