scholarly journals Contextualization using Context-Aware Publish and Subscribe (CAPS) based on IoT

2019 ◽  
Vol 9 (1) ◽  
pp. 1112-1119
Keyword(s):  
Internet Of Things ◽  
Data Processing ◽  
Hybrid Approach ◽  
Probabilistic Methods ◽  
Sensor Data ◽  
The Internet ◽  
Context Aware ◽  
Raw Data ◽  
Message Queue ◽  
Different Sources

The Internet of Things (IoT) activates massive data flow in the real world. Each computer can presently be linked to the internet and supply useful decision-making information. Virtually sensors are implemented in every aspect of life. From different sources of sensors can produce raw data. Due to the various data sources, the method of extracting information from the flow of data is mostly complicated, networks inadequate and criteria for real-time processing. In addition, an issue of context-aware data processing and architecture also present, despite the fact that they are essential criteria for stronger IoT structure. In order to meet this issue, we recommend a Context-aware Internet of Things Middleware (CAIM) architecture. This enables the incorporation of highly diverse IoT application context information by using light weigh protocol MQTT (Message Queue Telemetry Transport) for transmitting basic data streams from sensors to middleware and applications. In this paper, we propose a contextualization which means that obtain data from sensors of different sources. First have to create a context profile with the help of context type like user, activity, physical, and environment context. Then also is create a profile by using attributes. Finally, raw data can be change into contextualized data through CAPS (context-aware Publish-Subscribe) hybrid approach. This paper discusses the current context analysis strategies that use either rational models or probabilistic methods exclusively. The evaluation of identifying contextualization methods shows the shortcomings of IoT sensor data processing as well as offers alternative ways of identifying the context

Challenges and Solutions of Big Data and IoT

2019 ◽  
pp. 264-272
Author(s):  
Jayashree K. ◽  
Abirami R. ◽  
Rajeswari P.
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Collection ◽  
Internet Of Things ◽  
Data Processing ◽  
The Internet ◽  
Huge Amount ◽  
Collection Data ◽  
Different Sources ◽  
The Internet Of Things

The successful development of big data and the internet of things (IoT) is increasing and influencing all areas of technologies and businesses. The rapid increase of more devices that are connected to IoT from which enormous amount of data are consumed indicates the way how big data is related with IoT. Since huge amount of data are obtained from different sources, analysis of these data involves much of processing at each and every level to extract knowledge for decision making process. To manage big data in a continuous network that keeps expanding leads to few issues related to data collection, data processing, analytics, and security. To address these issues, certain solution using bigdata approach in IoT are examined. Combining these two areas provides several opportunities developing new systems and identify advanced techniques to solve challenges on big data and IoT.

scholarly journals A Data Processing Middleware Based on SOA for the Internet of Things

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Feng Wang ◽  
Liang Hu ◽  
Jin Zhou ◽  
Kuo Zhao
Keyword(s):  
Internet Of Things ◽  
Data Processing ◽  
Service Oriented Architecture ◽  
Heterogeneous Systems ◽  
Heterogeneous Data ◽  
Sensor Data ◽  
The Internet ◽  
Heterogeneous Information ◽  
New Research ◽  
The Internet Of Things

The Internet of Things (IoT) emphasizes on connecting every object around us by leveraging a variety of wireless communication technologies. Heterogeneous data fusion is widely considered to be a promising and urgent challenge in the data processing of the IoT. In this study, we first discuss the development of the concept of the IoT and give a detailed description of the architecture of the IoT. And then we design a middleware platform based on service-oriented architecture (SOA) for integration of multisource heterogeneous information. New research angle regarding flexible heterogeneous information fusion architecture for the IoT is the theme of this paper. Experiments using environmental monitoring sensor data derived from indoor environment are performed for system validation. Through the theoretical analysis and experimental verification, the data processing middleware architecture represents better adaptation to multisensor and multistream application scenarios in the IoT, which improves heterogeneous data utilization value. The data processing middleware based on SOA for the IoT establishes a solid foundation of integration and interaction for diverse networks data among heterogeneous systems in the future, which simplifies the complexity of integration process and improves reusability of components in the system.

