IoMT: Internet of Medical Things

• IoMT is a network of certified medical devices and sensors integrated with secure networks to provide real-time health monitoring and automated clinical decision support.
• It employs a multi-layered architecture—from sensor and network to edge/fog and cloud—to ensure reliable, low-latency, and secure data transmission.
• Applications include remote patient monitoring, chronic disease management, and telemedicine, enhanced by AI, blockchain, and federated learning for improved outcomes.

The Internet of Medical Things (IoMT) constitutes a clinically driven subset of the broader Internet of Things (IoT), integrating distributed medical sensors, actuators, devices, communication networks, and backend analytics platforms for seamless data-driven healthcare. It forms a foundational infrastructure for remote monitoring, clinical decision support, and automated intervention, leveraging a multi-layer architecture with stringent requirements for reliability, security, and regulatory compliance. As IoMT matures, the domain is characterized by a complex interplay of device heterogeneity, medical-grade quality-of-service demands, regulatory standards, and ethical imperatives targeting both operational efficiency and preservation of patient autonomy.

1. Definitions and Architectural Fundamentals

IoMT is defined as “the network of organized devices, sensors, and software that gather and communicate medical data to improve the features, effectiveness, and availability of healthcare services” (Mouanda, 2 Sep 2024). Unlike general-purpose IoT, IoMT platforms presuppose medical device certification, data-governance compliance (e.g., HIPAA, GDPR), and safety-critical workflows. Canonical architectures are represented as either three- or four-tiered stacks:

• Sensor/Perception Layer: Wearables, implantables, environmental and clinical devices acquire physiological and contextual signals. Sampling rates in practice vary from 1 Hz (basic vitals) to 250 Hz (ECG, EEG); energy budgets range from sub-milliwatt (implants) to tens of milliwatts (wearables) (Rajput et al., 2019, Mouanda, 2 Sep 2024).
• Network Layer: Data communication is realized via short-range protocols (Bluetooth LE, ZigBee, IEEE 802.15.4, 802.15.6) and long-range (LTE, 5G, NB-IoT, LoRaWAN, Wi-Fi). Star, mesh, peer-to-peer, and hybrid network topologies are employed depending on deployment scale, redundancy, and fault-tolerance requirements (Chango et al., 29 Jan 2024).
• Edge/Fog Layer: On-premise gateways or edge servers host local analytics, preliminary processing, filtering, and selective decision-making. Architectures range from simple filtering and preprocessing to low-latency, high-accuracy ML inference (see Transfer Learning with MobileNetV3, Section 2) (Mabrouk et al., 2023, Al-Masri, 2021).
• Cloud/Application Layer: Centralized repositories, EMR/EHR systems, data analytics (ML/DL, federated learning), dashboards, and orchestrated retraining processes. Secure storage, role-based access controls, and policy enforcement are standard (Mouanda, 2 Sep 2024, Kagita et al., 2020).

Network performance is usually assessed by reliability $R = \prod_{i=1}^n (1 - p_i)$ (with $p_i$ as per-link failure probability), end-to-end latency $L$, throughput $T$, and device power consumption $P$ (Mouanda, 2 Sep 2024).

2. IoMT Application Domains and Workflows

IoMT underpins critical application domains:

• Remote Patient Monitoring (RPM): Continuous collection and cloud-based analysis of vital signs, with studies showing 18% reduction in hospital readmission and 22% fewer emergency visits when RPM is deployed (Mouanda, 2 Sep 2024).
• Chronic Disease Management: Closed-loop insulin pump systems and smart inhalers yield significant clinical improvements, e.g., mean HbA1c reductions of 0.5% (Mouanda, 2 Sep 2024).
• Telemedicine & Smart Hospitals: High-definition video consults (<250 ms latency), tele-diagnostics, digitally instrumented wards, predictive maintenance on imaging hardware (e.g., downtime reduction by 25%), and context-aware clinical automation (Mouanda, 2 Sep 2024, Rajput et al., 2019).
• Wellness and Health Promotion: Patient engagement via wearables and mobile apps, with real-time feedback shown to double adherence rates and increase engagement by 42% in select studies (Mouanda, 2 Sep 2024).

3. Networking Topologies, Protocols, and Performance

IoMT deployments exhibit a diversity of networking topologies, each with distinct tradeoffs (Chango et al., 29 Jan 2024):

Topology	Redundancy	Typical Node Count	Pros/Cons
Star	1	up to 2000	Simplicity; single gateway failure is critical.
Mesh	>1	15–100	Redundant paths; greater complexity, overhead, and energy use.
Peer-to-Peer	variable	10–50	Flexible; unsuited for large scale due to energy/complexity issues.
Hybrid	mixed	20–200	Balances resilience and manageability at increased cost/complexity.

