Approximate Holistic Aggregation in Wireless Sensor Networks

Ji, Li; Cheng, Siyao; Cai, Zhipeng; Yu, Jiguo; Wang, Chaokun; Li, Yingshu

doi:10.1145/3027488

Cited by 80 publications

(30 citation statements)

References 26 publications

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“…To properly characterize the features of such data, techniques that transform the time-continuous raw data into discrete values are commonly used [46][47][48]. In this paper, the data are measurements on the conductivity of metal gas sensors array's responses to some gas compounds for continuous 36 months and have been preprocessed using Exponential Moving Average (EMA).…”

Section: Specification Of Datasetmentioning

confidence: 99%

Weighted Domain Transfer Extreme Learning Machine and Its Online Version for Gas Sensor Drift Compensation in E‐Nose Systems

Ma¹,

Luo²,

Qin³

et al. 2018

Wireless Communications and Mobile Computing

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Machine learning approaches have been widely used to tackle the problem of sensor array drift in E-Nose systems. However, labeled data are rare in practice, which makes supervised learning methods hard to be applied. Meanwhile, current solutions require updating the analytical model in an offline manner, which hampers their uses for online scenarios. In this paper, we extended Target Domain Adaptation Extreme Learning Machine (DAELM T) to achieve high accuracy with less labeled samples by proposing a Weighted Domain Transfer Extreme Learning Machine, which uses clustering information as prior knowledge to help select proper labeled samples and calculate sensitive matrix for weighted learning. Furthermore, we converted DAELM T and the proposed method into their online learning versions under which scenario the labeled data are selected beforehand. Experimental results show that, for batch learning version, the proposed method uses around 20% less labeled samples while achieving approximately equivalent or better accuracy. As for the online versions, the methods maintain almost the same accuracies as their offline counterparts do, but the time cost remains around a constant value while that of offline versions grows with the number of samples.

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Section: Specification Of Datasetmentioning

confidence: 99%

Weighted Domain Transfer Extreme Learning Machine and Its Online Version for Gas Sensor Drift Compensation in E‐Nose Systems

Ma¹,

Luo²,

Qin³

et al. 2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Proof First, the time complexity of broadcasting its own ID and distance messages in Phase I is O (1). Define N max as the maximum number of neighbors for any node, the time complexity of comparing the messages with their neighbors and sorting the ID in Phase I is O (N max log N max ).…”

Section: Theorem 2 the Time Complexity Of Algorithm 1 Ismentioning

confidence: 99%

“…A wireless sensor network (WSN) is spatially distributed to monitor and control the given area with a random or deterministic manner. WSNs have applications in a lot of fields such as military purpose environmental monitoring and disaster prevention [1][2][3][4][5]. Thus, a sensor network can be described as a collection of sensor nodes that can perform specific actions.…”

Section: Introductionmentioning

confidence: 99%

Multi-leader election in dynamic sensor networks

Meng

Jiang

et al. 2017

J Wireless Com Network

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The leader election problem is one of the fundamental problems in distributed computing. Different from most of the existing results studying the multi-leader election in static networks or one leader election in dynamic networks, in this paper, we focus on the multi-leader election in dynamic sensor networks where nodes are deployed randomly. A centralized simple leader election algorithm (VLE), a distributed leader election algorithm (NMDLE), and a multi-leader election algorithm (PSMLE) are proposed so as to elect multi-leaders for the purpose of saving energy and prolonging the network lifetime, respectively. Specifically, the proposed algorithms aim at using less leaders to control the whole network, which is controlled by at least k opt leaders, here k opt denotes the optimal number of network partitions. Then we analyze the impacts of the sleep scheme of nodes and node moving on energy consumption and establish a theoretical model for energy cost. Finally, we provide extensive simulation results valuating the correctness of theoretical analysis.

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“…One is the scheduling of data, including aggregation and extraction of large scale sensing data [5][6][7][8] and time-continuous data generation [9]. These works also minimize the size of the data transmitted by the network and reduce the network and computing overhead.…”

Section: Introductionmentioning

confidence: 99%

RTGOR: Reliability and Timeliness Guaranteed Opportunistic Routing in wireless sensor networks

Chen

et al. 2018

J Wireless Com Network

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Cyber-physical systems (CPSs) connect physical equipment and the information system to realize real-time perception and intelligent control of the equipment. With the rapid development of wireless sensors, wireless sensor networks (WSNs) now play a major role in CPS and have become a focus of research. However, WSNs in CPS face great challenges in ensuring the high reliability and time critical performance of data transmission because of the characteristics of the complex structure and dynamic connectivity. In order to meet the performance requirement of CPS data transmission, a Reliability and Timeliness Guaranteed Opportunistic Routing (RTGOR) protocol is proposed that is based on opportunistic routing and combined with quantified transmission reliability and time guarantees. Our simulation results demonstrate that RTGOR performs better than the state-of-the-art protocol QMOR (QoS-assured Opportunistic Routing Mechanism), in terms of the packet delivery rate and end-to-end delay metrics.

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Approximate Holistic Aggregation in Wireless Sensor Networks

Cited by 80 publications

References 26 publications

Weighted Domain Transfer Extreme Learning Machine and Its Online Version for Gas Sensor Drift Compensation in E‐Nose Systems

Weighted Domain Transfer Extreme Learning Machine and Its Online Version for Gas Sensor Drift Compensation in E‐Nose Systems

Multi-leader election in dynamic sensor networks

RTGOR: Reliability and Timeliness Guaranteed Opportunistic Routing in wireless sensor networks

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