arXiv CSopen access

AirLog: Store-Level Indoor Life Logging Made Easy

arXiv:2609.31864v1 [cs.NI] 25 Sep 2026

AirLog: Store-Level Indoor Life Logging Made Easy Zihui Yun

Jiaying Du

Yue Yu

University of Georgia Athens, Georgia, USA [email protected]

University of Georgia Athens, Georgia, USA [email protected]

University College London London, United Kingdom [email protected]

Zhewei Liu

Zhen Xiang

Longfei Shangguan

University of Toronto Mississauga Mississauga, Ontario, Canada [email protected]

University of Georgia Athens, Georgia, USA [email protected]

University of Pittsburgh Pittsburgh, Pennsylvania, USA [email protected]

Zhenlin An∗ University of Georgia Athens, Georgia, USA [email protected]

Abstract This paper presents AirLog, a smartphone-based life journaling system that automatically reconstructs users’ store visits in shopping malls and summarizes them into humanreadable journals. Unlike conventional indoor localization systems, AirLog avoids labor-intensive radio-map construction and dedicated wireless localization infrastructure and algorithm calibrations. Instead, it repurposes two cues already available in commercial spaces: semantic information exposed by ambient Wi-Fi SSIDs and indoor directory images. AirLog converts directory images into spatial maps and fuses Wi-Fi semantic anchors with inertial dead reckoning to recover store-level trajectories, which are then summarized into journals by an LLM. Such store-level life logs can support applications such as personal memory recall, activity reflection, and automated diary generation without requiring users to manually record where they have been. We implement AirLog on commodity smartphones and evaluate it on both a large-scale public dataset and a self-collected dataset. The results demonstrate that AirLog substantially improves store-level region recovery, semantic matching, trajectory reconstruction, and journal quality over existing baselines. A human evaluation further shows that the generated journals are coherent and faithful to users’ visits.

1

Introduction

Automatically recording daily experiences has long been a goal of mobile sensing and context-aware computing [17, 24]. In this paper, we focus on store-level indoor life logging: automatically reconstructing which stores or venues a user visited in a shopping mall, in what order, and for how long, ∗ Corresponding author.

and summarizing these visits into a human-readable journal. Such logs can support personal memory recall, visit retrospection, and context-aware assistants. As illustrated in Figure 1, instead of merely recording that a user spent two hours at a mall, a useful log could report that the user visited Macy’s, then Uniqlo, had lunch at the Cheesecake Factory, and later stopped by the Apple Store, together with the temporal order and dwell time of these visits. When users cannot remember the details of a past visit, they can also directly query the life log, e.g., “Where did I go for lunch after visiting Uniqlo?” At first glance, this task appears to be a straightforward extension of existing mobile lifelogging systems where coarsegrained mobility context is often obtained directly from GPS traces, digital maps, and nearby points of interest (POIs), after which an LLM can interpret or summarize the resulting context [45, 47, 62, 63, 69]. Indoors, however, the store-level trajectory itself is not readily available, largely because GPS is unreliable while conventional Wi-Fi localization typically requires site-specific surveys, fingerprint databases, or dedicated localization pipelines [4, 5, 25, 37, 68, 71]. Such requirements make store-level life logging difficult to scale across large and frequently changing commercial spaces. Our key observation is that commercial indoor environments already expose two useful spatial cues: (i): ambient Wi-Fi SSIDs, which often reveal semantic information about nearby stores [30, 52, 72]; and (ii): indoor directory images, which expose region locations and readable labels. This leads to our central question: Can these readily available but weak semantic cues be combined with smartphone motion sensing to recover store-level trajectories without relying on traditional Wi-Fi radio map or localization infrastructures? Answering this question introduces two challenges:

Yun et al. A day in the mall (Passive sensing) User carries a smartphone while visiting stores in a shopping mall.

Automatically generated life journal Airlog reconstructs store-level visits and summarizes them into a journal.

Yesterday (Nov 12, 2024) Ross Park Mall 10:02 AM

Wi-Fi 10:24 AM

IMU

10:02 10:20 (18 min) 10:24~ 10:48 (24 min) 11:15~ 11:58 (43 min)

11:15 AM

Floor-plan

12:05 PM Entrance

12:05~ 12:25 (20 min)

Macy's Browsed clothing and accessories.

Uniqlo Looked at jackets and tried.

Cheesecake Factory Had lunch.

Apple Store Explored products and left the mall.

Total time in mall: ~2 h 23 min Visited 4 stores

Querying the life log Users can ask retrospective questions about their past visits. After I went to Uniqlo yesterday, where did I go for lunch? You went to The Cheesecake Factory for lunch after visiting Uniqlo. ——— More examples ———

AutoLife (Prior Work)

AirLog (Ours)

General Daily Life

Wild Indoor Commercial Life SCOPE Fine Grained Floor Plan Image

GPS / GIS + Online Maps

Store-level Semantic Trajectory

Context-level

GRANULARITY Location Context

Wi-Fi Context

Store A (Visit)

Motion Context

Wi-Fi

IMU

Map/POI

Figure 1: AirLog turns passive smartphone sensing into a store-level indoor life log. (A) While the user visits a shopping mall, the smartphone passively collects Wi-Fi scans and inertial measurements, and a publicly available floor-plan image is used as spatial context. (B) AirLog reconstructs the store-level trajectory—what stores were visited, in what order, and for how long—and summarizes it into a human-readable journal. (C) The life log is queryable: users can ask retrospective questions, such as where they went for lunch after visiting a specific store.

(1) public indoor maps are rarely available as machine-readable region layers. While outdoor map services provide structured roads, venue boundaries, and POIs, indoor floorlevel information often exists only as directory images rather than labeled region geometry that downstream modules can query [21, 22, 38, 39]. Direct reasoning over these images is unreliable because current visionlanguage models (VLMs) still struggle with dense map text and fine-grained spatial relationships [61]. (2) Ambient Wi-Fi provides semantic but inherently ambiguous location evidence. An SSID may reveal that a particular store is nearby, but it does not uniquely determine whether the user is inside that store, walking past it, or located in an adjacent venue. RSSI further varies with multipath, crowd blockage, device orientation, and heterogeneous AP placement. Thus, Wi-Fi should be treated as a probabilistic semantic anchor rather than a direct location estimate. In this paper, we propose AirLog, a smartphone-based system for store-level indoor life journaling. As shown in Fig. 3, given passive smartphone Wi-Fi scans, inertial motion recordings, and a crowd-accessible floor-plan image, AirLog infers which stores a user likely visited, in what order, and for how long, and then summarizes these visits into a life journal. Rather than asking an LLM to infer the entire journal from raw sensor traces and a floor-plan image, AirLog decomposes the task into three structured tasks. • First, the map layer converts a human-facing indoor directory image into a labeled region map. The parser serializes

TECHNICAL DESIGN LLM/VLM Life Fusion Journal

Store B Waiting Area Store C (Visit) (Wait) (Visit)

Multi-Agent + Map Constraints Agentic System

LLM/ VLM Context Fusion

What did I do after Macy's? After Macy's, you visited Uniqlo, then had lunch at The Cheesecake Factory.

Corridor (Pass)

Wi-Fi Motion Map Planning Semantic Agent Context Agent Agent Agent

Review Agent

Store-level Trajectory

Figure 2: From general daily life logging to store-level indoor life journaling. AutoLife captures coarse multimodal context, while AirLog uses structured indoor maps and map-constrained multi-agent reasoning to reconstruct store-level trajectories.

this map as a GeoJSON FeatureCollection1 , allowing later modules to query region geometry and labels instead of reasoning over raw pixels. The parser itself does not emit corridor polygons, walkable areas, or connectivity. A separate deterministic map-preparation stage derives the corridor mask and trajectory graph used by the fusion layer. However, indoor directory images are designed for human viewing rather than machine parsing. VLMs can miss dense labels or distort region boundaries, while generic segmentation and traditional CV methods are brittle to text, icons, reflections, and perspective distortion [8, 55]. AirLog therefore uses a geometry-first map parser: GPT Image 2 canonicalizes the visual layout into a clean region mask, deterministic CV aligns and vectorizes the regions, and GPT-4o labels one polygon-masked crop at a time before writing the returned label set directly to the source GeoJSON feature. • Second, the trajectory layer grounds Wi-Fi and motion observations onto the map to recover store visits. Wi-Fi provides useful semantic hints: an SSID such as Uniqlo_Guest may suggest a nearby Uniqlo. However, the SSID names can be ambiguous, abbreviated, and do not reveal whether the user actually entered the store, walked past it, or visited a neighboring store. AirLog therefore treats SSIDs as probabilistic semantic anchors rather than direct location estimates. It filters and aggregates Wi-Fi scans, derives pedestrian dead reckoning (PDR) motion from inertial measurements, and jointly evaluates candidate store sequences against map connectivity, walking distance, and motion continuity. The decoded semantic trajectory is then reviewed against these tool outputs; 1 GeoJSON is an industry-standard JSON-based format for encoding map

features and their geometry [7]. In AirLog, each parser-produced feature represents one region using Polygon or MultiPolygon geometry and stores its readable strings in properties.labels.

