MAC Routing Protocol for Improving Efficiency in IEEE 802.15.4 Wireless Sensor Networks
Bibliographic record
Abstract
Wireless Sensor Network (WSN) are made of a number of nodes (aka., source nodes) and switches, which can communicate with each other through wireless channels. The infrastructureless nature and architecture of WSN makes it flexible and scalable but the nodes in this network are resource and energy constrained. In this paper we describe the design of a centralized MAC routing protocol (MCRP) to help improve the performance, the network lifetime and to detect wormhole attacks in 802.15.4 wireless sensor networks. A Wormhole attack occurs when an intruder creates a low latency tunnel between two or more sensor nodes to misguide other nodes and exhaust network resources by gaining access to sensitive data. Hence, we designed and implement a routing protocol that considers both performance and energy factors while detecting wormhole attacks. Experimental results from simulations carried out in NS-3 show that our proposed protocol exhibits a reduction in energy consumption of 54% and 42% over LEACH and LEACH-C, respectively. MCRP further improves the performance of the network in terms of propagation delay and packet loss ratio even as the number of nodes increases.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".