scholarly journals Development of a Low-Cost IoT System for Lightning Strike Detection and Location

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1512 ◽  
Author(s):  
Ismael Mialdea-Flor ◽  
Jaume Segura-Garcia ◽  
Santiago Felici-Castell ◽  
Miguel Garcia-Pineda ◽  
Jose M. Alcaraz-Calero ◽  
...  
Keyword(s):  
Emergency Services ◽  
Detection System ◽  
Low Cost ◽  
Economic Loss ◽  
Geographic Area ◽  
Limited Range ◽  
Mesh Network ◽  
Lightning Strike ◽  
Lightning Detection ◽  
Iot Devices

Lightning and thunder are some of the most violent natural phenomena. They generate a great deal of expenditure and economic loss, especially when they strike in cities. The identification of the concrete geographic area where they strike is of critical importance for emergency services in order to enhance their effectiveness by doing an intensive coverage of the affected area. To achieve this purpose at the city scale, this paper proposes the design, prototype, and validation of a distributed network of Internet of Things (IoT) devices. The IoT devices are empowered with lightning detection capabilities and are synchronized with the other devices in the sensor network. All of them cooperate within a network that is able to locate different events thanks to a trilateration algorithm implemented in a big data environment. The designed low-cost lightning detection system is based on the AS3935 sensor. This system alone has a limited range of effective detection, but when it is embedded in an IoT mesh network, the accuracy and performance are increased. A fully operational IoT network was deployed, and a functional validation and empirical measurements are provided.

scholarly journals The Morphology of Eyewall Lightning Outbreaks in Two Category 5 Hurricanes*

2008 ◽  
Vol 136 (5) ◽  
pp. 1706-1726 ◽  
Author(s):  
K. Squires ◽  
S. Businger
Keyword(s):  
Hurricane Katrina ◽  
Density Ratio ◽  
Structural Evolution ◽  
Tropical Rainfall Measuring Mission ◽  
Limited Range ◽  
Lightning Strike ◽  
Brightness Temperatures ◽  
Time Period ◽  
Lightning Detection ◽  
Ice Concentration

Abstract Data from the Long-Range Lightning Detection Network (LLDN), the Tropical Rainfall Measuring Mission (TRMM) satellite, and reconnaissance aircraft are used to analyze the morphology of lightning outbreaks in the eyewalls of Hurricanes Rita and Katrina, two of the strongest storms in the Atlantic hurricane record. Each hurricane produced eyewall lightning outbreaks during the period of most rapid intensification, during eyewall replacement cycles, and during the time period that encompassed the maximum intensity for each storm. Within the effective range of the aircraft radar, maxima in eyewall strike density were collocated with maxima in radar reflectivity. High lightning strike rates were also consistently associated with TRMM low brightness temperatures and large precipitation ice concentration (PIC) values. The strike density ratio between the eyewall region and the outer rainband region was 6:1 for Hurricane Rita and 1:1 for Hurricane Katrina. This result is in contrast to those of previous remote lightning studies, which found that outer rainbands dominated the lightning distribution. The differences are shown to be at least in part the result of the more limited range of the National Lightning Detection Network (NLDN) data used in the earlier studies. Finally, implications of the results for the use of LLDN lightning data to remotely examine changes in hurricane intensity and structural evolution are discussed.

Localised Single-Station Lightning Detection by Using TOA Method

2013 ◽  
Vol 64 (4) ◽  
Author(s):  
Behnam Salimi ◽  
Zulkurnain Abdul-Malek ◽  
Kamyar Mehranzamir ◽  
Saeed Vahabi Mashak ◽  
Hadi Nabipour Afrouzi
Keyword(s):  
Radiation Source ◽  
Detection System ◽  
Linear Equations ◽  
Lightning Strike ◽  
Time Of Arrival ◽  
Lightning Detection ◽  
Single Station ◽  
Mathematical Equations ◽  
Non Linear Equations

Lightning is an electrical discharge during storms that can be monitored continuously from certain distances. It can be either within clouds (intra cloud), or between clouds and the ground (cloud-ground). There are various techniques used nowadays to locate lightning, and to determine various parameters produced from lightning. Each technique provides its own claimed performances. This paper attempts to provide instantaneous detection of lightning strike lightning location using the Time of Arrival (TOA) method of a single detection station (comprises of four antennas). It also models the whole detection system using suitable mathematical equations so as to give some understanding on the differences between the measured and calculated (theoretical) results. The measurement system is based on the application of mathematical and geometrical formulas. Several parameters such as the distance from the radiation source to the station and the lightning path are significant in influencing the accuracy of the results (elevation and azimuth angles). The role of each parameter is examined in detail using Matlab. This study solved the resultant non-linear equations by Newton-Raphson techniques. Methods to determine the radiation source which include the exact coordinate of a given radiation source in 3-dimensions were also developed. Further clarifications on the cause of errors in the single-station TOA method and techniques to reduce the errors are given.

