Occupancy Detection via iBeacon on Android Devices for Smart Building Management

Corna, Andrea; Fontanab, L.; Nacci, Alessandro Antonio; Sciuto, D.

doi:10.7873/date.2015.0753

Cited by 67 publications

(51 citation statements)

References 12 publications

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“…With respect to indoor localisation and occupancy estimation, there are numerous approaches that target different area types. In [8], the authors present a system based on iBeacons for detecting the occupancy of a building. They have evaluated their system by predicting whether a user was inside or outside of a single room.…”

Section: Related Workmentioning

confidence: 99%

Bluetooth Low Energy Based Occupancy Detection for Emergency Management

Filippoupolitis

Oliff

Loukas

2016

2016 15th International Conference on Ubiquitous Computing and Communications and 2016 International Symposium on Cyberspace An

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Abstract-A reliable estimation of an area's occupancy can be beneficial to a large variety of applications, and especially in relation to emergency management. For example, it can help detect areas of priority and assign emergency personnel in an efficient manner. However, occupancy detection can be a major challenge in indoor environments. A recent technology that can prove very useful in that respect is Bluetooth Low Energy (BLE), which is able to provide the location of a user using information from beacons installed in a building. Here, we evaluate BLE as the primary means of occupancy estimation in an indoor environment, using a prototype system composed of BLE beacons, a mobile application and a server. We employ three machine learning approaches (k-nearest neighbours, logistic regression and support vector machines) to determine the presence of occupants inside specific areas of an office space and we evaluate our approach in two independent experimental settings. Our experimental results indicate that combining BLE with machine learning is certainly promising as the basis for occupancy estimation.

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Section: Related Workmentioning

confidence: 99%

Bluetooth Low Energy Based Occupancy Detection for Emergency Management

Filippoupolitis

Oliff

Loukas

2016

2016 15th International Conference on Ubiquitous Computing and Communications and 2016 International Symposium on Cyberspace An

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“…We gain this information through a variety of sensing methods, mainly device-based 5,6,11 or device-free. 13, 16, 23, 34, 38-40, 44, 45 Device-based methods utilize the devices that people carry with them (e.g., smart phone, GPS, etc.)…”

Section: Introductionmentioning

confidence: 99%

Occupant traffic estimation through structural vibration sensing

et al. 2016

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The number of people passing through different indoor areas is useful in various smart structure applications, including occupancy-based building energy/space management, marketing research, security, etc. Existing approaches to estimate occupant traffic include vision-, sound-, and radio-based (mobile) sensing methods, which have placement limitations (e.g., requirement of line-of-sight, quiet environment, carrying a device all the time). Such limitations make these direct sensing approaches difficult to deploy and maintain. An indirect approach using geophones to measure floor vibration induced by footsteps can be utilized. However, the main challenge lies in distinguishing multiple simultaneous walkers by developing features that can effectively represent the number of mixed signals and characterize the selected features under different traffic conditions. This paper presents a method to monitor multiple persons. Once the vibration signals are obtained, features are extracted to describe the overlapping vibration signals induced by multiple footsteps, which are used for occupancy traffic estimation. In particular, we focus on analysis of the efficiency and limitations of the four selected key features when used for estimating various traffic conditions. We characterize these features with signals collected from controlled impulse load tests as well as from multiple people walking through a real-world sensing area. In our experiments, the system achieves the mean estimation error of ±0.2 people for different occupant traffic conditions (from one to four) using k-nearest neighbor classifier.

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“…It can be used to develop indoor positioning systems [1] [2]. It can be used to build an indoor proximity estimation system to detect the number of moving objects in a room, and even gather the patterns of their movement [3]. Moreover, iBeacons can be also used as launching APPs on remote devices [4].…”

Section: Introductionmentioning

confidence: 99%

Using iBeacon for intelligent in-room presence detection

Yang

Pahlavan

2016

2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA

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iBeacon, a novel technique for proximity estimation, is utilized in our work to establish an intelligent in-room presence detection system. iBeacon is a kind of beacon device introduced in 2014 by Apple Inc. based on Bluetooth Low Energy (BLE) technology. The beacon signal can be broadcasted every certain interval. We collected data for line-of-sight (LOS) situation in in-room environment, and then recognize in-room presence according to path-loss readings. Data in other situations and environments such as obstructed-line-of-sight (OLOS) and outdoor scenario has been also considered to guarantee the in-room presence detection accuracy. Based on the empirical measurement results, we deeply investigate the system performance in terms of error detection possibility. This system is specifically promising for some particular purposes such as graduate seminar check-in, security system, in-and-out counting.

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Occupancy Detection via iBeacon on Android Devices for Smart Building Management

Cited by 67 publications

References 12 publications

Bluetooth Low Energy Based Occupancy Detection for Emergency Management

Bluetooth Low Energy Based Occupancy Detection for Emergency Management

Occupant traffic estimation through structural vibration sensing

Using iBeacon for intelligent in-room presence detection

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