scholarly journals A Cloud Based Disaster Management System

2020 ◽  
Vol 9 (1) ◽  
pp. 6 ◽  
Author(s):  
Omar Cheikhrouhou ◽  
Anis Koubaa ◽  
Anis Zarrad
Keyword(s):  
Real Time ◽  
Disaster Management ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Game Engine ◽  
New Paradigm ◽  
Lossy Networks ◽  
Training Environment ◽  
3D Environment

The combination of wireless sensor networks (WSNs) and 3D virtual environments opens a new paradigm for their use in natural disaster management applications. It is important to have a realistic virtual environment based on datasets received from WSNs to prepare a backup rescue scenario with an acceptable response time. This paper describes a complete cloud-based system that collects data from wireless sensor nodes deployed in real environments and then builds a 3D environment in near real-time to reflect the incident detected by sensors (fire, gas leaking, etc.). The system’s purpose is to be used as a training environment for a rescue team to develop various rescue plans before they are applied in real emergency situations. The proposed cloud architecture combines 3D data streaming and sensor data collection to build an efficient network infrastructure that meets the strict network latency requirements for 3D mobile disaster applications. As compared to other existing systems, the proposed system is truly complete. First, it collects data from sensor nodes and then transfers it using an enhanced Routing Protocol for Low-Power and Lossy Networks (RLP). A 3D modular visualizer with a dynamic game engine was also developed in the cloud for near-real time 3D rendering. This is an advantage for highly-complex rendering algorithms and less powerful devices. An Extensible Markup Language (XML) atomic action concept was used to inject 3D scene modifications into the game engine without stopping or restarting the engine. Finally, a multi-objective multiple traveling salesman problem (AHP-MTSP) algorithm is proposed to generate an efficient rescue plan by assigning robots and multiple unmanned aerial vehicles to disaster target locations, while minimizing a set of predefined objectives that depend on the situation. The results demonstrate that immediate feedback obtained from the reconstructed 3D environment can help to investigate what–if scenarios, allowing for the preparation of effective rescue plans with an appropriate management effort.

scholarly journals Outlier Detection in Wireless Sensor Networks Data by Entropy Based K-NN Predictor

2019 ◽  
Vol 8 (12) ◽  
pp. 5483-5489
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Outlier Detection ◽  
Window Size ◽  
Environmental Parameters ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Training Dataset ◽  
Time Motion

Anomaly (outlier) detection is plays very significant role in ESN based monitoring application using on large data used for biomedical and defence. Wireless Sensor network monitor environmental parameters (temperature, humidity, pressure, vibration etc). Group of sensor nodes forms a (WSN) and observations collected from these sensor produces low data quality and reliability due to the limited energy, memory, computation capability and bandwidth. The dynamic environment of network and roughness of the working condition are also responsible to generate inaccuracy in measurements. In this paper, an approach for outliers detection based entropy value of received sensor voltages is applied using KNN prediction model .The algorithm development and analysis involves a real time database generated on 14 sets of MICA2 wireless sensor kit with anomaly inserted by real time motion based intrusion in the lab by volunteers from Intel Berkeley lab. On each sensor data pair segmentation is applied by fixed window size in order get large outliers’ measurements training dataset. The analysis demonstrates the measurement accuracy in detection of number of outliers that its 86%. Moreover, the algorithm also provides an analysis in terms of impact of variation in distance types and number of nearest neighbours in the KNN prediction model. This work is helpful in the application in the situations where high amount of noise or distortions are present. The outlier part from distorted data can be figured out and recollected to enhance application accuracy.

scholarly journals A Remote Monitoring System of Logistics Carrier Based on Wireless Sensor Network

2018 ◽  
Vol 14 (01) ◽  
pp. 4
Author(s):  
Wang Weidong
Keyword(s):  
Wireless Sensor Network ◽  
Real Time ◽  
Sensor Network ◽  
Monitoring System ◽  
Remote Monitoring ◽  
Monitoring Network ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Remote Monitoring System ◽  
Monitoring Center

