Propositional Inference for IoT Based Dosage Calibration System Using Private Patient-Specific Prescription against Fatal Dosages
“Manufacturers of medical IoT devices should be prioritizing security, especially considering the potential detrimental consequences of a breach.”(Catherine Norcom, Hardware hacker for IBM’s X-Force Red)
- The dosage is increased resulting in extreme decomposition of blood glucose. The nominal result is fatigue, and in extreme cases, fatality.
- The dosage is decreased resulting in build-up of glucose, leading to hyperglycemia. The nominal result is shortness of breath and nausea, and in extreme cases, cardiovascular problems.
- The dosage dispersal frequency becomes chaotic. The basal and bolus factor is affected. The nominal result varies from patient to patient based on the severity of the disease.
2. Background and Challenges
- Basal—Nominal amount of insulin injected periodically to maintain blood sugar. Period depends upon program.
- Bolus—Heavy dosage of insulin to be initiated by the patient at mealtime, or hyperglycemia symptoms affect quality of life.
- A sensor and actuator that collect data and effect quantifiable change. In the case of an insulin pump, the motor, piston and cannula together form the insulin delivery actuator.
- A processor with the insulin delivery program installed, which is subject to change based on human intervention through a communication module.
- A battery power source that runs the device.
- A communication module, which, in IoT pumps may represent a wired or wireless communication link. Wireless technologies include BT-WiFi, BLE (Bluetooth Low Energy) & ZigBee. The primary vulnerabilities recognized in IoT insulin pumps that resulted in recall were present in the communications module that negatively affected the processor module. The processor module, in turn, affects the rest of the IoT components in a cascading fashion.
3. Related Work
3.1. A Brief Chronology of Medical Device Security—A.J Burns et al. 
- Identification. Identify processes and assets needing protection,
- Protection. Define available safeguards.
- Detection. Devise incident detection techniques.
- Response. Formulate a response plan.
- Recovery. Formalize a recovery plan.
3.2. Deep Insecurities: The Internet of Things Shifts Technology Risk—Samuel Greengard 
3.3. Internet of Things (IoT): Application Systems and Security Vulnerabilities 
3.4. Security and Privacy Considerations for IoT Application on Smart Grids: Survey and Research Challenges [22,23]
3.5. The Security Challenges in the IoT Enabled Cyber-Physical Systems and Opportunities for Evolutionary Computing and Other Computational Intelligence 
3.6. IoT Security Framework for Building Trustworthy Smart Car Services 
3.7. Summary of Inferences
4. Proposed Secure IoT Based Insulin Dosage Dispensary System Using Patient-Specific Private Blockchain
- The patient-specific private blockchain is bootstrapped by its miners (i.e., doctor, chief doctor and caretaker of the patient)
- The doctor decides the dosage information (i.e., specifies the quantity of insulin and the time of delivery) for the patient and generates the block in the chain containing the latest prescription information. The block is validated by the consensus mechanism that includes chief doctor, nurse and caretaker of the patient, and is appended to the blockchain.
- It sends the block hash value in one transaction.
- The entire block (i.e., dosage information and hash value) is transmitted in another transaction.
- The pump hashes the block contents and compares them against the block hash obtained in the separate transaction.
- If equal, go to step (v).
- Else, reject the new block and terminate the dosage procedure until a valid block arrives.
- The pump compares the previous hash of the new block with the current hash of the existing block .
- If equal, initiate insulin delivery.
- Else, reject the new block and terminate dosage until a valid block arrives.
- Battery replacement
- Insulin cartridge replacement
- Patient opts to remove it for a period of time.
