2,178 research outputs found
Data Analytics and Performance Enhancement in Edge-Cloud Collaborative Internet of Things Systems
Based on the evolving communications, computing and embedded systems technologies, Internet of Things (IoT) systems can interconnect not only physical users and devices but also virtual services and objects, which have already been applied to many different application scenarios, such as smart home, smart healthcare, and intelligent transportation. With the rapid development, the number of involving devices increases tremendously. The huge number of devices and correspondingly generated data bring critical challenges to the IoT systems. To enhance the overall performance, this thesis aims to address the related technical issues on IoT data processing and physical topology discovery of the subnets self-organized by IoT devices.
First of all, the issues on outlier detection and data aggregation are addressed through the development of recursive principal component analysis (R-PCA) based data analysis framework. The framework is developed in a cluster-based structure to fully exploit the spatial correlation of IoT data. Specifically, the sensing devices are gathered into clusters based on spatial data correlation. Edge devices are assigned to the clusters for the R-PCA based outlier detection and data aggregation. The outlier-free and aggregated data are forwarded to the remote cloud server for data reconstruction and storage. Moreover, a data reduction scheme is further proposed to relieve the burden on the trunk link for data uploading by utilizing the temporal data correlation. Kalman filters (KFs) with identical parameters are maintained at the edge and cloud for data prediction. The amount of data uploading is reduced by using the data predicted by the KF in the cloud instead of uploading all the practically measured data.
Furthermore, an unmanned aerial vehicle (UAV) assisted IoT system is particularly designed for large-scale monitoring. Wireless sensor nodes are flexibly deployed for environmental sensing and self-organized into wireless sensor networks (WSNs). A physical topology discovery scheme is proposed to construct the physical topology of WSNs in the cloud server to facilitate performance optimization, where the physical topology indicates both the logical connectivity statuses of WSNs and the physical locations of WSN nodes. The physical topology discovery scheme is implemented through the newly developed parallel Metropolis-Hastings random walk based information sampling and network-wide 3D localization algorithms, where UAVs are served as the mobile edge devices and anchor nodes. Based on the physical topology constructed in the cloud, a UAV-enabled spatial data sampling scheme is further proposed to efficiently sample data from the monitoring area by using denoising autoencoder (DAE). By deploying the encoder of DAE at the UAV and decoder in the cloud, the data can be partially sampled from the sensing field and accurately reconstructed in the cloud.
In the final part of the thesis, a novel autoencoder (AE) neural network based data outlier detection algorithm is proposed, where both encoder and decoder of AE are deployed at the edge devices. Data outliers can be accurately detected by the large fluctuations in the squared error generated by the data passing through the encoder and decoder of the AE
A Survey on the Security and the Evolution of Osmotic and Catalytic Computing for 5G Networks
The 5G networks have the capability to provide high compatibility for the new
applications, industries, and business models. These networks can tremendously
improve the quality of life by enabling various use cases that require high
data-rate, low latency, and continuous connectivity for applications pertaining
to eHealth, automatic vehicles, smart cities, smart grid, and the Internet of
Things (IoT). However, these applications need secure servicing as well as
resource policing for effective network formations. There have been a lot of
studies, which emphasized the security aspects of 5G networks while focusing
only on the adaptability features of these networks. However, there is a gap in
the literature which particularly needs to follow recent computing paradigms as
alternative mechanisms for the enhancement of security. To cover this, a
detailed description of the security for the 5G networks is presented in this
article along with the discussions on the evolution of osmotic and catalytic
computing-based security modules. The taxonomy on the basis of security
requirements is presented, which also includes the comparison of the existing
state-of-the-art solutions. This article also provides a security model,
"CATMOSIS", which idealizes the incorporation of security features on the basis
of catalytic and osmotic computing in the 5G networks. Finally, various
security challenges and open issues are discussed to emphasize the works to
follow in this direction of research.Comment: 34 pages, 7 tables, 7 figures, Published In 5G Enabled Secure
Wireless Networks, pp. 69-102. Springer, Cham, 201
Efficient Ambient LoRa Backscatter with On-Off Keying Modulation
Backscatter communication holds potential for ubiquitous and low-cost
connectivity among low-power IoT devices. To avoid interference between the
carrier signal and the backscatter signal, recent works propose a
frequency-shifting technique to separate these two signals in the frequency
domain. Such proposals, however, have to occupy the precious wireless spectrum
that is already overcrowded, and increase the power, cost, and complexity of
the backscatter tag. In this paper, we revisit the classic ON-OFF Keying (OOK)
modulation and propose Aloba, a backscatter system that takes the ambient LoRa
transmissions as the excitation and piggybacks the in-band OOK modulated
signals over the LoRa transmissions. Our design enables the backsactter signal
to work in the same frequency band of the carrier signal, meanwhile achieving
flexible data rate at different transmission range. The key contributions of
Aloba include: (1) the design of a low-power backscatter tag that can pick up
the ambient LoRa signals from other signals. (2) a novel decoding algorithm to
demodulate both the carrier signal and the backscatter signal from their
superposition. We further adopt link coding mechanism and interleave operation
to enhance the reliability of backscatter signal decoding. We implement Aloba
and conduct head-to-head comparison with the state-of-the-art LoRa backscatter
system PLoRa in various settings. The experiment results show Aloba can achieve
199.4 Kbps data rate at various distances, 52.4 times higher than PLoRa
Internet of Things and Sensors Networks in 5G Wireless Communications
This book is a printed edition of the Special Issue Internet of Things and Sensors Networks in 5G Wireless Communications that was published in Sensors
Internet of Things and Sensors Networks in 5G Wireless Communications
This book is a printed edition of the Special Issue Internet of Things and Sensors Networks in 5G Wireless Communications that was published in Sensors
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