Towards the Internet of Behaviors in Smart Cities through a Fog-To-Cloud Approach

Salis, Antonio

doi:10.28991/hij-2021-02-04-01

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…For example, penetration testing can be used to test the system against certain types of attacks, such as denial of service attacks [46], SQL injection attacks [47], and crosssite scripting attacks [48]. Where the tester starts by analyzing the computer system under test, generating a set of test cases, selecting the important test cases, executing those test cases, and reporting the results [29,[49][50][51][52].…”

Section: -1-penetration Testingmentioning

confidence: 99%

Overview on Case Study Penetration Testing Models Evaluation

2023

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Model evaluation is a cornerstone of scientific research as it represents the findings' accuracy and model performance. A case study is commonly used in evaluating software engineering models. Due to criticism in terms of generalization from a single case study and testers, deciding on the number of case studies used for evaluation and the number of testers has been one of the researchers’ challenges. Multiple case studies with multiple testers can be difficult in some domains, such as penetration testing, due to the complexity and time needed to prepare test cases. This study aims to review the literature and examine the evaluation methods used pertaining to the number of case studies and testers involved. This study is beneficial for researchers, students, and penetration testers as it provides case study design steps that are useful to determine the appropriate number of test cases and testers required. The paper's findings and novelty highlight that a single case study with a single tester is enough to evaluate a model. It also strikes a balance between what is enough for the evaluation and the need to reduce criticisms of a single case study by using two case studies with a single tester. Doi: 10.28991/ESJ-2023-07-03-025 Full Text: PDF

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Section: -1-penetration Testingmentioning

confidence: 99%

Overview on Case Study Penetration Testing Models Evaluation

2023

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“…Wherefore, deep learning comes to solve many obstacles that existed before. Using deep learning for extracting features from the satellite image saves the effort, time, cost spent by manual work, but the accuracy is a questionable point [19,20]. In our use case, buildings and landscape areas are extracted and find their geometries using Object Instance Segmentation which is a new approach developed to overcome some challenges faced before.…”

Section: Figure 6 2d Map For Mansoura University Campus From High Resolution Imagery (Hri) C) Using Deep Learningmentioning

confidence: 99%

Creation and Spatial Analysis of 3D City Modeling based on GIS Data

2022

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The 3D city model is one of the crucial topics that are still under analysis by many engineers and programmers because of the great advancements in data acquisition technologies and 3D computer graphics programming. It is one of the best visualization methods for representing reality. This paper presents different techniques for the creation and spatial analysis of 3D city modeling based on Geographical Information System (GIS) technology using free data sources. To achieve that goal, the Mansoura University campus, located in Mansoura city, Egypt, was chosen as a case study. The minimum data requirements to generate a 3D city model are the terrain, 2D spatial features such as buildings, landscape area and street networks. Moreover, building height is an important attribute in the 3D extrusion process. The main challenge during the creation process is the dearth of accurate free datasets, and the time-consuming editing. Therefore, different data sources are used in this study to evaluate their accuracy and find suitable applications which can use the generated 3D model. Meanwhile, an accurate data source obtained using the traditional survey methods is used for the validation purpose. First, the terrain was obtained from a digital elevation model (DEM) and compared with grid leveling measurements. Second, 2D data were obtained from: the manual digitization from (30 cm) high-resolution imagery, and deep learning structure algorithms to detect the 2D features automatically using an object instance segmentation model and compared the results with the total station survey observations. Different techniques are used to investigate and evaluate the accuracy of these data sources. The procedural modeling technique is applied to generate the 3D city model. TensorFlow & Keras frameworks (Python APIs) were used in this paper; moreover, global mapper, ArcGIS Pro, QGIS and CityEngine software were used. The precision metrics from the trained deep learning model were 0.78 for buildings, 0.62 for streets and 0.89 for landscape areas. Despite, the manual digitizing results are better than the results from deep learning, but the extracted features accuracy is accepted and can be used in the creation process in the cases not require a highly accurate 3D model. The flood impact scenario is simulated as an application of spatial analysis on the generated 3D city model. Doi: 10.28991/CEJ-2022-08-01-08 Full Text: PDF

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“…The Internet of Things (IoT) has become one of the most prominent technologies in the evolving digital era (1)(2)(3)(4)(5). IoT has transformed the way humans interact with devices and systems in their surroundings, ranging from smart homes to autonomous vehicles (6)(7)(8). IoT is often integrated with artificial intelligence (AI) (9).…”

Section: Introductionmentioning

confidence: 99%

Energy Efficiency with Internet of Things Based Fuzzy Inference System for Room Temperature and Humidity Regulation

Furizal,

Sunardi,

Yudhana

et al. 2024

IJE

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Energy consumption is a crucial aspect in the effort to optimize the utilization of resources and reduce energy wastage. Focusing on energy efficiency can result in operational cost savings, a reduction in greenhouse gas emissions, and support for environmental sustainability for future generations. Therefore, it is important to consider energy efficiency in daily life, especially in the use of electricity for electronic devices. This research aims to compare the energy efficiency of two different approaches to Air Conditioner (AC) usage: the manual method and the fuzzy logic method. The manual method involves eight tests with direct power measurements over a 30-minute period at various AC temperature settings, namely 18°C, 20°C, 23°C, 24°C, 25°C, 26°C, 27°C, and 30°C. On the other hand, the fuzzy logic method involves six tests allowing for dynamic temperature adjustments based on room conditions. The research findings indicate that the fuzzy logic method achieves lower average power consumption, except at 30°C, where the manual method is slightly more efficient (a difference of 140,745 watts). This difference is primarily attributed to the "cooling and fan" mode used at lower temperatures in the manual method, resulting in higher power consumption. Furthermore, this research reveals the potential of the fuzzy logic in optimizing AC power usage based on real-time conditions, achieving approximately a 41.96% energy savings. The primary contribution of this study is to provide practical insights into how the fuzzy logic method can significantly reduce AC energy consumption, support energy efficiency efforts, and contribute to environmental sustainability.

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Towards the Internet of Behaviors in Smart Cities through a Fog-To-Cloud Approach

Cited by 7 publications

References 21 publications

Overview on Case Study Penetration Testing Models Evaluation

Overview on Case Study Penetration Testing Models Evaluation

Creation and Spatial Analysis of 3D City Modeling based on GIS Data

Energy Efficiency with Internet of Things Based Fuzzy Inference System for Room Temperature and Humidity Regulation

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