scholarly journals A Fast Density-Based Clustering Algorithm for Real-Time Internet of Things Stream

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Amineh Amini ◽  
Hadi Saboohi ◽  
Teh Ying Wah ◽  
Tutut Herawan
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Data Streams ◽  
Clustering Algorithm ◽  
Computation Time ◽  
Density Based Clustering ◽  
Fast Processing ◽  
Iot Devices ◽  
Synthetic Datasets ◽  
Clustering Data

Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape clusters, to handle outlier, and it does not need the number of clusters in advance. Therefore, density-based clustering algorithm is a proper choice for clustering IoT streams. Recently, several density-based algorithms have been proposed for clustering data streams. However, density-based clustering in limited time is still a challenging issue. In this paper, we propose a density-based clustering algorithm for IoT streams. The method has fast processing time to be applicable in real-time application of IoT devices. Experimental results show that the proposed approach obtains high quality results with low computation time on real and synthetic datasets.

An Efficient Data Stream Clustering Algorithm Based on Dynamic Grids

2011 ◽  
Vol 186 ◽  
pp. 665-670
Author(s):  
Yun Wu ◽  
Feng Gao
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Arbitrary Shape ◽  
Clustering Algorithm ◽  
Stream Clustering ◽  
Research Fields ◽  
Efficient Data ◽  
Data Stream Clustering ◽  
Synthetic Datasets ◽  
Clustering Data

Data mining based on data stream has become one of hot research fields. In this paper we present a novel algorithm for clustering data streams based on dynamic grids named DG-CluStream. DG-CluStream partitions and prunes grids dynamically, improves the accuracy of grids gradually through saving feature tuples of grids. The algorithm can discover clusters with arbitrary shape and is more efficient than those static methods due to a notable decrease on the number of the grids. Through fading coefficient, DG-CluStream can also deal with the problem of concept drifting efficiently. The experimental results on real datasets and synthetic datasets demonstrate promising availabilities of the approach.

scholarly journals Practical Cross-Layer Radio Frequency-Based Authentication Scheme for Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4034
Author(s):  
Arie Haenel ◽  
Yoram Haddad ◽  
Maryline Laurent ◽  
Zonghua Zhang
Keyword(s):  
Internet Of Things ◽  
Radio Frequency ◽  
Real Life ◽  
Computation Time ◽  
Cost Effective ◽  
Battery Life ◽  
Cross Layer ◽  
Security Level ◽  
Cost Of Energy ◽  
Iot Devices

The Internet of Things world is in need of practical solutions for its security. Existing security mechanisms for IoT are mostly not implemented due to complexity, budget, and energy-saving issues. This is especially true for IoT devices that are battery powered, and they should be cost effective to be deployed extensively in the field. In this work, we propose a new cross-layer approach combining existing authentication protocols and existing Physical Layer Radio Frequency Fingerprinting technologies to provide hybrid authentication mechanisms that are practically proved efficient in the field. Even though several Radio Frequency Fingerprinting methods have been proposed so far, as a support for multi-factor authentication or even on their own, practical solutions are still a challenge. The accuracy results achieved with even the best systems using expensive equipment are still not sufficient on real-life systems. Our approach proposes a hybrid protocol that can save energy and computation time on the IoT devices side, proportionally to the accuracy of the Radio Frequency Fingerprinting used, which has a measurable benefit while keeping an acceptable security level. We implemented a full system operating in real time and achieved an accuracy of 99.8% for the additional cost of energy, leading to a decrease of only ~20% in battery life.

Applying Security to a Big Stream Cloud Architecture for the Internet of Things

2020 ◽  
pp. 1260-1284
Author(s):  
Laura Belli ◽  
Simone Cirani ◽  
Luca Davoli ◽  
Gianluigi Ferrari ◽  
Lorenzo Melegari ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Large Data ◽  
The Internet ◽  
Low Latency ◽  
Stream Data ◽  
Memory Processing ◽  
Cloud Architecture ◽  
Iot Devices ◽  
The Internet Of Things

The Internet of Things (IoT) is expected to interconnect billions (around 50 by 2020) of heterogeneous sensor/actuator-equipped devices denoted as “Smart Objects” (SOs), characterized by constrained resources in terms of memory, processing, and communication reliability. Several IoT applications have real-time and low-latency requirements and must rely on architectures specifically designed to manage gigantic streams of information (in terms of number of data sources and transmission data rate). We refer to “Big Stream” as the paradigm which best fits the selected IoT scenario, in contrast to the traditional “Big Data” concept, which does not consider real-time constraints. Moreover, there are many security concerns related to IoT devices and to the Cloud. In this paper, we analyze security aspects in a novel Cloud architecture for Big Stream applications, which efficiently handles Big Stream data through a Graph-based platform and delivers processed data to consumers, with low latency. The authors detail each module defined in the system architecture, describing all refinements required to make the platform able to secure large data streams. An experimentation is also conducted in order to evaluate the performance of the proposed architecture when integrating security mechanisms.

