Extending IEEE 802.11s Mesh Routing for 3-D Mobile Drone Applications in ns-3

Bautista, Oscar G.; Akkaya, Kemal

doi:10.1145/3389400.3389406

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…In the simulation tests, a 3D space scenario [40], [41] is used such as a practical situation, and the results showed that the proposed algorithm decreased the energy consumption by reducing unnecessary overheads [42], without having degradation in network throughput.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Hello Dissemination Model for FANET Routing Protocols

et al. 2022

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The routing mechanisms in flying ad-hoc networks (FANETs) using unmanned aerial vehicles (UAVs) have been a challenging issue for many reasons, such as its high speed and different directions of use. In FANETs, the routing protocols send hello messages periodically for the maintenance of routes. However, the hello messages that are sent in the network increase the bandwidth wastage on some occasions and the excessive number of hello messages can also cause the problem of energy loss. Scarce works deal with the problem of excessive hello messages in dynamic UAVs scenarios, and treat several other problems, such as bandwidth and energy wastage simultaneously. Generally, the existing solutions configure the hello interval to an excessive long or short time period originating delay in neighbors discovery. Thus, a selfacting approach is necessary for calculating the exact number of hello messages with the aim to reduce the bandwidth wastage of the network and the energy loss; this approach needs to be low complex in terms of computational resource consumption. In order to solve this problem, an intelligent Hello dissemination model, AI-Hello, based on reinforcement learning algorithms, that adapts the hello message interval scheme is proposed to produce a dense reward structure, and facilitating the network learning. Experimental results, considering FANET dynamic scenarios of high speed range with 40 UAVs, show that the proposed method implemented in two widely adopted routing protocols (AODV and OLSR) saved 30.86% and 27.57% of the energy consumption in comparison to the original AODV and OLSR protocols, respectively. Furthermore, our proposal reached better network performance results in relation to the state-of-the-art methods that are implemented in the same protocols, considering parameters, such as routing overhead, packet delivery ratio, throughput and delay.INDEX TERMS Unmanned aerial vehicles, FANET, hello messages, energy-efficient networking.

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Section: Introductionmentioning

confidence: 99%

Intelligent Hello Dissemination Model for FANET Routing Protocols

et al. 2022

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“…However, it optionally allows to write simulation scripts in Python. It has been widely used in the implementation and analysis of different types of wireless networks such as the work done in [86][87][88][89]. In all of our simulations, the ns-3.32 simulator version is used.…”

Section: Ns-3 Simulatormentioning

confidence: 99%

Adaptive Multicast Routing Protocol Based on Reinforcement Learning

Mohammed

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Current Mobile Ad-hoc Network (MANET) multicasting approaches either suffer from low Packet Delivery Ratio (PDR) or high overhead. These methods rely on metrics like hop count to find the optimal path to the destination. Once the path is selected, all packets are sent over the same path as long as it is available. However, a path that is deemed optimal at a specific instance of time may not retain its optimality at a subsequent moment due to node mobility. Moreover, using a metric like hop count that does not consider link quality can lead to poor PDR, as it can favor an unreliable path over a reliable one just because it is the shortest. To tackle these concerns, Q-Learning Adaptive -Multicast Ad hoc On-Demand Distance Vector Routing (QLA-MAODV) protocol is proposed. It is an adaptive and bandwidthefficient multicast routing protocol based on Q-learning. Unlike traditional methods, QLA-MAODV prioritizes link reliability over simple metrics like hop count, aiming to build a more stable multicast tree. The protocol utilizes the periodic Group Hello (GRPH) messages to explore the environment, identifying alternative paths for use in case of path degradation. The protocol continuously updates path costs, facilitating proactive switching to more reliable paths. Simulations in Network Simulator 3 (NS-3) reveal the protocol's superiority over the traditional Multicast Ad-hoc On-demand Distance Vector (MAODV) protocol. Additionally, it outperforms a modified version, MAODV-Route Reliability (MAODV-RR), that uses link reliability as the routing metric, demonstrating improvement in PDR and reduced multicast-related overhead.

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