A Novel Patient Monitoring Framework and Routing Protocols for Energy & QoS Aware Communication in Body Area Networks
Bibliographic record
Abstract
Significant challenges to patient monitoring systems in a hospital environment include the reliable and energy-efficient transmission of data and their real-time display. This thesis proposes innovative and novel mechanisms for the reliable transmission of patient data in Body Area Network (BAN) communication, which simultaneously ensure high throughput, low data latency, and low energy consumption by implementing energy and QoS aware routing protocols. Five main contributions are made in this regard. Firstly, a novel patient monitoring system (ZK-BAN peering framework) is proposed for real-time hospital BAN communication that displays patient data on the display units by considering data privacy, low energy consumption, better control on the devices, and patient mobility. Secondly, a novel energy-aware peering routing protocol (EPR) is introduced in which the choice of next hop is based on the residual energy and geographic information of the neighbor nodes. EPR contains three main components: a Hello protocol, a neighbor table constructor algorithm, and a routing table constructor algorithm. Thirdly, a new modular QoS-aware routing protocol (QPRD) is designed to handle the ordinary and delay-sensitive data for BAN communication in hospitals. QPRD provides an end-to-end path delay mechanism to calculate the path delays of all possible paths from a source to destination and then chooses the best path with the lowest path delay for delay-sensitive packets. Fourthly, a novel modular QoS-aware routing protocol (QPRR) is developed to handle ordinary and reliability-sensitive data for BAN communication in hospitals. The modular architecture of QPRR includes five modules: a reliability module, a packet classifier, a Hello protocol module, a routing services module, and a QoS-aware queuing module. The proposed mechanisms for end-to-end path reliability calculation and data transmission using redundant paths ensure more reliable BAN communication. Finally, a new integrated energy and QoS aware routing protocol (ZEQoS) is designed to deal with ordinary, delay-sensitive, and reliability-sensitive data packets. Extensive simulations in the OMNeT++ based Castalia 3.2 simulator show that EPR, QPRD, QPRR, and ZEQoS perform better than other similar energy and QoS aware routing protocols.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".