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

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.

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A Real-Time Detection Method for Abnormal Data of Internet of Things Sensors Based on Mobile Edge Computing

Mathematical Problems in Engineering ◽

10.1155/2021/6655346 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Xuguang Liu

Keyword(s):

Time Series ◽

Anomaly Detection ◽

Data Processing ◽

Real Time ◽

Detection Method ◽

Edge Computing ◽

Sensor Data ◽

Detection Accuracy ◽

Spatiotemporal Correlation ◽

Real Time Detection

Aiming at the anomaly detection problem in sensor data, traditional algorithms usually only focus on the continuity of single-source data and ignore the spatiotemporal correlation between multisource data, which reduces detection accuracy to a certain extent. Besides, due to the rapid growth of sensor data, centralized cloud computing platforms cannot meet the real-time detection needs of large-scale abnormal data. In order to solve this problem, a real-time detection method for abnormal data of IoT sensors based on edge computing is proposed. Firstly, sensor data is represented as time series; K-nearest neighbor (KNN) algorithm is further used to detect outliers and isolated groups of the data stream in time series. Secondly, an improved DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm is proposed by considering spatiotemporal correlation between multisource data. It can be set according to sample characteristics in the window and overcomes the slow convergence problem using global parameters and large samples, then makes full use of data correlation to complete anomaly detection. Moreover, this paper proposes a distributed anomaly detection model for sensor data based on edge computing. It performs data processing on computing resources close to the data source as much as possible, which improves the overall efficiency of data processing. Finally, simulation results show that the proposed method has higher computational efficiency and detection accuracy than traditional methods and has certain feasibility.

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INTERNET OF THINGS BASED SMART BIKE SYSTEM

INFORMATION TECHNOLOGY IN INDUSTRY ◽

10.17762/itii.v9i1.182 ◽

2021 ◽

Vol 9 (1) ◽

pp. 640-644

Author(s):

Dr. Siddhartha Choubey Et al.

Keyword(s):

Internet Of Things ◽

Real Time ◽

Ignition Temperature ◽

Road Accident ◽

Temperature Monitoring ◽

Sensor Data ◽

Schematic Diagram ◽

The Real ◽

Travelling Time ◽

Time Distance

Present invention relates to IoT enabled smart bike. The object of the proposed invention is to provide a smart bike which has many smart features like gesture controlled horn and ignition, temperature monitoring of engine, also shows the real time distance and time remaining to arrive the destination. The preferred embodiment comprises of Wi-Fi module, ultrasonic sensor,accelerometer and cloud platforms for managing and processing sensor data. The Smart Bike saves the travelling time and reduces the possibility of road accident by a warning buzzer system. Following invention is described in detail with the help of Figure 1 of sheet 1 showing the schematic diagram for the proposed invention.

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IoT-RECSM—Resource-Constrained Smart Service Migration Framework for IoT Edge Computing Environment

Sensors ◽

10.3390/s20082294 ◽

2020 ◽

Vol 20 (8) ◽

pp. 2294 ◽

Cited By ~ 1

Author(s):

Zhongyi Zhai ◽

Ke Xiang ◽

Lingzhong Zhao ◽

Bo Cheng ◽

Junyan Qian ◽

...

Keyword(s):

Edge Computing ◽

Sensor Data ◽

Prototype System ◽

Data Traffic ◽

Computing Environment ◽

Resource Constrained ◽

Edge Node ◽

Computing Paradigm ◽

Service Migration ◽

Smart Service

The edge-based computing paradigm (ECP) becomes one of the most innovative modes of processing distributed Interneit of Things (IoT) sensor data. However, the edge nodes in ECP are usually resource-constrained. When more services are executed on an edge node, the resources required by these services may exceed the edge node’s, so as to fail to maintain the normal running of the edge node. In order to solve this problem, this paper proposes a resource-constrained smart service migration framework for edge computing environment in IoT (IoT-RECSM) and a dynamic edge service migration algorithm. Based on this algorithm, the framework can dynamically migrate services of resource-critical edge nodes to resource-rich nodes. In the framework, four abstract models are presented to quantificationally evaluate the resource usage of edge nodes and the resource consumption of edge service in real-time. Finally, an edge smart services migration prototype system is implemented to simulate the edge service migration in IoT environment. Based on the system, an IoT case including 10 edge nodes is simulated to evaluate the proposed approach. According to the experiment results, service migration among edge nodes not only maintains the stability of service execution on edge nodes, but also reduces the sensor data traffic between edge nodes and cloud center.

