Experiences & Challenges with Server-Side WiFi Indoor Localization Using Existing Infrastructure

TL;DR

This paper explores the practical challenges of deploying server-side WiFi indoor localization in large venues with existing infrastructure, highlighting issues like cardinality mismatch and scan latency, and proposing heuristics to improve accuracy.

Contribution

It identifies key challenges in real-world server-side WiFi localization deployments and offers insights and heuristics to mitigate accuracy issues caused by device behavior and infrastructure constraints.

Findings

Ground truth data from four-year WiFi deployment shows significant AP reporting mismatch.

Client devices tend to reduce scans, decreasing localization accuracy.

Heuristics can partially mitigate accuracy loss due to reduced scans.

Abstract

Real-world deployments of WiFi-based indoor localization in large public venues are few and far between as most state-of-the-art solutions require either client or infrastructure-side changes. Hence, even though high location accuracy is possible with these solutions, they are not practical due to cost and/or client adoption reasons. Majority of the public venues use commercial controller-managed WLAN solutions, %provided by Aruba, Cisco, etc., that neither allow client changes nor infrastructure changes. In fact, for such venues we have observed highly heterogeneous devices with very low adoption rates for client-side apps. In this paper, we present our experiences in deploying a scalable location system for such venues. We show that server-side localization is not trivial and present two unique challenges associated with this approach, namely Cardinality Mismatch and High Client Scan Latency. The "Mismatch" challenge results in a significant mismatch between the set of access points (APs) reporting a client in the offline and online phases, while the "Latency" challenge results in a low number of APs reporting data for any particular client. We collect three weeks of detailed ground truth data (~200 landmarks), from a WiFi setup that has been deployed for more than four years, to provide evidences for the extent and understanding the impact of these problems. Our analysis of real-world client devices reveal that the current trend for the clients is to reduce scans, thereby adversely impacting their localization accuracy. We analyze how localization is impacted when scans are minimal. We propose heuristics to alleviate reduction in the accuracy despite lesser scans. Besides the number of scans, we summarize the other challenges and pitfalls of real deployments which hamper the localization accuracy.