Cooperative Body Channel Communications for Energy Efficient Internet of Bodies
Bibliographic record
Abstract
The Internet of Bodies is a network formed by wearable, implantable, ingestible, and injectable smart devices to collect physiological, behavioral, and structural information from the human body. Thus, the IoB technology can revolutionize the quality of human life by using these context-rich data in myriad smart-health applications. Radio frequency (RF) transceivers have been typically preferred due to their availability and maturity. However, for most RF standards (e.g. Bluetooth Low Energy), the highly radiative omnidirectional RF propagation (even at the lowest settings) reaches tens of meters of coverage, thereby reducing energy efficiency, causing interference and co-existence issues, and raising privacy and security concerns. On the other hand, body channel communication (BCC) confines low-power and low-frequency (10 kHz-100 MHz) signals to the human body, leading to more secure and efficient communications. Since energy efficiency is one of the critical design parameters of IoB networks, this paper focuses on energy-efficient orthogonal body channel access (OBA) and non-orthogonal body channel access (NOBA) schemes with and without cooperation. To this aim, three main BCC topologies are presented; point-to-point channel, medium access channel, and broadcast channel. These topologies are then used as building blocks to create IoB networks relying on OBA and NOBA schemes for downlink (DL) and uplink (UL) traffic. For all schemes and traffic directions, optimal transmit power and phase time allocations are derived in closed-form, which is essential to reduce energy consumption by eliminating computational power. The closed-form expressions are further leveraged to obtain maximum network size as a function of data rate requirement, bandwidth, and hardware parameters.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".