An Efficient Ray-Tracing Based Model Dedicated to Wireless Sensor Network Simulators for Smart Cities Environments

Alwajeeh, Taha; Combeau, Pierre; Aveneau, Lilian

doi:10.1109/access.2020.3037135

Cited by 11 publications

(11 citation statements)

References 55 publications

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“…Most of them achieve an average standard deviation of about 7 dB up to 9 dB. Also newer publications validate their introduced path loss prediction algorithms and acceleration techniques with the Mannesmann Munich dataset [29]- [32]. The standard deviation of these models is similarly within the above-mentioned value range.…”

Section: Validation Of Ray Tracing Pathloss Predictionsmentioning

confidence: 70%

Optimization and Evaluation of a 3-D Ray Tracing Channel Predictor Individually for Each Propagation Effect

Schweins,

Thielecke,

Grupe

et al. 2024

IEEE Open J. Antennas Propag.

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For 5G/6G system simulations and radio planning the availability of both accurate and run-time efficient channel predictors is required. This paper presents a 3D ray tracing channel predictor, which takes into account the relevant propagation phenomena like free space propagation, reflection, scattering, diffraction and transmission. The predictor is validated by two types of measurements and the relevance of the various propagation phenomena is analyzed based on predictions in a city using real base station locations and building data. Based on these results an acceleration methodology for the search of reflected paths is derived, which is able to reduce the computation time to 40% compared to the baseline prediction.

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Section: Validation Of Ray Tracing Pathloss Predictionsmentioning

confidence: 70%

Optimization and Evaluation of a 3-D Ray Tracing Channel Predictor Individually for Each Propagation Effect

Schweins,

Thielecke,

Grupe

et al. 2024

IEEE Open J. Antennas Propag.

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“…As a result of the analysis of research method 1, 249 (65.0%) of the total 383 quantitative studies constituted the highest number of those published, followed by 114 (29.8%) qualitative studies and 20 (5.2%) mixed studies (Table 4). The high number of quantitative studies includes statistical methods for data analysis (Alablani and Alenazi; Munjal et al) [31,32] and computer simulations for algorithm analysis (Reddy et al; Alwajee et al) [33,34]. Mixed studies include research that conducted both surveys and case studies (Deveci et al) [35], computer simulation and case studies (Elseidi et al) [36], literature research and survey research (Kirimtat et al) [37], and literature and case studies (Malik et al) [38].…”

Section: Results Of Research Methods Analysismentioning

confidence: 99%

“…The reason why experimental methods accounted for the largest portion with 241 papers is that among the papers to be studied, papers in the field of science and technology account for a large portion. The main papers of the experimental method are multidimensional modeling (Jia and Wu) [39] for sustainable smart cities (Alwajeeh, Combeau and Avenau) [33] and crowd management systems (Santana et al) [40] capable of detecting personal information. Literature research methods mainly consisted of an efficient smart city service framework (Li) [41] or a study comparing smart city services in various cities (Balasubramaniam) [42].…”

Section: Results Of Research Methods Analysismentioning

confidence: 99%

Research Models and Methodologies on the Smart City: A Systematic Literature Review

2022

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A smart city is a sustainable city that solves urban problems and improves citizens’ quality of life through the fourth industrial revolution technology and governance between stakeholders. With the advent of the fourth industrial revolution and the concept of smart cities changing, many smart city studies have been conducted. Still, studies on the overall flow of smart city research and major issues are insufficient. Therefore, this study analyzed the trends and characteristics of smart city research and proposes research directions through smart city literature analyses conducted over the past 10 years, from 2011 to 2020. This study conducted a systematic review of studies related to smart cities over the past 10 years. Smart city research was analyzed by dividing it into research methods and contents. In terms of research method, year, research methodology 1, research methodology 2, research purpose, data collection method, and use of research results were included in the analysis frame. In terms of research content, keywords related to research topics, analysis units, and the general classification criteria and security of smart cities were included. The research results mentioned in this study are expected to serve as useful guidelines for future smart city development projects.

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“…To evaluate the coverage, we use the ray-tracing method, which is one of the most efficient in terms of and realism [40] and allows to accurately compute the coverage area [19]. Its principle is to generate an image in which each pixel is independently processed [41]. A ray is launched from the camera towards the plane, and at each intersection, the nature and position of the corresponding pixel is determined [42].…”

Section: Sensor Modeling and Observation Processmentioning

confidence: 99%

An Efficient Placement Approach of Visual Sensors Under Observation Redundancy and Communication Connectivity Constraints

FAGA,

TOSSA,

ABDOU

et al. 2024

Preprint

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Visual Sensor Networks (VSNs) have been the focus of numerous studies, emphasizing the quality of the information they generate. The primary goal of these networks is to capture data from an area, extract the pertinent information from the visual scene, and facilitate communication. However, the presence of objects in the study area can impacted the network's efficiency, linking the quality of acquired data directly to the positioning of the cameras. Strategically positioning multiple cameras in a potentially complex area, while ensuring effective communication between nodes, presents a challenging issue in VSNs applications. This paper introduces a specific method to enhance the deployment of visual sensors in indoor environments. The research addresses the theme of multi-objective optimization of VSN deployment, integrating various criteria: maximizing overall visual coverage, maximizing the observation of zones of interest, maximizing observation redundancy of zones of interest and ensuring communication connectivity for data transfers. To tackle this problem, we simulate the observed environment incorporating cameras, obstacles, and considering zones of interest. Camera positioning is performed while considering visual constraints and the scene's characteristics. The quality of positioning is evaluated based on the defined objective functions. Initially, a comparative study is conducted with a state-of-the-art approach, integrating the first three objective functions. Subsequently, we incorporate the fourth objective function to ensure data transfers. The proposed method demonstrates efficient camera network deployments in indoor areas, considering global coverage and observation redundancy. In contrast to the state-of-the-art, these results are achieved in areas with visual obstacles while ensuring communication connectivity between the different cameras.

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An Efficient Ray-Tracing Based Model Dedicated to Wireless Sensor Network Simulators for Smart Cities Environments

Cited by 11 publications

References 55 publications

Optimization and Evaluation of a 3-D Ray Tracing Channel Predictor Individually for Each Propagation Effect

Optimization and Evaluation of a 3-D Ray Tracing Channel Predictor Individually for Each Propagation Effect

Research Models and Methodologies on the Smart City: A Systematic Literature Review

An Efficient Placement Approach of Visual Sensors Under Observation Redundancy and Communication Connectivity Constraints

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