A Novel Outlier Detection Model Based on One Class Principal Component Classifier in Wireless Sensor Networks

2015 ◽  
Author(s):  
Oussama Ghorbel ◽  
Mohamed Abid ◽  
Hichem Snoussi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Outlier Detection ◽  
Principal Component ◽  
Wireless Sensor ◽  
Detection Model ◽  
Model Based ◽  
Component Classifier

scholarly journals An Intrusion Detection Model Based On Danger Theory for Wireless Sensor Networks

2018 ◽  
Vol 14 (09) ◽  
pp. 53
Author(s):  
Linlin Li ◽  
Liangxu Sun ◽  
Gang Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Evaluation Method ◽  
False Negative ◽  
False Negative Rate ◽  
Wireless Sensor ◽  
Danger Theory ◽  
Detection Model ◽  
Model Based

<strong>This paper, due to the intrusion detection problem in Wireless Sensor Networks, proposes an intrusion detection model based on the Danger Theory instead of the traditional Self-NonSelf theory. The intrusion detection model has two layers structure including danger perception and control decision, and it uses a multi-node cooperation mechanism. The perception node can realize the danger perception with Projection Pursuit Algorithm, and the decision node can detect the intrusion in detail with Extreme Learning Machine Algorithm. The logic process between their layers is consistent with the Danger Theory. The proposed model can realize the data trust between nodes with the Beta distribution trust evaluation method. By the simulations in the MATLAB, the proposed intrusion detection model on the whole is better than the SNS model at the aspects including classification training, danger perception, false negative rate, false positive rate and energy consumption.</strong>

scholarly journals A Model-Based Approach for Adaptable Middleware Evolution in WSN Platforms

2021 ◽  
Vol 10 (1) ◽  
pp. 20
Author(s):  
Walter Tiberti ◽  
Dajana Cassioli ◽  
Antinisca Di Marco ◽  
Luigi Pomante ◽  
Marco Santic
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Embedded System ◽  
Operating Systems ◽  
Parallel Evolution ◽  
Wireless Sensor ◽  
New Techniques ◽  
Model Based ◽  
And Performance ◽  
Hardware Platforms

Advances in technology call for a parallel evolution in the software. New techniques are needed to support this dynamism, to track and guide its evolution process. This applies especially in the field of embedded systems, and certainly in Wireless Sensor Networks (WSNs), where hardware platforms and software environments change very quickly. Commonly, operating systems play a key role in the development process of any application. The most used operating system in WSNs is TinyOS, currently at its TinyOS 2.1.2 version. The evolution from TinyOS 1.x and TinyOS 2.x made the applications developed on TinyOS 1.x obsolete. In other words, these applications are not compatible out-of-the-box with TinyOS 2.x and require a porting action. In this paper, we discuss on the porting of embedded system (i.e., Wireless Sensor Networks) applications in response to operating systems’ evolution. In particular, using a model-based approach, we report the porting we did of Agilla, a Mobile-Agent Middleware (MAMW) for WSNs, on TinyOS 2.x, which we refer to as Agilla 2. We also provide a comparative analysis about the characteristics of Agilla 2 versus Agilla. The proposed Agilla 2 is compatible with TinyOS 2.x, has full capabilities and provides new features, as shown by the maintainability and performance measurement presented in this paper. An additional valuable result is the architectural modeling of Agilla and Agilla 2, missing before, which extends its documentation and improves its maintainability.

scholarly journals A Data-Based Fault-Detection Model for Wireless Sensor Networks

2019 ◽  
Vol 11 (21) ◽  
pp. 6171 ◽  
Author(s):  
Jangsik Bae ◽  
Meonghun Lee ◽  
Changsun Shin
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Detection ◽  
Data Transmission ◽  
Sensor Node ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Sensors And Actuators ◽  
Detection Model ◽  
Management Platform

With the expansion of smart agriculture, wireless sensor networks are being increasingly applied. These networks collect environmental information, such as temperature, humidity, and CO2 rates. However, if a faulty sensor node operates continuously in the network, unnecessary data transmission adversely impacts the network. Accordingly, a data-based fault-detection algorithm was implemented in this study to analyze data of sensor nodes and determine faults, to prevent the corresponding nodes from transmitting data; thus, minimizing damage to the network. A cloud-based “farm as a service” optimized for smart farms was implemented as an example, and resource management of sensors and actuators was provided using the oneM2M common platform. The effectiveness of the proposed fault-detection model was verified on an integrated management platform based on the Internet of Things by collecting and analyzing data. The results confirm that when a faulty sensor node is not separated from the network, unnecessary data transmission of other sensor nodes occurs due to continuous abnormal data transmission; thus, increasing energy consumption and reducing the network lifetime.

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