Smart Lightning Detection System for Smart-City Infrastructure Using Artificial Neural Network

Ullah, Irshad; Baharom, M. N. R.; Ahmad, H.; Wahid, Fazli; Luqman, H. M.; Zainal, Zainab; Das, Bhagwan

doi:10.1007/s11277-018-5383-4

Cited by 14 publications

(9 citation statements)

References 16 publications

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“…The following are examples of IoT applications in smart cities: Smart homes: controls energy and water consumption, home applications remotely, and acts as a security system [ 83 ]. Smart lighting: controls lighting to support energy consumption, adjusting lights based on city conditions [ 84 ]. Smart roads: enhances the safety of drivers, traffic management [ 85 ], and energy efficiency [ 86 ].…”

Section: Key Survey Topicsmentioning

confidence: 99%

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Edge-Computing Architectures for Internet of Things Applications: A Survey

Hamdan

Ayyash

Almajali

2020

Sensors

136

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The rapid growth of the Internet of Things (IoT) applications and their interference with our daily life tasks have led to a large number of IoT devices and enormous sizes of IoT-generated data. The resources of IoT devices are limited; therefore, the processing and storing IoT data in these devices are inefficient. Traditional cloud-computing resources are used to partially handle some of the IoT resource-limitation issues; however, using the resources in cloud centers leads to other issues, such as latency in time-critical IoT applications. Therefore, edge-cloud-computing technology has recently evolved. This technology allows for data processing and storage at the edge of the network. This paper studies, in-depth, edge-computing architectures for IoT (ECAs-IoT), and then classifies them according to different factors such as data placement, orchestration services, security, and big data. Besides, the paper studies each architecture in depth and compares them according to various features. Additionally, ECAs-IoT is mapped according to two existing IoT layered models, which helps in identifying the capabilities, features, and gaps of every architecture. Moreover, the paper presents the most important limitations of existing ECAs-IoT and recommends solutions to them. Furthermore, this survey details the IoT applications in the edge-computing domain. Lastly, the paper recommends four different scenarios for using ECAs-IoT by IoT applications.

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Section: Key Survey Topicsmentioning

confidence: 99%

“…Smart lighting: controls lighting to support energy consumption, adjusting lights based on city conditions [ 84 ].…”

Section: Key Survey Topicsmentioning

confidence: 99%

Edge-Computing Architectures for Internet of Things Applications: A Survey

Hamdan

Ayyash

Almajali

2020

Sensors

136

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“…ANNs have many applications in smart cities, including hazard detection, water supply, energy, and urban transport. Ullah et al [55], for instance, develop infrastructures for smart lightning detection system aided by ANNs. Yuan et al [56] provide an approach to improve the stability of wind turbines in smart cities.…”

Section: Artificial Neural Network In Smart Citiesmentioning

confidence: 99%

“…Application domains Ullah et al [55] Utilizing artificial neural network to develop Infrastructures for the smart lightning detection system…”

Section: Contributionsmentioning

confidence: 99%

State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

Nosratabadi

Mosavi

Keivani

et al. 2020

Lecture Notes in Networks and Systems

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Deep learning (DL) and machine learning (ML) methods have recently contributed to the advancement of models in the various aspects of prediction, planning, and uncertainty analysis of smart cities and urban development. This paper presents the state of the art of DL and ML methods used in this realm. Through a novel taxonomy, the advances in model development and new application domains in urban sustainability and smart cities are presented. Findings reveal that five DL and ML methods have been most applied to address the different aspects of smart cities. These are artificial neural networks; support vector machines; decision trees; ensembles, Bayesians, hybrids, and neurofuzzy; and deep learning. It is also disclosed that energy, health, and urban transport are the main domains of smart cities that DL and ML methods contributed in to address their problems.

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“…This system constitutes committees of ANN trained to interrogate data collected from accelerometers mounted on the railroad bridge then to classify the event as either a passing train or a potentially damaging impact. Concerning the application of ANN in other infrastructure, Ullah et al (2019) innovatively proposed the use of ANN to build up a smart lighting detection system for smart-city support, which is one of the crucial parameters for building protection. To sum up, ANN plays a vital role as an algorithm to analyze various scientific issues related to nonlinear datasets.…”

Section: Studies Of Artificial Neural Networkmentioning

confidence: 99%

Reliability Improvement On Feasibility Study For Selection Of Infrastructure Projects Using Data Mining And Machine Learning

Hu¹

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21. Temporal Distribution of Pedestrians (15, 30, and 60 minutes, respectively) 22. Temporal Distribution of Vehicles (15, 30, and 60 minutes, respectively) 23. Temporal Distribution of Trains (15, 30, and 60 minutes, respectively) 24. Residence Location of Participants in Map 25. Pie Chart of Residence Location of Survey Participants 26. Train Blocking Frequency and Block Time 27. Ballot for Four Types of Bridge 28. Four Types of Pedestrian Bridge 29. One Aspect of Social Impacts the Proposed Pedestrian Overpass Have 30. Sustainable and Environmental Impacts of the Pedestrian Bridge 31. EIA Process and Project Lifecycle 32. Application of ANN in MATLAB R2018b 33. Basic Layout of Time-Series Neural Network 34. The Layout of the Most Optimal Time-Series Neural Network 35. Training Performance of 15-Minute NOP Simulation 36. Error Histogram of 15-Minute NOP Simulation 37. Regressions of 15-Minute NOP Simulation 38. Time-Series Response and Error of 15-Minute NOP Simulation 39. Training Performance of 15-Minute NOV Simulation 40. Error Histogram of 15-Minute NOV Simulation 41. Regressions of 15-Minute NOV Simulation ix 42. Time-Series Response of 15-Minute NOV Simulation 43. Training Performance of 30-Minute NOP Simulation 44. Error Histogram of 30-Minute NOP Simulation 45. Regressions of 30-Minute NOP Simulation 46. Time-Series Response of 30-Minute NOP Simulation 47. Training Performance of 30-min NOV Simulation 48. Error Histogram of 30-min NOV Simulation 49. Regressions of 30-Minute NOV Simulation 50. Time-Series Response of 30-Minute NOV Simulation 51. Training Performance of 60-Minute NOP Simulation 52. Error Histogram of 60-Minute NOP Simulation 53. Regressions of 60-Minute NOP Simulation 54. Time-Series Response of 60-Minute NOP Simulation 55. Training Performance of 60-Minute NOV Simulation 56. Error Histogram of 60-Minute NOV Simulation 57. Regressions of 60-Minute NOV Simulation 58. Time-Series Response of 60-Minute NOV Simulation 59. Time-Series Response of NOP Simulation for Additional Testing 60. Time-Series Response of NOV Simulation for Additional Testing 61.

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Smart Lightning Detection System for Smart-City Infrastructure Using Artificial Neural Network

Cited by 14 publications

References 16 publications

Edge-Computing Architectures for Internet of Things Applications: A Survey

Edge-Computing Architectures for Internet of Things Applications: A Survey

State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

Reliability Improvement On Feasibility Study For Selection Of Infrastructure Projects Using Data Mining And Machine Learning

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