scholarly journals Semantic Ids Using Wireless Sensor Network

2011 ◽  
pp. 20-25
Author(s):  
K. Sri Ganesh ◽  
V. Vaideh ◽  
M. Raja Sekar
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Time Domain ◽  
Streaming Data ◽  
Wireless Sensor ◽  
Surveillance Systems ◽  
Coverage Area ◽  
Rule Based ◽  
Sensory Data ◽  
Monitoring Application

Emerging technologies have metamorphosed the nature of surveillance and monitoring application, but the sensory data collected using various gadgets still remain changeable and poorly synchronized. An event detected by WSN formulates patterns. The sink receives the information about several events happening in the coverage area. Sink has to correlate these streaming data (events) in spatial domain (several sensors) and time domain. This paper proposes a scheme to formulate patterns based on sensing elements and a methodology for detecting an intruder using rule-based semantics. This scheme can be integrated with the surveillance systems to detect the entry of an unauthorized person into a secured area. Real Time implementations prove that events, patterns, rules can efficiently detect an intruder with the help of a wired network with appropriate database. The semantic rules are developed using ANTLR tool.

Ultra-Wide-Band Wireless Sensor Network for Transmission of Streaming Data

2018 ◽  
Vol 7 (1) ◽  
pp. 91-102
Author(s):  
Yu.V. Andreyev ◽  
◽  
L.V. Kuzmin ◽  
M.G. Popov ◽  
A.I. Ryshov ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wide Band ◽  
Streaming Data ◽  
Wireless Sensor ◽  
Ultra Wide Band

A new hierarchical belief-rule-based method for reliability evaluation of wireless sensor network

2018 ◽  
Vol 87 ◽  
pp. 33-51 ◽  
Author(s):  
Wei He ◽  
Guan-Yu Hu ◽  
Zhi-Jie Zhou ◽  
Pei-Li Qiao ◽  
Xiao-Xia Han ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Reliability Evaluation ◽  
Wireless Sensor ◽  
Rule Based

scholarly journals Distributed Learning Fractal Algorithm for Optimizing a Centralized Control Topology of Wireless Sensor Network Based on the Hilbert Curve L-System

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1442 ◽  
Author(s):  
Jaime Moreno ◽  
Oswaldo Morales ◽  
Ricardo Tejeida ◽  
Juan Posadas ◽  
Hugo Quintana ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Physical Space ◽  
Distributed Learning ◽  
Wireless Sensor ◽  
Surveillance Systems ◽  
Centralized Control ◽  
Hilbert Curve ◽  
Service Routing ◽  
Control Topology

Wireless sensor networks (WSNs) consist of a large number of small devices or nodes, called micro controller units (MCUs) and located in homes and/or offices, to be operated through the internet from anywhere, making these devices smarter and more efficient. Quality of service routing is one of the critical challenges in WSNs, especially in surveillance systems. To improve the efficiency of the network, in this article we proposes a distributed learning fractal algorithm (DFLA) to design the control topology of a wireless sensor network (WSN), whose nodes are the MCUs distributed in a physical space and which are connected to share parameters of the sensors such as concentrations of C O 2 , humidity, temperature within the space or adjustment of the intensity of light inside and outside the home or office. For this, we start defining the production rules of the L-systems to generate the Hilbert fractal, since these rules facilitate the generation of this fractal, which is a fill-space curve. Then, we model the optimization of a centralized control topology of WSNs and proposed a DFLA to find the best two nodes where a device can find the highly reliable link between these nodes. Thus, we propose a software defined network (SDN) with strong mobility since it can be reconfigured depending on the amount of nodes, also we employ a target coverage because distributed learning fractal algorithm (DLFA) only consider reliable links among devices. Finally, through laboratory tests and computer simulations, we demonstrate the effectiveness of our approach by means of a fractal routing in WSNs, by using a large amount of WSNs devices (from 16 to 64 sensors) for real time monitoring of different parameters, in order to make efficient WSNs and its application in a forthcoming Smart City.

A High-Accuracy Least-Time-Domain Mixture Features Machine-Fault Diagnosis Based on Wireless Sensor Network

2020 ◽  
Vol 14 (3) ◽  
pp. 4101-4109
Author(s):  
Cuicui Du ◽  
Shang Gao ◽  
Naishu Jia ◽  
Deren Kong ◽  
Jian Jiang ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Fault Diagnosis ◽  
Sensor Network ◽  
Time Domain ◽  
High Accuracy ◽  
Wireless Sensor ◽  
Machine Fault Diagnosis ◽  
Machine Fault

scholarly journals Wireless Sensor Network for Real-time Flood Monitoring Based on 6loWPAN Communication Standard

2016 ◽  
Vol 1 (1) ◽  
pp. 12-22 ◽  
Author(s):  
Nuhu B. K. ◽  
Arulogun O. T. ◽  
Adeyanju I. A. ◽  
Abdullahi I. M.
Keyword(s):  
Wireless Sensor Network ◽  
Water Level ◽  
Real Time ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Major Disaster ◽  
Flood Monitoring ◽  
Monitoring Application ◽  
Level Monitoring ◽  
Water Level Monitoring

Riverine flood is a major disaster faced by most countries and has significant adverse effect on long term economic growth of affected regions and their environments. Several systems have previously employed different technologies to monitor riverine flood but are expensive with low accuracy and consumes high amount of energy. In this paper, we proposed an energy efficient and accurate flood monitoring system. The system leverages on Internet Protocol Version 6 over Low Power Wireless Personal Area Network (6loWPAN) technology to construct a Wireless Sensor Network (WSN) comprising of two XM1000 motes and a rule-base water level monitoring application. The motes were configured using NesC programming for flood monitoring with Basestation and water level sensing applications. The water level sensing mote samples and transmits real-time water level information to the Basestation mote which interfaces with a rule-based water level monitoring application. The application compares current water level with a predetermined threat level and alerts relevant agencies when flood is imminent via an email. The results obtained from the emulation of the developed system showed that, it achieved an accuracy of 95.3% in water level monitoring with a Mean Squared Error of 5.1. The power consumed in transmitting a packet of 2 bytes payload plus other overhead was 0.4µJ and 0.0396mJ with and without 6loWPAN configuration respectively.

Sign in / Sign up

Export Citation Format

Share Document