scholarly journals Improving Fire Safety Systems Based On Internet Of Things And Deep Learning

2021 ◽  
Author(s):  
Mohamed Gamaleldin
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Fire Safety ◽  
Ground Truth ◽  
Current Status ◽  
Dynamic Nature ◽  
Actual Fire ◽  
People Movement ◽  
Safety Systems

Structure fires are one of the main concerns for fire safety systems. The actual fire safety of a building depends on not only how it is designed and constructed, but also on how it is operated. Computational fluid dynamics software is the current solution to reduce the casualties in the fire circumstances. However, it consumes hours to provide the results in some cases that makes it hard to run in real-time. It also does not accept any changes after starting the simulation, which makes it unsuitable for running in the dynamic nature of the fire. On the other hand, the current evacuation signs are fixed, which might guide occupants and firefighter to dangerous zones.<div><br><div>In this research, we present a smoke emulator that runs in real-time to reflect what is happening on the ground-truth. This system is achieved using a light-weight smoke emulator engine, deep learning, and internet of things. The IoT sensors are sending the measurements to correct the emulator from any deviation and reflect events such as fire starting, people movement, and the door’s status. This emulator helps the firefighter by providing them with a map that shows the smoke development in the building. They can take a snapshot from the current status of the building and try different virtual evacuation and firefighting plans to pick the best and safest for them to proceed. The system will also control the exit signs to have adaptive exit routes that guide occupants away from fire and smoke to minimize the exposure time to the toxic gases<br></div></div>

scholarly journals Improving Fire Safety Systems Based On Internet Of Things And Deep Learning

2021 ◽  
Author(s):  
Mohamed Gamaleldin
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Fire Safety ◽  
Ground Truth ◽  
Current Status ◽  
Dynamic Nature ◽  
Actual Fire ◽  
People Movement ◽  
Safety Systems

Structure fires are one of the main concerns for fire safety systems. The actual fire safety of a building depends on not only how it is designed and constructed, but also on how it is operated. Computational fluid dynamics software is the current solution to reduce the casualties in the fire circumstances. However, it consumes hours to provide the results in some cases that makes it hard to run in real-time. It also does not accept any changes after starting the simulation, which makes it unsuitable for running in the dynamic nature of the fire. On the other hand, the current evacuation signs are fixed, which might guide occupants and firefighter to dangerous zones.<div><br><div>In this research, we present a smoke emulator that runs in real-time to reflect what is happening on the ground-truth. This system is achieved using a light-weight smoke emulator engine, deep learning, and internet of things. The IoT sensors are sending the measurements to correct the emulator from any deviation and reflect events such as fire starting, people movement, and the door’s status. This emulator helps the firefighter by providing them with a map that shows the smoke development in the building. They can take a snapshot from the current status of the building and try different virtual evacuation and firefighting plans to pick the best and safest for them to proceed. The system will also control the exit signs to have adaptive exit routes that guide occupants away from fire and smoke to minimize the exposure time to the toxic gases<br></div></div>

Toward Polymorphic Internet of Things Receivers Through Real-Time Waveform-Level Deep Learning

2022 ◽  
Vol 25 (3) ◽  
pp. 28-33
Author(s):  
Francesco Restuccia ◽  
Tommaso Melodia
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Software Defined Radio ◽  
Wireless Systems ◽  
Physical World ◽  
Wireless Protocols ◽  
Key Innovation ◽  
Custom Made ◽  
Time Waveform

Wireless systems such as the Internet of Things (IoT) are changing the way we interact with the cyber and the physical world. As IoT systems become more and more pervasive, it is imperative to design wireless protocols that can effectively and efficiently support IoT devices and operations. On the other hand, today's IoT wireless systems are based on inflexible designs, which makes them inefficient and prone to a variety of wireless attacks. In this paper, we introduce the new notion of a deep learning-based polymorphic IoT receiver, able to reconfigure its waveform demodulation strategy itself in real time, based on the inferred waveform parameters. Our key innovation is the introduction of a novel embedded deep learning architecture that enables the solution of waveform inference problems, which is then integrated into a generalized hardware/software architecture with radio components and signal processing. Our polymorphic wireless receiver is prototyped on a custom-made software-defined radio platform. We show through extensive over-the-air experiments that the system achieves throughput within 87% of a perfect-knowledge Oracle system, thus demonstrating for the first time that polymorphic receivers are feasible.

scholarly journals Real-Time Mapping: Contemporary Challenges and the Internet of Things as the Way Forward

2016 ◽  
Vol 65 (2) ◽  
pp. 129-137
Author(s):  
Kazimierz Bęcek
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Current Status ◽  
The Internet ◽  
Virtual Sensor ◽  
Ip Address ◽  
Intelligent Sensors ◽  
Using Data ◽  
Psychological Sense ◽  
The Internet Of Things