AirLog : Store-Level Indoor Life Logging Made Easy

detected semantic or physical inconsistencies are fed back into the loop to improve the final trajectory. • Third, the journal layer converts the validated trajectory and motion sensor data into a human-readable account of the visit. A store trajectory alone does not distinguish, for example, spending 25 minutes shopping inside a store from briefly passing its entrance. AirLog therefore combines dwell time, POI type, motion-derived activity cues, and trajectory confidence to derive a sequence of supported behavior events. An LLM then summarizes this validated sequence into a journal, rather than generating entries directly from raw Wi-Fi or sensor observations. We evaluate AirLog with two main data sources: largescale Microsoft public indoor traces containing over 19.7k labeled Wi-Fi scan samples [35], and a self-collected six-city corpus containing 374,567 Wi-Fi observations over 603.0 active hours. From the latter, we construct 185,140 candidate SSID–POI pairs for semantic analysis. The public traces support quantitative semantic trajectory recovery, while the self-collected data support SSID semantic validation and end-to-end journal generation validation. We test AirLog’s local deployment on edge devices, where sensing data can be collected during the day and processed locally afterward. On-device execution takes longer than cloud, but it preserves privacy by keeping raw data and journals on the user’s device, while smaller models still achieve comparable accuracy. In summary, this paper makes the following contributions: • We propose AirLog, a novel agentic platform for surveyfree, site services-free indoor life journaling in wild environments using smartphone sensing and crowd-accessible map artifacts. • We design a geometry-first map parser that converts humanfacing indoor directory images into labeled region maps serialized as GeoJSON, making region polygons and their readable labels directly queryable by downstream modules. • We introduce a grounded semantic trajectory and journaling layer that treats ambient Wi-Fi SSIDs as semantic landmarks, fuses them with PDR and floor-plan topology through graph-constrained inference, and generates third-person journals from validated visits rather than raw sensor context.

2 Related Works 2.1 Life Journaling Lifelogging systems transform daily observations into records for behavior understanding [6, 13, 17, 24]. Existing approaches broadly use either vision-based or non-visual sensing. Wearable cameras and smart glasses capture rich egocentric context [23, 28, 32, 58, 65], but incur high processing costs and privacy concerns, while smartphone-based approaches exploit motion, wireless, audio, and location signals [31, 54,

56, 70]. Recent LLM-based systems further translate heterogeneous sensor data into open-vocabulary event descriptions [2, 11, 29, 36, 45, 51, 62, 63], but largely rely on promptlevel context and target outdoor mobility or coarse activities. As illustrated in Fig. 2, AirLog instead grounds Wi-Fi and motion evidence on indoor map structure to recover store-level semantic trajectories before journal generation.

2.2

Indoor Localization

Existing indoor localization methods [1, 12, 25, 48, 71] broadly follow fingerprinting- or model-based paradigms. Fingerprinting methods require site-specific radio measurements and machine learning [4, 5, 26, 68], while ranging- or AoAbased methods depend on calibrated infrastructure and assumptions specific to each deployment [60, 71], making both costly to deploy across changing commercial environments. Prior survey-free localization systems avoid manual site surveys through crowdsourcing, PDR, map constraints, and opportunistic landmarks, but still primarily target metric coordinates and typically benefit from observations accumulated across users or visits [50, 53, 57, 66]. AirLog instead targets site-free semantic localization, inferring visited stores from a single visit without site-specific fingerprints or accumulated crowdsourced observations. Wi-Fi SSIDs provide a promising survey-free semantic signal by encoding business names and location-related tokens [30, 52, 72]; AirLog revalidates this signal at scale and fuses it with PDR and map topology to recover store-level trajectories rather than isolated place labels.

2.3

Map Understanding with VLMs

Recent work uses VLMs for map reading, landmark reasoning, and navigation from floor-plan images [9, 33, 74], yet current LVLMs struggle to jointly handle OCR, topology, and route planning [61]. Meanwhile, public indoor-map services rarely expose floor-level geometry and topology as queryable layers [21, 22, 38, 39], and recovering queryable regions and labels from human-facing maps remains challenging. AirLog addresses these gaps by converting directory images into labeled GeoJSON region maps for reasoning over explicit polygons and labels. Existing map-parsing pipelines combine OCR and classical segmentation [34, 55, 64], but remain brittle on photographed directories with text, icons, legends, reflections, and perspective distortion; AirLog instead canonicalizes region geometry with image generation, vectorizes polygons using deterministic CV, and labels each region with a per-region semantic subagent [16].

3

A Glimpse of AirLog

Imagine Bob visiting Rose Park Mall on a Saturday afternoon. Before entering, he photographs the mall directory. He first enters Uniqlo and browses, then walks past KFC, and later

Yun et al.

Floor Plan Generation Readable Map

Semantic Trajectory Trajectory

Journal Generation

Floor Plan (image)

GeoJson

Trajectory Construction

…

Sensors ✓ Dining (Eating)

POI: McDonald's Duration: 1,200s ✓ Retail (Shopping)

4

Map Parsing

…

Behavior Inference

Journal Generation

In the late morning, the user enters a shopping mall…

Life journal

Figure 3: AirLog overview. AirLog decomposes life logging into three structured layers: (1) Labeled Region Map Construction: a geometry-first parser converts an indoor directory image into a labeled region map serialized as GeoJSON. (2) Trajectory Reconstruction: a trajectory agent grounds sensor data and Wi-Fi semantics using downstream map constraints to infer store-level movement. (3) Journal Generation: a journal agent summarizes the validated trajectory into a third-person life journal.

spends about 25 minutes at Starbucks. Bob never checks in or labels these visits; during the trip, his phone records only passive Wi-Fi scans and IMU readings. To reconstruct Bob’s path, AirLog must first convert this directory photo into a labeled region map (§4). Although readable to Bob, the raw image cannot directly serve as map context for the trajectory agents, which need explicit store geometry and POI labels. The resulting GeoJSON stores labeled region polygons for Uniqlo, KFC, and Starbucks so later modules can combine the map with Bob’s Wi-Fi and motion observations. As Bob walks, his phone periodically observes SSIDs, including Uniqlo_WiFi, KFC_FREE_WiFi, and Starbucks_WiFi. However, these SSID observations are sometimes ambiguous: seeing KFC_FREE_WiFi, for example, does not imply that Bob entered KFC: he may simply be walking past it. AirLog therefore treats Wi-Fi observations as probabilistic semantic anchors, uses PDR to estimate how Bob moves between observations, and jointly decodes them under the floor-plan topology. Said differently, the Wi-Fi SSIDs indicate where Bob may be, motion sensing indicates how Bob moved, and the map constrains where Bob can move. These cues together can help distinguish a brief pass near KFC from sustained visits to Uniqlo and Starbucks (§5). Finally, AirLog aggregates the reconstructed trajectory into store-level visits using dwell time and motion cues, and summarizes the validated sequence into a human-readable journal, e.g., “In the afternoon, Bob visited Uniqlo, walked through the dining area, and later spent about 25 minutes at Starbucks.” (§6).

Constructing Labeled Region Maps from Floor-Plan Images

We begin with the map-representation bottleneck in Bob’s example. His floor-plan image contains the store-level information needed to reconstruct his visit—where Uniqlo, KFC, Starbucks, and other POIs are located—but exposes this information only as pixels. As shown in Figure 4(a)– (b), a public map may identify the mall while providing no queryable floor-level POI geometry, whereas the floor-plan image visually encodes both store regions and their names. For the trajectory reconstruction in the next section, however, AirLog needs more than an image: it needs an explicit mapping from each POI label to its polygon so that downstream modules can query where stores are and reason about their spatial relationships. Challenge: recovering both region geometry and readable labels. Turning a human-facing floor-plan image into such a representation requires solving two coupled problems. First, region geometry must be recovered from a visually cluttered image. Store boundaries are mixed with text, logos, icons, legends, decorative backgrounds, and weak separators. Second, each recovered region must be associated with the correct readable label set. Recognizing a store name alone is insufficient: the system must know exactly which region that name describes. A mistake in either geometry or semantic association produces an incorrect map and can subsequently mislead trajectory reconstruction. Why conventional and direct approaches fall short. The closest prior work on this problem is the shopping-mallplan parser of Su et al. [55]. Their pipeline first recognizes a mall directory, applies threshold/edge preprocessing and two-stage region growing to segment individual rooms, and then uses OCR-recognized room identifiers to retrieve the corresponding room names. This work establishes that explicitly coupling segmentation and recognition can recover both geometry and semantics from structured shopping-mall plans. AirLog advances this line of work to more heterogeneous human-facing floor-plan images, where text, logos, icons, legends, reflections, weak separators, and perspective distortion make classical region growing and OCR-based matching brittle. Rather than requiring an OCR-readable room identifier that can be matched back to a directory, AirLog canonicalizes the visual layout into explicit region geometry and then assigns semantics directly to each recovered region with a per-region semantic subagent. Other direct alternatives expose complementary limitations. Classical color-filling methods can merge neighboring stores or retain non-POI structures when colors and backgrounds vary, while edge detection can mistake text strokes

AirLog : Store-Level Indoor Life Logging Made Easy (a) Public Map

(b) Floor-plan regions (GT)

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(c) SAM 3

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(c) AirLog (Ours)

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(c) CV based Mask Alignment

(d) CV based Region polygons

(B) Semantic pass (a)Input floor-plan crop Apply each polygon as a mask

15 16

Phone Mania pandora (e) Polygon-masked crop

(f) Semantic subagent VLM

LUNA

FeatureCollection { "properties": { "labels": ["Nordstrom"] } "geometry": {"type":"Polygon" , ... }}}