Wireless medical-grade protocols include IEEE 802.15.4 (ZigBee/6LoWPAN, 250 kbps), IEEE 802.15.6 (BAN, multi-PHY), Bluetooth LE (125 kbps–2 Mbps), Wi-Fi (hundreds of Mbps), and LPWANs (tens of kbps, long-range) (Rajput et al., 2019, Zhou et al., 3 Apr 2025). Quality-of-service is achieved through mechanisms such as TDMA, polling, guaranteed time slots, and multi-criteria optimization for edge resource allocation (e.g., TOPSIS-based in Edgify) (Al-Masri, 2021).

Key performance formulas include channel capacity $C = B \log_2(1 + SNR)$, max latency for time-critical medical apps, and AoI (Age of Information) constraints (Rajput et al., 2019, Zhou et al., 3 Apr 2025).

4. AI and Data Analytics in IoMT

State-of-the-art IoMT leverages edge/cloud AI for diagnosis, triage, filtering, and security:

• Transfer Learning and Mobile Image Classification: The “MobileNetV3+CGO+SGD” pipeline for medical image classification demonstrated in (Mabrouk et al., 2023) achieved top-tier accuracy (ISIC-2016 skin lesion detection: 88.39%, PH2: 97.52%, Blood-Cell: 88.79%) by extracting 128-dimensional embeddings with MobileNetV3 and selecting the most discriminative ~30 features via Chaos Game Optimization, all deployable on embedded fog nodes with sub-500 ms latency.
• Federated Learning and Filtering: Federated filtering preserves privacy and reduces communication loads by up to 95%, balancing local filtering with cloud/fog model aggregation, and guaranteeing performance bounds via eigen-spectrum perturbation theory (Sanyal et al., 2019, Si-ahmed et al., 14 Mar 2024).
• Explainable AI & XAI: SHAP explanations on ML-based anomaly detection frameworks enable regulatory compliance and clinical interpretability of real-time security and reliability assessments (Si-ahmed et al., 14 Mar 2024).
• Intelligent Sensing & Self-Powered Wearables: FTES-based triboelectric sensing, coupled with deep learning (CNN-BiLSTM-Attention), achieves posture and identity recognition for Parkinson’s patients at >97% accuracy (Mao et al., 2023).

5. Security, Privacy, and Trust Management

IoMT security departs fundamentally from traditional IT due to safety-critical exigencies, legacy device constraints, and fine-grained privacy mandates (Deb et al., 25 Jul 2025, Kagita et al., 2020, Ghubaish et al., 2023, Allouzi et al., 16 Feb 2024):

• Attack Surfaces & Threat Taxonomy: Risks span device/firmware tampering, weak authentication, protocol flaws (unauthenticated BLE/Wi-Fi channels), data eavesdropping, ransomware, and application layer exploits. Attacks can result in direct patient harm (e.g., manipulated insulin pumps), not merely data loss (Deb et al., 25 Jul 2025).
• Quantitative Risk Metrics: Attack surface $S = \sum_i w_i v_i$; aggregate risk $R = \sum_a L_a \times I_a$, with explicit weighting for safety-criticality (Deb et al., 25 Jul 2025).
• Layered Security Frameworks: Defense-in-depth architectures combine lightweight symmetric/asymmetric cryptography (ECC, AES-128), multi-factor authentication, homomorphic and proxy re-encryption, blockchain-based audit, and AI-driven intrusion detection. Two-factor authentication, secure boot, anomaly monitoring at edge, CoAP over DTLS or HTTPS/TLS1.3, and certificateless authenticated encryption are core components (Ghubaish et al., 2023).
• Zero Trust and Adaptive Access Control: Soter’s zero-trust architecture for IoMT edge networks employs dynamic trust negotiation, MedDL policy enforcement (Datalog-with-constraints), and context-aware access decisioning under minimal CPU/memory footprint (Allouzi et al., 16 Feb 2024).
• Trusted Clustering and Secure 5G IoMT: Interval type-2 fuzzy trust clouds, peer recommendation, and adaptive classification in 5G+D2D clusters yield 88% malicious detection even with 50% adversarial device presence (Yang et al., 2022).

