Anomalous Path Detection for Spatial Crowdsourcing-Based Indoor Navigation System
Bibliographic record
Abstract
Indoor navigation system provides customized path planning for requesters who are unfamiliar with the indoor environment, such as shopping mall and airport. Spatial crowd-sourcing technology can be applied to indoor navigation to offer fundamental services related to location. However, spatial crowdsourcing-based indoor navigation is vulnerable to the intrusion of injected anomalous paths from attackers. In this paper, we propose an anomalous path detection (APD) scheme to classify attackers according to their reputation management and abnormal trajectory sequence. Specifically, we first develop a crowdsourcing system to support the indoor location service using the fog as the spatial crowdsourcing server. Then, we identify two levels of attackers, i.e., the malicious responders and the semi-honest responders in the indoor environment according to their attacking purposes. Through the responders' historical records from the fog server, we analyze a series of trajectory sequences consisting of the distance between the current position and the destination to distinguish the semi-honest responders from the normal. In addition, we propose a semi-supervised learning with hidden Markov model (HMM) to detect the semi-honest responders. Finally, the extensive simulations show that the APD scheme can achieve higher accuracy with the acceptable false rate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".