Using Bluetooth Low Energy devices to monitor visitor activity in remote amenity spaces

Tracking of pedestrian behaviour, particularly route selection and temporal behaviours, can be difficult to undertake. This is especially true of studies at a community or campus level where the anonymity of pedestrians can be difficult to protect. The introduction of the EU’s General Data Protection Regulations 2016 (GDPR) has increased the complexity of this challenge. Advances in Bluetooth Low Energy (BLE) technology in recent years have increased the potential to monitor human behaviour by tracking and triangulating pedestrians. This paper describes an experiment undertaken along The Great South Wall at the Port of Dublin, which is considered a leading amenity location. Monitoring of visitor behaviour in places of this type can provide valuable information about the use of this and other public resources. The aims of this study were to test two prototypes to: i) determine the direction of participants carrying BLE devices, ii) determine the capabilities of two BLE scanning prototypes, (ESP32 & Raspberry Pi3), iii) test the ability of detecting a small number of BLE devices simultaneously while minimising interference or loss of passers-by data, iv) to investigate the use of a hash encoding scheme to anonymise BLE device identifiers. The findings show that the direction of the visitors to the pier can be detected by correlating the received signal strength indicator (RSSI) from multiple Bluetooth scanning devices and this can work where scanning devices are as close as 10m apart. The locations of the BLE scanners has a slight effect on detecting the RSSI from different distances and the distance between scanners needs to be considered to facilitate accurate measurement of direction. As a pier like the South Wall has only one entrance and exit point, this approach can also be used to determine the length of time spent on the pier. The technical performance of the two BLE scanners was also reviewed and the ESP32 was shown to have significantly lower power consumption with only a slight decrease in performance. Finally, it was shown that the BLE scanners can detect multiple carried BLE devices successfully without interference or loss of data as long as those devices are within range of the BLE scanners

Capturing the Behaviour of Volunteer Pedestrians in a Newly-Developed University Campus Using a Distributed Array of Bluetooth Low Energy Devices

Contemporary public infrastructure projects emphasise sustainable options that integrate pedestrian routes, leisure facilities and convenient access to public transport systems. It is important to understand the effectiveness of these contemporary designs. In the age of the General Data Protection Regulation (GDPR), there is a need to develop technologybased solutions that collect information about the behaviour of pedestrians in public spaces as they commute and engage in leisure pursuits while simultaneously preserving the privacy of these citizens. Bluetooth Low Energy (BLE), has privacypreserving features that make it worth considering as part of a technology solution for studies of this type. This work presents the preliminary results of a multi-stakeholder study that collected data via BLE from 28 volunteer pedestrians who regularly used the public domain of the newly developed Grangegorman campus in Dublin’s north inner city. Before the commencement of the data collection, each volunteer completed a short questionnaire about their intended movements on the campus, and for the next three weeks, they each carried a small keyring-sized BLE beacon with them as they passed through the campus. Bluetooth received signal strength indication from these beacons was collected at 17 points around the campus over the study period. The data for the volunteers were anonymised at the point of capture by hash encoding the MAC address of the beacons. The results of the work show that BLE can be used to monitor the approximate movements of volunteer pedestrians and so provide valuable privacy-preserving data on the utilisation of public infrastructure