Cluster-head based feedback for simplified time reversal prefiltering in ultra-wideband systems

Soleimani, Hossein; Tomasin, Stefano; Alizadeh, Tohid; Shojafar, Mohammad

doi:10.1016/j.phycom.2017.09.005

Cited by 15 publications

(10 citation statements)

References 43 publications

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“…However, only two-level heterogeneity about initial energy is considered in P-SEP, and pre-deployment of advanced nodes makes it quite limited for execution in practical scenarios. In [ 26 ], clustering techniques were used to solve the feedback problem in ultra-wideband systems, where large amounts of feedback are required when transmitting the channel impulse response from receiver to transmitter. By adopting machine learning methods, the estimated channels are clustered into several groups and correspondingly the appropriate channel cluster head (CCH) is selected within each group.…”

Section: Related Workmentioning

confidence: 99%

A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks

Yang

Xiong

et al. 2017

Sensors

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Clustering is an effective topology control method in wireless sensor networks (WSNs), since it can enhance the network lifetime and scalability. To prolong the network lifetime in clustered WSNs, an efficient cluster head (CH) optimization policy is essential to distribute the energy among sensor nodes. Recently, game theory has been introduced to model clustering. Each sensor node is considered as a rational and selfish player which will play a clustering game with an equilibrium strategy. Then it decides whether to act as the CH according to this strategy for a tradeoff between providing required services and energy conservation. However, how to get the equilibrium strategy while maximizing the payoff of sensor nodes has rarely been addressed to date. In this paper, we present a game theoretic approach for balancing energy consumption in clustered WSNs. With our novel payoff function, realistic sensor behaviors can be captured well. The energy heterogeneity of nodes is considered by incorporating a penalty mechanism in the payoff function, so the nodes with more energy will compete for CHs more actively. We have obtained the Nash equilibrium (NE) strategy of the clustering game through convex optimization. Specifically, each sensor node can achieve its own maximal payoff when it makes the decision according to this strategy. Through plenty of simulations, our proposed game theoretic clustering is proved to have a good energy balancing performance and consequently the network lifetime is greatly enhanced.

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Section: Related Workmentioning

confidence: 99%

A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks

Yang

Xiong

et al. 2017

Sensors

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“…In UWASNs, the energy efficiency is widely considered as the most important challenge. To prolong the network lifetime, various approaches have been proposed to solve this issue in clustered networks [ 3 , 4 , 5 ]. Figure 3 shows the clustered UWASN considered in this paper.…”

Section: An Overview Of the Characteristics Of The Underwater Chanmentioning

confidence: 99%

Power Allocation Scheme for Non-Orthogonal Multiple Access in Underwater Acoustic Communications

Cheon

Cho

2017

Sensors

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In this paper, we propose a power allocation scheme for non-orthogonal multiple access (NOMA) in underwater acoustic sensor networks (UWASNs). The existing terrestrial sum-rate maximization (SRM) power allocation scheme suffers from the degradation of the overall sum-rate in UWASNs due to wasteful resource created by unequal transmission times between each transmission path. To address this issue, we propose the equal transmission times (ETT) power allocation scheme, which can prevent wasteful resource generation by guaranteeing equal transmission times between each transmission path. ETT considers the number of packets waiting for transmission in the sender’s buffer for creating equal transmission times. Numerical results show that the proposed ETT outperforms SRM in terms of the overall sum-rate, while having nearly identical maximum sum-rate to the SRMs.

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“…Using this system, people can easily detect disease in their vegetable farming area, which can be used widely in the agriculture sector in the near future. For text and structured data, data analysis as well as data mining with many sub-problems such as pattern mining [4][5][6], erasable pattern mining [7], high average-utility pattern mining [8], weighted closed pattern mining [9], association rules mining [10], clustering [11,12], and classification [13,14] become the most common techniques to analyze data. Using these techniques, several intelligent systems perform a number of intelligent tasks such as medical diagnosis [15], congestion control in wireless sensor networks [16], personalized facets for semantic search [17], a recommender system [18], and an interpolation-based hiding scheme [19].…”

Section: Introductionmentioning

confidence: 99%

A Cluster-Based Boosting Algorithm for Bankruptcy Prediction in a Highly Imbalanced Dataset

Sơn

et al. 2018

Symmetry

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Bankruptcy prediction has been a popular and challenging research topic in both computer science and economics due to its importance to financial institutions, fund managers, lenders, governments, as well as economic stakeholders in recent years. In a bankruptcy dataset, the problem of class imbalance, in which the number of bankruptcy companies is smaller than the number of normal companies, leads to a standard classification algorithm that does not work well. Therefore, this study proposes a cluster-based boosting algorithm as well as a robust framework using the CBoost algorithm and Instance Hardness Threshold (RFCI) for effective bankruptcy prediction of a financial dataset. This framework first resamples the imbalance dataset by the undersampling method using Instance Hardness Threshold (IHT), which is used to remove the noise instances having large IHT value in the majority class. Then, this study proposes a Cluster-based Boosting algorithm, namely CBoost, for dealing with the class imbalance. In this algorithm, the majority class will be clustered into a number of clusters. The distance from each sample to its closest centroid will be used to initialize its weight. This algorithm will perform several iterations for finding weak classifiers and combining them to create a strong classifier. The resample set resulting from the previous module, will be used to train CBoost, which will be used to predict bankruptcy for the validation set. The proposed framework is verified by the Korean bankruptcy dataset (KBD), which has a very small balancing ratio in both the training and the testing phases. The experimental results of this research show that the proposed framework achieves 86.8% in AUC (area under the ROC curve) and outperforms several methods for dealing with the imbalanced data problem for bankruptcy prediction such as GMBoost algorithm, the oversampling-based method using SMOTEENN, and the clustering-based undersampling method for bankruptcy prediction in the experimental dataset.

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Cluster-head based feedback for simplified time reversal prefiltering in ultra-wideband systems

Cited by 15 publications

References 43 publications

A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks

A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks

Power Allocation Scheme for Non-Orthogonal Multiple Access in Underwater Acoustic Communications

A Cluster-Based Boosting Algorithm for Bankruptcy Prediction in a Highly Imbalanced Dataset

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