Empowering Self-Organized Feature Maps for AI-Enabled Modeling of Fake Task Submissions to Mobile Crowdsensing Platforms
Bibliographic record
Abstract
Mobile crowdsensing (MCS) has emerged as a ubiquitous solution for data collection from embedded sensors of smart devices to improve the sensing capacity and reduce sensing costs in large regions. Due to the ubiquitous nature of MCS services, smart devices require awareness of against misbehaving users that are becoming smarter to clog the resources in such a nondedicated sensing environment. In an MCS setting, the primary goal of a fake sensing task submission is to keep participant devices occupied, such as the battery, sensing, storage, and computing. Since the development of robust sensing campaigns highly depends on the existence of a realistic model of misbehaving users, this article leverages artificial intelligence and introduces a region-based self-organizing feature map (SOFM)-based model on user movement patterns so as to place the fake sensing tasks with the objective of maximum impacted participants and recruits. Uniformly and randomly initialized neurons are designed with fixed and adaptive quantities that are determined based upon the affected area on the covered terrain. Through numerical studies, we show that the impact of the SOFM structures can affect up to 46% of the participants and up to 37% of the recruits under various SOFM topologies. Furthermore, SOFM-based task submission models can increase the energy consumption in recruited devices by up to 39% due to the illegitimate task submission.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".