scholarly journals A Survey on Behavioral Pattern Mining From Sensor Data in Internet of Things

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 33318-33341 ◽  
Author(s):  
Md. Mamunur Rashid ◽  
Joarder Kamruzzaman ◽  
Mohammad Mehedi Hassan ◽  
Sakib Shahriar Shafin ◽  
Md. Zakirul Alam Bhuiyan
Keyword(s):  
Internet Of Things ◽  
Pattern Mining ◽  
Behavioral Pattern ◽  
Sensor Data

scholarly journals Developing an On-Demand Cloud-Based Sensing-as-a-Service System for Internet of Things

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Mihui Kim ◽  
Mihir Asthana ◽  
Siddhartha Bhargava ◽  
Kartik Krishnan Iyyer ◽  
Rohan Tangadpalliwar ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Internet Of Things ◽  
Service System ◽  
Value Added ◽  
Computing System ◽  
Sensor Data ◽  
Dynamic Provisioning ◽  
On Demand ◽  
Cloud Computing System ◽  
Iot Devices

The increasing number of Internet of Things (IoT) devices with various sensors has resulted in a focus on Cloud-based sensing-as-a-service (CSaaS) as a new value-added service, for example, providing temperature-sensing data via a cloud computing system. However, the industry encounters various challenges in the dynamic provisioning of on-demand CSaaS on diverse sensor networks. We require a system that will provide users with standardized access to various sensor networks and a level of abstraction that hides the underlying complexity. In this study, we aim to develop a cloud-based solution to address the challenges mentioned earlier. Our solution, SenseCloud, includes asensor virtualizationmechanism that interfaces with diverse sensor networks, amultitenancymechanism that grants multiple users access to virtualized sensor networks while sharing the same underlying infrastructure, and adynamic provisioningmechanism to allow the users to leverage the vast pool of resources on demand and on a pay-per-use basis. We implement a prototype of SenseCloud by using real sensors and verify the feasibility of our system and its performance. SenseCloud bridges the gap between sensor providers and sensor data consumers who wish to utilize sensor data.

scholarly journals AdaGUM: An Adaptive Graph Updating Model-Based Anomaly Detection Method for Edge Computing Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiang Yu ◽  
Chun Shan ◽  
Jilong Bian ◽  
Xianfei Yang ◽  
Ying Chen ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Anomaly Detection ◽  
Real Time ◽  
Detection Method ◽  
Edge Computing ◽  
Sensor Data ◽  
Computing Environment ◽  
The Real ◽  
Edge Node ◽  
Model Based

With the rapid development of Internet of Things (IoT), massive sensor data are being generated by the sensors deployed everywhere at an unprecedented rate. As the number of Internet of Things devices is estimated to grow to 25 billion by 2021, when facing the explicit or implicit anomalies in the real-time sensor data collected from Internet of Things devices, it is necessary to develop an effective and efficient anomaly detection method for IoT devices. Recent advances in the edge computing have significant impacts on the solution of anomaly detection in IoT. In this study, an adaptive graph updating model is first presented, based on which a novel anomaly detection method for edge computing environment is then proposed. At the cloud center, the unknown patterns are classified by a deep leaning model, based on the classification results, the feature graphs are updated periodically, and the classification results are constantly transmitted to each edge node where a cache is employed to keep the newly emerging anomalies or normal patterns temporarily until the edge node receives a newly updated feature graph. Finally, a series of comparison experiments are conducted to demonstrate the effectiveness of the proposed anomaly detection method for edge computing. And the results show that the proposed method can detect the anomalies in the real-time sensor data efficiently and accurately. More than that, the proposed method performs well when there exist newly emerging patterns, no matter they are anomalous or normal.

scholarly journals Sensing as a Service for the Internet of Things

2016 ◽  
Vol 4 (3) ◽  
pp. 28
Author(s):  
Omar Subhi Aldabbas
Keyword(s):  
Internet Of Things ◽  
Cost Effective ◽  
Sensor Data ◽  
Detection Accuracy ◽  
Heterogeneous Services ◽  
Coordination Languages ◽  
Spatio Temporal ◽  
Temporal Events ◽  
New Applications ◽  
Common Application

Internet of Things (IoT) is a ubiquitous embedded ecosystem known for its capability to perform common application functions through coordinating resources, which are distributed on-object or on-network domains. As new applications evolve, the challenge is in the analysis and usage of multimodal data streamed by diverse kinds of sensors. This paper presents a new service-centric approach for data collection and retrieval. This approach considers objects as highly decentralized, composite and cost effective services. Such services can be constructed from objects located within close geographical proximity to retrieve spatio-temporal events from the gathered sensor data. To achieve this, we advocate Coordination languages and models to fuse multimodal, heterogeneous services through interfacing with every service to achieve the network objective according to the data they gather and analyze. In this paper we give an application scenario that illustrates the implementation of the coordination models to provision successful collaboration among IoT objects to retrieve information. The proposed solution reduced the communication delay before service composition by up to 43% and improved the target detection accuracy by up to 70%, while maintaining energy consumption 20% lower than its best rivals in the literature.

