Adaptive Data-Gathering Protocols with Mobile Collectors for Vehicular Ad Hoc and Sensor Networks
Bibliographic record
Abstract
Energy efficiency and low message delay are the basic QoS requirements for data collection in vehicular ad-hoc and sensor networks. In order to achieve these requirements, many solutions have been developed by attaching collectors/sinks to vehicle or aircraft and moving them either randomly or along a predefined route. This paper presents an adaptive data gathering protocol (ADG) that employs multiple mobile collectors (instead of sinks) to help an existing wireless sensor network achieve such requirements. In the proposed ADG protocol, a virtual elastic-force model is used to help mobile collectors adjust their moving speed and direction while adapting to changes within the network. Because of the irregularity of the information generation rate as well as the cost of mobile collectors, the number of collectors can not be predefined. Mobile collectors are sent out by sink if the information in the network is beyond the capabilities of existing mobile collectors, and are called back when they become redundant. In this paper we discuss our ADG protocol, highlight how its performance can be enhanced using both multi-hop OCOPS and LEACH protocols. We report on the performance evaluation of ADG protocol using an extensive set of simulation experiments. Our simulation results show that the proposed ADG algorithm can be a flexible means of meeting the constraints and different requirements of monitoring applications.
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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 | 0.001 |
| Open science | 0.001 | 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".