Broadcast scheduling in wireless sensor networks using fuzzy Hopfield neural network

Shen, Yi; Wang, Ming-Shi

doi:10.1016/j.eswa.2006.10.024

Cited by 58 publications

(24 citation statements)

References 15 publications

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“…A fuzzy Hopfield neural network is proposed in [97] to solve the TDMA broadcast scheduling problem in WSNs. The problem is treated as discrete energy minimization problem that is mapped into a Hopfield neural network.…”

Section: ) Fuzzy Neural Networkmentioning

confidence: 99%

Computational Intelligence in Wireless Sensor Networks: A Survey

Kulkarni

Förster

Venayagamoorthy

2011

IEEE Commun. Surv. Tutorials

617

270

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Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. However, WSN developers are usually not or not completely aware of the potential CI algorithms offer. On the other side, CI researchers are not familiar with all real problems and subtle requirements of WSNs. This mismatch makes collaboration and development difficult. This paper intends to close this gap and foster collaboration by offering a detailed introduction to WSNs and their properties. An extensive survey of CI applications to various problems in WSNs from various research areasand publication venues is presented in the paper. Besides, a discussion on advantages and disadvantages of CI algorithms over traditional WSN solutions is offered. In addition, a general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for WSNs.

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Section: ) Fuzzy Neural Networkmentioning

confidence: 99%

Computational Intelligence in Wireless Sensor Networks: A Survey

Kulkarni

Förster

Venayagamoorthy

2011

IEEE Commun. Surv. Tutorials

617

270

View full text Add to dashboard Cite

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“…This includes the work of [193] proposing a combination of HNN and genetic algorithm (GA) and [194] using sequential vertex coloring (SVC) and noisy chaotic neural network (NCNN). Subsequently, these solutions were shown to be outperformed by fuzzy hopfield neural network (FHNN) proposed in [195]. Here, we describe how [195] tackles BSP.…”

Section: Data Link Layermentioning

confidence: 99%

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

et al. 2019

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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.

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“…The QoS control field in the MAC frame format is a 16 bits field which facilitates the description of QoS requirements of application flows. Its TID (4 bits) identifies the TC (0-7) or the TS (8)(9)(10)(11)(12)(13)(14)(15) to which the corresponding MSDU in the FB field belongs. The last eight bits are used usually by QAP to receive the queue size of QSTAs.…”

Section: Qos Comparison Between Wifi and Wimax Mesh Modementioning

confidence: 99%

“…Ref. [12] uses fuzzy hopfield neural network technique to solve the TDMA broadcast scheduling problem in wireless sensor networks. Artificial neural network with reinforcement learning has been introduced in [13] to schedule downlink traffic of wireless networks.…”

Section: Related Workmentioning

confidence: 99%