Abstract
We report a 3-year city-wide study of an operational indoor sensing system based on Bluetooth Low Energy (BLE) called <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aBeacon</monospace> (short for <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> libaba <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Beacon</u> ). <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aBeacon</monospace> is pilot-studied, A/B tested, deployed, and operated in Shanghai, China to infer the indoor status of Alibaba couriers, e.g., arrival and departure at the merchants participating in the Alibaba Local Services platform. In its full operation stage (2018/01-2020/04), <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aBeacon</monospace> consists of customized BLE devices at 12,109 merchants, interacting with 109,378 couriers to infer their status to assist the scheduling of 64 million delivery orders for 7.3 million customers with a total amount of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula> 600 million order values. Although in an academic setting, using BLE devices to detect arrival and departure looks straightforward, it is non-trivial to design, build, deploy, and operate <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aBeacon</monospace> from its conception to its retirement at city scale in a metric-based approach by considering the tradeoffs between various practical factors (e.g., cost and performance) during long-term system evolution. We report our study in two phases, i.e., an 8-month pilot study and a 28-month deployment and operation in the wild. We focus on an in-depth reporting on the five lessons learned and provide their implications in other systems with long-term operation and broad geospatial coverage, e.g., Edge Computing.
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