Fault tolerant, energy efficient and secure clustering scheme for mobile machine‐to‐machine communications
Bibliographic record
Abstract
ABSTRACT Designing energy efficient, fault tolerant and secure clustering scheme is important for machine‐to‐machine (M2M) communications that comprise a large number of sensors. Existing works on M2M communications include designing M2M layered architecture, device model, Quality of Service (QoS) categorisation of M2M services and potential M2M applications. However, designing secure and fault tolerant clustering schemes has not received much attention in M2M research. Thus, this paper introduces a fault tolerant, energy efficient and secure clustering scheme for M2M (FESM) area networks that minimises the number of cluster heads (CHs) and active nodes to reduce network energy consumption. The machine type communication gateway and CHs transmit beacon messages to discover the failure of CHs and member nodes, respectively. The security mechanism is lightweight but efficient. It uses simple permutation‐based shared keys between (i) member nodes and CHs; (ii) gateway nodes and CHs; and (iii) CHs and machine type communication gateway. Experimental results demonstrate that the FESM clustering scheme reduces network energy consumption and increases network lifetime as compared with the existing Fault Tolerant and Energy Efficient Clustering Protocol (FTEEC), Dynamic Static Clustering Protocol (DSC) and Low Energy Adaptive Clustering Hierarchy (LEACH) protocols. We also analyse the security mechanism of the FESM protocol and find that it is very effective against well‐known attacks such as sybil, wormhole and black hole. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".