scholarly journals Simulation of a Fine Dust Value-Based False Data Detection System to Improve Security In WSN-Based Air Purification IoT

2021 ◽  
Vol 10 (6) ◽  
pp. 148-153
Author(s):  
Ye-lim Kang ◽  
◽  
Tae-ho Cho ◽  
Keyword(s):  
Detection System ◽  
Discrete Event ◽  
Sensor Nodes ◽  
Air Purification ◽  
Data Detection ◽  
Fine Dust ◽  
System Specification ◽  
Error Range ◽  
Purification System ◽  
Air Purifier

Fine dust refers to harmful substances floating in the air. It is divided into PM 2.5 and PM 10, and has the characteristic that the particles are small enough to be invisible to the naked eye. When fine dust enters a room, it can enter the human body through the bronchi and cause lung or respiratory diseases. To solve the health problems caused by fine dust, research and development about various air purification systems are progressing. In this paper, we introduce a Wireless Sensor Networks (WSNs)-based Internet of Things (IoT) air purification system. This WSNs-based IoT air purification system refers to a system in which an IoT air purifier and a window are automatically controlled based on fine dust values detected by sensor nodes. Therefore, because it is important to maintain the integrity of the fine dust values, SSL/TLS, an encryption protocol, is applied to this system. However, the existing SSL/TLS has a problem in which, if an attacker attempts a false data injection attack, the symmetric key itself used to encrypt and decrypt the data is stolen, so it cannot be detected. To solve this problem, in this paper we propose a Discrete Event System Specification (DEVS) model based on Data Calibration that verifies whether the fine dust values detected by sensor nodes and an IoT air purifier is within a preset error range. If the fine dust value is not within the preset error range, it is detected as false data, filtered, and not stored in the database. Because this proposed scheme verifies the integrity of the fine dust values, it not only raises the accuracy of collected sensing data, but also prevents abnormal operation of an IoT air purifier and a window in advance. Therefore, the security of the WSNs-based IoT air purification system is improved.

scholarly journals Energy Efficient Operation Cycle Determination Scheme of Fuzzy Based IHA in Air Purification IoT

2020 ◽  
Vol 9 (2) ◽  
pp. 1126-1131
Keyword(s):  
Indoor Air ◽  
Sensor Nodes ◽  
Air Purification ◽  
Fine Dust ◽  
Chronic Respiratory Diseases ◽  
Injection Attacks ◽  
Purification System ◽  
Air Purifier ◽  
False Report ◽  
Operation Cycle

Fine dust is a harmful particulate substance floating in the air and is divided into PM10 (which is 10 um in diameter or less) and PM2.5 (which is 2.5 um in diameter or less). Fine dust is a major cause of chronic respiratory diseases, which may occur naturally through forest fires or yellow dust, but it is mainly caused by combustion of fossil fuels such as oil and coal, or by automobile exhaust gases. When this type of bad outdoor fine dust flows into buildings, the indoor air becomes polluted, making it easier for workers or students who spend a lot of time indoors to be at risk for chronic respiratory diseases. To minimize this risk, recent research and development has focused on systems to purify indoor air by filtering fine dust. In this paper, we introduce a Wireless Sensor Networks (WSNs)-based Internet of Things (IoT) air purification system. In the WSNs-based IoT air purification system, it is important to maintain the integrity of the sensing data because the IoT air purifier operates based on the sensing data detected by sensor nodes. To defend the IoT air purifier against false report injection attacks, the existing fuzzy-based Interleaved Hop-by-Hop Authentication (IHA) detects false report injection attacks through Data Calibration. In addition to the existing fuzzy-based IHA sets, the security limit changes according to the network situation using fuzzy logic and adjusts the security and energy. However, the existing fuzzy-based IHA executes a fuzzy system every time it detects a normal event or false report injection attack, which requires additional message overhead and increases the transmission/reception energy, which increases the energy burden of the sensor nodes. To address this problem, we propose a method to control the operation cycle of the fuzzy system using the evaluation function. This proposed method has the advantage that the trade-off relationship between energy and security can be appropriately used to adjust the operation cycle and increase the lifetime of the network.

scholarly journals Fuzzy-based Dynamic Security Parameters Determination Method to Improve Energy Efficiency

2020 ◽  
Vol 9 (4) ◽  
pp. 1952-1958
Keyword(s):  
Energy Efficiency ◽  
Indoor Air ◽  
Base Station ◽  
The Body ◽  
Security Protocol ◽  
Sensor Nodes ◽  
Air Purification ◽  
Fine Dust ◽  
Purification System ◽  
Event Information