17

Sensitive to noise

Multiple Background Stores Merged wrongly recognized

Stores missing

Icons Wrong Recognized

Sensitive to noise

Figure 4: From public maps to labeled region maps. (a) The public map provides venue-level context but lacks floor-level region geometry. (b) The floor-plan image contains the annotated regions and readable labels. (c) SAM 3 absorbs background structures and merges adjacent regions. (d) Color Filling is distracted by icons and other visual structures. (e) Boundary Detection extracts text and noise as candidate boundaries. (f) AirLog more closely recovers the annotated region geometry and labels. This example is qualitative; aggregate results appear in Table 1.

and decorations for region boundaries. Generic segmentation models such as SAM 3 [8] face a different mismatch: they are designed primarily for object-like regions in natural images rather than tenant-scale graphic regions, and can therefore absorb background structures or merge adjacent POIs. Semantic extraction alone does not solve the problem either. Standalone OCR can miss small, stylized, rotated, or low-resolution labels, and even a correctly recognized string does not specify which region polygon it belongs to. A fullmap VLM faces a related coupling problem: it must recognize every POI, predict each label’s position, and bind that position to a region. A correctly recognized name can therefore still be omitted, misplaced, or attached to the wrong polygon. Figure 4 illustrates representative geometry failures. Appendix A.4 pairs two exact-region OCR failures in Figure 18 and places the coordinate-binding and geometry-to-semantics mechanisms together in Figure 19, beside the claims that they support. These observations motivate a geometry-first, sequential factorization. Instead of asking one model to jointly recover region boundaries, recognize readable labels, predict label coordinates, and bind labels to regions, AirLog first recovers explicit region polygons and then uses each polygon to define one constrained semantic task. As shown in Figure 5, the geometry stages in (a)–(d) produce a geometry-only region map serialized as GeoJSON. Each recovered polygon is then projected onto the original floor-plan image to produce the polygon-masked crop in (e). The per-region semantic subagent processes one crop at a time in (f), and its returned label set is written directly to the same GeoJSON feature

Figure 5: Geometry-first map-parsing pipeline. From (a) an input floor-plan crop, AirLog generates (b) a clean region mask, performs (c) mask alignment, and extracts (d) region polygons. Each polygon produces (e) a polygon-masked crop for (f) the per-region semantic subagent. The returned label set is written directly to its source feature, producing (g) a labeled region map in GeoJSON.

in (g). The current pipeline therefore predicts no label coordinate and requires no separate polygon–label binding stage.

4.1

Geometry Pass: Recovering Regions

The geometry pass addresses the first question: where are the regions? Directly extracting boundaries from Figure 5(a) is unreliable because region geometry is entangled with text, logos, icons, and background variation. AirLog therefore first canonicalizes the map’s visual appearance before extracting precise geometry. Starting from the input crop in (a), AirLog uses GPT Image 2 [43] to generate the clean region mask in (b). The model redraws floor-plan regions as solid regions over a uniform background while suppressing text, icons, legends, and other visual clutter. The generated colors carry no POI semantics; they only make individual spatial regions explicit. This use of image generation is intentional: the generative model handles heterogeneous appearance, while deterministic processing remains responsible for spatial precision. The generated mask cannot be used directly because image generation may introduce a small global translation or scale change relative to the source floor-plan image. AirLog therefore aligns structural gradients between the generated mask and source image under a restricted transformation consisting of uniform scaling and horizontal and vertical translation, producing (c). Rotation and non-rigid deformation are excluded, and the system falls back to the identity transformation when reliable alignment cannot be established. Deterministic CV then separates the uniform background from foreground blocks, extracts 4-connected components, traces exterior contours and holes, and converts them into valid Polygon or MultiPolygon features. Candidates with insufficient support in the original image are removed. The output in (d) is a geometry-only map that explicitly represents the floor-plan regions but does not yet label them.

Yun et al.

4.2

Per-Region Semantic Subagent

Once the region polygons are fixed, AirLog uses them to constrain semantic labeling. Here, per-region describes the invocation unit: the subagent processes one recovered region at a time, while the region polygon is used only to construct its input crop. For each polygon, the system rasterizes its exact geometry over the original floor-plan image, retains source pixels inside the polygon, suppresses pixels outside the polygon and inside any holes, and crops the result to the polygon’s bounding box. This produces one polygon-masked crop per GeoJSON feature while preserving the printed POI name and excluding neighboring labels, legends, and decorations. The current system invokes this GPT-4o semantic subagent once for each polygon-masked crop. Under the shared visible-label policy, the subagent returns "labels":[...] containing all readable strings in the target region. Because each crop is generated from a known polygon, the returned labels are written directly to that same feature. In contrast to the earlier full-map baseline, the current method predicts no label coordinates and requires no point-in-polygon, nearestneighbor, or learned cross-region binding stage.

4.3

Generate GeoJSON

After all recovered regions are labeled, AirLog exports a GeoJSON FeatureCollection. Each feature contains Polygon or MultiPolygon geometry, along with a canonical label field, properties.labels, containing the recognized strings. The parser does not emit name, category, confidence, corridor polygons, walkable areas, traversability, or connectivity fields. The result is a machine-queryable labeled region map. For Bob’s example, Uniqlo, KFC, and Starbucks become labeled region polygons that later modules can query directly. A separate deterministic map-preparation stage derives the corridor mask and navigability graph used in the next section; these downstream artifacts are not part of the parser’s GeoJSON output.

5

Semantic Trajectory Reconstruction

Given the labeled region map obtained from §4, AirLog next reconstructs the user’s store-level trajectory from Wi-Fi and inertial sensing. Rather than asking an agent to directly predict locations, in AirLog we propose a multi-agent pipeline (§5.1) that coordinates semantic Wi-Fi anchoring (§5.2), PDRbased motion estimation (§5.3), and map-constrained inference (§5.4) to construct a trajectory that is both semantically plausible and physically feasible.

Planning Agent (Re-planning & Active Sensing)

1. Wi-Fi Semantic Agent (Location Context)

Feedback for Re-planning

Collect Data

2. Motion Agent (Motion Context)

Semantic Wi-Fi LLM Location Scans Reasoning Context

Location Context

Set Next Goal / Path

IMU Motion Motion Data Estimation Context

Motion Context

3. Map Context Agent (Spatial Constraints)

GeoJSON Navigability Map Graph

Map Constraints

4. Context Fusion & Review Agent (Review, Reasoning & Fusion) Review & Understand

Viterbi Inference Tool

Supporting Data / Tools

POI / Store Database Venue Information Time & Calendar

Trajectory Estimate

Observation / Inference Result

Figure 6: Trajectory Reconstruction in AirLog. The framework organizes three complementary contexts for indoor trajectory recovery: Wi-Fi Semantic Agent derives semantic location context, Motion Agent derives motion context, and Map Context Agent derives GeoJSON map constraints. The Context Fusion & Review Agent integrates these inputs and invokes structured inference to recover a geometrically consistent, store-level trajectory.

5.1

Agent-Orchestrated Trajectory Reconstruction

Figure 6 illustrates the agent-orchestrated context-fusion pipeline. Before processing each episode, a Planning Agent selects and schedules the required modules in a ReAct-style loop [67], revising the plan when evidence is missing or conflicting. AirLog organizes the resulting evidence into three complementary context streams: Wi-Fi semantic context for location candidates, motion context for relative displacement and heading changes, and map context for physically admissible transitions. A structured fusion decoder combines these contexts to recover a globally consistent trajectory, while a review stage detects failures and triggers targeted replanning when necessary.

5.2

Wi-Fi Semantic Agent

Validating SSID semantics in the wild. Prior work has shown that Wi-Fi SSIDs can encode location and service information [52]. We re-evaluate their usefulness in current commercial environments using Wi-Fi observations collected across six major cities (Table 6). Associating businessclass SSIDs with nearby OpenStreetMap POIs yields 185,140 candidate SSID–POI pairs. As summarized in Table 7, SSID semantics remain observable at scale, but lexical matching alone is too sparse and noisy to serve as a direct signal (representative failure cases in Table 8). We therefore treat SSIDs as weak semantic evidence and jointly reason over SSID strings, RSSI rank, POI names, and map context. We further verify that SSID distributions are venue- and floor-specific using cross-session observations from a public benchmark [35], spanning over 5.5 hours of data across 2 venues and 14 floors. Simple SSID-set Jaccard matching achieves 87.88% accuracy in identifying the correct site–floor pair, suggesting that

AirLog : Store-Level Indoor Life Logging Made Easy

SSIDs provide a useful cue for automatic floor identification without site-specific surveys. 5.2.1 SSID Semantic-noise filtering. Although SSID strings provide useful place-level cues, raw Wi-Fi scans contain substantial semantic noise that can mislead the anchoring agent and inflate the LLM context. We make two observations from our traces. • Generic infrastructure SSIDs (e.g., T-mobile, China-mobile) are prevalent but they provide less location information. Many visible SSIDs correspond to carrier hotspots, venuewide guest networks, or router-default names. These networks provide little store-specific evidence and would increase LLM input length, so we remove them with a keyword blocklist. • The strongest RSSI is not necessarily the nearest or most useful POI cue. Indoor RSSI is unstable under multipath propagation, wall attenuation, crowd blockage, AP placement, and transmit-power variation [25, 71]. Therefore, AirLog does not assign location using the strongest SSID alone. After filtering, we aggregate scans into sliding windows, represent each SSID by its maximum RSSI within the window, and pass the top-ranked SSID-RSSI pairs to the LLM as soft semantic evidence. This allows the model to reason over multiple possible store cues instead of relying on a single nearest-signal rule. Empty windows are dropped, and gaps longer than 600 s are flagged to avoid incorrect PDR integration across disconnected sessions. 5.2.2 LLM-based semantic anchoring. After semantic-noise filtering, each Wi-Fi window is represented by a compact set of top-ranked SSID–RSSI pairs. AirLog then converts this evidence into store-level semantic anchors. We formulate SSID-to-POI grounding as a language understanding task [30]: the model must interpret noisy SSID strings, abbreviations, guest-network names, and RSSI ranks in the context of the venue’s POI list and floor-plan topology. For each window, AirLog constructs a structured prompt containing three inputs: the venue’s POI names, map-derived spatial descriptions, and the filtered top-𝑁 SSID–RSSI pairs. The prompt instructs the model to treat RSSI as relative proximity evidence rather than a deterministic distance measurement; the full prompt is shown in Appendix D.2. To provide spatial grounding, we augment each POI with a natural-language descriptor derived from the navigability graph. The descriptor summarizes the POI’s relative position and nearby stores, e.g., “Uniqlo is north of KFC and adjacent to the main corridor entrance,” as illustrated in Figure 7. These relational cues help the model disambiguate SSIDs that are lexically similar or spatially overlapping, and prevent it from treating all name matches as equally plausible. Rather than forcing a single location decision, the model outputs all