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An edge-cloud collaboration architecture for pattern anomaly detection of time series in wireless sensor networks

Complex & Intelligent Systems ◽

10.1007/s40747-021-00442-6 ◽

2021 ◽

Author(s):

Cong Gao ◽

Ping Yang ◽

Yanping Chen ◽

Zhongmin Wang ◽

Yue Wang

Keyword(s):

Time Series ◽

Wireless Sensor Networks ◽

Sensor Networks ◽

Anomaly Detection ◽

Estimation Method ◽

Feature Representation ◽

Sensor Data ◽

Wireless Sensor ◽

Data Sets ◽

Edge Node

AbstractWith large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.

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Design of a model based engineering deep learning scheduler in cloud computing environment using Industrial Internet of Things (IIOT)

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-020-02862-7 ◽

2021 ◽

Author(s):

P. Senthilkumar ◽

K. Rajesh

Keyword(s):

Cloud Computing ◽

Deep Learning ◽

Internet Of Things ◽

Computing Environment ◽

Industrial Internet Of Things ◽

Cloud Computing Environment ◽

Model Based ◽

Industrial Internet

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Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

International Journal of Ambient Computing and Intelligence ◽

10.4018/ijaci.2017040106 ◽

2017 ◽

Vol 8 (2) ◽

pp. 88-105 ◽

Cited By ~ 49

Author(s):

Gunasekaran Manogaran ◽

Daphne Lopez

Keyword(s):

Climate Change ◽

Big Data ◽

Data Processing ◽

Real Time ◽

Surveillance System ◽

Disease Surveillance ◽

Sensor Data ◽

Climate Data ◽

The Real ◽

Disease Surveillance System

Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.

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Detection of Human Body Motion Based on Electrostatic Measurements in the Indoor Environment

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.480-481.1329 ◽

2011 ◽

Vol 480-481 ◽

pp. 1329-1334

Author(s):

Wei Zheng ◽

Zhan Zhong Cui

Keyword(s):

Theoretical Analysis ◽

Real Time ◽

Human Body ◽

Motion Detection ◽

Average Velocity ◽

Indoor Environment ◽

Detection Method ◽

Body Motion ◽

The Real ◽

Multiple Electrodes

An effective non-contact electrostatic detection method is used for human body motion detection. Theoretical analysis and pratical experiments are carried out to prove that this method is effective in the field of human body monitoring, in which a model for human body induced potential by stepping has been proposed. Furthermore, experiment results also prove that it’s feasible to measure the average velocity and route of human body motion by multiple electrodes array. What’s more the real-time velocity and direction of human body motion can be determined by orthogonal electrostatic detector array, and the real-time velocity and direction of human body motion can be obtained within the range of 2 meters.

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IoT Device Onboarding, Monitoring, and Management

Advances in Web Technologies and Engineering - Challenges and Opportunities for the Convergence of IoT, Big Data, and Cloud Computing ◽

10.4018/978-1-7998-3111-2.ch013 ◽

2021 ◽

pp. 227-255

Author(s):

Selvaraj Kesavan ◽

Senthilkumar J. ◽

Suresh Y. ◽

Mohanraj V.

Keyword(s):

Internet Of Things ◽

Real Time ◽

Secure Communication ◽

Mutual Authentication ◽

Modern World ◽

Cloud Platform ◽

Privacy And Security ◽

The Real ◽

Healthy Environment

In establishing a healthy environment for connectivity devices, it is essential to ensure that privacy and security of connectivity devices are well protected. The modern world lives on data, information, and connectivity. Various kinds of sensors and edge devices stream large volumes of data to the cloud platform for storing, processing, and deriving insights. An internet of things (IoT) system poses certain difficulties in discretely identifying, remotely configuring, and controlling the devices, and in the safe transmission of data. Mutual authentication of devices and networks is crucial to initiate secure communication. It is important to keep the data in a secure manner during transmission and in store. Remotely operated devices help to monitor, control, and manage the IoT system efficiently. This chapter presents a review of the approaches and methodologies employed for certificate provisioning, device onboarding, monitoring, managing, and configuring of IoT systems. It also examines the real time challenges and limitations in and future scope for IoT systems.

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