Design guidelines for routing protocols in ad hoc and sensor networks with a realistic physical layer
Bibliographic record
Abstract
We present guidelines on how to design network layer protocols when the unit disk graph (UDG) model is replaced by a more realistic physical layer model. Instead of merely using the transmission radius in the UDG model, physical, MAC, and network layers share the information about a bit and/or packet reception probability as a function of distance between nodes. We assume that all nodes use the same transmission power for sending messages, and that a packet is received when all its bits are correctly received. The MAC layer reacts to this probabilistic reception information by adjusting the number of acknowledgments and/or retransmissions. We observe that an optimal route discovery protocol cannot be based on a single retransmission by each node, because such a search may fail to reach the destination or find the optimal path. Next, we discuss that gaining neighbor knowledge information with "hello" packets is not a trivial protocol. We describe localized position-based routing protocols that aim to minimize the expected hop count (in case of hop-by-hop acknowledgments and fixed bit rate) or maximize the probability of delivery (when acknowledgments are not sent). We propose a guideline for the design of greedy position-based routing protocols with known destination locations. The node currently holding the message forwards it to a neighbor (closer to the destination than itself) that minimizes the ratio of cost over progress, where the cost measure depends on the assumptions and metrics used, while the progress measures the difference in distances to the destination. We consider two basic medium access layer approaches, with fixed and variable packet lengths. This article serves as a preliminary contribution toward the development of network layer protocols that match the assumptions and criteria already used in simulators and ultimately in real equipment.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.010 |
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".