Spatial Context Description: Uniqlo: North of KFC, adjacent to the main corridor entrance. Zara: Next to Starbucks, across from the information desk. H&M: Between Starbucks and Sephora … Map Nodes: [Uniqlo, KFC, Zara, Starbucks, H&M, ...]

Spatial Context Description & POI Context

Window size: Δw=1s

--- WIFI WINDOW (T=25.0s) --1 KFC_FREE_WiFi : -48 dBm 2 Uniqlo_Store : -54 dBm 3 MALL_FREE_WIFI : -65 dBm 4 TP-Link_AP_98 : -78 dBm 5 ChinaNet_Auto : -82 dBm *... (5 more signals omitted)*

LLM

{ "results": [ { "time": 0.5, "locs": [ {"name": "Uniqlo", "confidence": 0.9}, {"name": "KFC", "confidence": 0.4} ] }, ... ]

Filtered Wi-Fi Window System Role: You are a precise positioning assistant. Output ONLY valid JSON. SYSTEM TASK: Identify ALL possible store locations for each timestamp... MAP NODES: [Uniqlo, KFC, Zara, Starbucks, ...] INSTRUCTIONS: 1. List ALL SSIDs that match the Map Nodes. 2. Do NOT limit to one store per timestamp.

Prompt

}

Structured JSON Output

Figure 7: LLM-based semantic anchoring pipeline. Wi-Fi RSSI observations, spatial context, and POI context are combined into a structured prompt, producing multi-label POI hypotheses with confidence scores.

plausible nearby stores with confidence scores between zero and one. The resulting predictions are post-processed into semantic anchors. Each predicted POI name is matched to the corresponding GeoJSON feature and mapped to its polygon centroid or store region in the navigability graph. Generic outputs such as floor identifiers, restroom tokens, and corridor labels are discarded. Each surviving tuple (𝑡, 𝑛, 𝑐), consisting of timestamp 𝑡, store name 𝑛, and confidence 𝑐, becomes a probabilistic claim that the user was near store 𝑛 at time 𝑡. These sparse anchors provide semantic location evidence, which complements the continuous but drift-prone PDR estimate used next.

5.3

Motion Context via PDR

Wi-Fi semantic anchors provide place-level cues but are sparse and intermittent, leaving gaps between valid observations. AirLog bridges these gaps with continuous relative motion from the phone’s inertial sensors using a standard PDR pipeline [25, 31, 59]: step detections and rotation-vectorderived headings are integrated into relative displacement and heading change between consecutive sensing windows. Wi-Fi anchors provide semantic positions, while PDR provides continuous motion between them. The earliest highconfidence anchor can initialize the PDR path when no surveyed starting point is available. Residual heading errors are corrected during fusion using well-separated anchors. PDR remains a motion context rather than a store-level trajectory because its errors accumulate over time [37].

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5.4

Context Fusion & Review Agent

Prior Wi-Fi IMU floor-plan fusion systems can learn dense location histories by training fusion models over collected traces [27]. AirLog targets a different setting: wild indoor venues where no site-specific fingerprint survey or training trajectory is available. We therefore use a semantic-priority Viterbi decoder that performs explicit probabilistic optimization over the GeoJSON-derived navigability graph G [15, 49], as shown in Figure 8. The decoder combines three scores. WiFi semantic anchors provide the emission term: nodes near a high-confidence candidate store receive higher likelihood. PDR provides the transition term: candidate graph edges are favored when their distance and heading agree with the observed displacement and turn angle. The map provides the hard topological constraint: Viterbi only considers adjacent nodes in G, which restricts the search to physically walkable routes. We summarize the decoded trajectory as ∑︁  𝑆ˆ1:𝑇 = arg max 𝛽𝐸𝑡 (𝑆𝑡 ) + 𝛼𝐴𝑡 (𝑆𝑡 −1, 𝑆𝑡 ) 𝑆 1:𝑇 ∈ G

WiFi Semantic (Emission) How well each node matches semantic anchors

IMU Motion (Transition) How well movement matches distance & heading on map

Semantic-Priority Viterbi Fusion

Figure 8: Semantic-priority Viterbi fusion. Wi-Fi semantic anchors score where the user is likely to be, PDR motion primitives score how the user could move, and the GeoJSON graph restricts the search to physically walkable transitions. The decoder first estimates a globally consistent trajectory, which is then reviewed by the multimodal feedback agent. 15:07-15:42

(1)

where 𝐸𝑡 is semantic consistency with Wi-Fi anchors, 𝐴𝑡 and 𝐷𝑡 are IMU heading and distance consistency, and 𝐶𝑡 is an anchor-based heading-calibration bonus when consecutive high-confidence semantic anchors imply a reliable direction. Concretely, for a candidate transition from node 𝑢 to node 𝑣, with edge direction 𝜃 (𝑢, 𝑣), edge length ℓ (𝑢, 𝑣), IMU motion primitive (𝑑𝑡 , Δ𝜙𝑡 ), and active semantic anchors 𝐿ˆ𝑡 = {(𝑛 𝑗 , 𝑐 𝑗 )}, these terms are ! ∑︁ dist(𝑣, P𝑛 𝑗 ) 2 𝐸𝑡 (𝑣) = 𝑐𝑗 − , (2) 2𝜎 2 ˆ (𝑛 𝑗 ,𝑐 𝑗 ) ∈ 𝐿𝑡

𝐴𝑡 (𝑢, 𝑣) = log max (cos(𝜃 (𝑢, 𝑣) − Δ𝜙𝑡 ), 𝜖) , 2

𝐷𝑡 (𝑢, 𝑣) = − (𝑑𝑡 − ℓ (𝑢, 𝑣)) .

(3) (4)

Here P𝑛 𝑗 denotes the polygon or centroid of the POI named by anchor 𝑛 𝑗 , and 𝜎 controls the spatial bandwidth of semantic evidence. In short, Wi-Fi estimates where the user is likely to be, IMU estimates how the user moved, and the map defines where the user can move. Review and Feedback Loop: This optimization produces an initial trajectory estimate, which AirLog then evaluates using diagnostic signals such as position jumps, stuck segments, and PDR-to-map scale drift. If these indicate a lowquality decode, a deterministic grid search re-runs Viterbi over preset (𝛼, 𝛽, 𝛾, calib_weight) configurations and keeps the lowest-variance result; if issues persist, the Planning Agent inspects the flagged windows using read-only Wi-Fi, anchor, spatial, and PDR context and selects one bounded corrective action, such as re-running semantic anchoring with adjusted parameters or known SSID mappings, reweighting

Most Likely Path

Viterbi finds the most likely path that best explains both WiFi semantics and IMU motion under map constraints

14:12-14:57

16:00-16:51

17:15-17:41

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𝑡

 + 𝛾𝐷𝑡 (𝑆𝑡 −1, 𝑆𝑡 ) + 𝐶𝑡 (𝑆𝑡 −1, 𝑆𝑡 ) ,

Topological Map (Constraints) Only allow transitions along walkable edges

"A productive afternoon! After grabbing a latte at Starbucks, The user spent some time at Zara and then headed to Apple to check out the new gadgets..." Life journal LLM

Reconstructed Trajectory & Map Information LLM

POI: Food Court Behavior: Browsing Dwell Time: 3 min (18:30-18:33) Motion Disambiguation LLM

PDR/ IMU signals

"browsing","passing",... (low linearity, few steps)

Candidate Motion

Behavior Inference

Motion Calibration

Figure 9: Workflow of Diary Generation. The reconstructed trajectory is aggregated into store-level visits by a behavior inference module (A). A motion calibration module (B) then refines each activity label using PDR geometry and location context. Finally, a diary agent (C) synthesizes the calibrated behavior sequence into a third-person Life Journal.

the fusion decoder within a fixed valid range, falling back to substring-based Wi-Fi matching, patching a single behavior label, or accepting the result. Only Wi-Fi anchoring and fusion may be revisited, each within a small retry budget, while map parsing and PDR are fixed; the investigation itself is capped by a round limit and a wall-clock budget, after which the best result found is used. The Planning Agent’s decision calls use a low sampling temperature, so repeated runs can select different parameter values within these bounded ranges rather than always reaching an identical trajectory.