Abstract The Internet of Things (IoT) is an emerging technology that was conceived in 1999. The key components of the IoT are intelligent sensors, which represent objects of interest. The adjective ‘intelligent’ is used here in the information gathering sense, not the psychological sense. Some 30 billion sensors that ‘know’ the current status of objects they represent are already connected to the Internet. Various studies indicate that the number of installed sensors will reach 212 billion by 2020. Various scenarios of IoT projects show sensors being able to exchange data with the network as well as between themselves. In this contribution, we discuss the possibility of deploying the IoT in cartography for real-time mapping. A real-time map is prepared using data harvested through querying sensors representing geographical objects, and the concept of a virtual sensor for abstract objects, such as a land parcel, is presented. A virtual sensor may exist as a data record in the cloud. Sensors are identified by an Internet Protocol address (IP address), which implies that geographical objects through their sensors would also have an IP address. This contribution is an updated version of a conference paper presented by the author during the International Federation of Surveyors 2014 Congress in Kuala Lumpur. The author hopes that the use of the IoT for real-time mapping will be considered by the mapmaking community.

IoT4Emergency

2021 ◽  
Vol 46 (1) ◽  
pp. 33-36
Author(s):  
Julie Dugdale ◽  
Mahyar T. Moghaddam ◽  
Henry Muccini
Keyword(s):  
Internet Of Things ◽  
Emergency Management ◽  
Real Time ◽  
International Workshop ◽  
The Internet ◽  
Time Data ◽  
Time Requirements ◽  
Safety Systems ◽  
Crises Management ◽  
The Internet Of Things

The increasing natural and man-induced disasters such as res, earthquakes, oods, hurricanes, overcrowding, or pandemic viruses endanger human lives. Hence, designing infrastructures to handle those possible crises has become an ever-increasing need. The Internet of Things (IoT) has changed our approach to safety systems by connecting sensors and providing real-time data to managers, rescuers, and endangered people. IoT systems can monitor and react to progressive disasters, people's movements and their behavioral patterns. The community faces challenges in using IoT for crises management: i) how to take advantage of technological advancements and deal with IoT resources installation issues? ii) what environmental contexts should be considered while designing IoT-based emergency handling systems? iii) how should system design comply with various levels of real-time requirements? This paper reports on the results of the First International Workshop on Internet of Things for Emergency Management (IoT4Emergency 2020), which speci cally focuses on challenges and envisioned solutions in using smart connected systems to handle disasters.

scholarly journals Hybrid Deep-Learning Model to Detect Botnet Attacks over Internet of Things Environments

2021 ◽  
Author(s):  
Mohammed Y. Alzahrani ◽  
Alwi M Bamhdi
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Short Term Memory ◽  
Weighted Average ◽  
Learning Model ◽  
Attack Detection ◽  
Data Sets ◽  
Intelligence Gathering ◽  
Deep Learning Model

Abstract In recent years, the use of the internet of things (IoT) has increased dramatically, and cybersecurity concerns have grown in tandem. Cybersecurity has become a major challenge for institutions and companies of all sizes, with the spread of threats growing in number and developing at a rapid pace. Artificial intelligence (AI) in cybersecurity can to a large extent help face the challenge, since it provides a powerful framework and coordinates that allow organisations to stay one step ahead of sophisticated cyber threats. AI provides real-time feedback, helping rollover daily alerts to be investigated and analysed, effective decisions to be made and enabling quick responses. AI-based capabilities make attack detection, security and mitigation more accurate for intelligence gathering and analysis, and they enable proactive protective countermeasures to be taken to overwhelm attacks. In this study, we propose a robust system specifically to help detect botnet attacks of IoT devices. This was done by innovatively combining the model of a convolutional neural network with a long short-term memory algorithm mechanism to detect two common and serious IoT attacks (BASHLITE and Mirai) on four types of security camera. The data sets, which contained normal malicious network packets, were collected from real-time lab-connected camera devices in IoT environments. The results of the experiment showed that the proposed system achieved optimal performance, according to evaluation metrics. The proposed system gave the following weighted average results for detecting the botnet on the Provision PT-737E camera: camera precision: 88%, recall: 87% and F1 score: 83%. The results of system for classifying botnet attacks and normal packets on the Provision PT-838 camera were 89% for recall, 85% for F1 score and 94%, precision. The intelligent security system using the advanced deep learning model was successful for detecting botnet attacks that infected camera devices connected to IoT applications.

scholarly journals Lightweight Cryptographic Algorithms for Guessing Attack Protection in Complex Internet of Things Applications

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Mohammad Kamrul Hasan ◽  
Muhammad Shafiq ◽  
Shayla Islam ◽  
Bishwajeet Pandey ◽  
Yousef A. Baker El-Ebiary ◽  
...  
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Encryption Algorithm ◽  
Support Vector ◽  
Iot Applications ◽  
Encryption And Decryption ◽  
Cryptographic Algorithms ◽  
Encryption Algorithms ◽  
Guessing Attack

