Reinforce Based Optimization in Wireless Communication Technologies and Routing Techniques Using Internet of Flying Vehicles

Khan, Inam Ullah; Zukhraf, Syeda Zillay Nain; Abdollahi, Asrin; Imran, Shahbaz Ali; Qureshi, Ijaz Mansoor; Aziz, Muhammad Adnan; Shah, Syed Bilal Hussian

doi:10.1145/3440749.3442621

Cited by 14 publications

(7 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Numerous routing protocols can also be merged based on the task demands. Some protocols rely on topology-based routing protocols, such as the Topology Construction Method (TCM) [34], Distributed Priority Tree-Based Routing Protocol (DPTR) [35], Topology Change Awarding Based Routing Choosing Scheme (TARCS) [36], and Q-learning-based topology-aware routing (QTAR) [37], and Temporally ordered routing algorithm (TORA) [38]. Also, other researchers have incorporated software defined networks (SDN) in topology aware routing for FANETs.…”

Section: A Topology Aware Routing Protocols (Tarps)mentioning

confidence: 99%

Cluster-Based Routing Protocols for Flying Ad Hoc Networks (FANETs)

2022

View full text Add to dashboard Cite

Flying Ad-Hoc Network (FANET) is a set of Unmanned Aerial Vehicles (UAVs) interconnected wirelessly. FANETs self-organize and provide low-cost, adaptable, and simple-to-implement flying nodes, enabling them to complete complicated tasks more quickly and collectively. The high mobility of nodes and the highly dynamic topology pose challenges to communication design, particularly when creating a routing protocol for UAV networks; this has inspired researchers to contribute and develop this technology. Hierarchical routing technique known as clustering is necessary to offer scalability, survivability, and distribute payload among UAVs to maintain the performance. This study has proposed a comprehensive survey of the cluster-based routing protocols (CBRPs) in terms of their strengths, weaknesses, specific applications, method, number of nodes, and future improvements for serving FANETs. Moreover, 21 CBRPs based FANETs were reviewed in terms of their topology, challenges, scalability, characteristics, clustering strategy, outstanding features, cluster head (CH) selection, routing metrics, and performance measures.In addition, open issues that need to be addressed in future studies in the field of routing protocols for UAV networks were also debated. INDEX TERMSClustering algorithm, flying ad hoc network, routing protocol, scalability, unmanned aerial vehicle.

show abstract

Section: A Topology Aware Routing Protocols (Tarps)mentioning

confidence: 99%

Cluster-Based Routing Protocols for Flying Ad Hoc Networks (FANETs)

2022

View full text Add to dashboard Cite

show abstract

“…Boukerche et al [24] consider mobile phones as the gateway and Bluetooth profiles as a proxy while communicating with the Bluetooth devices. These routing methods are more feasible and best suited for processing and power constraints [25][26][27][28][29][30][31].…”

Section: Related Workmentioning

confidence: 99%

Routing with Cooperative Nodes Using Improved Learning Approaches

Raja¹,

Satheesh²,

Dennis³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

In IoT, routing among the cooperative nodes plays an incredible role in fulfilling the network requirements and enhancing system performance. The evaluation of optimal routing and related routing parameters over the deployed network environment is challenging. This research concentrates on modelling a memory-based routing model with Stacked Long Short Term Memory (s − LSTM) and Bi-directional Long Short Term Memory (b − LSTM). It is used to hold the routing information and random routing to attain superior performance. The proposed model is trained based on the searching and detection mechanisms to compute the packet delivery ratio (PDR), end-to-end (E2E) delay, throughput, etc. The anticipated s − LSTM and b − LSTM model intends to ensure Quality of Service (QoS) even in changing network topology. The performance of the proposed b − LSTM and s − LSTM is measured by comparing the significance of the model with various prevailing approaches. Sometimes, the performance is measured with Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for measuring the error rate of the model. The prediction of error rate is made with Learning-based Stochastic Gradient Descent (L − SGD). This gradual gradient descent intends to predict the maximal or minimal error through successive iterations. The simulation is performed in a MATLAB 2020a environment, and the model performance is evaluated with diverse approaches. The anticipated model intends to give superior performance in contrast to prevailing approaches.

show abstract

“…Ant colony optimization approaches, or metaheuristics, in general, have shown improved dependability and performance compared to other conventional optimal path selection algorithms [27]. The progress of wireless communication technology was thoroughly compared and evaluated for various parameter configurations in this research study [28]. The architecture, optimization techniques, mobility models, heuristic calculations, and routing protocols used in flying ad hoc networks were all explicitly covered in the introduction [29].…”

Section: Related Workmentioning

confidence: 99%

Energy Based Random Repeat Trust Computation in Delay Tolerant Network

Dheenathayalan¹,

Paramasivan²

2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

As the use of mobile devices continues to rise, trust administration will significantly improve security in routing the guaranteed quality of service (QoS) supply in Mobile Ad Hoc Networks (MANET) due to the mobility of the nodes. There is no continuance of network communication between nodes in a delay-tolerant network (DTN). DTN is designed to complete recurring connections between nodes. This approach proposes a dynamic source routing protocol (DSR) based on a feed-forward neural network (FFNN) and energybased random repetition trust calculation in DTN. If another node is looking for a node that swerved off of its path in this situation, routing will fail since it won't recognize it. However, in the suggested strategy, nodes do not stray from their pathways for routing. It is only likely that the message will reach the destination node if the nodes encounter their destination or an appropriate transitional node on their default mobility route, based on their pattern of mobility. The EBRRTC-DTN algorithm (Energy based random repeat trust computation) is based on the time that has passed since nodes last encountered the destination node. Compared to other existing techniques, simulation results show that this process makes the best decision and expertly determines the best and most appropriate route to send messages to the destination node, which improves routing performance, increases the number of delivered messages, and decreases delivery delay. Therefore, the suggested method is better at providing better QoS (Quality of Service) and increasing network lifetime, tolerating network system latency.

show abstract

Reinforce Based Optimization in Wireless Communication Technologies and Routing Techniques Using Internet of Flying Vehicles

Cited by 14 publications

References 31 publications

Cluster-Based Routing Protocols for Flying Ad Hoc Networks (FANETs)

Cluster-Based Routing Protocols for Flying Ad Hoc Networks (FANETs)

Routing with Cooperative Nodes Using Improved Learning Approaches

Energy Based Random Repeat Trust Computation in Delay Tolerant Network

Contact Info

Product

Resources

About