A Framework for a User Friendly Wireless Sensor Network Configuration System

2011 ◽  
pp. 57-61
Author(s):  
Julia C. Lee ◽  
Lawrence J. Henschen
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Network Configuration ◽  
Configuration System ◽  
User Friendly

scholarly journals SDWSN: Software-Defined Wireless Sensor Network

2019 ◽  
Vol 8 (12) ◽  
pp. 1064-1071
Keyword(s):  
Wireless Sensor Network ◽  
Network Management ◽  
Sensor Network ◽  
Network Architecture ◽  
Wireless Sensor ◽  
The Internet ◽  
Network Technology ◽  
Network Configuration ◽  
Distributed Management ◽  
Data Plane

Nowadays, several protocols and treaties coexist in the Internet world that dispenses the services to the users. But the distributed management and the control decisions make the network modest to control. Due to these problems, the behavior of the network becomes unpredictable and insufficient. Hence, there is a lack of flexibility in the conventional network architecture like in Wireless Sensor Network (WSN). Therefore, Software-Defined Networking (SDN) finds the deficiencies of the previous technologies and isolated both the planes named as control and data plane. It aims to make the network more simplified and flexible with respect to that of the traditional one. The SDN application in WSN is very commanding in terms of network configuration and network management, leading to an emerging network technology known as Software-Defined Wireless Sensor Network (SDWSN). Therefore, the paper presents the challenges in SDWSN in terms of the management and configuration of the network. The pitch is to comprehend the current challenges with an end goal to ensure more security, efficiency, and dependability. We reviewed several works of literature such as SDN, WSN, and SDWSN and present the findings in terms of architecture, challenges, and their solution. This paper shows how SDN is included in WSN to solve the existing challenges

An Intelligent Monitoring System Using a ZigBee-Base Sensor Network

2012 ◽  
Vol 182-183 ◽  
pp. 1393-1401
Author(s):  
Kun Yung Lu
Keyword(s):  
Wireless Sensor Network ◽  
Expert System ◽  
Sensor Network ◽  
Monitoring System ◽  
Monitoring Network ◽  
Wireless Sensor ◽  
Network Configuration ◽  
Intelligent Monitoring ◽  
Expert System Shell ◽  
Configuration Module

This paper presents an intelligent monitoring system based on a ZigBee-base wireless sensor network. The proposed system includes a sensor network configuration module, an expert system shell, and an event-condition-action (ECA) engine. The sensor network configuration module is used to configure the working properties of the ZigBee components and set up the monitoring network. The expert system shell enables users to define the related events occurred at system running, the threshold of working conditions for triggering the corresponding events, and the required commands for fitting the system. The ECA engine is used to monitor the system and provide a suitable command for system fitting in a specified event being fired. The proposed system enables user to quickly establish a wireless sensor network system without well-experienced knowledge about the wireless sensor network and the expert system.

scholarly journals Study of Wireless Sensor Network in SCADA System for Power Plant

2012 ◽  
pp. 157-160
Author(s):  
Mrs. uma sharad patil
Keyword(s):  
Wireless Sensor Network ◽  
Power Plant ◽  
Real Time ◽  
Sensor Network ◽  
Corrective Action ◽  
Wireless Sensor ◽  
Paper Study ◽  
Scada System ◽  
Warning Message ◽  
User Friendly

In this paper, study of wireless SCADA system which is powerful setup for monitoring and controlling the various applications from remotely placed is presented. All communication between GSM modem and user are wireless based, this translates into lowest cost compared to all other systems. Wireless based solutions have universally accepted, familiar and user friendly system. Real-time logging would allow warnings to be flagged to the relevant personnel (e.g. an SMS warning message to the supervisors) and allow corrective action to be taken before the quality and value of the catch is degraded.

Optimal wireless sensor network configuration for structural monitoring using automatic-learning firefly algorithm

2018 ◽  
Vol 22 (4) ◽  
pp. 907-918 ◽  
Author(s):  
Guang-Dong Zhou ◽  
Mei-Xi Xie ◽  
Ting-Hua Yi ◽  
Hong-Nan Li
Keyword(s):  
Wireless Sensor Network ◽  
Structural Health Monitoring ◽  
Sensor Network ◽  
Health Monitoring ◽  
Firefly Algorithm ◽  
Wireless Sensor ◽  
Network Configuration ◽  
Automatic Learning ◽  
Structural Health ◽  
Health Monitoring Systems

Wireless sensor networks are becoming attractive data communication patterns in structural health monitoring systems. Designing and applying effective wireless sensor network–based structural health monitoring systems for large-scale civil infrastructure require a great number of wireless sensors and the optimal wireless sensor networks configuration becomes critical for such spatially separated large structures. In this article, optimal wireless sensor network configuration for structural health monitoring is treated as a discrete optimization problem, where parameter identification and network performance are simultaneously addressed. To solve this rather complicated optimization problem, a novel swarm intelligence algorithm called the automatic-learning firefly algorithm is proposed by integrating the original firefly algorithm with the Lévy flight and the automatic-learning mechanism. In the proposed algorithm, the Lévy flight is adopted to maximize the searching capability in unknown solution space and avoid premature convergence and the automatic-learning mechanism is designed to drive fireflies to move toward better locations at high speed. Numerical experiments are performed on a long-span bridge to demonstrate the effectiveness of the proposed automatic-learning firefly algorithm. Results indicate that automatic-learning firefly algorithm can find satisfactory wireless sensor network configurations, which facilitate easy discrimination of identified mode vectors and long wireless sensor network lifetime, and the innovations in automatic-learning firefly algorithm make it superior to the simple discrete firefly algorithm as to solution quality and convergence speed.

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