Sensor data transmission through passive RFID tags to feed wireless sensor networks

2010 ◽  
Author(s):  
Luca Catarinucci ◽  
Riccardo Colella ◽  
Luciano Tarricone
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Transmission ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Rfid Tags ◽  
Passive Rfid

scholarly journals Pseudo-Random Encryption for Security Data Transmission in Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2452 ◽  
Author(s):  
Liang Liu ◽  
Wen Chen ◽  
Tao Li ◽  
Yuling Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Data Transmission ◽  
Multiple Scales ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Fusion Center ◽  
Inverse Mapping ◽  
The Matrix

The security of wireless sensor networks (WSN) has become a great challenge due to the transmission of sensor data through an open and wireless network with limited resources. In the paper, we discussed a lightweight security scheme to protect the confidentiality of data transmission between sensors and an ally fusion center (AFC) over insecure links. For the typical security problem of WSN’s binary hypothesis testing of a target’s state, sensors were divided into flipping and non-flipping groups according to the outputs of a pseudo-random function which was held by sensors and the AFC. Then in order to prevent an enemy fusion center (EFC) from eavesdropping, the binary outputs from the flipping group were intentionally flipped to hinder the EFC’s data fusion. Accordingly, the AFC performed inverse flipping to recover the flipped data before data fusion. We extended the scheme to a more common scenario with multiple scales of sensor quantification and candidate states. The underlying idea was that the sensor measurements were randomly mapped to other quantification scales using a mapping matrix, which ensured that as long as the EFC was not aware of the matrix, it could not distract any useful information from the captured data, while the AFC could appropriately perform data fusion based on the inverse mapping of the sensor outputs.

An Energy Efficient Routing Approach to Enhance Coverage for Application Specific Wireless Sensor Networks using Genetic Algorithm

2019 ◽  
Vol 13 ◽  
Author(s):  
Amandeep Kaur Sohal ◽  
Ajay Kumar Sharma ◽  
Neetu Sood
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Transmission ◽  
Energy Efficient ◽  
Search Algorithm ◽  
Cluster Formation ◽  
Green Computing ◽  
Wireless Sensor ◽  
Energy Efficient Routing

Background: An information gathering is a typical and important task in agriculture monitoring and military surveillance. In these applications, minimization of energy consumption and maximization of network lifetime have prime importance for green computing. As wireless sensor networks comprise of a large number of sensors with limited battery power and deployed at remote geographical locations for monitoring physical events, therefore it is imperative to have minimum consumption of energy during network coverage. The WSNs help in accurate monitoring of remote environment by collecting data intelligently from the individual sensors. Objective: The paper is motivated from green computing aspect of wireless sensor network and an Energy-efficient Weight-based Coverage Enhancing protocol using Genetic Algorithm (WCEGA) is presented. The WCEGA is designed to achieve continuously monitoring of remote areas for a longer time with least power consumption. Method: The cluster-based algorithm consists two phases: cluster formation and data transmission. In cluster formation, selection of cluster heads and cluster members areas based on energy and coverage efficient parameters. The governing parameters are residual energy, overlapping degree, node density and neighbor’s degree. The data transmission between CHs and sink is based on well-known evolution search algorithm i.e. Genetic Algorithm. Conclusion: The results of WCEGA are compared with other established protocols and shows significant improvement of full coverage and lifetime approximately 40% and 45% respectively.

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

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.

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.

Design of Tungsten Tailing Reservoir Safety Monitoring and Warning System Based on Wireless Sensor Networks and LabVIEW

2013 ◽  
Vol 427-429 ◽  
pp. 1268-1271
Author(s):  
Xue Wen He ◽  
Ying Fei Sheng ◽  
Kuan Gang Fan ◽  
Le Ping Zheng ◽  
Qing Mei Cao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Transmission ◽  
Communication Protocol ◽  
Warning System ◽  
Safety Monitoring ◽  
Wireless Sensor ◽  
Serial Port ◽  
Rf Transceiver ◽  
New Type

In view of the existing flaws of traditional manual observations, a new type of tailing reservoir safety monitoring and warning system based on ZigBee and LabVIEW was designed. The system chose SoC chip CC2530 as the RF transceiver and designed the low-power wireless sensor networks nodes to collect and process the data of tailing reservoir. It chose ZigBee 2007 as the network communication protocol, and uploaded the data to PC by RS232 serial port. The monitoring and warning interface of PC was completed with LabVIEW. The testing results show that the data transmission of the network is stable and the system is suitable for real-time monitoring and warning of the tungsten tailing reservoir.

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