To improve the efficiency of the remote monitoring system for logistics transportation, we proposed a remote monitoring system based on wireless sensor network and GPRS communication. The system can collect information from the wireless sensor network and transmit the information to the ZigBee interpreter. The monitoring system mainly includes the following parts: Car terminal, GPRS transmission network and monitoring center. Car terminal mainly consists by the Zigbee microcontroller and peripherals, wireless sensor nodes, RFID reader, GPRS wireless communication module composed of a micro-wireless monitoring network. The information collected by the sensor communicates through the GPRS and the monitoring center on the network coordinator, sends the collected information to the monitoring center, and the monitoring center realizes the information of the logistics vehicle in real time. The system has high applicability, meets the design requirements in the real-time acquisition and information transmission of the information of the logistics transport vehicles and goods, and realizes the function of remote monitoring.

scholarly journals Anomaly Detection in Medical Wireless Sensor Networks using SVM and Linear Regression Models

2014 ◽  
Vol 5 (1) ◽  
pp. 20-45 ◽  
Author(s):  
Osman Salem ◽  
Alexey Guerassimov ◽  
Ahmed Mehaoua ◽  
Anthony Marcus ◽  
Borko Furht
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Patient Data ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Smart Devices ◽  
Support Vector ◽  
Processing Unit

This paper details the architecture and describes the preliminary experimentation with the proposed framework for anomaly detection in medical wireless body area networks for ubiquitous patient and healthcare monitoring. The architecture integrates novel data mining and machine learning algorithms with modern sensor fusion techniques. Knowing wireless sensor networks are prone to failures resulting from their limitations (i.e. limited energy resources and computational power), using this framework, the authors can distinguish between irregular variations in the physiological parameters of the monitored patient and faulty sensor data, to ensure reliable operations and real time global monitoring from smart devices. Sensor nodes are used to measure characteristics of the patient and the sensed data is stored on the local processing unit. Authorized users may access this patient data remotely as long as they maintain connectivity with their application enabled smart device. Anomalous or faulty measurement data resulting from damaged sensor nodes or caused by malicious external parties may lead to misdiagnosis or even death for patients. The authors' application uses a Support Vector Machine to classify abnormal instances in the incoming sensor data. If found, the authors apply a periodically rebuilt, regressive prediction model to the abnormal instance and determine if the patient is entering a critical state or if a sensor is reporting faulty readings. Using real patient data in our experiments, the results validate the robustness of our proposed framework. The authors further discuss the experimental analysis with the proposed approach which shows that it is quickly able to identify sensor anomalies and compared with several other algorithms, it maintains a higher true positive and lower false negative rate.

scholarly journals Time-selective data fusion for in-network processing in ad hoc wireless sensor networks

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881130 ◽  
Author(s):  
Jaanus Kaugerand ◽  
Johannes Ehala ◽  
Leo Mõtus ◽  
Jürgo-Sören Preden
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Data Streams ◽  
Ad Hoc ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Temporal Consistency ◽  
Variable Delays

This article introduces a time-selective strategy for enhancing temporal consistency of input data for multi-sensor data fusion for in-network data processing in ad hoc wireless sensor networks. Detecting and handling complex time-variable (real-time) situations require methodical consideration of temporal aspects, especially in ad hoc wireless sensor network with distributed asynchronous and autonomous nodes. For example, assigning processing intervals of network nodes, defining validity and simultaneity requirements for data items, determining the size of memory required for buffering the data streams produced by ad hoc nodes and other relevant aspects. The data streams produced periodically and sometimes intermittently by sensor nodes arrive to the fusion nodes with variable delays, which results in sporadic temporal order of inputs. Using data from individual nodes in the order of arrival (i.e. freshest data first) does not, in all cases, yield the optimal results in terms of data temporal consistency and fusion accuracy. We propose time-selective data fusion strategy, which combines temporal alignment, temporal constraints and a method for computing delay of sensor readings, to allow fusion node to select the temporally compatible data from received streams. A real-world experiment (moving vehicles in urban environment) for validation of the strategy demonstrates significant improvement of the accuracy of fusion results.