5. Proof of Impenetrability through Propositional Calculus
- Successful State: L
- Failure State: BC^H (or) !L
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Meal Time||B. Sugar Threshold||Outcome||Duration||Programmable |
|✓||✓||Bolus||1–4 h||Quantity of Insulin|
|✘||✓||Bolus||Symptoms abation||Quantity of Insulin|
|✘||✘||Basal||Periodic||Quantity of Insulin & Frequency|
|✓||✘||-||Patient recovery imminent||Abstinence|
|Paper Name||Issues Addressed||Solution Proposed||Observations||IoT Relevance|
|A brief chronology of medical device security||Five step security protocols.||Organized criteria for medical IoT.||Prescription of organization adopted.|
|Deep insecurities: the internet of things shifts technology risk||NA||Recommendation of blockchain architecture resolution.||Need for external verifications with supervision.|
|Internet of Things (IoT): Application Systems and Security Vulnerabilities||None Provides |
consolidation of threats only
|Vulnerability implies fatality.|
|Security and Privacy Considerations for IoT Application on Smart Grids: Survey and Research Challenges||Management of vast amounts of data amongst others.||None||Consolidation of IoT challenges provided.||Study of data in transit.|
|The security challenges in the IoT enabled cyber-physical systems and opportunities for evolutionary computing & other computational intelligence||Attack surfaces indentified.||None||Areas of counter-threat measures identified.|
|IoT Security Development Framework for building trustworthy smart car services||Security is an afterthought in IoT.||Layered architecture and threat ranking protocols are prescribed.||False positives in sensor data are 4.2% of positives observed.||Blockchain architecture replacement considered.|
|Duration (h)||Meal Time||Insulin Status Expected||Ongoing Insulin Status||User Triggered||Inference|
|06:00 h to 08:00||✘||Basal||Basal||NA||No Deviation|
|Basal||Bolus||Y||Update Care giver|
|08:01 h to 09:00||✓||Bolus||Bolus||NA||No Deviation|
|09:01 h to 12:30||✘||Basal||Basal||NA||No Deviation|
|Basal||Bolus||Y||Update Care giver|
|Data in Blockchain is immutable.||BC|
|Two dosage settings basal and bolus. Basal and Bolus are mutually exclusive.||BS, BL; BS = !BL|
|Two triggers normal time and meal time. Normal time and meal time are mutually exclusive.||NT, MT; NT = !MT|
|Normal time triggers basal dosage state||NT→BS|
|Normal time basal dosage triggers continual loop.||BS→L|
|Meal time triggers bolus dosage.||MT→BL|
|Meal time bolus dosage triggers basal dosage at normal time.||BL^NT→BS|
|User can trigger bolus dosage.||U→ BL|
|Outsider should not change dosage.||!U→!BS; !U→!BL|
|Outsider changes dosage triggers loop break and triggers BC data entry & system halt.||(!U→BS)→BC^H(!U→BL)→BC^H|
|Registration and halt happens only during failure.||BC^H ←→!L|
|!(BC^H)→(U→BL)||BS and BL are mutually exclusive|
|L→(U→BL)||Double negation (8)|
|!U→!BL||Double negation (3)|
|!L||Modus Tollens of (9) & (11)|
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Gopalakrishnan, K.; Balakrishnan, A.; Govardhanan, K.; Selvarasu, S. Propositional Inference for IoT Based Dosage Calibration System Using Private Patient-Specific Prescription against Fatal Dosages. Sensors 2023, 23, 336. https://doi.org/10.3390/s23010336
Gopalakrishnan K, Balakrishnan A, Govardhanan K, Selvarasu S. Propositional Inference for IoT Based Dosage Calibration System Using Private Patient-Specific Prescription against Fatal Dosages. Sensors. 2023; 23(1):336. https://doi.org/10.3390/s23010336Chicago/Turabian Style
Gopalakrishnan, Karthikeyan, Arunkumar Balakrishnan, Kousalya Govardhanan, and Sadagopan Selvarasu. 2023. "Propositional Inference for IoT Based Dosage Calibration System Using Private Patient-Specific Prescription against Fatal Dosages" Sensors 23, no. 1: 336. https://doi.org/10.3390/s23010336