6

Journal Generation

Spatial fusion produces a corrected trajectory, but the result remains a localization sequence rather than a humanreadable account. As shown in Fig. 9, AirLog transforms this sequence into a diary through three stages: behavior

AirLog : Store-Level Indoor Life Logging Made Easy

inference (A), motion calibration (B), and journal generation (C).

6.1

Behavior Inference

As shown in Fig. 9(A), behavior inference converts the fused trajectory into store-level visits. For each POI, AirLog merges consecutive contacts inside or near the store region, producing an enter time, exit time, dwell duration, semantic confidence, and local motion summary. Consecutive contacts are merged using a trajectory-adaptive temporal tolerance, allowing segmentation to adapt to different sampling rates. Short contacts are treated as passing behavior, whereas longer dwell periods form candidate visits. Structural POIs such as corridors, elevators, and restrooms are excluded regardless of dwell time. For each candidate visit, the inference agent uses the store name and category, dwell duration, neighboring POIs, and an IMU-derived activity cue to infer the most likely behavior. The motion cue can come from either a conventional smartphone activity recognizer or a zero-shot activity model [29, 31, 54]. The output is a compact behavior sequence containing the store, time interval, dwell duration, inferred action, and confidence.

6.2

Journal Generation

Motion Calibration. As illustrated in Fig. 9(B), motion calibration refines candidate activity labels using PDR-derived motion features and location context. Step count, displacement, path linearity, and mean speed first produce a candidate label from stationary, walking, browsing, transit, which an LLM then disambiguates using the store category, dwell time, and neighboring POIs. Before narration, an evidencefiltering step removes unreliable WiFi anchors. Specifically, a POI’s WiFi evidence is discarded when its active window exceeds a predefined fraction of the visit while scan density remains low, indicating repeated background detections rather than a concentrated visit. This rule overrides even high-confidence WiFi anchors. Journal Generation. As shown in Fig. 9(C), journal generation transforms the validated visits and calibrated actions into a third-person Life Journal. The diary agent first constructs a structured outline containing the chronological flow, dominant activity, inferred purpose, and salient visit characteristics, and then rewrites the outline as a fluent narrative. This two-stage process keeps the journal grounded in validated behaviors while allowing natural transitions and varied phrasing.

7

Implementation

Prototype. AirLog is implemented as a Python prototype. A preprocessing module normalizes timestamps, converts

Table 1: Map-parsing results on the complete benchmark. Higher is better. The two AirLog rows share identical region geometry and differ only in semantic-labeling configuration. Method

Region Matched Area F1 ↑ Purity ↑ Label F1 ↑

Color Filling Su et al.-style Boundary Detection SAM 3

0.277 0.144 0.037

0.510 0.468 0.306

0.523 0.506 0.155

AirLog w/o semantic subagent AirLog w/ semantic subagent

0.705 0.705

0.767 0.767

0.734 0.814

raw Wi-Fi records into SSID–RSSI snapshots, resamples IMU streams for PDR, and serializes each session into a structured JSON file. The JSON representation contains Wi-Fi windows, PDR motion primitives, and map context including POI names, polygon centroids, and graph-based spatial descriptors. To facilitate reproducibility, we will publicly release the implementation and experimental configurations upon publication. Model backends. We evaluate AirLog with both closedsource and open-weight LLMs. GPT-4o is the default backbone, while GPT-5.4, Claude Sonnet 4.6, gpt-oss-120b, Gemma 4, Qwen 3, and DeepSeek-R1 are used for cross-model evaluation [3, 40, 41, 44]. The map-semantic comparison additionally uses GPT-5.5 [42]. Across backbones, preprocessing, graph construction, Viterbi parameters, prompts, and postprocessing remain fixed. Prompting and tools. AirLog uses a lightweight ReActstyle agent loop [67], with LLMs handling semantic reasoning and deterministic Python modules performing validation and spatially constrained trajectory fusion. Agent outputs follow structured JSON schemas for semantic anchoring, behavior inference, and diary generation. Public dataset. We use the sample data from Indoor Location Competition 2.0 [35], which provides smartphone sensor traces, Wi-Fi scans, floor-plan images, and GeoJSON maps from two large shopping malls in Hangzhou. Site 1 covers five floors with 642 traces totaling 563.8 minutes, while Site 2 covers nine floors with 429 traces totaling 290.8 minutes. Overall, the dataset contains 1,071 traces, 19,712 Wi-Fi scans, and 8,704 surveyor-labeled waypoints. Each trace provides time-synchronized sensor streams and surveyed waypoints with absolute coordinates, enabling quantitative evaluation of zero-shot trajectory reconstruction in multi-floor indoor environments. Self-collected dataset. We collect smartphone traces using Sensor Logger and export the recorded data for offline processing [10]. A total of 603.0 hour data was collected (Table 6). For each experiment, a volunteer starts recording before entering the venue and carries the phone naturally while performing ordinary activities. Wi-Fi scans are recorded at the application default interval 10 s, and IMU

Yun et al.

streams are resampled to 50 Hz before PDR processing. Each session is paired with a venue floor plan and a manual reference record of visited stores and dwell intervals. The floor plan is captured by the volunteer as a photo of the mall directory before the session. AirLog uses readable floor-plan images obtained from public venue websites, online map directories, or crowdsourced photos when available. We use the self-collected dataset primarily for Wi-Fi semantics analysis and journaling validation. For journaling evaluation, we recruited 11 volunteers to annotate a subset of the traces in Table 6 with reference journals, yielding a total of 33 h 37 min of recorded time, of which 20 h 55 min correspond to indoor activity.

8 Evaluation 8.1 Map Parsing Evaluation Metrics. We report three complementary scores. (1) Area F1 measures tolerant one-to-one region recovery, capturing whether annotated regions are successfully recovered without requiring exact boundaries. (2) Region Purity measures the fraction of predicted area overlapping annotated foreground, reflecting how well predictions avoid background and other clutter. (3) Matched Label F1 evaluates semantic label accuracy after geometry-based matching between predicted and ground-truth regions. Detailed matching rules, averaging procedures, and label normalization are provided in Appendix A.1. Baselines. We compare three visual pipelines and two AirLog semantic-labeling configurations. (1) Color Filling groups connected regions by color and binds OCR labels by spatial overlap. (2) Boundary Detection / Su et al. represents the classical edge/region-growing approach in mall-plan parsing [55], with OCR applied separately to candidate regions; because no official implementation is used, this is our implementation of the procedure described in the paper. (3) SAM 3 [8] uses the fixed text-prompted configuration documented in Appendix A.2, followed by full-crop OCR with the same spatial binding strategy as Color Filling. We also report a uniform threshold-0.5 SAM 3 sensitivity rerun in the appendix. Since these methods differ in both region extraction and label binding, Table 1 presents an end-to-end pipeline comparison rather than a controlled comparison under a shared semantic-labeling module. The two AirLog configurations use identical frozen predicted polygons and the same visible-label policy. Full-map uses GPT-5.5 to jointly predict labels and coordinates for the entire map before binding them to polygons, whereas the current configuration invokes GPT-4o on each polygonmasked crop and writes the returned label list directly to the corresponding GeoJSON feature.

Table 2: Semantic labeling comparison. The GPT-4o per-region run contains 1,920 completed semantic records from 1,927 application attempts; all seven extra attempts were recovered by retries and no record was excluded. Model Input

Calls Tokens (I/O) Time (s) Label F1

GPT-5.5 Full map 24/24 127.4k/137.0k GPT-4o Region crop 1,920/1,927 524.2k/24.6k

1,984.0 1,815.9

0.734 0.814

Setting and map complexity. The complete benchmark contains 24 floor-plan images spanning both public webpublished mall directories and in-the-wild photographs. Twenty correspond to publicly discoverable malls with official websites and public directory/floor-plan images; the remaining four were photographed during our in-the-wild sensing collection and may contain realistic perspective distortion, illumination variation, and color shift. Region geometry was manually maintained in QGIS against the exact pixel-aligned crop. GPT-5.5 was used only as an auxiliary label-auditing tool, and all final ground-truth labels were manually verified against the source images. All method–image evaluations completed under evaluator v8; the archived benchmark snapshot and runtime details are documented in Appendix A.2. Figure 10 summarizes the density of OCR text relative to geometry-valid annotated regions, characterizing visual clutter rather than recognition accuracy. Region recovery. AirLog substantially outperforms the visual baselines in both Area F1 and Region Purity, indicating more complete region recovery and cleaner foreground localization. Area F1 increases from 0.277 for Color Filling to 0.705 for AirLog, while Region Purity increases from 0.510 to 0.767. These scores reflect tolerant region matching and foreground coverage, respectively, rather than exact pixel overlap. The conclusion is unchanged under standard IoUbased metrics: AirLog obtains 0.590 Region [email protected] and 0.475 [email protected], compared with 0.229 and 0.199 for Color Filling; Appendix A.1 reports the full sanity check. Semantic-labeling comparison. Table 2 compares the two production semantic-labeling configurations under identical frozen region geometry and the same visible-label policy. Calls denotes completed semantic records/application attempts, Tokens reports aggregate input/output tokens, and Time is the summed model-response latency rather than wall-clock batch time. Label F1 is the Matched Label F1 defined above. Because the backend model and input scope change together, this is an operational comparison rather than a controlled ablation; we do not attribute the difference independently to model choice or region cropping. Semantic labeling. With region geometry and visible-label policy fixed, the GPT-4o region-crop configuration reaches 0.814 Matched Label F1 (precision 0.789, recall 0.842), compared with 0.734 for full-map GPT-5.5 (precision 0.728, recall

AirLog : Store-Level Indoor Life Logging Made Easy

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100 Number

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(a) Ground truth (b) AirLog (Ours) (c) SenLLM (d) AutoLife Figure 10: Floor-plan image Figure 11: Spatial distribution of subset hit/miss results across localization methods. Green dots complexity. indicate waypoints where the GT location falls within the predicted subset; red dots indicate misses.