Design of Urban Vehicle Dynamic and Real-Time Navigation System Based on WSNs, GPS, and GPRS Techniques

2013 ◽  
Vol 765-767 ◽  
pp. 3291-3294
Author(s):  
Cheng Lin Li ◽  
Zhi Yong Jiang
Keyword(s):  
Real Time ◽  
Global Positioning System ◽  
Navigation System ◽  
Traffic Congestion ◽  
Road Construction ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
General Packet Radio Service ◽  
Vehicle Navigation System ◽  
Global Positioning

Currently, the traffic congestion is a significant problem encountered in urban development, which should be resolved depending primarily on the management and deployment under the circumstance that road construction isn't able to keep the pace of automobile growth. WSNs (Wireless sensor networks), made up of numerous sensor nodes, form a multi-hop and self-organizing cellular system by wireless communication, which can realize real-time monitoring and collecting environmental information by cooperation. In this paper, a design of real-time and dynamic city vehicle navigation system is presented based on WSNs, GPS(Global Positioning System), and GPRS(General Packet Radio Service) techniques..

Using Line Cameras for Monitoring and Surveillance Sensor Networks

2013 ◽  
pp. 1032-1050
Author(s):  
Jiang Yu Zheng
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Real Time ◽  
Sensor Network ◽  
Wireless Sensor ◽  
New Paradigm ◽  
Visual Sensor Network ◽  
Visual Monitoring ◽  
Surveillance Technology ◽  
Monitoring And Surveillance

The innovative combination of wireless sensor network (WSN) technology with visual monitoring and surveillance technology in computer vision has been emerging as an important new paradigm. This emerging technology will play a crucial role in visual monitoring and surveillance for automatic object detection and tracking in applications such as real-time traffic monitoring and control, vehicle parking control, intrusion detection, security surveillance, military battlefield monitoring, and so on. Compared to traditional WSNs with scalar sensing data, the development of WVSNs presents much greater challenges in terms of node’s computation power, storage, wireless bandwidth capacity and energy conservation due to the processing and transmission of the huge amount of two-dimensional (2D) image data. We introduce the use of linear CCD sensors for wireless sensor network here. It reads temporal data from a CCD array continuously and stores them to form a 2D image profile. Compared to most of the sensors in the current sensor networks that output temporal signals, it delivers more information such as color, shape, and event of a flowing scene. On the other hand, it abstracts passing objects in the profile without heavy computation and transmits much less data than a video from normal cameras. This paper focus on several unsolved issues of line sensors in capturing targets in the 3D space such as sensor setting, shape analysis, robust object extraction, and real time background adapting to ensure long-term sensing and visual data collection via networks. All the developed algorithms are executed in constant complexity for reducing the sensor and network burden. A sustainable visual sensor network can thus be established in a large area to monitor passing objects and people for surveillance, traffic assessment, invasion alarming, etc.

Applications of Wireless Sensor Networks

2010 ◽  
pp. 1076-1090 ◽  
Author(s):  
Corinna Schmitt ◽  
Georg Carle
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Radio Communication ◽  
Power Resources ◽  
Limited Power

Today the researchers want to collect as much data as possible from different locations for monitoring reasons. In this context large-scale wireless sensor networks are becoming an active topic of research (Kahn1999). Because of the different locations and environments in which these sensor networks can be used, specific requirements for the hardware apply. The hardware of the sensor nodes must be robust, provide sufficient storage and communication capabilities, and get along with limited power resources. Sensor nodes such as the Berkeley-Mote Family (Polastre2006, Schmitt2006) are capable of meeting these requirements. These sensor nodes are small and light devices with radio communication and the capability for collecting sensor data. In this chapter the authors review the key elements for sensor networks and give an overview on possible applications in the field of monitoring.

