A new QoS routing algorithm based on self-organizing maps for wireless sensor networks

Barbancho, Julio; León, Carlos; Molina, J.M.; Barbancho, Antonio Ceballos

doi:10.1007/s11235-007-9061-1

Cited by 30 publications

(21 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An unsupervised learning based routing referred to as Sensor Intelligence Routing (SIR) is proposed in [225]. They modify the Dijkstra's algorithm utilizing SOM.…”

Section: Mac Protocol ML Algorithm Objectivementioning

confidence: 99%

“…Proposed to improve reliability by predicting disconnection probabilities Barbancho et al [225] SOM Modified version of Dijkstra's Algorithm Dong et al [228] RL Energy efficient geographical routing Hu and Fei [229] RL Liftime-aware routing for UAN that aims to distribute traffic load among nodes Forster and Murphy [230] RF Near-Optimal routing for multiple source to multiple sinks others) that must be considered during decision making. This challenge is further exacerbated when a distributed operation is required to reduce the overhead and ensure scalability.…”

Section: Cmacmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

et al. 2019

View full text Add to dashboard Cite

The Internet of Things (IoT) is expected to require more effective and efficient wireless communications than ever before. For this reason, techniques such as spectrum sharing, dynamic spectrum access, extraction of signal intelligence and optimized routing will soon become essential components of the IoT wireless communication paradigm. In this vision, IoT devices must be able to not only learn to autonomously extract spectrum knowledge on-the-fly from the network but also leverage such knowledge to dynamically change appropriate wireless parameters (e.g., frequency band, symbol modulation, coding rate, route selection, etc.) to reach the network's optimal operating point. Given that the majority of the IoT will be composed of tiny, mobile, and energy-constrained devices, traditional techniques based on a priori network optimization may not be suitable, since (i) an accurate model of the environment may not be readily available in practical scenarios; (ii) the computational requirements of traditional optimization techniques may prove unbearable for IoT devices. To address the above challenges, much research has been devoted to exploring the use of machine learning to address problems in the IoT wireless communications domain. The reason behind machine learning's popularity is that it provides a general framework to solve very complex problems where a model of the phenomenon being learned is too complex to derive or too dynamic to be summarized in mathematical terms.This work provides a comprehensive survey of the state of the art in the application of machine learning techniques to address key problems in IoT wireless communications with an emphasis on its ad hoc networking aspect. First, we present extensive background notions of machine learning techniques. Then, by adopting a bottom-up approach, we examine existing work on machine learning for the IoT at the physical, data-link and network layer of the protocol stack. Thereafter, we discuss directions taken by the community towards hardware implementation to ensure the feasibility of these techniques. Additionally, before concluding, we also provide a brief discussion of the application of machine learning in IoT beyond wireless communication. Finally, each of these discussions is accompanied by a detailed analysis of the related open problems and challenges.

show abstract

“…An unsupervised learning based routing referred to as Sensor Intelligence Routing (SIR) is proposed in [225]. They modify the Dijkstra's algorithm utilizing SOM.…”

Section: Mac Protocol ML Algorithm Objectivementioning

confidence: 99%

Section: Cmacmentioning

confidence: 99%

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The composition of a routing issue in wireless sensor networks customarily is conducted as a graphing issue G = (V, E), where V shows the pair of all nodes and E represents the pair of bidirectional communication channels linking the nodes [20,21]. Using this graph modeling strategy, the routing issue can be called the process of getting the minimal cost way from the source vertex to all destination vertices, by utilizing the available graph edges.…”

Section: Issues For Routing In Wsnmentioning

confidence: 99%

A Study of Machine Learning in Wireless Sensor Network

Khan¹,

Samad²

2017

ijcna

View full text Add to dashboard Cite

-Within this Paper, a concept of machine learning strategies suggested. In this investigation to address the design issues in WSNs is introduced. As can be viewed within this paper, countless endeavors have induced up to now; several layout issues in wireless sensor networks have been remedied employing numerous machine learning strategies. Utilizing machine learning based algorithms in WSNs need to deem numerous constraints, for instance, minimal sources of the network application that really needs distinct events to be tracked as well as other operational and non-operational aspects.

show abstract

“…SIR [104] adopts neural networks in every sensor node to discover paths. Each node obtains mean values of latency, error rate, duty cycle and throughput by testing every neighbor link quality with the transmissions of a specific packet named ping.…”

Section: Artificial Neural Network Algorithmmentioning

confidence: 99%

An overview of performance trade-off mechanisms in routing protocol for green wireless sensor networks

et al. 2015

View full text Add to dashboard Cite

Along with the development of the technologies of various kinds, wireless sensor networks are gradually entering into the practicalization phase. Therefore, whether its performance is good or not will directly affect the use of WSNs. Routing is one of the most important technologies. In addition, the routing performance is a significant part of the evaluation index system of wireless sensor networks. Energy efficiency is focused on in the traditional routing protocols, while quality of service (QoS) (i.e., delay, reliability, robustness) becomes important in practical application. Routing protocols should not only ensure the energy efficiency, but also realize QoS performances. As a consequence how to achieve the equilibrium between energy efficiency and other performances is the issue to be solved. In this paper, some performance trade-off mechanisms and methods that have existed in the routing protocols of wireless sensor networks are analyzed and summarized. At the same time, the related technical features of the routing protocols are mentioned, the purpose of which is to provide a better understanding of the current research issues within the field of performance trade-off mechanisms and to guide the researcher to develop the routing protocols with better performance in order to fulfill the actual requirements.

show abstract

A new QoS routing algorithm based on self-organizing maps for wireless sensor networks

Cited by 30 publications

References 24 publications

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

A Study of Machine Learning in Wireless Sensor Network

An overview of performance trade-off mechanisms in routing protocol for green wireless sensor networks

Contact Info

Product

Resources

About