0.0

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(b) Recall & Avg SeqSim

Figure 12: Trajectory reconstruction evaluation.

0.740). The 0.080 difference describes the two deployed configurations only because model and input scope change together. The GPT-4o run completed all 1,920 semantic records; seven transient failed attempts across six polygons were recovered within the retry budget, so no sample was excluded from evaluation.

8.2 Store-Level Trajectory & Visit Recovery Next, we evaluate how accurately AirLog reconstructs a user’s trajectory and recovers the stores visited along the trajectory. Metrics. We evaluate recovery at two complementary levels. For point-level trajectory recovery, (1) Subset Hit measures whether the predicted set covers all ground-truth POIs, while (2) Avg Hit Ratio measures the fraction of ground-truth POIs covered, using the five nearest POIs and an 8 m spatial threshold with a ±5 s temporal window. These metrics are evaluated on the public benchmark with surveyed ground truth (§7). For store-level visit recovery, we further evaluate self-collected diary cases (§7), where (3) Recall measures recovered ground-truth visits and (4) SeqSim measures ordering consistency between predicted and ground-truth POI sequences using normalized edit distance. Baselines. We compare AirLog against three baselines. (1) SenLLM, inspired by Penetrative AI [62], is a pure LLM reasoning baseline that receives the same formatted sensing context and infers the POI sequence from motion and WiFi data. (2) AutoLife [63] is adapted to indoor settings by using the provided floor-plan image as VLM location context instead of a GPS-queried map. (3) Rule-based Trajectory Reconstruction assigns each timestamp to the POI with the strongest nearby Wi-Fi signal. Results. Figure 12 shows that AirLog outperforms all baselines, improving Subset Hit and Avg Hit Ratio over AutoLife by 24.8% and 24.6%, respectively. It also achieves 84.6% Recall

Figure 13: Comparison of generated journals. Underlined italic denotes ground-truth POI names. Red denotes hallucinated or incorrect content. More details in the Appendix C.

and 0.778 SeqSim, compared with 63.6% and 0.327 for AutoLife. The limited gain of AutoLife over SenLLM suggests that floor-plan images alone are insufficient for reliable indoor trajectory reasoning, whereas AirLog combines map topology, Wi-Fi semantics, and motion continuity for more accurate and physically consistent reconstruction; remaining errors mainly occur in anchor-sparse corridors and dense areas with overlapping Wi-Fi semantics (Fig. 11).

8.3

Journalling Recovery

Metrics. To evaluate the quality of generated journals, we measure their similarity to reference journals using chrF [46] and BERTScore [73]. We also report hallucination rate, which is marked as hallucinated if the response contains content that is not supported by evidence and is factually inconsistent with the target context. Settings: We recruit 11 volunteers to collect indoor shopping traces. Each participant carries a smartphone during natural shopping activities while the device records Wi-Fi scans and IMU data. After each session, the participant writes a concise reference journal describing the stores visited, dwell periods, and main activities. Results. Table 3 shows that AirLog achieves the best performance across all metrics. It reduces the hallucination rate from 0.667 for SenLLM and 0.636 for AutoLife to 0.000, while improving chrF by 26.9% and 10.2%, and BERTScore F1 by 53.3% and 23.7%, respectively. Figure 13 further shows that the baselines introduce unsupported stores, whereas AirLog

Yun et al.

Table 3: Diary generation quality evaluation. Hall. Rate indicates the hallucination rate, chrF denotes the character F-score, and P, R, and F1 refer to the precision, recall, and F1 score of BERTScore. Method

Hall. Rate ↓

BERTScore ↑

chrF ↑ P

R

F1

SenLLM AutoLife

0.667 0.636

0.331 0.381

0.263 0.363

0.403 0.458

0.330 0.409

AirLog

0.000

0.420

0.485

0.529

0.506

Table 4: Token usage and nominal estimated cost per session. Estimates use GPT-4o-equivalent rates of $2.50/1M input and $10.00/1M output tokens; they are not gateway invoices. Module

Input

Output

Cost (USD)

Calls

Spatial description Wi-Fi semantic agent Trajectory validation Behavior inference Journal generation

22.3k 118.7k 62.8k 1.9k 3.0k

1.2k 13.2k 26.9k 0.8k 1.3k

6.8 ×10 −2 /hr 4.3 ×10 −1 /hr 4.3 ×10 −1 /hr 1.3 ×10 −2 /hr 2.1 ×10 −2 /hr

1 per floor 𝑁𝑤 1 per session 1 per session 1 per session

Total per session

208.7k

43.4k

9.6 ×10 −1 /hr

All modules

generates journals grounded in the recovered POI visit sequence.

8.4

Ablation Study

Figure 14 shows the contribution of each component to storelevel recovery. Removing the Wi-Fi Semantic Agent causes the largest degradation, reducing Subset Hit and Avg Hit Ratio from 71.2%/75.5% to 38.3%/42.3%, and Recall from 84.6% to 36.0%. This highlights semantic Wi-Fi evidence as the key source for identifying visited stores. Removing the Map Agent or Context Fusion also substantially reduces pointlevel recovery, with Avg Hit Ratio dropping to 56.0% and 55.2%, respectively. The Motion Agent mainly improves visitlevel consistency, while the Planning Agent has the smallest effect on point-level metrics. Overall, the results show that semantic anchoring provides the strongest contribution, with map, motion, and fusion providing complementary constraints.

8.5

Cross-Model Evaluation

We evaluate AirLog with different LLM backbones to assess its robustness to model choice. Figure 15 shows that trajectory recovery remains relatively stable across backbones, while diary generation is more sensitive to model choice. GPT-5.4 achieves the strongest overall trajectory performance (73.9% Subset Hit, 77.3% Avg Hit Ratio, 83.3% Recall, and 0.833 SeqSim), while Qwen3 (32B) achieves the highest Recall (91.7%) but a substantially higher hallucination rate (0.313). This gap highlights that high visit recall alone does not guarantee reliable diary generation. Overall, AirLog remains effective across both proprietary and open-weight backbones, demonstrating robustness to the underlying LLM.

8.6

Human Evaluation Study

Setting. Following AutoLife [63], we conduct a user study to evaluate generated journals along five dimensions: clarity, conciseness, correctness, completeness, and relevance. Six volunteers rated 11 generated diaries. All dimensions are rated on a 1–4 scale, where 1 indicates that the generated journal completely does not meet the criterion and 4 indicates that it completely meets the criterion. Fleiss’ 𝜅 is used to measure inter-rater agreement. For self-evaluation, users assessed the end-to-end system using their own collected data. Results. Figure 16 shows that the generated journals are generally perceived as clear, relevant, and faithful to the collected data. Evaluations from third-party raters are consistent with the authors’ self-assessment, and raters show substantial agreement in their judgments. Correctness emerges as a particular strength, while conciseness remains an area for improvement.

8.7

Token Cost

We report nominal API-equivalent cost by logging input and output tokens for each LLM/VLM call and applying the nominal GPT-4o rates stated in Table 4. Requests in our experiments were routed through a custom third-party APIcompatible gateway, and no billing invoice or verified gateway pricing was available; the reported dollar values are therefore estimates rather than actual paid cost. Processing one hour of trace consumes 208.7k input tokens and 43.4k output tokens, corresponding to $0.96 per hour. In other words, the token cost is roughly $1 per hour of processed trajectory. The cost is dominated by Wi-Fi semantic anchoring and trajectory validation, while behavior inference and journal generation add only a small fraction of the total.

8.8

Mobile and Edge Deployment

As location information is privacy sensitive, we evaluate the local deployment cost of AirLog in terms of sensing overhead and on-device processing. Sensing System Cost. During daytime collection, the phone only records passive Wi-Fi scans, IMU samples, timestamps, and optional venue context using Sensor Logger [10] on commodity phones (Xiaomi Flipmix2, vivo S12, and iQoo12). Across the tested phones, a 1-hour trace consumes 153 mAh (3% battery) and produces 70 MB of local data on average. These measurements indicate modest overhead for hourscale passive sensing. Running on edge device. We run our agent with Gemma 4 12B [20] locally with LM Studio [14] on a Mac mini equipped with an M4 chip and 24 GB memory. Processing a 1-hour daytime trace takes roughly 75 min end-to-end, making the local setup suitable for deferred trajectory analysis and diary generation rather than interactive use. This runtime is

AirLog : Store-Level Indoor Life Logging Made Easy Third-Party

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Figure 14: Ablation study.

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Conclusion

We have presented the design and implementation of AirLog, an agentic mobile system that converts a floor plan intended for human interpretation into a machine-readable spatial structure to which ambient Wi-Fi can be anchored, making store-level indoor logging possible without dedicated infrastructure. We believe this points to a broader direction: agents that interpret the spatial artifacts already present in built environments and ground everyday signals against them.

Acknowledgments This work was supported in part by the National Science Foundation under award No. 2554332, No. 2337537, No. 2433914, and No. 2302724.