scholarly journals Context-aware security in the internet of things: a survey

2021 ◽  
Vol 14 (3) ◽  
pp. 231
Author(s):  
Tidiane Sylla ◽  
Mohamed Aymen Chalouf ◽  
Francine Krief ◽  
Karim Samaké
Keyword(s):  
Internet Of Things ◽  
The Internet ◽  
Context Aware ◽  
The Internet Of Things

scholarly journals An Improved Unsupervised Single-Channel Speech Separation Algorithm for Processing Speech Sensor Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Dazhi Jiang ◽  
Zhihui He ◽  
Yingqing Lin ◽  
Yifei Chen ◽  
Linyan Xu
Keyword(s):  
Internet Of Things ◽  
Speech Signal ◽  
Single Channel ◽  
Low Noise ◽  
Sensor Data ◽  
The Internet ◽  
Speech Separation ◽  
Adaptive Wavelet ◽  
Enhancement Technique ◽  
The Internet Of Things

As network supporting devices and sensors in the Internet of Things are leaping forward, countless real-world data will be generated for human intelligent applications. Speech sensor networks, an important part of the Internet of Things, have numerous application needs. Indeed, the sensor data can further help intelligent applications to provide higher quality services, whereas this data may involve considerable noise data. Accordingly, speech signal processing method should be urgently implemented to acquire low-noise and effective speech data. Blind source separation and enhancement technique refer to one of the representative methods. However, in the unsupervised complex environment, in the only presence of a single-channel signal, many technical challenges are imposed on achieving single-channel and multiperson mixed speech separation. For this reason, this study develops an unsupervised speech separation method CNMF+JADE, i.e., a hybrid method combined with Convolutional Non-Negative Matrix Factorization and Joint Approximative Diagonalization of Eigenmatrix. Moreover, an adaptive wavelet transform-based speech enhancement technique is proposed, capable of adaptively and effectively enhancing the separated speech signal. The proposed method is aimed at yielding a general and efficient speech processing algorithm for the data acquired by speech sensors. As revealed from the experimental results, in the TIMIT speech sources, the proposed method can effectively extract the target speaker from the mixed speech with a tiny training sample. The algorithm is highly general and robust, capable of technically supporting the processing of speech signal acquired by most speech sensors.

Cloud Provisioning and Modeling of Internet of Things (IoT) Systems for Evaluating Big Data Uncertainties

2020 ◽  
pp. 125-130
Author(s):  
Yuji Huang ◽  
Aravindhan K
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Actual World ◽  
The Internet ◽  
Data Systems ◽  
Internet Service ◽  
Research Gap ◽  
Big Data Systems ◽  
Different Sources ◽  
The Internet Of Things

The present cyber-physical schemes and the Internet of Things (IoT) schemes comprise of both complex and simple interactions defining the different sources of the IoT systems such as cloud information and the edge internet service centres. All the modeling frameworks have been established on the virtualization dimensions that include both the cloud and the edge structures. Apart from that, the systems deal with big data based on the connections of various forms of services and networks. In that case, various forms of data uncertainties are evident. These uncertainties include elasticity and actuation uncertainties. As a result, this leads to a number of challenges that affect the process of testing these uncertainties in the big data systems. Nonetheless, there is a research gap present to effectively model and design the precise infrastructure frameworks that handle the necessities for evaluating these emergent big data uncertainties. With that regard, this scholastic paper focusses on the techniques used to generate and determine the deployment configurations used in the process of evaluating both the cloud and IoT systems. In this research, the survey will consider the actual-world application for analysing and monitoring the transceiver frameworks.

scholarly journals A Novel Sensor Data Pre-Processing Methodology for the Internet of Things Using Anomaly Detection and Transfer-By-Subspace-Similarity Transformation

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4536 ◽  
Author(s):  
Yan Zhong ◽  
Simon Fong ◽  
Shimin Hu ◽  
Raymond Wong ◽  
Weiwei Lin
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Sensor Data ◽  
Detection Methods ◽  
The Internet ◽  
Quality Of Data ◽  
Data Mining Algorithms ◽  
Sensing Applications ◽  
The Internet Of Things