Fine dust is a harmful substance that floats in the atmosphere. It is not filtered well in the bronchus and causes health problems when it accumulates in the body. Fine dust that is present in the air outside can flow into buildings through windows and other openings, adversely affecting indoor air quality. It has negative health effects on people who live indoors. As attention to problems associated with fine dust gradually rises, the importance of continuously managing the quality of indoor air through air purification rises. Therefore, recently, research and development into systems to periodically purify indoor air are being carried out. This paper introduces an air purification system, a Wireless Sensor Networks (WSNs)-based Internet of Things (IoT) air purification system. In the WSNs-based IoT air purification system, the IoT air purifier is controlled based on event information that the WSNs senses, so it is important to maintain the security of the event information. To this end, a WSNs security protocol, Interleaved Hop by hop Authentication (IHA), is used in this system. IHA is a security protocol in which sensor nodes and a Base Station (BS) detect and drop false reports if the number of compromised sensor nodes does not exceed a security threshold. That is, because the false report injection attack that the number of compromised sensor nodes exceeds security threshold can’t defend by IHA, it is detected and defended through Data Calibration. However, considerable energy of the sensor nodes is unnecessarily consumed in the process of forwarding false reports. Thus, this paper proposes a method of decreasing and increasing security thresholds dynamically according to the network situation using fuzzy logic. This proposed scheme has the advantages of improvements in both the overall energy efficiency and network lifetime in WSNs

scholarly journals Detection of False Report Injection At Wsns Based on Data Calibration in Iot Environment

2019 ◽  
Vol 8 (4) ◽  
pp. 8956-8960
Keyword(s):  
Indoor Air ◽  
Security Protocol ◽  
Sensor Nodes ◽  
Abnormal Behavior ◽  
Air Purification ◽  
Fine Dust ◽  
Detection Scheme ◽  
Sensing Data ◽  
Air Cleaner ◽  
False Report

Unlike ordinary dust, fine dust has a small particle size and is thus not well-filtered out of the human nose or bronchus. It accumulates in the human body where it can generate various respiratory and bronchial illnesses. If this fine dust is introduced into a room from the outside, the indoor air is worsened which has negative effects on the health of the people there. The continuous management of indoor air through air purification is necessary to protect the health of students, office workers, and others who spend most of the day indoors. Various air purification systems exist for this purpose, and research and development are actively progressing. This paper discusses an indoor air purification system that uses Internet of Things (IoT) based on Wireless Sensor Networks (WSNs). In such a system, false reports are generated when the number of sensor nodes compromised by an attacker exceeds the security threshold. The WSNs security protocol, Interleaved Hop-by-hop Authentication (IHA), cannot defend against such false reports, resulting in abnormal behavior of the IoT air cleaner. Therefore, a false report detection scheme that uses sensing data is proposed in this paper. When fine dust occurs, the WSNs and IoT air cleaner sense fine dust at the same time. The IoT air cleaner, which has normally received the WSNs event report, calculates the average and the deviation about the cumulative fine dust sensing data values of the WSNs and the IoT. Then, it compares the deviation for the current event with the deviation for the previous event to calculate the variation of deviation. If the variation of deviation is within a predetermined error range, it is determined that a normal event occurs. Otherwise, it is determined that the false report injection attack occurs and it is prevented from running abnormal behavior of it. In conclusion, this scheme not only improves security by preventing the IoT air cleaner from running abnormally, but also contributes to improved energy efficiency in the WSNs.

RHELP (Regenerative High Efficiency Low Pressure) Air Purification System

2009 ◽  
Author(s):  
Chang-Yu Wu ◽  
Brian Damit ◽  
Qi Zhang ◽  
Myung-Heui Woo ◽  
Wolfgang Sigmund ◽  
...  
Keyword(s):  
High Efficiency ◽  
Low Pressure ◽  
Air Purification ◽  
Purification System

scholarly journals Specification of dynamic structure discrete event systems using single point encapsulated control functions

2014 ◽  
Vol 05 (03) ◽  
pp. 1450012 ◽  
Author(s):  
Alexandre Muzy ◽  
Bernard P. Zeigler
Keyword(s):  
Discrete Event Systems ◽  
Single Point ◽  
Discrete Event ◽  
Dynamic Structure ◽  
System Specification ◽  
Discrete Event System Specification ◽  
Usual Definition ◽  
Control Functions ◽  
Event System ◽  
Structure Changes

In Discrete Event System Specification (DEVS), the dynamics of a network is constituted only by the dynamics of its basic components. The state of each component is fully encapsulated. Control in the network is fully decentralized to each component. At dynamic structure level, DEVS should permit the same level of decentralization. However, it is hard to ensure structure consistency while letting all components achieve structure changes. Besides, this solution can be complex to implement. To avoid these difficulties, usual dynamic structure approaches ensure structure consistency allowing structure changes to be done only by the network having newly added dynamics change capabilities. This is a safe and simple way to achieve dynamic structure. However, it should be possible to simply allow components of a network to modify the structure of their network, other components and/or their own structure — without having to modify the usual definition a DEVS network. In this manuscript, it is shown that a simple fully decentralized approach is possible while ensuring full modularity and structure consistency.