References [1] Abdulrahman Alarifi et al. 2016. Ultra wideband indoor positioning technologies: Analysis and recent advances. Sensors (2016). [2] Tuo An, Yunjiao Zhou, Han Zou, and Jianfei Yang. 2025. IoT-LLM: Enhancing Real-World IoT Task Reasoning with Large Language Models. arXiv:2410.02429 [cs.AI] https://arxiv.org/abs/2410.02429 [3] Anthropic. 2026. Claude Sonnet 4.6. https://www.anthropic.com/ claude/sonnet. Accessed: 2026-05-28. [4] Paramvir Bahl and Venkata N. Padmanabhan. 2000. RADAR: An inbuilding RF-based user location and tracking system. IEEE INFOCOM (2000). [5] Nayan Sanjay Bhatia and Katia Obraczka. 2025. Transforming DecoderOnly Transformers for Accurate WiFi-Telemetry Based Indoor Localization. arXiv:2505.15835 [cs.NI] https://arxiv.org/abs/2505.15835 [6] Marc Bolaños, Mariella Dimiccoli, and Petia Radeva. 2016. Toward Storytelling from Visual Lifelogging: An Overview. In Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval. [7] Howard Butler, Martin Daly, Allan Doyle, Sean Gillies, Stefan Hagen, and Tim Schaub. 2016. The GeoJSON Format. RFC 7946. Internet Engineering Task Force. https://doi.org/10.17487/RFC7946

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(a) Trajectory Reconstruction (b) Diary Generation Figure 15: Cross-model robustness evaluation.

therefore suitable for deferred journal generation rather than interactive use during the trip. The map can be parsed once offline and reused across visits to the same venue, so map construction is not included in the per-trace runtime. We further note that AirLog can also be deployed on mobile devices through the Google AI Edge Gallery framework [18, 19]; however, phone-side execution is expected to take longer and is mainly an additional deployment option.

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Correctness

Figure 16: User study results.

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A

Map Parsing Evaluation and Failure Analysis

This appendix expands the map-parsing claims made in Section 4 and the results reported in Section 8.1. The main text gives the headline comparison; here we explain why the three reported metrics are appropriate, how to interpret the corresponding scores, and which qualitative cases support the design choices and limitations discussed in the method.

A.1

Metric Design and Interpretation

Table 1 in the main text evaluates two questions: whether AirLog recovers each floor-plan region as a usable polygon, and whether the recovered polygon receives the correct readable labels. We therefore report two geometry metrics and one semantic metric. Area F1: did we recover the regions? For an annotated region 𝑔 and a predicted region 𝑝, we first compute the two directed area coverages Area(𝑔 ∩ 𝑝) , Area(𝑔) Area(𝑔 ∩ 𝑝) 𝑐 𝑝 (𝑔, 𝑝) = . Area(𝑝) 𝑐𝑔 (𝑔, 𝑝) =

(5)

A pair is eligible when max(𝑐𝑔 , 𝑐 𝑝 ) ≥ 0.5,

min(𝑐𝑔 , 𝑐 𝑝 ) ≥ 0.05,

(6)

after which we form a label-independent one-to-one matching. Let 𝑇 𝑃𝐴 be the matched pairs, 𝐹 𝑃𝐴 the unmatched predictions, and 𝐹 𝑁𝐴 the unmatched annotations. We compute 𝑇 𝑃𝐴 , 𝑇 𝑃𝐴 + 𝐹 𝑃𝐴 𝑇 𝑃𝐴 𝑅𝐴 = , 𝑇 𝑃𝐴 + 𝐹 𝑁 𝐴 2𝑃𝐴 𝑅𝐴 AreaF1 = . 𝑃𝐴 + 𝑅𝐴 𝑃𝐴 =

(7)

This tolerant criterion reflects the downstream requirement of AirLog: later modules need one queryable polygon for each floor-plan region, while small boundary deviations are less important than missing, splitting, or duplicating a region. Thus, the increase from 0.277 to 0.705 in Table 1 indicates substantially more successful one-to-one region recovery; it should not be interpreted as 70.5% pixel overlap. For reproducibility, this paper-facing Area F1 corresponds to the association_f1 field in evaluator v8. Region Purity: did a polygon absorb unrelated content? Let ΩGT be the union of all evaluator-valid annotated regions. For each predicted polygon 𝑝, we compute Purity(𝑝) =

Area(𝑝 ∩ ΩGT ) . Area(𝑝)

(8)

Predictions receive equal weight within each image, and image-level means are macro-averaged. Region Purity complements Area F1 because a prediction may recover the intended region while also absorbing corridor, neighboringstore, icon, or background pixels. The increase from 0.510 to 0.767 in Table 1 therefore indicates cleaner region predictions with less unrelated content. Matched Label F1: are the labels correct once geometry is fixed? Semantic evaluation first forms a separate labelindependent one-to-one geometry matching at IoU ≥ 0.5. Label strings are canonicalized identically for all methods and compared as sets. With pooled semantic true positives, false positives, and false negatives denoted by 𝑇 𝑃𝐿 , 𝐹 𝑃𝐿 , and 𝐹 𝑁𝐿 , we report 𝑇 𝑃𝐿 𝑃𝐿 = , 𝑇 𝑃𝐿 + 𝐹 𝑃𝐿 𝑇 𝑃𝐿 , 𝑅𝐿 = (9) 𝑇 𝑃𝐿 + 𝐹 𝑁𝐿 2𝑃𝐿 𝑅𝐿 LabelF1 = . 𝑃 𝐿 + 𝑅𝐿 Unmatched polygons are left to the geometry metrics, so geometry failures are not counted again as semantic errors. Under frozen geometry, the deployed GPT-4o region-crop configuration obtains 0.814 Matched Label F1, compared with 0.734 for the GPT-5.5 full-map configuration in Table 2. Because the backend model and visual scope change together, this comparison does not isolate the effect of region cropping or model choice. Standard-metric sanity check. Area F1 uses a task-motivated tolerant matching rule. To verify that the geometry conclusion does not depend on this rule, we additionally evaluate the same frozen predictions using standard one-to-one matching at IoU ≥ 0.5. Table 5 reports Region [email protected] and [email protected]. The method ordering remains unchanged under both metrics. Table 5: Standard-metric sanity check on map geometry. Region [email protected] and [email protected] use one-to-one matching at IoU ≥ 0.5 and are macro-averaged across the 24 maps. Method Color Filling Boundary Detection / Su et al. SAM 3 AirLog

A.2

Region [email protected] ↑

[email protected] ↑

0.229 0.095 0.020 0.590

0.199 0.075 0.016 0.475

Benchmark and Comparison Protocol

The benchmark used by Section 8.1 contains 24 floor-plan images with 1,488 geometry-valid annotated polygons and 1,400 label strings; 190 polygons contain multiple readable labels. Twenty images correspond to publicly discoverable

AirLog : Store-Level Indoor Life Logging Made Easy

malls with official websites and publicly available directory SAM 3 configuration sensitivity. To test whether the low or floor-plan images. The remaining four are mall-directory SAM 3 result is primarily an artifact of the permissive conphotographs captured during the same in-the-wild sensing figuration above, we reran all 24 maps using the same checkcollection used elsewhere in AirLog. These photographs may point, text prompts, resize cap, and OCR pipeline, while contain perspective distortion, illumination variation, and restoring the detection-confidence threshold to 0.5 and recolor shift that are less pronounced in web-published maps. taining one residual component per query. This more conThe benchmark therefore covers both web-published floorservative configuration removes the large false-positive fragplan artifacts and photographed mall directories encountered mentation, but produces predictions on only one of the 24 in the target deployment setting. maps. With empty-prediction maps assigned zero, it obtains The multi-label cases motivate evaluating label sets rather Region [email protected] of 0.042 and [email protected] of 0.033. Thus, the senthan forcing every region to contain a single store-name sitivity check does not indicate robust SAM 3 performance string. Ground-truth polygon geometry was manually mainon this benchmark; instead, the more conservative configutained in QGIS against the exact pixel-aligned source image. ration suppresses nearly all detections. GPT-5.5 was used only as an auxiliary tool for label auditTable 1 is an end-to-end map-parsing comparison: the viing; every final ground-truth label was manually reviewed sual baselines may differ in both region extraction and label against the source floor-plan image. binding. By contrast, Table 2 freezes the predicted polygons All reported values use the same archived evaluator-v8 and visible-label policy, then compares the full-map GPT-5.5 snapshot and prediction artifacts under OpenCV 4.10.0. Baseline- labeling configuration with the GPT-4o per-region semantic specific settings were fixed globally and applied uniformly subagent. Because model and input scope still change toacross the 24 maps. We did not tune parameters separately gether, this table compares the two deployed configurations for individual maps or select settings against the benchmark but does not independently isolate either factor. ground truth. For the GPT-4o per-region run, all 1,920 semantic records Baseline configurations. Color Filling uses gray-world were completed from 1,927 application attempts. Seven trannormalization, OKLab clustering with 𝑘 = 6, merge threshold sient failed attempts affected six polygons, and all six reached 0.04, random seed 13, and a 30-pixel minimum connecteda valid terminal response within the retry budget. No semancomponent area. PaddleOCR (lang=ch) is applied to the full tic record was dropped from evaluation. map, and OCR results are bound to predicted regions by A.3 Downstream Map Preparation maximum overlap with center fallback. Boundary Detection / Su et al. is our implementation of Section 4 ends with a labeled region map serialized as a the edge/region-growing procedure described by Su et al., GeoJSON FeatureCollection. Corridor geometry and conrather than an official author-code reproduction. It applies nectivity are constructed afterward, before the trajectory the fixed threshold/edge pipeline at the original resolution, stage in Section 5. Figure 17 visualizes this interface: the retains regions of at least 30 pixels, runs OCR on regions of parser-produced regions are converted into a complemenat least 1,200 pixels among at most the 80 largest candidates, tary corridor mask, region targets are placed in the same and retries OCR once after a 90◦ rotation when necessary. metric space, and the corridor is skeletonized into the naviFor SAM 3, the configuration reported in Table 1 uses the gability graph used by the Viterbi decoder. text prompts map region and enclosed spatial region, This separation also clarifies the scope of Table 1: Area F1, longest-side preprocessing at 2,048 pixels, and a mask-binarization Region Purity, and Matched Label F1 evaluate the parser’s threshold of 0.5. Separately, the wrapper’s omitted objectregion geometry and labels, rather than the downstream query score threshold resolves to a detection-confidence corridor or graph construction. threshold of 0.0 rather than the processor default of 0.5. The configuration uses up to 500 masks, no overlap NMS, and exports each residual connected component as a polygon. This permissive configuration produces 24,939 predicted polygons for 1,482 annotated polygons, and 68.05% of the predicted polygons have no evaluator-raster overlap with annotated foreground. This large amount of over-segmentation explains the unusually low 0.037 Area F1 reported for SAM 3.