The Internet of Things (IoT) and sensors are becoming increasingly popular, especially in monitoring large and ambient environments. Applications that embrace IoT and sensors often require mining the data feeds that are collected at frequent intervals for intelligence. Despite the fact that such sensor data are massive, most of the data contents are identical and repetitive; for example, human traffic in a park at night. Most of the traditional classification algorithms were originally formulated decades ago, and they were not designed to handle such sensor data effectively. Hence, the performance of the learned model is often poor because of the small granularity in classification and the sporadic patterns in the data. To improve the quality of data mining from the IoT data, a new pre-processing methodology based on subspace similarity detection is proposed. Our method can be well integrated with traditional data mining algorithms and anomaly detection methods. The pre-processing method is flexible for handling similar kinds of sensor data that are sporadic in nature that exist in many ambient sensing applications. The proposed methodology is evaluated by extensive experiment with a collection of classical data mining models. An improvement over the precision rate is shown by using the proposed method.

scholarly journals The Lightweight Autonomous Vehicle Self-Diagnosis (LAVS) Using Machine Learning Based on Sensors and Multi-Protocol IoT Gateway

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2534 ◽  
Author(s):  
YiNa Jeong ◽  
SuRak Son ◽  
ByungKwan Lee
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Autonomous Vehicle ◽  
Sensor Data ◽  
The Internet ◽  
Message Queue ◽  
Road Side Unit ◽  
The Neural Network ◽  
Total Condition ◽  
Vehicle Sensors

This paper proposes the lightweight autonomous vehicle self-diagnosis (LAVS) using machine learning based on sensors and the internet of things (IoT) gateway. It collects sensor data from in-vehicle sensors and changes the sensor data to sensor messages as it passes through protocol buses. The changed messages are divided into header information, sensor messages, and payloads and they are stored in an address table, a message queue, and a data collection table separately. In sequence, the sensor messages are converted to the message type of the other protocol and the payloads are transferred to an in-vehicle diagnosis module (In-VDM). The LAVS informs the diagnosis result of Cloud or road side unit(RSU) by the internet of vehicles (IoV) and of drivers by Bluetooth. To design the LAVS, the following two modules are needed. First, a multi-protocol integrated gateway module (MIGM) converts sensor messages for communication between two different protocols, transfers the extracted payloads to the In-VDM, and performs IoV to transfer the diagnosis result and payloads to the Cloud through wireless access in vehicular environment(WAVE). Second, the In-VDM uses random forest to diagnose parts of the vehicle, and delivers the results of the random forest as an input to the neural network to diagnose the total condition of the vehicle. Since the In-VDM uses them for self-diagnosis, it can diagnose a vehicle with efficiency. In addition, because the LAVS converts payloads to a WAVE message and uses IoV to transfer the WAVE messages to RSU or the Cloud, it prevents accidents in advance by informing the vehicle condition of drivers rapidly.

Exploiting Context-Aware Capabilities over the Internet of Things for Industry 4.0 Applications

IEEE Network ◽  
2018 ◽  
Vol 32 (3) ◽  
pp. 101-107 ◽  
Author(s):  
Igor Bisio ◽  
Chiara Garibotto ◽  
Aldo Grattarola ◽  
Fabio Lavagetto ◽  
Andrea Sciarrone
Keyword(s):  
Internet Of Things ◽  
Industry 4.0 ◽  
The Internet ◽  
Context Aware ◽  
The Internet Of Things

scholarly journals SlowITe, a Novel Denial of Service Attack Affecting MQTT

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2932
Author(s):  
Ivan Vaccari ◽  
Maurizio Aiello ◽  
Enrico Cambiaso
Keyword(s):  
Internet Of Things ◽  
Denial Of Service ◽  
The Internet ◽  
Message Queue ◽  
Plain Text ◽  
Denial Of Service Attack ◽  
Service Attack ◽  
Iot Devices ◽  
The Internet Of Things ◽  
Low Rate

Security of the Internet of Things is a crucial topic, due to the criticality of the networks and the sensitivity of exchanged data. In this paper, we target the Message Queue Telemetry Transport (MQTT) protocol used in IoT environments for communication between IoT devices. We exploit a specific weakness of MQTT which was identified during our research, allowing the client to configure the behavior of the server. In order to validate the possibility to exploit such vulnerability, we propose SlowITe, a novel low-rate denial of service attack aimed to target MQTT through low-rate techniques. We validate SlowITe against real MQTT services, considering both plain text and encrypted communications and comparing the effects of the threat when targeting different daemons. Results show that the attack is successful and it is able to exploit the identified vulnerability to lead a DoS on the victim with limited attack resources.

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