scholarly journals Real Time Monitoring and Fire Detection using Internet of Things and Cloud based Drones

2020 ◽  
Vol 2 (3) ◽  
pp. 168-174 ◽  
Author(s):  
Dr. Akey Sungheetha ◽  
Dr. Rajesh Sharma R
Keyword(s):  
System Integration ◽  
Agricultural Land ◽  
Early Stage ◽  
Smart Cities ◽  
Environmental Parameters ◽  
Fire Detection ◽  
Environmental Data ◽  
True Detection ◽  
Fire Accidents ◽  
Network Technologies

Smart cities with smart infrastructure is a rapidly flourishing field of research in the modern days. Open areas, agricultural land, forests, office, homes and several areas can have occurrences of fire accidents leading to loss of significant resources. Unmanned Aerial Vehicle (UAV) and wireless sensor network technologies are used fir detection of fire at an early stage in this paper. This helps in avoiding serious fire accidents. The environmental parameters are monitored using the sensor architecture. The sensors uses IoT based applications for processing the gathered environmental data. Cloud computing, IoT sensors, wireless technology and UAVs are combined for the purpose of fire detection in this paper. In order to improve the accuracy of the system, integration of image processing schemes is done in this system. The rules are formulated such that the true detection rate is improved. The existing state-of-the-art models are compared with the proposed system. The simulation results show that the rate of fire detection of the proposed system is improved for up to 98% when compared to the traditional models.

scholarly journals Fire Detection Method in Smart City Environments Using a Deep-Learning-Based Approach

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 73
Author(s):  
Kuldoshbay Avazov ◽  
Mukhriddin Mukhiddinov ◽  
Fazliddin Makhmudov ◽  
Young Im Cho
Keyword(s):  
Deep Learning ◽  
Urban Areas ◽  
High Speed ◽  
Data Augmentation ◽  
Smart Cities ◽  
Fire Detection ◽  
Training Dataset ◽  
Digital Cameras ◽  
Augmentation Techniques ◽  
Fire Accidents

In the construction of new smart cities, traditional fire-detection systems can be replaced with vision-based systems to establish fire safety in society using emerging technologies, such as digital cameras, computer vision, artificial intelligence, and deep learning. In this study, we developed a fire detector that accurately detects even small sparks and sounds an alarm within 8 s of a fire outbreak. A novel convolutional neural network was developed to detect fire regions using an enhanced You Only Look Once (YOLO) v4network. Based on the improved YOLOv4 algorithm, we adapted the network to operate on the Banana Pi M3 board using only three layers. Initially, we examined the originalYOLOv4 approach to determine the accuracy of predictions of candidate fire regions. However, the anticipated results were not observed after several experiments involving this approach to detect fire accidents. We improved the traditional YOLOv4 network by increasing the size of the training dataset based on data augmentation techniques for the real-time monitoring of fire disasters. By modifying the network structure through automatic color augmentation, reducing parameters, etc., the proposed method successfully detected and notified the incidence of disastrous fires with a high speed and accuracy in different weather environments—sunny or cloudy, day or night. Experimental results revealed that the proposed method can be used successfully for the protection of smart cities and in monitoring fires in urban areas. Finally, we compared the performance of our method with that of recently reported fire-detection approaches employing widely used performance matrices to test the fire classification results achieved.

scholarly journals Image-Based Automatic Watermeter Reading under Challenging Environments

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 434
Author(s):  
Qingqi Hong ◽  
Yiwei Ding ◽  
Jinpeng Lin ◽  
Meihong Wang ◽  
Qingyang Wei ◽  
...  
Keyword(s):  
Smart Cities ◽  
Rapid Development ◽  
Mobile Network ◽  
Spatial Layout ◽  
Single Image ◽  
Automatic Instrument ◽  
Intelligent Sensors ◽  
Fifth Generation ◽  
Instrument Reading ◽  
Network Technologies

With the rapid development of artificial intelligence and fifth-generation mobile network technologies, automatic instrument reading has become an increasingly important topic for intelligent sensors in smart cities. We propose a full pipeline to automatically read watermeters based on a single image, using deep learning methods to provide new technical support for an intelligent water meter reading. To handle the various challenging environments where watermeters reside, our pipeline disentangled the task into individual subtasks based on the structures of typical watermeters. These subtasks include component localization, orientation alignment, spatial layout guidance reading, and regression-based pointer reading. The devised algorithms for orientation alignment and spatial layout guidance are tailored to improve the robustness of our neural network. We also collect images of watermeters in real scenes and build a dataset for training and evaluation. Experimental results demonstrate the effectiveness of the proposed method even under challenging environments with varying lighting, occlusions, and different orientations. Thanks to the lightweight algorithms adopted in our pipeline, the system can be easily deployed and fully automated.