Yun et al.

crop therefore removes the visual evidence needed by the semantic subagent. The adjacent Crew component, whose polygon retains the text, is labeled correctly. (a) Correct text, wrong polygon anchor

(a) Corridor mask and region targets.

exact region

VOGUE

(b) Navigability graph.

no binding step

Figure 17: Interface between map parsing and trajectory fusion. The labeled GeoJSON region map produced in Section 4 is converted by a separate deterministic stage into the corridor mask and navigability graph consumed in Section 5. These artifacts are not parser outputs and are not included in the map-parsing metrics.

A.4

anchor in UBERFONE

(b) Geometry removes label evidence predicted polygon text outside

Qualitative Evidence and Remaining Failures

Tables 1 and 2 report the aggregate geometry and label results. The examples below illustrate specific failure mechanisms discussed in Section 4; they are qualitative evidence rather than additional quantitative comparisons. Exact-region OCR errors. Figure 18 fixes the target geometry and gives PaddleOCR only the exact region crop. The readable vertical HMV label is returned as AWH, while O2 is returned as 02. Region isolation therefore removes spatialassignment ambiguity but does not, by itself, resolve OCR orientation or glyph confusion. (a) Orientation

(b) Glyph confusion

GROUND TRUTH

HMV

PADDLEOCR

AWH

GT

O2 →

OCR 02

O→0

Figure 18: Exact-region OCR failures. PaddleOCR reads (a) a visible vertical HMV crop as AWH and (b) O2 as 02.

Full-map coordinate-binding error. Figure 19(a) provides an illustrative cached full-map failure case. GPT-5.5 recognizes VOGUE, yet its predicted coordinate falls inside the adjacent UBERFONE polygon. Per-region labeling removes this additional coordinate-prediction and polygon-binding step. This example illustrates the failure mechanism and is not used for the quantitative comparison. Geometry-to-semantics cascade. Figure 19(b) illustrates a remaining limitation of the factorized pipeline. For one matched Crew-associated component, the predicted polygon excludes the readable word. The resulting polygon-masked

masked crop

labels = []

Figure 19: Two map-parsing interface failures. (a) A cached full-map GPT-5.5 result recognizes VOGUE but places its coordinate in the adjacent UBERFONE polygon; an exact-region input removes this binding step. (b) For one Crew-associated component, the predicted polygon removes the readable word from the masked crop, leaving no readable evidence for the semantic subagent.

B

Wi-Fi Semantic Evidence and Collection Context

This appendix groups the evidence supporting the use of ambient Wi-Fi as a weak semantic cue: collection coverage, the sparsity and failure modes of lexical SSID–POI matching, and the ambiguity of RSSI-based nearest-signal decisions.

B.1

Collection Coverage

Figure 20 summarizes representative urban and session-level coverage. These figures document collection context; storelevel reconstruction remains grounded in indoor Wi-Fi observations, PDR motion continuity, and structured indoor topology.

B.2

Lexical Similarity and Representative Failure Cases

The character-level distribution shows a clear non-zero tail, indicating that many SSIDs still contain partial POI-related strings. However, only a small fraction of pairs reach strong lexical overlap: 647 pairs exceed 0.8 at character level with a precision of 52.2%, and 14,516 additional pairs fall between 0.6 and 0.8. Among word-level and TF-IDF pairs, high-similarity matches (>0.8) achieve up to 100% and 90.0% precision respectively, but account for only 11 and 40 pairs out of 185,140,

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(a) Shenzhen density.

(b) Shanghai density.

(c) Hong Kong session context.

Figure 20: Representative Wi-Fi collection context. The density maps summarize two urban collection areas, while the Hong Kong panel provides a session-level view of mobility coverage. Table 6: Dataset statistics across six cities. Metric Hong Kong Beijing Shanghai Shenzhen Dalian Nanjing Total Active hours (h) 59.3 5.8 208.4 225.5 2.6 101.3 603.0 GPS points 157,360 2,137 277,356 319,865 6,696 168,041 931,455 Wi-Fi observations 260,394 2,711 25,798 45,801 10,391 29,472 374,567 Unique SSIDs (raw) 21,303 984 1,730 2,125 1,604 1,274 29,020 Unique BSSIDs (raw) 56,615 1,742 2,541 3,199 2,832 1,922 68,851 Avg. SSIDs per scan 81.0 46.7 3.0 5.9 48.8 6.2 59.4

Table 8: Representative bad cases of Wi-Fi SSID–POI matching. String similarity algorithms fail where LLM semantic reasoning succeeds.

Table 7: Number of SSID–POI pairs by similarity range and precision. Precision is computed over manually annotated samestore labels. Range

Cosine (Char)

Cosine (Word)

Cosine (TF-IDF)

0.0 0.0–0.2 0.2–0.4 0.4–0.6 0.6–0.8 0.8–1.0

9,731 33,454 72,318 54,474 14,516 647 (52.2%)

184,841 3 131 137 (34.3%) 17 (58.8%) 11 (100%)

184,871 3 65 100 (9.0%) 61 (44.3%) 40 (90.0%)

Total

185,140

185,140

185,140

Wi-Fi SSID

POI

SKH-SchoolDevice Beauty MNSGUEST

SKH Holy Carpenter Church A Boutea Monster Sushi

Best Sim. 0.693 0.833 0.820

𝑎 SKH-SchoolDevice: SKH is a dominant institutional prefix of the

Hong Kong Sheng Kung Hui; “SchoolDevice” identifies a campus device network, pointing to an affiliated lodging facility rather than the church itself. 𝑏 Beauty: “A Boutea” is a beverage brand whose name superficially resembles “Beauty” in spelling; the SSID more plausibly belongs to a cosmetics or personal-care venue. 𝑐 MNSGUEST : MNS follows a typical three-letter property code convention; combined with its location in the Miramar cluster in Tsim Sha Tsui, the network is better attributed to Miramar Shopping Centre’s guest Wi-Fi than to a restaurant abbreviation.

showing that exact token matches are extremely sparse in practice. Table 8 illustrates a complementary failure mode: surface string similarity can rank misleading SSID–POI pairs highly even when the semantic explanation points elsewhere. These examples justify using semantic reasoning over names, local context, and map constraints; they are not presented as a standalone quantitative benchmark.

B.3

RSSI Ambiguity

Figure 21 expands the Wi-Fi anchoring example from the main paper. Nearby access points overlap substantially along a single walk, and one signal window can remain compatible with multiple neighboring stores. This motivates treating the top-𝐾 SSIDs as soft evidence rather than selecting the strongest signal as the user’s location.

C

Additional Journal Examples

The following examples supplement the journal comparison in the main paper. Underlined italic denotes ground-truth POI names, and red denotes hallucinated or incorrect content.

Yun et al. -40 dBm

ambiguity zone

signal rebound

RSSI (dBm)

-50 dBm -60 dBm -70 dBm -80 dBm

KFC_FREE_WiFi Uniqlo_Store Zara_Guest Starbucks_WiFi

-90 dBm

in top-10 (LLM visible) out of top-10 (LLM invisible)

t=0 t = 60 entrance KFC

t = 120 Uniqlo

t = 180 t = 240 t = 300 t = 360 atrium Starbucks H&M zone rebound

Time (s)

(a) RSSI variation. Solid lines indicate SSIDs in the top-𝐾 window; faded dashed lines indicate lower-ranked signals.

Figure 23: Journal generation case 3: shopping and dining visit. (b) Spatial layout. The corresponding mall trajectory remains compatible with multiple nearby stores.

Figure 21: RSSI ambiguity and spatial layout. The profiles and map show why semantic anchoring is formulated as soft multi-label inference rather than a deterministic nearest-signal decision.

Figure 22: Journal generation case 2: restaurant visit.

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D Prompt Templates D.1 Spatial Description Generation This prompt converts GeoJSON store features into normalized names, neighbor relations, and natural-language position descriptions. The output is a compact spatial context that later prompts can use without directly reasoning over raw polygon coordinates.

D.2

Wi-Fi Semantic Anchoring

The following prompt maps Wi-Fi SSIDs to candidate store locations via semantic similarity, relative signal strength, and spatial context.

Figure 25: Prompt template for Wi-Fi semantic anchoring using SSID semantics and signal evidence. Figure 24: Prompt template for spatial description generation from GeoJSON floor-plan features.