Real-Time Data Management on a Wireless Sensor Network

2005 ◽  
Vol 1 (2) ◽  
pp. 215-225 ◽  
Author(s):  
Chris Roadknight ◽  
Laura Parrott ◽  
Nathan Boyd ◽  
Ian W. Marshall
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Statistical Tests ◽  
Adaptive Method ◽  
Wireless Sensor ◽  
Limited Resources ◽  
Time Data ◽  
Real Time Data ◽  
Local Feedback ◽  
Material Exchange

A multi-layered algorithm is proposed that provides a scalable and adaptive method for handling data on a wireless sensor network. Statistical tests, local feedback, and global genetic style material exchange ensure limited resources such as battery and bandwidth which are used efficiently by manipulating data at the source and important features in the time series are not lost when compression needs to be made. The approach leads to a more ‘hands off’ implementation which is demonstrated by a real world oceanographic deployment of the system.

Evaluating Authentication Schemes for Real-Time Data in Wireless Sensor Network

2020 ◽  
Vol 114 (1) ◽  
pp. 629-655
Author(s):  
Deepti Singh ◽  
Bijendra Kumar ◽  
Samayveer Singh ◽  
Satish Chand
Keyword(s):  
Wireless Sensor Network ◽  
Real Time ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Time Data ◽  
Real Time Data ◽  
Authentication Schemes

scholarly journals Monitoring of Drainage System in Urban Using Device Free Localization Neural Networks and Cloud computing

2018 ◽  
Vol 7 (04) ◽  
pp. 08-14
Author(s):  
Mr. Rahul Sharma
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Drainage System ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Urban Drainage ◽  
Time Data ◽  
Wireless Sensor Nodes ◽  
Real Time Data ◽  
Device Free

Wireless Sensor Network is a Wi-Fi community consisting of spatially propagated and self-sufficient devices using sensors to detect physical or environmental conditions. During heavy rainfall, the urban drainage system cannot drain the water. A wireless sensor with many interconnected wireless sensor nodes captures real-time data from the network environment and transmits this data to a base station for analysis and operation. With wireless sensor nodes, it is possible to capture and monitor the amount of water in drainages and the difference in water flow between the two points in the drainage system. Nevertheless, the majority localization techniques aims on device based localization, which can find target with festinated devices. It is not suitable for applications such as terrain, drainage flow and flooding. Here device free wireless localization system using artificial neural networks and a cluster based wireless sensor network system to monitor urban drainage is proposed. There are two stages in the system. During the off-line preparation stage, Acceptable Signal Strength (RSS) differential metrics are calculated between the RSS metrics together while the monitor area is empty and calculated by a specialized in the region. Some RSS dissimilarity values ​​are selected in the RSS Difference Matrix. The RSS dissimilarity standards ​​and associated matrix indices are taken as the inputs of the ANN representation in addition to the identified position coordinate are in its outputs. The real-time data collected from the wireless sensor network is used to detect overflow and provide alarms before disturbances arise.

scholarly journals An Efficient Mobile Sink based Data Collection Model for Clustered based Wireless Sensor Network

2021 ◽  
Vol 58 (1) ◽  
pp. 1836-1843
Author(s):  
Naveen Ghorpade, Dr. P. Vijaykarthik
Keyword(s):  
Wireless Sensor Network ◽  
Data Collection ◽  
Real Time ◽  
Sensor Network ◽  
Data Communication ◽  
Data Access ◽  
Wireless Sensor ◽  
Low Latency ◽  
Time Data ◽  
Real Time Data

The Wireless Sensor Network (WSN) is considered to be a core component of tomorrow's real-time data communication networks, such as the Internet of Things (IoT). Modern networks need low-latency and high-throughputs in real-time due to a heterogeneous network. The availability of low-latency real-time data access incurs energy costs from the sensor systems. Clustering helped in maintaining the scalability and energy usage of sensors. However, it incurs overhead of the independent cluster head and sensor device within the close range of the sump pump. Since it would take longer transmission and recovery time. This Mine Research Paper introduces an Accessible Mobile Sensor Dependent Data Collection (EMSDC) Model for Cluster Based WSN (CWSN). Experiments are carried out to verify the efficiency of EMSDC and to equate it with the existing versions. The findings of the Latency and Overhead benchmarks demonstrated a lot of progress over the state-of-the-art versions.

Sign in / Sign up

Export Citation Format

Share Document