An energy‐aware load‐balanced routing protocol for ad hoc M2M communications
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Bibliographic record
Abstract
Abstract Machine‐to‐machine (M2M) communications will become ubiquitous in the future Internet of Things, and it is important that current wireless networks are developed to support M2M communications. In this paper, we propose an energy‐aware load‐balanced routing protocol for ad hoc M2M communications. Routing is a challenging issue in ad hoc M2M communication networks due to a large number of machine‐type communication (MTC) devices and a lack of infrastructure. Wireless nodes that are used as MTC devices are powered by batteries only. This makes energy efficiency a major issue as each MTC device in the network acts as a router and consumes energy in the routing process. The energy consumption should be reduced to prolong the lifetime of the batteries. Most existing research on energy‐aware routing only focuses on minimising the total energy consumption from the source to the destination device, regardless of the performance degradation caused by the heavy traffic load in the network. The unbalanced traffic load distribution among devices may cause more packet loss and quick battery depletion due to the frequent usage. Unlike existing research, we classify the devices as energy‐critical and load‐critical devices and protect them from participating in the routing frequently. Simulation results demonstrate that the proposed routing protocol significantly increases the packet delivery ratio, reduces delay and prolongs the network lifetime compared to ad hoc on‐demand distance vector and dynamic source routing in the heavy load network. Energy‐aware load‐balanced can also provide better performance compared with delay‐aware MChannel and energy‐aware MChannel protocols when devices have very low energy levels in congested networks. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.008 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it