As the world keeps advancing, the need for automated interconnected devices has started to gain significance; to cater to the condition, a new concept Internet of Things (IoT) has been introduced that revolves around smart devicesʼ conception. These smart devices using IoT can communicate with each other through a network to attain particular objectives, i.e., automation and intelligent decision making. IoT has enabled the users to divide their household burden with machines as these complex machines look after the environment variables and control their behavior accordingly. As evident, these machines use sensors to collect vital information, which is then the complexity analyzed at a computational node that then smartly controls these devicesʼ operational behaviors. Deep learning-based guessing attack protection algorithms have been enhancing IoT security; however, it still has a critical challenge for the complex industries’ IoT networks. One of the crucial aspects of such systems is the need to have a significant training time for processing a large dataset from the networkʼs previous flow of data. Traditional deep learning approaches include decision trees, logistic regression, and support vector machines. However, it is essential to note that this convenience comes with a price that involves security vulnerabilities as IoT networks are prone to be interfered with by hackers who can access the sensor/communication data and later utilize it for malicious purposes. This paper presents the experimental study of cryptographic algorithms to classify the types of encryption algorithms into the asymmetric and asymmetric encryption algorithm. It presents a deep analysis of AES, DES, 3DES, RSA, and Blowfish based on timing complexity, size, encryption, and decryption performances. It has been assessed in terms of the guessing attack in real-time deep learning complex IoT applications. The assessment has been done using the simulation approach and it has been tested the speed of encryption and decryption of the selected encryption algorithms. For each encryption and decryption, the tests executed the same encryption using the same plaintext for five separate times, and the average time is compared. The key size used for each encryption algorithm is the maximum bytes the cipher can allow. To the comparison, the average time required to compute the algorithm by the three devices is used. For the experimental test, a set of plaintexts is used in the simulation—password-sized text and paragraph-sized text—that achieves target fair results compared to the existing algorithms in real-time deep learning networks for IoT applications.

Smart Surveillance System Using Computer Vision and Internet of Things

2020 ◽  
Vol 17 (1) ◽  
pp. 68-73
Author(s):  
M. Hemaanand ◽  
V. Sanjay Kumar ◽  
R. Karthika
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Tracking System ◽  
Single Shot ◽  
Stream Data ◽  
Advanced Technique ◽  
The Real ◽  
Smart Surveillance ◽  
Bounding Boxes

With the evolution of technology ensuring people for their safety and security all around the time constantly is a big challenge. We propose an advanced technique based on deep learning and artificial intelligence platform that can monitor the people, their homes and their surroundings providing them a quantifiable increase in security. We have surveillance cameras in our homes for video capture as well as security purposes. Our proposed technique is to detect and classify as well as inform the user if there is any breach in security of the classified object using the cameras by implementing deep learning techniques and the technology of internet of things. It can serve as a perimeter monitoring and intruder alert system in smart surveillance environment. This paper provides a well-defined structure for live stream data analysis. It overcomes the challenge of static closed circuit cameras television as it serves as a motion based tracking system and monitors events in real time to ensure activities are limited to specific persons within authorized areas. It has the advantage of creating multiple bounding boxes to track down the objects which could be any living or non-living thing based on the trained modules. The trespasser or intruder can be efficiently detected using the CCTV camera surveillance which is being supported by the real-time object classifier algorithm at the intermediate module. The proposed method is mainly supported by the real time object detection and classification which is implemented using Mobile Net and Single shot detector.

scholarly journals Health Assessment of Eucalyptus Trees Using Siamese Network from Google Street and Ground Truth Images

2021 ◽  
Vol 13 (11) ◽  
pp. 2194
Author(s):  
Asim Khan ◽  
Warda Asim ◽  
Anwaar Ulhaq ◽  
Bilal Ghazi ◽  
Randall W. Robinson
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Algorithm ◽  
Health Assessment ◽  
Ground Truth ◽  
Urban Ecosystem ◽  
City Council ◽  
Local Council ◽  
Deep Learning Algorithm ◽  
Urban Greenery

Urban greenery is an essential characteristic of the urban ecosystem, which offers various advantages, such as improved air quality, human health facilities, storm-water run-off control, carbon reduction, and an increase in property values. Therefore, identification and continuous monitoring of the vegetation (trees) is of vital importance for our urban lifestyle. This paper proposes a deep learning-based network, Siamese convolutional neural network (SCNN), combined with a modified brute-force-based line-of-bearing (LOB) algorithm that evaluates the health of Eucalyptus trees as healthy or unhealthy and identifies their geolocation in real time from Google Street View (GSV) and ground truth images. Our dataset represents Eucalyptus trees’ various details from multiple viewpoints, scales and different shapes to texture. The experiments were carried out in the Wyndham city council area in the state of Victoria, Australia. Our approach obtained an average accuracy of 93.2% in identifying healthy and unhealthy trees after training on around 4500 images and testing on 500 images. This study helps in identifying the Eucalyptus tree with health issues or dead trees in an automated way that can facilitate urban green management and assist the local council to make decisions about plantation and improvements in looking after trees. Overall, this study shows that even in a complex background, most healthy and unhealthy Eucalyptus trees can be detected by our deep learning algorithm in real time.

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