An alternative approach to monitor occupancy using bluetooth low energy technology in an office environment

Tekler, Zeynep Duygu; Low, Raymond; Blessing, Luciënne

doi:10.1088/1742-6596/1343/1/012116

Cited by 16 publications

(11 citation statements)

References 7 publications

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“…Lastly, LBSNs rely on their vast network of end-users to maintain the relevancy of their database by encouraging their users to share their location information and visiting experiences with other users on the platform in the form of user reviewers and ratings. Some platforms even rely on the users' smartphone connection to nearby cell towers and wireless networks to infer the users' last visited locations within the building by combining it with various indoor localisation techniques [13,14]. Some examples of these LBSNs includes Swarm by Foursquare [15] and Google Maps [16].…”

Section: Poi Data Sourcesmentioning

confidence: 99%

An End-to-End Point of Interest (POI) Conflation Framework

Low

Tekler

Cheah

2021

IJGI

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Point of interest (POI) data serves as a valuable source of semantic information for places of interest and has many geospatial applications in real estate, transportation, and urban planning. With the availability of different data sources, POI conflation serves as a valuable technique for enriching data quality and coverage by merging the POI data from multiple sources. This study proposes a novel end-to-end POI conflation framework consisting of six steps, starting with data procurement, schema standardisation, taxonomy mapping, POI matching, POI unification, and data verification. The feasibility of the proposed framework was demonstrated in a case study conducted in the eastern region of Singapore, where the POI data from five data sources was conflated to form a unified POI dataset. Based on the evaluation conducted, the resulting unified dataset was found to be more comprehensive and complete than any of the five POI data sources alone. Furthermore, the proposed approach for identifying POI matches between different data sources outperformed all baseline approaches with a matching accuracy of 97.6% with an average run time below 3 min when matching over 12,000 POIs to result in 8699 unique POIs, thereby demonstrating the framework’s scalability for large scale implementation in dense urban contexts.

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Section: Poi Data Sourcesmentioning

confidence: 99%

An End-to-End Point of Interest (POI) Conflation Framework

Low

Tekler

Cheah

2021

IJGI

Self Cite

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“…The algorithm follows an iterative functional gradient descent approach that minimizes its loss function L R y i ; g ð Þ by iteratively introducing base learners b mx j ð Þ ð Þ, defined based on the errors made by the current model F mÀ1x j ð Þ ð Þ to boost model performance. Because of its robust performance, it has also been used in many other application areas such as activity recognition and indoor localization (21)(22)(23)(24). A preliminary implementation of the GAMIN algorithm described up to this point showed a rapid convergence in the performance of discriminator D within the first few iterations and prevents generator G from further improving its performance.…”

Section: Algorithmmentioning

confidence: 99%

Predicting Commercial Vehicle Parking Duration using Generative Adversarial Multiple Imputation Networks

Low

Tekler

Cheah

2020

Transportation Research Record

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As the world rapidly urbanizes in pace with economic growth, the rising demand for products and services in cities is putting a strain on the existing road infrastructure, leading to traffic congestion and other negative externalities. To mitigate the impacts of freight movement within commercial areas, city planners have begun focusing their attention on the parking behaviors of commercial vehicles. Unfortunately, there is a general lack of information on such activities because of the heterogeneity of practices and the complex nature of urban goods movement. Furthermore, field surveys and observations of truck parking behavior are often faced with significant challenges, resulting in the collection of sparse and incomplete data. The objective of this study is to develop a regression model to predict the parking duration of commercial vehicles at the loading bays of retail malls and identify significant factors that contribute to this dwell time. The dataset used in this study originates from a truck parking and observation survey conducted at the loading bays of nine retail malls in Singapore, containing information about the trucks’ and drivers’ activities. However, because of the presence of incomplete fields found in the dataset, the authors propose the use of a generative adversarial multiple imputation networks algorithm to impute the incomplete fields before developing the regression model using the imputed dataset. Through the parking duration model, the activity type, parking location, and volume of goods delivered (or picked up) were identified as significant features influencing vehicle dwell time, corroborating with findings in the literature.

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“…The indoor localization of individuals is crucial for "smart city" applications, such as smart homes [1], elderly care monitoring [2], building emergency management [3], occupancy tracking in office spaces [4], smart building controls [5], and energy-saving grids [6]. It is of great value for improving residents' quality of life [1].…”

Section: Introductionmentioning

confidence: 99%

Indoor Localization Method of Personnel Movement Based on Non-Contact Electrostatic Potential Measurements

Man¹,

Zhang

Ma³

et al. 2022

Sensors

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The indoor localization of people is the key to realizing “smart city” applications, such as smart homes, elderly care, and an energy-saving grid. The localization method based on electrostatic information is a passive label-free localization technique with a better balance of localization accuracy, system power consumption, privacy protection, and environmental friendliness. However, the physical information of each actual application scenario is different, resulting in the transfer function from the human electrostatic potential to the sensor signal not being unique, thus limiting the generality of this method. Therefore, this study proposed an indoor localization method based on on-site measured electrostatic signals and symbolic regression machine learning algorithms. A remote, non-contact human electrostatic potential sensor was designed and implemented, and a prototype test system was built. Indoor localization of moving people was achieved in a 5 m × 5 m space with an 80% positioning accuracy and a median error absolute value range of 0.4–0.6 m. This method achieved on-site calibration without requiring physical information about the actual scene. It has the advantages of low computational complexity and only a small amount of training data is required.

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An alternative approach to monitor occupancy using bluetooth low energy technology in an office environment

Cited by 16 publications

References 7 publications

An End-to-End Point of Interest (POI) Conflation Framework

An End-to-End Point of Interest (POI) Conflation Framework

Predicting Commercial Vehicle Parking Duration using Generative Adversarial Multiple Imputation Networks

Indoor Localization Method of Personnel Movement Based on Non-Contact Electrostatic Potential Measurements

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