Neural networks for MANET AODV: an optimization approach

Hua, Yang; Li, Zhimei; Li, Yong

doi:10.1007/s10586-017-1086-y

Cited by 23 publications

(11 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A similar outcome is obtained in [293], where the authors employ Hopfield neural networks for routing, achieving better usability and survivability in mobile ad hoc network application scenarios. Geyer et al represent the network using graphs, and design a dedicated Graph-Query NN to address the distributed routing problem [395].…”

Section: G Deep Learning Driven Network Controlsupporting

confidence: 64%

“…Analysis [17], [112], [235]- [266] [73], [97], [187], [267]- [291] Mobility Analysis [227], [292]- [310] User Localization [272], [273], [311]- [315] [111], [316]- [334] Wireless Sensor Networks [335]- [346], [346]- [356] Network Control [186], [293], [357]- [368] [234], [368]- [403] Network Security [185], [345], [404]- [419] [223], [420]- [429], [429]- [436] Signal Processing [378], [380], [437]- [444] [322], [445]- [458] Emerging Applications For each domain, we summarize work broadly in tabular form, providing readers with a general picture of individual topics. Most important works in each domain are discussed in more details in text.…”

Section: App-level Mobile Datamentioning

confidence: 99%

“…Network-Level Mobile Data Analysis [77], [102], [210]- [234] App-Level Mobile Data Analysis [17], [112], [235]- [266] [73], [97], [187], [267]- [291] Mobility Analysis [227], [292]- [310] User Localization [272], [273], [311]- [315] [111], [316]- [334] Wireless Sensor Networks [335]- [346], [346]- [356] Network Control [186], [293], [357]- [368] [234], [368]- [403] Network Security [185], [345], [404]- [419] [223], [420]- [429], [429]- [436] Signal Processing [378], [380], [437]- [444] [322], [445]- [458] Emerging Applications [459]- [463] Fig.…”

Section: Deep Learning Driven Mobile and Wireless Networkmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,383

838

View full text Add to dashboard Cite

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space.In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

show abstract

Section: G Deep Learning Driven Network Controlsupporting

confidence: 64%

Section: App-level Mobile Datamentioning

confidence: 99%

Section: Deep Learning Driven Mobile and Wireless Networkmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,383

838

View full text Add to dashboard Cite

show abstract

“…Therefore, improving the convergence speed of the route finding algorithm of reactive routing and applying it to FANETs will achieve better results. The more famous algorithms in reactive routing are AODV (Perkins et al, 2003 ; Yang et al, 2017 ), DSR (Johnson et al, 2001 ; Yang et al, 2019 ), etc.…”

Section: Related Workmentioning

confidence: 99%

A Genetic-Algorithm-Based Optimization Routing for FANETs

2021

Self Cite

View full text Add to dashboard Cite

In view of the characteristics of high mobility of FANETs nodes, combined with the features of Topology-based class routing protocol on-demand search, a Genetic-algorithm-based routing (GAR) protocol is proposed for FANETs which based on improved genetic algorithm for FANETs route search, and it taking into account the link stability, link bandwidth, node energy, and other factors. GAR improves the selection, crossover, and variation operators of the genetic algorithm so that GAR can finally plan an optimized route from the communication initiating node to the destination node quickly using a smaller cost. The experimental results show that GAR can largely improve the throughput, reduce the delay and improve the stability of the network, which is more suitable for FANETs.

show abstract

“…Therefore, such routes are known as on-demand routing protocols. This type of routing protocol has poor stability in highly dynamic networks and is also less secure, for example, Dynamic Source Routing (DSR) [11][12][13] and ad hoc on-demand distance vector (AODV) [11,14,15].…”

Section: Reactive Routingmentioning

confidence: 99%

An optimization routing protocol for FANETs

Hua

2019

J Wireless Com Network

Self Cite

View full text Add to dashboard Cite

With the wide-ranging application of mobile ad hoc networks, flying ad hoc networks (FANETs) have received more and more attention from the industry. Routing technology is a key technology of ad hoc networks. The high-speed mobility of nodes poses a greater challenge to FANET routing technology. Based on the Dynamic Source Routing (DSR) protocol, the continuous Hopfield neural network is used to optimize the route to be adapted to the highspeed movement of the FANET node. In a simulation using NS3, the result shows that the optimized DSR protocol has greatly improved key indicators such as end-to-end average delay, throughput, and packet delivery ratio.

show abstract

Neural networks for MANET AODV: an optimization approach

Cited by 23 publications

References 24 publications

Deep Learning in Mobile and Wireless Networking: A Survey

Deep Learning in Mobile and Wireless Networking: A Survey

A Genetic-Algorithm-Based Optimization Routing for FANETs

An optimization routing protocol for FANETs

Contact Info

Product

Resources

About