Privacy attacks are systematized via LINDDUN, including identification, linkage, profiling, and unawareness, with mitigation spanning PETS deployment (encryption, deniable access control, lightweight authentication) and policy frameworks to reduce linkage risk in aggregate health datasets (Bookert et al., 2022).

6. Blockchain and Decentralized Security

Blockchain integration addresses core IoMT threats of data integrity, auditability, and decentralized control:

• EHR Security and Control: Patient-owned EC keypairs, off-chain encrypted storage, and on-chain hash-anchoring for auditibility and unlinkability (no direct identifiers) (Nkenyereye et al., 2020). All access, modification, and authorization flows are cryptographically enforced, with ETH-based access tokens.
• COVID-19 Use Cases: Blockchain-enabled IoMT facilitates secure contact tracing, quarantine compliance, and remote telemedicine, combining edge preprocessing, zero-knowledge proofs for privacy-aware location tracking, and smart contracts for compliance enforcement (Dai et al., 2020).

Scalability remains an area for research; private/consortium chains and sharding are needed for high-frequency telemetry (Dai et al., 2020). Emerging integration paths include federated learning round-tripped with on-chain model provenance and selective update auditing.

7. Standards, Compliance, and Future Trajectories

IoMT mandates comprehensive compliance with FDA (TPLC, SBOM management), AAMI TIR57, ISO 27001, NIST SP 800-53, GDPR, and HIPAA (Deb et al., 25 Jul 2025, Mouanda, 2 Sep 2024). The research agenda highlights:

• Edge AI and Privacy-Preserving Inference: On-device, lightweight models for anomaly detection, explainable medical decision support, and federated learning for privacy and regulatory mandates (Mouanda, 2 Sep 2024, Si-ahmed et al., 14 Mar 2024).
• Interoperability and Standardization: Progress on medical-grade FHIR-IoMT schemas, multi-phy protocols, and cross-vendor credential frameworks remains limited but critical (Mouanda, 2 Sep 2024, Kagita et al., 2020).
• Secure, Ultra-Low-Latency Communications: 6G/THz for sub-ms telemedicine (e.g., robotic surgery), URLLC, and new physical-layer security primitives (Zhou et al., 3 Apr 2025).
• Socio-Technical and Ethical Studies: Evaluations of equity, patient consent, digital literacy, and ethical oversight (IRB) for AI-driven IoMT diagnostics and interventions (Mouanda, 2 Sep 2024, Jr. et al., 2019).
• Network Topology Research: Focus on hybrid and mesh topologies for improved fault tolerance, dynamic adaption, and real-time responsiveness, along with energy-aware protocol design under the DSR framework (Chango et al., 29 Jan 2024).

• (Mabrouk et al., 2023) Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things
• (Mouanda, 2 Sep 2024) Comprehensive up-to-date impact of the IoMT in healthcare and patients
• (Rajput et al., 2019) Characterizing IOMT/Personal Area Networks Landscape
• (Deb et al., 25 Jul 2025) Securing the Internet of Medical Things (IoMT): Real-World Attack Taxonomy and Practical Security Measures
• (Al-Masri, 2021) An Edge-Based Resource Allocation Optimization for the Internet of Medical Things (IoMT)
• (Kagita et al., 2020) A Review on Security and Privacy of Internet of Medical Things
• (Bookert et al., 2022) Privacy Threats on the Internet of Medical Things
• (Allouzi et al., 16 Feb 2024) Enabling Zero Trust Security in IoMT Edge Network
• (Yang et al., 2022) An Intelligent Trust Cloud Management Method for Secure Clustering in 5G enabled Internet of Medical Things
• (Zhou et al., 3 Apr 2025) Revolutionizing Medical Data Transmission with IoMT: A Comprehensive Survey of Wireless Communication Solutions and Future Directions
• (Sanyal et al., 2019) A Federated Filtering Framework for Internet of Medical Things
• (Si-ahmed et al., 14 Mar 2024) Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems
• (Nkenyereye et al., 2020) Blockchain-Enabled EHR Framework for Internet of Medical Things
• (Mao et al., 2023) A Health Monitoring System Based on Flexible Triboelectric Sensors for Intelligence Medical Internet of Things and its Applications in Virtual Reality
• (Chango et al., 29 Jan 2024) Topologies in the Internet of Medical Things (IoMT), literature review
• (Dai et al., 2020) Blockchain-enabled Internet of Medical Things to Combat COVID-19
• (Jr. et al., 2019) Exploring Challenges and Opportunities in Cybersecurity Risk and Threat Communications Related To The Medical Internet Of Things (MIoT)