scholarly journals Setting Up Telemedicine: Internet of Things to Support Rural Oncology Clinics in a Low- and Middle-Income Country—A Pilot Project in Kenya

2020 ◽  
Vol 6 (Supplement_1) ◽  
pp. 58-58
Author(s):  
Lamech Sigu ◽  
Fredrick Chite ◽  
Emma Achieng ◽  
Andrew Koech
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Length Of Hospital Stay ◽  
Human Interaction ◽  
Sub Saharan Africa ◽  
Patient Treatment ◽  
The Internet ◽  
Tumor Boards ◽  
Iot Devices ◽  
Set Up

PURPOSE The Internet of Things (IoT) is a technology that involves all things connected to the Internet that share data over a network without requiring human-to-human interaction or human-to-computer interaction. Information collected from IoT devices can help physicians identify the best treatment process for patients and reach accurate and expected outcomes. METHODS The International Cancer Institute is partnering to set up remote oncology clinics in sub-Saharan Africa. Medical oncologists and expert teams from across the world connect with oncology clinics in other Kenyan counties—Kisumu, Meru, Makueni, Garissa, Kakamega, Bungoma, Siaya, and Vihiga counties. The furthest county is Garissa, approximately 651.1 km from Eldoret, and the nearest is Vihiga at 100.4 km from Eldoret. This study began July 2019, and as of November 30th, the team has hosted 21 sessions with an average of 11 participants attending a session led by a medical oncologist. RESULTS IoT devices have become a way by which a patient gets all the information he or she needs from a physician without going to the clinic. Patient monitoring can be done in real time, allowing access to real-time information with improved patient treatment outcomes and a decrease in cost. Through IoT-enabled devices, the International Cancer Institute has set up weekly virtual tumor boards during which cancer cases are presented and discussed by all participating counties. An online training module on cancer is also offered. Furthermore, remote monitoring of a patient’s health helps to reduce the length of hospital stay and prevents readmissions. CONCLUSION In our setting, which has a few oncologists, use of IoT and tumor boards has helped to improve patient decision support as well as training for general physicians.

scholarly journals Density Based Clustering Data Association Procedure for Real–Time HFSWRs Tracking at OTH Distances

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 39907-39919
Author(s):  
Nikola Stojkovic ◽  
Dejan Nikolic ◽  
Snezana Puzovic
Keyword(s):  
Real Time ◽  
Data Association ◽  
Density Based Clustering ◽  
Clustering Data

scholarly journals Internet of Things: A Comprehensive Study on Machine Learning-based Intrusion Detection approaches

2021 ◽  
Author(s):  
Priyanka Gupta ◽  
Lokesh Yadav ◽  
Deepak Singh Tomar
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Real Time ◽  
Detection System ◽  
Research Work ◽  
Security And Privacy ◽  
Iot Security ◽  
Advantages And Disadvantages ◽  
Iot Devices

The Internet of Things (IoT) connects billions of interconnected devices that can exchange information with each other with minimal user intervention. The goal of IoT to become accessible to anyone, anytime, and anywhere. IoT has engaged in multiple fields, including education, healthcare, businesses, and smart home. Security and privacy issues have been significant obstacles to the widespread adoption of IoT. IoT devices cannot be entirely secure from threats; detecting attacks in real-time is essential for securing devices. In the real-time communication domain and especially in IoT, security and protection are the major issues. The resource-constrained nature of IoT devices makes traditional security techniques difficult. In this paper, the research work carried out in IoT Intrusion Detection System is presented. The Machine learning methods are explored to provide an effective security solution for IoT Intrusion Detection systems. Then discussed the advantages and disadvantages of the selected methodology. Further, the datasets used in IoT security are also discussed. Finally, the examination of the open issues and directions for future trends are also provided.

scholarly journals An Efficient Data Stream Analytics Model for Real Time Internet of Things (Iot) Applications

2020 ◽  
Vol 8 (6) ◽  
pp. 495-500
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Real Time ◽  
Private Information ◽  
Large Scale ◽  
Layer System ◽  
Collection Data ◽  
Efficient Data ◽  
Multiple Environments ◽  
Iot Devices

