Recursive Principal Component Analysis-Based Data Outlier Detection and Sensor Data Aggregation in IoT Systems

Yu, Tianqi; Wang, Xianbin; Shami, Abdallah

doi:10.1109/jiot.2017.2756025

Cited by 142 publications

(67 citation statements)

References 32 publications

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“…If a cloud server confronts a physical or software malfunction, it may impact the entire network functionality [15]. Also, a breach in a single device connected to a server or the cloud can wreak havoc with the entire system through denial of service (DoS) attacks by sending malicious messages to other devices, leaking private data, or manipulating the gathered data [16], [17], [18], [19]. Collected data from different devices is stored, processed and forwarded by intermediate systems which can tamper the data.…”

Section: B Current Iot Technologies and Limitationsmentioning

confidence: 99%

A Survey on Consensus Methods in Blockchain for Resource-constrained IoT Networks

Salimitari¹,

Chatterjee²,

Fallah³

2020

Preprint

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The success of blockchain as the underlying technology for cryptocurrencies has opened up possibilities for its use in other application domains as well. The main advantages of blockchain for its potential use in other domains are its inherent security mechanisms and immunity to different attacks. A blockchain relies on a consensus method for agreeing on any new data. Most of the consensus methods which are currently used for the blockchain of different cryptocurrencies require high computational power and thus are not apt for resourceconstrained systems.In this article, we discuss and survey the various blockchain based consensus methods that are applicable to resource constrained IoT devices and networks. A typical IoT network consists of several devices which have limited computational and communications capabilities. Most often, these devices cannot perform intensive computations and are starved for bandwidth. Therefore, we discuss the possible measures that can be taken to reduce the computational power and convergence time for the underlying consensus methods. We also talk about some of the alternatives to the public blockchain like private blockchain and tangle, along with their potential adoption for IoT networks. Furthermore, we review the existing consensus methods that have been implemented and explore the possibility of utilizing them to realize a blockchain based IoT network. Some of the open research challenges are also put forward.

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Section: B Current Iot Technologies and Limitationsmentioning

confidence: 99%

A Survey on Consensus Methods in Blockchain for Resource-constrained IoT Networks

Salimitari¹,

Chatterjee²,

Fallah³

2020

Preprint

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“…By extracting the principal components of spatially correlated sensor data collected from member nodes, a cluster-based data analysis framework is proposed to aggregate the redundant data as well as detect the outliers in the meantime [16]. In [17], an energy efficient hybrid node scheduling scheme (EEHS) in cluster-based WSNs is proposed to improve the overall efficiency and network lifetime, which can identify the nodes with redundant coverage in each round.…”

Section: Related Workmentioning

confidence: 99%

An energy-efficient sleep scheduling mechanism with similarity measure for wireless sensor networks

Wan

Xiong

Lộc

2018

Hum. Cent. Comput. Inf. Sci.

112

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In wireless sensor networks, the high density of node’s distribution will result in transmission collision and energy dissipation of redundant data. To resolve the above problems, an energy-efficient sleep scheduling mechanism with similarity measure for wireless sensor networks (ESSM) is proposed, which will schedule the sensors into the active or sleep mode to reduce energy consumption effectively. Firstly, the optimal competition radius is estimated to organize the all sensor nodes into several clusters to balance energy consumption. Secondly, according to the data collected by member nodes, a fuzzy matrix can be obtained to measure the similarity degree, and the correlation function based on fuzzy theory can be defined to divide the sensor nodes into different categories. Next, the redundant nodes will be selected to put into sleep state in the next round under the premise of ensuring the data integrity of the whole network. Simulations and results show that our method can achieve better performances both in proper distribution of clusters and improving the energy efficiency of the networks with prerequisite of guaranteeing the data accuracy.

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“…Some researchers focus on specific IoT data analysis technology –. Onal et al propose an extended IoT framework with semantics, big data, and machine learning and use the framework in a weather data analysis use case.…”

Section: Related Workmentioning

confidence: 99%

“…Some researchers focus on specific IoT data analysis technology. [21][22][23][24] Onal et al 21 propose an extended IoT framework with semantics, big data, and machine learning and use the framework in a weather data analysis use case. They implement weather clustering and sensor anomaly detection using a publicly available data set.…”

Section: Iot Data Analysismentioning

confidence: 99%

FRFB: Top‐k Followee Recommendation by exploring the Following Behaviors in social networks

Xue

et al. 2018

Concurrency and Computation

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Summary As social networks such as micro‐blogging sites rapidly grow, deciding whom to follow (followee recommendation) becomes a significantly important problem. Most existing works exclusively rely on two traditional factors: the proximity between two users in the network topology or the similarity of the user‐generated contents in the social network, disregarding the effect of users' following behaviors. The challenge of how to effectively combine these two factors remains largely open. Moreover, most research studies simply sort the scores to find top‐k users, which is time‐consuming, especially for large‐scale networks. In this paper, we propose the idea that “predict users' following behaviors by following behaviors themselves.” We consider a user's following to others as a normal process of dynamic and coherent behavior, and we model the potential propagation of the users' following behaviors. Furthermore, based on our previous research on top‐k selection problem, we propose an effective top‐k followee recommendation algorithm, called FRFB. FRFB has low complexity and high scalability and, moreover, good adaptability to real‐life dynamic social networks. We conduct extensive experiments, with two real social network data sets (Wiki and Twitter), which show that FRFB outperforms the well‐known topology‐based followee recommendation algorithms.

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Recursive Principal Component Analysis-Based Data Outlier Detection and Sensor Data Aggregation in IoT Systems

Cited by 142 publications

References 32 publications

A Survey on Consensus Methods in Blockchain for Resource-constrained IoT Networks

A Survey on Consensus Methods in Blockchain for Resource-constrained IoT Networks

An energy-efficient sleep scheduling mechanism with similarity measure for wireless sensor networks

FRFB: Top‐k Followee Recommendation by exploring the Following Behaviors in social networks

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