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.

Internet of Things in E-Health

2017 ◽  
pp. 191-197 ◽  
Author(s):  
Branka Rodić Trmčić ◽  
Aleksandra Labus ◽  
Svetlana Mitrović ◽  
Vesna Buha ◽  
Gordana Stanojević
Keyword(s):  
Internet Of Things ◽  
Health Behavior ◽  
Well Being ◽  
Sensor Data ◽  
Main Task ◽  
Preventive Actions ◽  
Framework Model ◽  
The Impact ◽  
Health And Well Being ◽  
Improve Health

The main task of Internet of Things in eHealth solutions is to collect data, connect people, things and processes. This provides a wealth of information that can be useful in decision-making, improving health and well-being. The aim of this study is to identify framework of sensors and application health services to detect sources of stress and stressors and make them visible to users. Also, we aim at extracting relationship between event and sensor data in order to improve health behavior. Evaluation of the proposed framework model will be performed. Model is based on Internet of Things in eHealth and is going to aim to improve health behavior. Following the established pattern of behavior realized through wearable system users will be proposed a preventive actions model. Further, it will examine the impact of changing health behavior on habits, condition and attitudes in relation to well-being and prevention.

scholarly journals Advances in Future Internet and the Industrial Internet of Things

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 244 ◽  
Author(s):  
Jong Park
Keyword(s):  
Internet Of Things ◽  
Communication Networks ◽  
Future Internet ◽  
Resource Provisioning ◽  
Sensor Data ◽  
Third Wave ◽  
Industrial Internet Of Things ◽  
Privacy And Security ◽  
Industrial Internet ◽  
Use Of Internet

After the emergence of the Internet and mobile communication networks, the IoT has been considered as the third wave of information technology. The Industrial Internet of Things (IIoT) is the use of Internet of Things (IoT) technologies in manufacturing. IIoT incorporates machine learning and big data technology, sensor data, and machine-to-machine (M2M) communications that have existed in industrial areas for years. In the future, people and objects will be connected at any time, any place, with anything and anyone and will utilize any network and services. IIoT is creating a new world in which people and businesses can manage their assets in more informed ways and can make more opportune and better-informed decisions. Many advanced IIoT and 5G technologies have been successfully applied in everyday life, but there are still many practical problems tackled by traditional methods which are generally difficult to experimentally solve in the advanced Industrial Internet of Things. Therefore, in this special issue, we accepted five articles in three different dimensions: communication networks, optimized resource provisioning and data forwarding, privacy and security.

scholarly journals Proximity Environmental Feature Based Tree Health Assessment Scheme Using Internet of Things and Machine Learning Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3115 ◽  
Author(s):  
Yang Wei ◽  
Hao Wang ◽  
Kim Fung Tsang ◽  
Yucheng Liu ◽  
Chung Kit Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Health Assessment ◽  
Sensor Data ◽  
Machine Learning Algorithm ◽  
Tree Health ◽  
Environmental Feature ◽  
Feature Based

Improperly grown trees may cause huge hazards to the environment and to humans, through e.g., climate change, soil erosion, etc. A proximity environmental feature-based tree health assessment (PTA) scheme is proposed to prevent these hazards by providing guidance for early warning methods of potential poor tree health. In PTA development, tree health is defined and evaluated based on proximity environmental features (PEFs). The PEF takes into consideration the seven surrounding ambient features that strongly impact tree health. The PEFs were measured by the deployed smart sensors surrounding trees. A database composed of tree health and relative PEFs was established for further analysis. An adaptive data identifying (ADI) algorithm is applied to exclude the influence of interference factors in the database. Finally, the radial basis function (RBF) neural network (NN), a machine leaning algorithm, has been identified as the appropriate tool with which to correlate tree health and PEFs to establish the PTA algorithm. One of the salient features of PTA is that the algorithm can evaluate, and thus monitor, tree health remotely and automatically from smart sensor data by taking advantage of the well-established internet of things (IoT) network and machine learning algorithm.

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