Indoor Localization: A Cost-Effectiveness vs. Accuracy Study

Mohebbi, Parisa; Stroulia, Eleni; Nikolaidis, Ioanis

doi:10.1109/cbms.2017.126

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…This is because, unlike motion sensors, Estimote errors are not bounded; although we are assuming that RSSI values higher than

dBm imply that the target is within one meter of the Estimote, that may not be always the case. According to our previous study [ 12 ], the RSSI value can vary drastically (over a range of 10 dBm or more), even when the target is stationary at a fixed distance; this did not happen frequently in our experiment (so the accuracy is still better when adding Estimotes), but it is sufficient to make the standard deviation slightly higher (28 cm on average when window size was 1). In other words, the coverage area of the Estimote thresholded at

dBm is “fuzzy”.…”

Section: Discussionmentioning

confidence: 87%

“…RSSI thresholding is applied to the Estimote data stream: a minimum threshold of

dBm is used to select the RSSI readings that are “significant” enough to be used for location estimation. This specific threshold value was motivated by experiments reported in [ 12 ]: roughly speaking,

dBm (or higher) RSSI strength suggests that the device is within approximately one meter of the transmitting beacon. In this fashion, the RSSI sensing effectively becomes an eponymous proximity sensor.…”

Section: The Location-estimation Methodsmentioning

confidence: 99%

“…The weights are described in 4 as

and

. In this study, their values were set to 0.7 and 0.9, respectively; these values are based on the observed accuracy of the Estimote and motion sensors in our previous studies [ 1 , 12 ], which revealed that PIR motion sensors can be more accurate than Estimotes.…”

Section: The Location-estimation Methodsmentioning

confidence: 99%

“…The approach we discuss in this paper considers only RSSI values higher (stronger) than

dBm. The choice for this threshold comes from our previous work [ 12 ], and reflects situations where the subject is very close (approximately within one meter) of the Estimote beacons we use. In this fashion, the RSSI values only matter when they are strong enough to act as a proximity sensors rather than as a model for distance calculation.…”

Section: Related Workmentioning

confidence: 99%

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Sensor-Data Fusion for Multi-Person Indoor Location Estimation

Mohebbi

Stroulia

Nikolaidis

2017

Sensors

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We consider the problem of estimating the location of people as they move and work in indoor environments. More specifically, we focus on the scenario where one of the persons of interest is unable or unwilling to carry a smartphone, or any other “wearable” device, which frequently arises in caregiver/cared-for situations. We consider the case of indoor spaces populated with anonymous binary sensors (Passive Infrared motion sensors) and eponymous wearable sensors (smartphones interacting with Estimote beacons), and we propose a solution to the resulting sensor-fusion problem. Using a data set with sensor readings collected from one-person and two-person sessions engaged in a variety of activities of daily living, we investigate the relative merits of relying solely on anonymous sensors, solely on eponymous sensors, or on their combination. We examine how the lack of synchronization across different sensing sources impacts the quality of location estimates, and discuss how it could be mitigated without resorting to device-level mechanisms. Finally, we examine the trade-off between the sensors’ coverage of the monitored space and the quality of the location estimates.

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“…This is because, unlike motion sensors, Estimote errors are not bounded; although we are assuming that RSSI values higher than

dBm is “fuzzy”.…”

Section: Discussionmentioning

confidence: 87%

“…RSSI thresholding is applied to the Estimote data stream: a minimum threshold of

Section: The Location-estimation Methodsmentioning

confidence: 99%

“…The weights are described in 4 as

and

Section: The Location-estimation Methodsmentioning

confidence: 99%

“…The approach we discuss in this paper considers only RSSI values higher (stronger) than

Section: Related Workmentioning

confidence: 99%

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Sensor-Data Fusion for Multi-Person Indoor Location Estimation

Mohebbi

Stroulia

Nikolaidis

2017

Sensors

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“…However, only in the last years, some descriptive and predictive models are emerging aiming specifically the better description of occupants for buildings energy modeling [72]. The description of the localization of occupants in real-time is fundamental for a large variety of smart buildings services; specifically, energy management and indoor environmental control [73]. For example, the proper load calculation due to occupants and their spatial distribution could avoid over-heating/cooling or under-heating/cooling of areas which is of a major importance especially for large public spaces [72,74].…”

Section: People Movement Between Zonesmentioning

confidence: 99%

Modeling occupant behavior in buildings

Carlucci

Simone

Firth

et al. 2020

Building and Environment

154

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In the last four decades several methods have been used to model occupants' presence and actions (OPA) in buildings according to different purposes, available computational power, and technical solutions. This study reviews approaches, methods and key findings related to OPA modeling in buildings. An extensive database of related research documents is systematically constructed, and, using bibliometric analysis techniques, the scientific production and landscape are described. The initial literature screening identified more than 750 studies, out of which 278 publications were selected. They provide an overarching view of the development of OPA modeling methods. The research field has evolved from longitudinal collaborative efforts since the late 1970s and, so far, covers diverse building typologies mostly concentrated in a few climate zones. The modeling approaches in the selected literature are grouped into three categories (rule-based models, stochastic OPA modeling, and data-driven methods) for modeling occupancy-related target functions and a set of occupants' actions (window, solar shading, electric lighting, thermostat adjustment, clothing adjustment and appliance use). The explanatory modeling is conventionally based on the model-based paradigm where occupant behavior is assumed to be stochastic, while the Revised Manuscript with No Changes MarkedClick here to view linked References data-driven paradigm has found wide applications for the predictive modeling of OPA, applicable to control systems. The lack of established standard evaluation protocols was identified as a scientifically important yet rarely addressed research question. In addition, machine learning and deep learning are emerging in recent years as promising methods to address OPA modeling in real-world applications.

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