Molecular Communication in the Human Body for Implanted Electronic Medical Devices
Bibliographic record
Abstract
Implanted electronic medical devices (IEMDs) are becoming an integral part of patient care for treatment and monitoring purposes. Existing IEMDs communicate using electromagnetic (EM) waves as information carriers to program/reprogram or change their behavior by adjusting stored parameters. However, EM makes IEMDs vulnerable to remote eavesdropping, interference, and even cyber-attacks. Inspired by biological systems, we investigate in this thesis the use of molecular communication (MC) as an alternative wireless link to an IEMD. MC links provide privacy and security because the signaling molecules are released in and confined to bodily fluids, which provides high robustness against eavesdropping, particularly when compared to EM waves. To enable MC between any points inside the human body, regardless of their distance and even if tissues and fluids separate them, we propose a new method and a new system for molecular communication in the body, denoted MoSiMe and MoCoBo, respectively. The MoSiMe method accounts for pharmacokinetics to take advantage of how the absorption, distribution, metabolization, and excretion (ADME) bodily processes affect the distribution of drugs in the body. An analytical model for the body channel is derived, and experimental MoCoBo proof of concept platform which mimics the ADME processes is constructed and validated against clinical trials and animal tests. Then, we introduce a two-step verification access control protocol (2-SVMoCo) and a break-the-glass access procedure to an IEMD using MC (BTGMoCo). The 2-SVMoCo protocol uses MC in the body to secure an IEMD against battery draining, privacy, and EM link hijacking attacks. The BTGMoCo mechanism provides emergency access to an IEMD using a syringe and a signaling solution such as saline, which are widely available in any medical facility. Experimental results show that 2-SVMoCo can secure an IEMD against attacks with minimal size and energy budget overhead. Furthermore, we design and evaluate multilevel concentration shift keying modulation schemes to improve the bit rate of the MoCoBo system. Finally, we design, build prototypes, and test a new generation of IEMDs denoted MoCoSwitch, which use MC as their main communication link.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".