Sensor Data Geographic Forwarding in Two-Dimensional and Three-Dimensional Spaces

2017 ◽  
pp. 317-352
Author(s):  
Habib M. Ammari ◽  
Amer Ahmed
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Three Dimensional ◽  
Geographic Routing ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Two Dimensional ◽  
Geographic Forwarding ◽  
Limited Power

A wireless sensor network is a collection of sensor nodes that have the ability to sense phenomena in a given environment and collect data, perform computation on the gathered data, and transmit (or forward) it to their destination. Unfortunately, these sensor nodes have limited power, computational, and storage capabilities. These factors have an influence on the design of wireless sensor networks and make it more challenging. In order to overcome these limitations, various power management techniques and energy-efficient protocols have been designed. Among such techniques and protocols, geographic routing is one of the most efficient ways to solve some of the design issues. Geographic routing in wireless sensor networks uses location information of the sensor nodes to define a path from source to destination without having to build a network topology. In this paper, we present a survey of the existing geographic routing techniques both in two-dimensional (2D) and three-dimensional (3D) spaces. Furthermore, we will study the advantages of each routing technique and provide a discussion based on their practical possibility of deployment.

scholarly journals Design and Simulation of a Wireless Sensor Network Greenhouse-Monitoring System Based on 3G Network Communication

2016 ◽  
Vol 12 (05) ◽  
pp. 48 ◽  
Author(s):  
Y. H. Zhou ◽  
J. G. Duan
Keyword(s):  
Wireless Sensor Network ◽  
Real Time ◽  
Sensor Network ◽  
Monitoring System ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Network Communication ◽  
Target Area ◽  
Wireless Communication Network ◽  
Environmental Monitoring System

A greenhouse provides a stable and suitable environment for the growth of plants. Temperature and humidity are closely related to plant growth. These factors directly affect the water content of plants and the quality of fruits. To solve the problems in the current monitoring system of greenhouse cultivation, such as complicated wiring, large node power consumption, and so on, this study proposes a wireless sensor network greenhouse-monitoring system based on third-generation network communication for the real-time monitoring of the temperature, humidity, light, and CO<sub>2</sub> concentration in a greenhouse. GS1011M is regarded as the core in developing wireless terminal nodes. PC software is used to build a real-time observation platform. Sensor data are received in real time through a wireless communication network to complete the monitoring of the target area. A simulation research is also conducted. Results show that the power dissipation of the greenhouse environmental monitoring system is low, its data accuracy is high, and its operation is stable.

scholarly journals Anomaly Detection in Wireless Sensor Networks: A Proposed Framework

2020 ◽  
Vol 14 (10) ◽  
pp. 150
Author(s):  
Dina M. Ibrahim ◽  
Nada M. Alruhaily
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Detection System ◽  
Denial Of Service ◽  
Research Problem ◽  
Sensor Nodes ◽  
Base Stations ◽  
Sensor Data ◽  
Wireless Sensor

With the rise of IOT devices and the systems connected to the internet, there was, accordingly, an ever-increasing number of network attacks (e.g. in DOS, DDOS attacks). A very significant research problem related to identifying Wireless Sensor Networks (WSN) attacks and the analysis of the sensor data is the detection of the relevant anomalies. In this paper, we propose a framework for intrusion detection system in WSN. The first two levels are located inside the WSN, one of them is between sensor nodes and the second is between the cluster heads. While the third level located on the cloud, and represented by the base stations. In the first level, which we called light mode, we simulated an intrusion traffic by generating data packets based on TCPDUMP data, which contain intrusion packets, our work, is done by using WSN technology. We used OPNET simulation for generating the traffic because it allows us to collect intrusion detection data in order to measure the network performance and efficiency of the simulated network scenarios. Finally, we report the experimental results by mimicking a Denial-of-Service (DOS) attack. <em> </em>

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