A simulation-driven online scheduling algorithm for the maintenance and operation of wind farm systems

SIMULATION ◽  
2021 ◽  
pp. 003754972110286
Author(s):  
Eduardo Pérez
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Computational Study ◽  
Scheduling Algorithm ◽  
Discrete Event ◽  
Wind Farms ◽  
Online Scheduling ◽  
Maintenance Scheduling ◽  
Lead Times ◽  
System Specification

Wind turbines experience stochastic loading due to seasonal variations in wind speed and direction. These harsh operational conditions lead to failures of wind turbines, which are difficult to predict. Consequently, it is challenging to schedule maintenance actions that will avoid failures. In this article, a simulation-driven online maintenance scheduling algorithm for wind farm operational planning is derived. Online scheduling is a suitable framework for this problem since it integrates data that evolve over time into the maintenance scheduling decisions. The computational study presented in this article compares the performance of the simulation-driven online scheduling algorithm against two benchmark algorithms commonly used in practice: scheduled maintenance and condition-based monitoring maintenance. An existing discrete event system specification simulation model was used to test and study the benefits of the proposed algorithm. The computational study demonstrates the importance of avoiding over-simplistic assumptions when making maintenance decisions for wind farms. For instance, most literature assumes maintenance lead times are constant. The computational results show that allowing lead times to be adjusted in an online fashion improves the performance of wind farm operations in terms of the number of turbine failures, availability capacity, and power generation.

scholarly journals Physical parametrisation of fire-spotting for operational fire spread models: response analysis with a model based on the Level Set Method

2018 ◽  
Author(s):  
Inderpreet Kaur ◽  
Anton Butenko ◽  
Gianni Pagnini
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Discrete Event ◽  
Fire Spread ◽  
Response Analysis ◽  
System Specification ◽  
Time Step ◽  
Processing Scheme ◽  
Model Based ◽  
The Many

Abstract. Fire-spotting is often responsible for a dangerous flare up in the wildfire and causes secondary ignitions isolated from the primary fire zone leading to perilous situations. In this paper a complete physical parametrisation of fire-spotting is presented within a formulation aimed to include random processes into operational fire spread models. This formulation can be implemented into existing operational models as a post-processing scheme at each time step, without calling for any major changes in the original framework. In particular, the efficacy of this formulation has already been shown for wildfire simulators based on an Eulerian moving interface method, namely the Level Set Method (LSM) that forms the baseline of the operational software WRF-SFIRE, and for wildfire simulators based on a Lagrangian front tracking technique, namely the Discrete Event System Specification (DEVS) that forms the baseline of the operational software FOREFIRE. The simple and computationally less expensive parametrisation includes the important parameters necessary for describing the landing behavior of the firebrands. The results from different simulations with a simple model based on the LSM highlight the response of the parametrisation to varying fire intensities, wind conditions and different firebrand radii. The contribution of the firebrands towards increasing the fire perimeter varies according to different concurrent conditions and the simulation results prove to be in agreement with the physical processes. Among the many rigorous approaches available in literature to model the firebrand transport and distribution, the approach presented here proves to be simple yet versatile for application to operational fire spread models.

scholarly journals Effect of Air Purifier Use in the Classrooms on Indoor Air Quality—Case Study

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1606
Author(s):  
Małgorzata Basińska ◽  
Michał Michałkiewicz ◽  
Katarzyna Ratajczak
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Air Purification ◽  
Portable Devices ◽  
Microscopic Fungi ◽  
Before And After ◽  
Air Purifier ◽  
High Level

Dissatisfaction with indoor air quality is common even in relatively new or renovated Polish school buildings. To improve air quality in educational buildings, portable devices have seen increased use, for which manufacturers guarantee a high level of indoor air purification. However, their optimized operation largely depends on their correct use. The aim of this article was to determine the effectiveness of air purification in a primary school using an air purification device with an analysis of the classroom indoor air quality (IAQ). Two criteria were used, microbiological and particulate matter concentration. Measurements were made before device installation and during its continuous operation, and before and after lessons on chosen days. Measurements related to IAQ did not detect clear differences in the analyzed measurement periods. For microbiological contamination, in the morning before lessons, the total count for all bacteria and microscopic fungi was definitely lower than after lessons. Comparing the periods before and after device installation, no clear tendency for reducing the bacteria count or microscopic fungi occurred during air purifier operation, nor was there any noticeable trend in the reduction of particulate matter. There was no improvement in air quality in the classrooms during the operation of the purification devices.

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