scholarly journals A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 768
Author(s):  
Jin Pan ◽  
Xiaoming Ou ◽  
Liang Xu
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Learning Strategy ◽  
Early Stage ◽  
Fire Detection ◽  
Surveillance Systems ◽  
Region Detection ◽  
Decision Network ◽  
Fire Smoke ◽  
Weakly Supervised

Forest fires are serious disasters that affect countries all over the world. With the progress of image processing, numerous image-based surveillance systems for fires have been installed in forests. The rapid and accurate detection and grading of fire smoke can provide useful information, which helps humans to quickly control and reduce forest losses. Currently, convolutional neural networks (CNN) have yielded excellent performance in image recognition. Previous studies mostly paid attention to CNN-based image classification for fire detection. However, the research of CNN-based region detection and grading of fire is extremely scarce due to a challenging task which locates and segments fire regions using image-level annotations instead of inaccessible pixel-level labels. This paper presents a novel collaborative region detection and grading framework for fire smoke using a weakly supervised fine segmentation and a lightweight Faster R-CNN. The multi-task framework can simultaneously implement the early-stage alarm, region detection, classification, and grading of fire smoke. To provide an accurate segmentation on image-level, we propose the weakly supervised fine segmentation method, which consists of a segmentation network and a decision network. We aggregate image-level information, instead of expensive pixel-level labels, from all training images into the segmentation network, which simultaneously locates and segments fire smoke regions. To train the segmentation network using only image-level annotations, we propose a two-stage weakly supervised learning strategy, in which a novel weakly supervised loss is proposed to roughly detect the region of fire smoke, and a new region-refining segmentation algorithm is further used to accurately identify this region. The decision network incorporating a residual spatial attention module is utilized to predict the category of forest fire smoke. To reduce the complexity of the Faster R-CNN, we first introduced a knowledge distillation technique to compress the structure of this model. To grade forest fire smoke, we used a 3-input/1-output fuzzy system to evaluate the severity level. We evaluated the proposed approach using a developed fire smoke dataset, which included five different scenes varying by the fire smoke level. The proposed method exhibited competitive performance compared to state-of-the-art methods.

scholarly journals A Self-Powered PMFC-Based Wireless Sensor Node for Smart City Applications

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Daniel Ayala-Ruiz ◽  
Alejandro Castillo Atoche ◽  
Erica Ruiz-Ibarra ◽  
Edith Osorio de la Rosa ◽  
Javier Vázquez Castillo
Keyword(s):  
Energy Harvesting ◽  
Low Power ◽  
Sensor Node ◽  
Smart Cities ◽  
Cloud Services ◽  
Environmental Data ◽  
Security And Privacy ◽  
Wireless Sensor ◽  
Ultra Low Power ◽  
Self Powered

Long power wide area networks (LPWAN) systems play an important role in monitoring environmental conditions for smart cities applications. With the development of Internet of Things (IoT), wireless sensor networks (WSN), and energy harvesting devices, ultra-low power sensor nodes (SNs) are able to collect and monitor the information for environmental protection, urban planning, and risk prevention. This paper presents a WSN of self-powered IoT SNs energetically autonomous using Plant Microbial Fuel Cells (PMFCs). An energy harvesting device has been adapted with the PMFC to enable a batteryless operation of the SN providing power supply to the sensor network. The low-power communication feature of the SN network is used to monitor the environmental data with a dynamic power management strategy successfully designed for the PMFC-based LoRa sensor node. Environmental data of ozone (O3) and carbon dioxide (CO2) are monitored in real time through a web application providing IoT cloud services with security and privacy protocols.

scholarly journals The impact of environmental variables on the spread of COVID-19 in the Republic of Korea

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yong Kwan Lim ◽  
Oh Joo Kweon ◽  
Hye Ryoun Kim ◽  
Tae-Hyoung Kim ◽  
Mi-Kyung Lee
Keyword(s):  
Environmental Factors ◽  
Virus Disease ◽  
Environmental Parameters ◽  
Environmental Data ◽  
Republic Of Korea ◽  
Metropolitan Region ◽  
Health Concern ◽  
Ozone Level ◽  
The Republic ◽  
The Impact

