Machine Learning-based Prevention of Battery-oriented Illegitimate Task Injection in Mobile Crowdsensing
Bibliographic record
Abstract
Mobile crowdsensing (MCS) is a cloud-inspired and non-dedicated sensing paradigm to enable ubiquitous sensing via built-in sensors of personalized devices. Due to disparate participants and sensing tasks, MCS is vulnerable to threats initiated by malicious participants, which can either be a participant providing sensory data or an end user injecting a fake task aiming at resource (e.g. battery, sensor, etc.) clogging at the participating devices. This paper builds on machine learning-based detection of illegitimate tasks, and investigates the impact of machine learning-based prevention of battery-oriented illegitimate task injection in MCS campaigns. To this end, we introduce two different attack strategies, and test the impact of ML-based detection and elimination of fake tasks on task completion rate, as well as the overall battery drain of participating devices. Simulation results confirm that up to 14% battery power can be saved at the expense of a slight decrease in the completion rate of legitimate tasks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".