Effect of Sensor Mobility and Channel Fading on Wireless Sensor Network Clustering Algorithms
Bibliographic record
Abstract
Clustering is an effective topology approach in wireless sensor network, which can increase network lifetime and scalability. Either Node Mobility or Channel fading has a negative impact on various clustering protocol. In case of Node Mobility when all sensor nodes are mobile the current nearest cluster head may be the farthest one for the sensor node when message transmission phase starts. In the present research the received signal strength is used to estimate the sensor location. Consequently, channel fading affects the path loss between the nodes thus affecting the estimated distance between them. This paper introduces a new clustering protocol which is built on Adaptive Decentralized re-clustering protocol called E-ADRP (Enhanced Adaptive Decentralized re-clustering protocol). Simulations are performed to test the effect of node mobility using Random Walk Mobility model (RWM) on Low Energy Adaptive Clustering Hierarchy (LEACH) and Enhanced Adaptive Decentralized re-clustering protocol (E-ADRP). The simulation results show that the applied mobility on LEACH affected the network lifetime and energy dissipation negatively while in contrast E-ADRP simulation results were much better. On the other side, Rayleigh channel model also was applied on LEACH and E-ADRP clustering algorithms. The simulation results show that network lifetime and energy dissipation at mobile nodes were nearly stable compared to static nodes in case of E-ADRP while in case of LEACH mobile nodes were negatively affected by rate up to 24% less than static nodes, at fading E-ADRP and LEACH were both negatively affected where E-ADRP was affected by rate up to 40% less than static nodes and LEACH was affected by rate up to 50% less than static nodes.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".