A Dynamical Slot Assignment Method for Wireless Sensor Networks Based on Hopfield Network

This paper proposes an improved Hopfield neural network (I-HNN) algorithm to optimize the slot assignment scheme in wireless sensor networks. The key advantage of the proposed algorithm is to increase the convergence probability under different traffic loads. To achieve this, nodes can adjust their...

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Main Authors: Qi Yang, Xiao Lin, Yuxiang Zhuang, Xuemin Hong
Format: Article
Language:English
Published: Wiley 2014-05-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1155/2014/805142
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author Qi Yang
Xiao Lin
Yuxiang Zhuang
Xuemin Hong
author_facet Qi Yang
Xiao Lin
Yuxiang Zhuang
Xuemin Hong
author_sort Qi Yang
collection DOAJ
description This paper proposes an improved Hopfield neural network (I-HNN) algorithm to optimize the slot assignment scheme in wireless sensor networks. The key advantage of the proposed algorithm is to increase the convergence probability under different traffic loads. To achieve this, nodes can adjust their slot demands according to the traffic load, slots number, and demand history. Various aspects of the network performances with the proposed I-HNN algorithm are evaluated via simulation. The results indicate that I-HNN is suitable for wireless sensor networks with dynamically varying traffic. In particular, it can increase the convergence probability and slot utilization under the heavy traffic load.
format Article
id doaj-art-e5bd9da7a75643de9b8a0925fe089239
institution Kabale University
issn 1550-1477
language English
publishDate 2014-05-01
publisher Wiley
record_format Article
series International Journal of Distributed Sensor Networks
spelling doaj-art-e5bd9da7a75643de9b8a0925fe0892392025-08-20T03:25:02ZengWileyInternational Journal of Distributed Sensor Networks1550-14772014-05-011010.1155/2014/805142805142A Dynamical Slot Assignment Method for Wireless Sensor Networks Based on Hopfield NetworkQi YangXiao LinYuxiang ZhuangXuemin HongThis paper proposes an improved Hopfield neural network (I-HNN) algorithm to optimize the slot assignment scheme in wireless sensor networks. The key advantage of the proposed algorithm is to increase the convergence probability under different traffic loads. To achieve this, nodes can adjust their slot demands according to the traffic load, slots number, and demand history. Various aspects of the network performances with the proposed I-HNN algorithm are evaluated via simulation. The results indicate that I-HNN is suitable for wireless sensor networks with dynamically varying traffic. In particular, it can increase the convergence probability and slot utilization under the heavy traffic load.https://doi.org/10.1155/2014/805142
spellingShingle Qi Yang
Xiao Lin
Yuxiang Zhuang
Xuemin Hong
A Dynamical Slot Assignment Method for Wireless Sensor Networks Based on Hopfield Network
International Journal of Distributed Sensor Networks
title A Dynamical Slot Assignment Method for Wireless Sensor Networks Based on Hopfield Network
title_full A Dynamical Slot Assignment Method for Wireless Sensor Networks Based on Hopfield Network
title_fullStr A Dynamical Slot Assignment Method for Wireless Sensor Networks Based on Hopfield Network
title_full_unstemmed A Dynamical Slot Assignment Method for Wireless Sensor Networks Based on Hopfield Network
title_short A Dynamical Slot Assignment Method for Wireless Sensor Networks Based on Hopfield Network
title_sort dynamical slot assignment method for wireless sensor networks based on hopfield network
url https://doi.org/10.1155/2014/805142
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AT xueminhong adynamicalslotassignmentmethodforwirelesssensornetworksbasedonhopfieldnetwork
AT qiyang dynamicalslotassignmentmethodforwirelesssensornetworksbasedonhopfieldnetwork
AT xiaolin dynamicalslotassignmentmethodforwirelesssensornetworksbasedonhopfieldnetwork
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