IRIS: A Flexible and Extensible Experiment Management and Data Analysis Tool for Wireless Sensor Networks

2013 ◽  
pp. 94-110
Author(s):  
Richard Figura ◽  
Chia-Yen Shih ◽  
Songwei Fu ◽  
Roberta Daidone ◽  
Sascha Jungen ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Data Analysis ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Analysis Tool ◽  
Experiment Management ◽  
Data Analysis Tool

scholarly journals IRIS: Efficient Visualization, Data Analysis and Experiment Management for Wireless Sensor Networks

2014 ◽  
Vol 1 (3) ◽  
pp. e4 ◽  
Author(s):  
Richard Figura ◽  
Matteo Ceriotti ◽  
Chia-Yen Shih ◽  
Margarita Mulero-Pázmány ◽  
Songwei Fu ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Data Analysis ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Experiment Management

An Overview of Memory Provisioning in Real-Time Wireless Sensor Networks

2013 ◽  
Vol 5 (4) ◽  
pp. 73-91
Author(s):  
Jacqueline Stewart ◽  
Thomas McCabe ◽  
Robert Stewart ◽  
Sean Kennedy
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Service Delivery ◽  
Real Time ◽  
Service Providers ◽  
Wireless Sensor ◽  
Analysis Tool ◽  
Time Service ◽  
Main Challenge ◽  
Stack Analysis

Wireless Sensor Networks and the smart applications designed to operate upon them have enjoyed a rapid increase in popularity over the last decade. The main challenge currently is the provision of real-time service delivery for wireless sensor networks to cater for new applications with guaranteed Quality of Service (QoS) requirements. However each application has a different service requirement. In order to deliver real-time services the dimensioning of such networks is important to service providers in order to meet these service requirements. If packets cannot be stored due to insufficient memory they are lost. Lost packets result in the resending of the packets and hence an increase in delay in delivery of the application traffic. It is this memory provisioning of these wireless sensor networks that is the focus of the work presented in this paper. More specifically the relationship between the application design, implementation and memory resources required to run the service are explored using a stack analysis tool. This stack analysis tool enables the stack footprint to be measured. Results of memory usage for two different WSN applications are presented. Recommendations based on this study for efficient memory provisioning and ultimately real-time service delivery are given.

The Design of Wireless Sensor Networks and Data Analysis in Smart Grids

2014 ◽  
Vol 978 ◽  
pp. 257-260
Author(s):  
Ying Zhao ◽  
Ru Kun Li ◽  
Kun Le Xu
Keyword(s):  
Wireless Sensor Networks ◽  
Data Analysis ◽  
Sensor Networks ◽  
Communication Technology ◽  
Smart Grids ◽  
Wireless Sensor ◽  
Current Status ◽  
Sensing Technology ◽  
Close Relationship ◽  
Technology And Communication

Smart grids have a close relationship to advanced sensing technology and communication technology and also have provided a new application platform for wireless sensor networks. From wireless sensor networks research development and characteristics, combined with current status of wireless sensor networks applications in power system, this paper describes its key technologies of wireless sensor networks, then analyze applications and data analysis of the wireless sensor network in the smart grids.

scholarly journals Demand Analysis of Online Chinese Behavior Expression in Wireless Sensor Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zheng Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Big Data ◽  
Data Analysis ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Semantic Analysis ◽  
Big Data Analysis ◽  
Wireless Sensor ◽  
Data Set ◽  
Analysis Models

Due to the common progress and interdependence of wireless sensor networks and language, Chinese semantic analysis under wireless sensor networks has become more and more important. Although there are many research results on wireless networks and Chinese semantics, there are few researches on the influence and relationship between them. Wireless sensor networks have strong application relevance, and the key technologies that need to be solved are also different for different application backgrounds. In order to reveal the basic laws and development trends of online Chinese semantic behavior expression in the context of wireless sensor networks, this paper adopts big data analysis methods and semantic model analysis methods and constructs semantic analysis models through PLSA method calculations, so that the λ construction process conforms to this research topic. Research the accuracy and applicability of the semantic analysis model. Through word extraction of 1.05 million word data of 1,103 documents on Baidu Tieba, HowNet, and citeulike websites, the data set was integrated into a data set, and the PLSA model was verified with this data set. In addition, through the construction of the wireless sensor network, the semantic analysis results in the expression of Chinese behavior are obtained. The results show that the accuracy of the data set extracted from 1103 documents increases with the increase of the number of documents. Second, after using the PLSA model to perform semantic analysis on the data set, the accuracy of the data set is improved. Compared with traditional semantic analysis, the model and the big data analysis framework have obvious advantages. With the continuous development of Internet big data, the big data methods used to count Chinese semantics are also constantly updated, and their efficiency is constantly improving. These updated semantic analysis models and statistical methods are constantly eliminating the uncertainty of modern online Chinese. The basic laws and development trends of statistical Chinese semantics also provide new application scenarios for online Chinese behavior. It also laid a ladder for subsequent scholars.

scholarly journals Fault Detection Method and Simulation Based on Abnormal Data Analysis in Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xiaogang Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Data Analysis ◽  
Sensor Networks ◽  
Fault Detection ◽  
Wireless Network ◽  
Optimization Algorithm ◽  
Detection Rate ◽  
Rapid Development ◽  
Wireless Sensor ◽  
Data Detection

With the rapid development of Internet of things and information technology, wireless sensor network technology is widely used in industrial monitoring. However, limited by the architecture characteristics, software and hardware characteristics, and complex external environmental factors of wireless sensor networks, there are often serious abnormalities in the monitoring data of wireless sensor networks, which further affect the judgment and response of users. Based on this, this paper optimizes and improves the fault detection algorithm of related abnormal data analysis in wireless sensor networks from two angles and verifies the algorithm at the same time. In the first level, aiming at the problem of insufficient spatial cooperation faced by the network abnormal data detection level, this paper first establishes a stable neighbor screening model based on the wireless network and filters and analyzes the reliability of the network cooperative data nodes and then establishes the detection data stability evaluation model by using the spatiotemporal correlation corresponding to the data nodes. Realize abnormal data detection. On the second level, aiming at the problem of wireless network abnormal event detection, this paper proposes a spatial clustering optimization algorithm, which mainly clusters the detection data flow in the wireless network time window through the clustering algorithm, and analyzes the clustering data, so as to realize the detection of network abnormal events, so as to retain the characteristics of events and further classify the abnormal data events. This paper will verify the realizability and superiority of the improved optimization algorithm through simulation technology. Experiments show that the fault detection rate based on abnormal data analysis is as high as 97%, which is 5% higher than the traditional fault detection rate. At the same time, the corresponding fault false detection rate is low and controlled below 1%. The efficiency of this algorithm is about 10% higher than that of the traditional algorithm.

Sign in / Sign up

Export Citation Format

Share Document