AbstractCorona virus disease 2019 (COVID-19) has been declared a global pandemic and is a major public health concern worldwide. In this study, we aimed to determine the role of environmental factors, such as climate and air pollutants, in the transmission of COVID-19 in the Republic of Korea. We collected epidemiological and environmental data from two regions of the Republic of Korea, namely Seoul metropolitan region (SMR) and Daegu-Gyeongbuk region (DGR) from February 2020 to July 2020. The data was then analyzed to identify correlations between each environmental factor with confirmed daily COVID-19 cases. Among the various environmental parameters, the duration of sunshine and ozone level were found to positively correlate with COVID-19 cases in both regions. However, the association of temperature variables with COVID-19 transmission revealed contradictory results when comparing the data from SMR and DGR. Moreover, statistical bias may have arisen due to an extensive epidemiological investigation and altered socio-behaviors that occurred in response to a COVID-19 outbreak. Nevertheless, our results suggest that various environmental factors may play a role in COVID-19 transmission.

scholarly journals A Place-Based Approach to Agricultural Nonmaterial Intangible Cultural Ecosystem Service Values

2020 ◽  
Vol 12 (2) ◽  
pp. 699 ◽  
Author(s):  
Joy R. Petway ◽  
Yu-Pin Lin ◽  
Rainer F. Wunderlich
Keyword(s):  
Land Cover ◽  
Ecosystem Service ◽  
Agricultural Landscape ◽  
Agricultural Land ◽  
Environmental Data ◽  
Farming Practices ◽  
Environmental Characteristics ◽  
Cultural Ecosystem Service ◽  
Context Specific ◽  
Landscape Biodiversity

Though agricultural landscape biodiversity and ecosystem service (ES) conservation is crucial to sustainability, agricultural land is often underrepresented in ES studies, while cultural ES associated with agricultural land is often limited to aesthetic and tourism recreation value only. This study mapped 7 nonmaterial-intangible cultural ES (NICE) valuations of 34 rural farmers in western Taiwan using the Social Values for Ecosystem Services (SolVES) methodology, to show the effect of farming practices on NICE valuations. However, rather than a direct causal relationship between the environmental characteristics that underpin ES, and respondents’ ES valuations, we found that environmental data is not explanatory enough for causality within a socio-ecological production landscape where one type of land cover type (a micro mosaic of agricultural land cover) predominates. To compensate, we used a place-based approach with Google Maps data to create context-specific data to inform our assessment of NICE valuations. Based on 338 mapped points of 7 NICE valuations distributed among 6 areas within the landscape, we compared 2 groups of farmers and found that farmers’ valuations about their landscape were better understood when accounting for both the landscape’s cultural places and environmental characteristics, rather than environmental characteristics alone. Further, farmers’ experience and knowledge influenced their NICE valuations such that farm areas were found to be sources of multiple NICE benefits demonstrating that farming practices may influence ES valuation in general.

Mobility Assistants to Support Multi-Modal Routes in Smart Cities: A Scoping Review

2021 ◽  
Vol 3 (1) ◽  
pp. 26-40
Author(s):  
Nelson Pacheco Rocha ◽  
Gonçalo Santinha ◽  
Mário Rodrigues ◽  
Carlos Rodrigues ◽  
Alexandra Queirós ◽  
...  
Keyword(s):  
Scoping Review ◽  
Early Stage ◽  
Smart Cities ◽  
Research Trends ◽  
Exclusion Criteria ◽  
Major Barrier ◽  
Stage Of Development ◽  
Electronic Search ◽  
Inclusion Results

Objectives - This study aimed to identify: (i) the current research trends related to mobility assistants to support multi-modal routes in smart cities; (ii) the types of smart cities’ data being used; (iii) the methods applied to assess the proposed solutions; and iv) the major barriers for their dissemination. Methods - An electronic search was conducted in several databases, combining relevant keywords. Then titles and abstracts were screened against inclusion and exclusion criteria. Finally, the full texts of the eligible articles were retrieved and screened for inclusion. Results - A total of 19 articles were included. These articles either propose algorithms to optimize routes planning or presenting specific applications that make use of a broad range of smart cities’ data. Conclusion - The number of included articles is very reduced when compared with the total number of articles related to smart cities, which means that the mobility assistants to support multi-modal routes are still not significant within the smart cities’ research. Moreover, most of the included articles report applications in an early stage of development, which is a major barrier for the respective dissemination.

scholarly journals CHANNEL ALLOCATION FOR WIRELESS NETWORKS BASED ON INTELLIGENT METHOD

