Energy Efficient Guidelines for iOS Core Location Framework
Bibliographic record
Abstract
Several types of apps require accessing user location, including map navigation, food ordering, and fitness tracking apps. To access user location, app developers use frameworks that the underlying platform provides to them. For the iOS platform, the Core Location framework enables developers to configure various services to obtain user location information. But how does a particular configuration affect the energy consumption of an app? The available Core Location framework documentation is insufficient to help developers reason about the tradeoff between choosing a particular configuration and energy consumption. In this paper, we present a set of guidelines that will help developers make an energy-efficient design choice while configuring the Core Location framework for their app. To achieve that, we have created microbenchmark configurations of the various services that the Core Location framework provides. We have then run several test-scenarios on these configurations to extract their energy profiles. To extract energy-efficient guidelines for developers, we have carefully examined those energy profile results. The guidelines show several configurations that not only reduce energy consumption but also access locations more frequently than other configurations. To evaluate those guidelines, we analyzed three real-world apps and a location service sample app provided by Apple. Our results show that the guidelines help reduce energy: 0.42% for a property search app, 10.59% for a weather app, 26.91% for a location utility app, and 11.37% for Apple's sample app. Additionally, our empirical evaluation shows that choosing an energy-hungry configuration can increase the energy consumption by up to a maximum of 23.97%. Our guidelines are effective on 3 real-world apps, and our methodology may be used to extract energy-efficient guidelines for frameworks other than the Core Location framework.
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 | 0.000 | 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".