Internet of Things (IoT), data analytics is supporting multiple applications. These numerous applications try to gather data from different environments, here the gathered data may be homogeneous or heterogeneous, but most of the data collected from multiple environments were heterogeneous, the task of gathering, processing, storing and the analysis that is being performed on data are still challenging. Providing security to all these things is also a challenging task due to untrusted networks and big data. Big data management in the ever-expanding network may rise several non-trivial concerns on data collection, data-efficient processing, analytics, and security. However, the above said scenarios depends on large scale sensor deployed. Sensors continuously transmit data to clouds for real time use, which can raise the issue of privacy disclosure because IoT devices may gather data including a kind of sensitive private information. In this context, we propose a two-layer system or model for analyzing IoT data, collected from multiple applications. The first layer is mainly used for gathering data from multiple environments and acts as a service-oriented interface to ingest data. The second layer is responsible for storing and analyses data securely. The Proposed solutions are implemented by the use of open source components.

scholarly journals Affecting Young Children’s Knowledge, Attitudes, and Behaviors for Ultraviolet Radiation Protection through the Internet of Things: A Quasi-Experimental Study

Computers ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 137
Author(s):  
Sotiroula Theodosi ◽  
Iolie Nicolaidou
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Uv Radiation ◽  
Small Scale ◽  
Post Intervention ◽  
Attitudes And Behaviors ◽  
Quasi Experimental ◽  
And Behaviors ◽  
Iot Devices ◽  
Experimental Group

Prolonged exposure to ultraviolet (UV) radiation is linked to skin cancer. Children are more vulnerable to UV harmful effects compared to adults. Children’s active involvement in using Internet of Things (IoT) devices to collect and analyze real-time UV radiation data is suggested to increase their awareness of UV protection. This quasi-experimental pre-test post-test control group study implemented light sensors in a STEM inquiry-based learning environment focusing on UV radiation and protection in primary education. This exploratory, small-scale study investigated the effect of a STEM environment implementing IoT devices on 6th graders’ knowledge, attitudes, and behaviors about UV radiation and protection. Participants were 31 primary school students. Experimental group participants (n = 15) attended four eighty-minute inquiry-based lessons on UV radiation and protection and used sensors to measure and analyze UV radiation in their school. Data sources included questionnaires on UV knowledge, attitudes, and behaviors administered pre- and post-intervention. Statistically significant learning gains were found only for the experimental group (t14 = −3.64, p = 0.003). A statistically significant positive behavioral change was reported for experimental group participants six weeks post-intervention. The study adds empirical evidence suggesting the value of real-time data-driven approaches implementing IoT devices to positively influence students’ knowledge and behaviors related to socio-scientific problems affecting their health.

Fog Computing: A Primer

2017 ◽  
Vol 7 (7) ◽  
pp. 405
Author(s):  
Matthew N. O. Sadiku ◽  
Mahamadou Tembely ◽  
Sarhan M. Musa
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Fog Computing ◽  
Resource Constrained ◽  
Computing Model ◽  
Computing Services ◽  
Iot Devices ◽  
Real Time Computing ◽  
And Storage ◽  
The Ideal

Fog computing (FC) was proposed in 2012 by Cisco as the ideal computing model for providing real-time computing services and storage to support the resource-constrained Internet of Things (IoT) devices. Thus, FC may be regarded as the convergence of the IoT and the Cloud, combining the data-centric IoT services and pay-as-you-go characteristics of clouds.  This paper provides a brief introduction of fog computing.

IMPROVED DENSITY BASED ALGORITHM FOR DATA STREAM CLUSTERING

2015 ◽  
Vol 77 (18) ◽  
Author(s):  
Maryam Mousavi ◽  
Azuraliza Abu Bakar
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Clustering Algorithm ◽  
Local Density ◽  
Clustering Methods ◽  
Clustering Techniques ◽  
Stream Clustering ◽  
Density Based Clustering ◽  
Clustering Quality ◽  
Data Stream Clustering

In recent years, clustering methods have attracted more attention in analysing and monitoring data streams. Density-based techniques are the remarkable category of clustering techniques that are able to detect the clusters with arbitrary shapes and noises. However, finding the clusters with local density varieties is a difficult task. For handling this problem, in this paper, a new density-based clustering algorithm for data streams is proposed. This algorithm can improve the offline phase of density-based algorithm based on MinPts parameter. The experimental results show that the proposed technique can improve the clustering quality in data streams with different densities.

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