2021 ◽  
Vol 20 (4) ◽  
pp. 32-37
Author(s):  
Dominik NEZNÍK ◽  
◽  
Ľubomír DOBOŠ
Keyword(s):  
Wireless Networks ◽  
Data Transfer ◽  
Channel Allocation ◽  
Smart Cities ◽  
Data Rates ◽  
Different Types ◽  
Fuzzy Logic Method ◽  
Network Technologies ◽  
Near Future

In this paper, will be presented actual research of the intelligent channel allocation. The intelligent channel allocation is based on combination of fuzzy logic method and game theory attributes to increase quality of link in network. The channel allocations will become an important phenomenon in different types of networks such as 5G technology, wireless networks (IEEE 802.11xx), Z-Wave, LoRa, 3G, 4G, etc. In the near future, new network technologies, Internet of Things (IoT) and Smart Cities will need to have intelligent channel allocation to prevent interference on the channels used for data transfer. These networks along with IoT are considered as promising technology, that interconnects different types of networks into one fully functional network. The aim of this paper is to present the concept of a methods for channel allocation in wireless networks, where channels work as communication medium based on IEEE 802.11xx technology. The simulations prove, that proposed method is able to provide lower interference, improve data rates and increase quality of links.

scholarly journals DESIGN AND CONTROL OF SPIDER ROBOT MODEL

2019 ◽  
Vol 1 (1) ◽  
pp. 90-93
Author(s):  
Tan Thanh Nguyen ◽  
Duy Khanh Nguyen
Keyword(s):  
Operating System ◽  
High Stability ◽  
Low Cost ◽  
Environmental Parameters ◽  
Environmental Data ◽  
Control Methods ◽  
Multiple Control ◽  
Android Operating System ◽  
And Control ◽  
Mit App Inventor

Robots imitating spider’s moving have many advantages such as flexible movement, high stability, diversity in movements performed, especially in terrain  crossing, in military reconnaissance, in surveying and collecting environmental data in dangerous areas,.... In this article  with the main objective is to exploit multiple control methods to support applications of a spider robot with low-cost, a spider robot with 6 legs and 18 joints was designed. The ESPWROOM-32 module (ESP32-D0WDQ6 chip) and MIT App Inventor were used as the main tools for conducting this research. As a result, the robot is controlled via Bluetooth and Wifi to move, making some actions by self-written software running on the Android operating system. In addition, the robot has the capacity of self-propelled to avoid simple obstacles and send some environmental parameters to the software, including obstacles distance, humidity and temperature.

scholarly journals Incorporating Geographical Scale and Multiple Environmental Factors to Delineate the Breeding Distribution of Sea Turtles

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 142
Author(s):  
Liam C. Dickson ◽  
Kostas A. Katselidis ◽  
Christophe Eizaguirre ◽  
Gail Schofield
Keyword(s):  
Sea Turtles ◽  
Regional Distribution ◽  
Environmental Parameters ◽  
Environmental Data ◽  
Offshore Wind ◽  
Loggerhead Sea Turtles ◽  
Change Models ◽  
Terrestrial Environments ◽  
Aircraft System ◽  
Marine Megafauna

Temperature is often used to infer how climate influences wildlife distributions; yet, other parameters also contribute, separately and combined, with effects varying across geographical scales. Here, we used an unoccupied aircraft system to explore how environmental parameters affect the regional distribution of the terrestrial and marine breeding habitats of threatened loggerhead sea turtles (Caretta caretta). Surveys spanned four years and ~620 km coastline of western Greece, encompassing low (<10 nests/km) to high (100–500 nests/km) density nesting areas. We recorded 2395 tracks left by turtles on beaches and 1928 turtles occupying waters adjacent to these beaches. Variation in beach track and inwater turtle densities was explained by temperature, offshore prevailing wind, and physical marine and terrestrial factors combined. The highest beach-track densities (400 tracks/km) occurred on beaches with steep slopes and higher sand temperatures, sheltered from prevailing offshore winds. The highest inwater turtle densities (270 turtles/km) occurred over submerged sandbanks, with warmer sea temperatures associated with offshore wind. Most turtles (90%) occurred over nearshore submerged sandbanks within 10 km of beaches supporting the highest track densities, showing the strong linkage between optimal marine and terrestrial environments for breeding. Our findings demonstrate the utility of UASs in surveying marine megafauna and environmental data at large scales and the importance of integrating multiple factors in climate change models to predict species distributions.

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