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On-demand delivery is a fast developing business where gig couriers deliver online orders within a short time from merchants to customers. Couriers' accurate indoor locations play an essential role in the business. Most of the existing indoor localization methods cannot be applied in practice due to the high cost or data unavailable on off-the-shelf smartphones. This paper explores a new angle to solve the problem in a relative and infrastructure-free fashion. We design a person-to-person localization system that can (1) detect encounter events via Bluetooth on couriers' smartphones, and (2) infer couriers' relative locations to all the indoor merchants via deep learning on a graph neural network. The system is infrastructure-free, map-free, and compatible for off-the-shelf devices. We deploy the system on a real-world industry platform. The system runs on the smartphones of 4,075 couriers around 79 merchants for a month. The evaluation in a mall area shows that P2-Loc improves the mean average error compared with state-of-art infrastructure-based, report-based, and encounter-based methods. We also use an application analysis based on real-world orders and trajectory data to show that the P2-Loc can save around $40,000 for the platform every day with improved indoor localization results.

We report a 3-year city-wide study of an operational indoor sensing system based on Bluetooth Low Energy (BLE) calledaBeacon (short for alibaba Beacon). aBeacon 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),aBeacon 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 $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 operateaBeacon 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.

On-demand delivery is a rapidly developing business worldwide, where meals and groceries are delivered door to door from merchants to customers by the couriers. Couriers' real-time localization plays a key role in on-demand delivery for all parties like the platform's order dispatching, merchants' order preparing, couriers' navigation, and customers' shopping experience. Although GPS has well solved outdoor localization, indoor localization is still challenging due to the lack of large-coverage, low-cost anchors. Given the high penetration of smartphones in merchants and frequent rendezvous between merchants and couriers, we employ merchants' smartphones as indoor anchors for a new sensing opportunity. In this paper, we design, implement and evaluate SmartLOC, a map-free localization system that employs merchants' smartphones as anchors to obtain couriers' real-time locations. Specifically, we design a rendezvous detection module based on Bluetooth Low Energy (BLE), build indoor shop graphs for each mall, and adopt graph embedding to extract indoor shops' topology. To guarantee anchors' accuracy and privacy, we build a mutual localization module to iteratively infer merchants' state (in-shop or not) and couriers' locations with transformer models. We implement SmartLOC in a large on-demand delivery platform and deploy the system in 566 malls in Shanghai, China. We evaluate SmartLOC in two multi-floor malls in Shanghai and show that it can improve the accuracy of couriers' travel time estimation by 24%, 43%, 70%, and 76% compared with a straightforward graph solution, GPS, Wi-Fi, and TransLoc.