Bringing Security to The Smallest Embedded Systems

2017 ◽  
Author(s):  
JOSEPH YIU
Keyword(s):  
Internet Of Things ◽  
Embedded Systems ◽  
Low Cost ◽  
Security Requirements ◽  
The Internet ◽  
Security Measures ◽  
Significant Challenge ◽  
Iot Devices

The increasing need for security in microcontrollers Security has long been a significant challenge in microcontroller applications(MCUs). Traditionally, many microcontroller systems did not have strong security measures against remote attacks as most of them are not connected to the Internet, and many microcontrollers are deemed to be cheap and simple. With the growth of IoT (Internet of Things), security in low cost microcontrollers moved toward the spotlight and the security requirements of these IoT devices are now just as critical as high-end systems due to:

scholarly journals Virtualizing AI at the Distributed Edge Towards Intelligent IoT Applications

2021 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Claudia Campolo ◽  
Giacomo Genovese ◽  
Antonio Iera ◽  
Antonella Molinaro
Keyword(s):  
Resource Constraints ◽  
Smart Cities ◽  
Traditional Approach ◽  
Low Cost ◽  
Iot Applications ◽  
Software And Hardware ◽  
Iot Devices ◽  
Consumer Devices ◽  
Hardware Platforms ◽  
Smart Factories

Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution.

scholarly journals A novel automated autism spectrum disorder detection system

2021 ◽  
Author(s):  
Shu Lih Oh ◽  
V. Jahmunah ◽  
N. Arunkumar ◽  
Enas W. Abdulhay ◽  
Raj Gururajan ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Developmental Disorder ◽  
Detection System ◽  
Brain Activity ◽  
Limited Range ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Nonlinear Features ◽  
Genetic And Environmental Factors ◽  
Future Work

AbstractAutism spectrum disorder (ASD) is a neurological and developmental disorder that begins early in childhood and lasts throughout a person’s life. Autism is influenced by both genetic and environmental factors. Lack of social interaction, communication problems, and a limited range of behaviors and interests are possible characteristics of autism in children, alongside other symptoms. Electroencephalograms provide useful information about changes in brain activity and hence are efficaciously used for diagnosis of neurological disease. Eighteen nonlinear features were extracted from EEG signals of 40 children with a diagnosis of autism spectrum disorder and 37 children with no diagnosis of neuro developmental disorder children. Feature selection was performed using Student’s t test, and Marginal Fisher Analysis was employed for data reduction. The features were ranked according to Student’s t test. The three most significant features were used to develop the autism index, while the ranked feature set was input to SVM polynomials 1, 2, and 3 for classification. The SVM polynomial 2 yielded the highest classification accuracy of 98.70% with 20 features. The developed classification system is likely to aid healthcare professionals as a diagnostic tool to detect autism. With more data, in our future work, we intend to employ deep learning models and to explore a cloud-based detection system for the detection of autism. Our study is novel, as we have analyzed all nonlinear features, and we are one of the first groups to have uniquely developed an autism (ASD) index using the extracted features.

scholarly journals Multi-Blockchain-Based IoT Data Processing Techniques to Ensure the Integrity of IoT Data in AIoT Edge Computing Environments

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3515
Author(s):  
Sung-Ho Sim ◽  
Yoon-Su Jeong
Keyword(s):  
Low Cost ◽  
Third Party ◽  
Monitoring And Control ◽  
Constant Size ◽  
Computing Environments ◽  
Iot Devices ◽  
Cloud Servers ◽  
The Cost ◽  
Processing Techniques ◽  
And Control

As the development of IoT technologies has progressed rapidly recently, most IoT data are focused on monitoring and control to process IoT data, but the cost of collecting and linking various IoT data increases, requiring the ability to proactively integrate and analyze collected IoT data so that cloud servers (data centers) can process smartly. In this paper, we propose a blockchain-based IoT big data integrity verification technique to ensure the safety of the Third Party Auditor (TPA), which has a role in auditing the integrity of AIoT data. The proposed technique aims to minimize IoT information loss by multiple blockchain groupings of information and signature keys from IoT devices. The proposed technique allows IoT information to be effectively guaranteed the integrity of AIoT data by linking hash values designated as arbitrary, constant-size blocks with previous blocks in hierarchical chains. The proposed technique performs synchronization using location information between the central server and IoT devices to manage the cost of the integrity of IoT information at low cost. In order to easily control a large number of locations of IoT devices, we perform cross-distributed and blockchain linkage processing under constant rules to improve the load and throughput generated by IoT devices.

scholarly journals IoT Botnet Anomaly Detection Using Unsupervised Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1876
Author(s):  
Ioana Apostol ◽  
Marius Preda ◽  
Constantin Nila ◽  
Ion Bica
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
False Positive Rate ◽  
Empirical Evaluation ◽  
Learning Approaches ◽  
Promising Alternative ◽  
Positive Rate ◽  
Unsupervised Deep Learning ◽  
Attack Surface ◽  
Iot Devices

The Internet of Things has become a cutting-edge technology that is continuously evolving in size, connectivity, and applicability. This ecosystem makes its presence felt in every aspect of our lives, along with all other emerging technologies. Unfortunately, despite the significant benefits brought by the IoT, the increased attack surface built upon it has become more critical than ever. Devices have limited resources and are not typically created with security features. Lately, a trend of botnet threats transitioning to the IoT environment has been observed, and an army of infected IoT devices can expand quickly and be used for effective attacks. Therefore, identifying proper solutions for securing IoT systems is currently an important and challenging research topic. Machine learning-based approaches are a promising alternative, allowing the identification of abnormal behaviors and the detection of attacks. This paper proposes an anomaly-based detection solution that uses unsupervised deep learning techniques to identify IoT botnet activities. An empirical evaluation of the proposed method is conducted on both balanced and unbalanced datasets to assess its threat detection capability. False-positive rate reduction and its impact on the detection system are also analyzed. Furthermore, a comparison with other unsupervised learning approaches is included. The experimental results reveal the performance of the proposed detection method.

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