A new selfish thing detection method based on Voronoi diagram for Internet of Things

2022 ◽  
Author(s):  
Nasim Razzaghi ◽  
Shahram Babaie
Keyword(s):  
Internet Of Things ◽  
Voronoi Diagram ◽  
Detection Method

scholarly journals An Automata Based Intrusion Detection Method for Internet of Things

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Yulong Fu ◽  
Zheng Yan ◽  
Jin Cao ◽  
Ousmane Koné ◽  
Xuefei Cao
Keyword(s):  
Open Access ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection Method ◽  
Detection System ◽  
Network Communication ◽  
5G Networks ◽  
Machine To Machine ◽  
Security Of Iot

Internet of Things (IoT) transforms network communication to Machine-to-Machine (M2M) basis and provides open access and new services to citizens and companies. It extends the border of Internet and will be developed as one part of the future 5G networks. However, as the resources of IoT’s front devices are constrained, many security mechanisms are hard to be implemented to protect the IoT networks. Intrusion detection system (IDS) is an efficient technique that can be used to detect the attackers when cryptography is broken, and it can be used to enforce the security of IoT networks. In this article, we analyzed the intrusion detection requirements of IoT networks and then proposed a uniform intrusion detection method for the vast heterogeneous IoT networks based on an automata model. The proposed method can detect and report the possible IoT attacks with three types: jam-attack, false-attack, and reply-attack automatically. We also design an experiment to verify the proposed IDS method and examine the attack of RADIUS application.

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 A high-sensitivity multilayer soil moisture monitoring sensor based on a double high-frequency tuning detection circuit

2020 ◽  
Vol 16 (2) ◽  
pp. 155014772090782
Author(s):  
Shi Qinglan ◽  
Shi Yujiao ◽  
Liu Xiaochen ◽  
Mei Shuli ◽  
Feng Lei
Keyword(s):  
Soil Moisture ◽  
Internet Of Things ◽  
Root Mean Square Error ◽  
High Frequency ◽  
Detection Method ◽  
Mean Square ◽  
Time Division Multiplexing ◽  
Detection Circuit ◽  
Moisture Detection ◽  
Time Division

The multilayer soil moisture Internet of things sensor is designed to monitor the moisture of multiple soil profiles in real time. Its sensitivity and accuracy are of great concern to improve the performance of sensors. This article introduces the system composition of the end-cloud integrated multilayer soil moisture Internet of things sensor and then focuses on the design of key technologies, such as the moisture detection circuit, the time division multiplexing detection technology, and the deredundancy circuit in the analog–digital integrated design. The performance of the soil moisture detection circuit is directly related to the measurement accuracy of the sensor. A detection method is proposed using a high-frequency double-resonance circuit, which can detect small changes in moisture by changing the circuit detuning voltage. The maximum root mean square error of the calibration is less than 1.35% for five typical soils from different places. Compared with that of an independent detection method, the output consistency of the time division multiplexing detection is significantly improved by using the time division multiplexing detection method, which has a root mean square error of only 0.12%. In order to reduce errors caused by inconsistency in each burial, the gravimetric analysis is used in the sensitivity monitoring test, which shows that small changes in soil moisture can be detected by the circuit.

Voronoi Diagram Based Retrieval Method for the Internet of Things

2012 ◽  
Vol 472-475 ◽  
pp. 3420-3424
Author(s):  
Wen Bin Zhao ◽  
Zheng Xu Zhao
Keyword(s):  
Internet Of Things ◽  
Vector Space ◽  
Voronoi Diagram ◽  
Vector Space Model ◽  
The Internet ◽  
Retrieval Method ◽  
Space Model ◽  
Feature Vectors ◽  
Search Service ◽  
The Internet Of Things

In the Internet of Things, search service is that information which is corresponding to entities in real world is retrieve so as to help people achieve information that they need. From the angle of visualization, this paper study the search service in the Internet of Things. With the Vector-Space Model, entities' information is expressed as entity vectors, and a Voronoi diagram based Vector-Space Model visualization method is presented. According to similarity between entity vectors, entity vectors' position in two-dimensional plane is calculated and this plane is divided into Voronoi diagram by nodes as which feature vectors of entity vectors’ clustering are taken. According to similarity between query vector and feature vector, a Voronoi diagram based Vector-Space Model retrieval method is put forward. This method restricts the searching scope in Voronoi domain of feature vectors that are the most similar to query vector, thereby the number of compared entity vectors is reduced in the retrieval process. The experiment result indicates that this method can ensure retrieval precision and improve retrieval efficiency.

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