Advanced Applications in IoT, Wearables, and Edge Computing

Overview:

Advanced applications in IoT, wearables, and edge computing are transforming sectors such as healthcare through secure medical IoT devices and innovative materials like graphene, while also introducing new challenges and opportunities in security-particularly as Edge AI and private 5G wireless converge.

Secure Development and Validation: Medical IoT and Graphene Wearables : Secure medical IoT and graphene wearables focus on protecting patient data and ensuring device reliability by implementing strong encryption, authentication, and access control. They must address resource constraints that increase attack risks, use secure design and validation, and provide ongoing updates. Advanced materials like graphene offer benefits but require evolving security and reliability standards.

Edge AI and Private 5G: Security Challenges : Edge AI combined with private 5G offers speed, low latency, and local data processing but poses complex security challenges. These include a larger attack surface from distributed devices, vulnerabilities in AI models, supply chain and insider threats, and gaps in regulatory compliance like insufficient encryption and access controls. Robust security measures are essential to mitigate these risks.

Security Opportunities and Best Practices : To tackle these challenges, leading organizations use Zero Trust architectures, unified security platforms, and AI-driven tools for continuous threat monitoring and adaptive authentication. Key strategies include AI-powered predictive threat intelligence, self-healing networks for automated response, managed security services to fill skill gaps, and strict data anonymization and retention policies to protect sensitive medical and wearable IoT data.

Why you should Attend:

Here are three targeted FUD (Fear, Uncertainty, Doubt) liners, tailored to highlight critical security risks and emphasize the urgency of attending the event:

For IoT and Medical Device Companies : With over a million exposed devices causing data leaks and disruptions, outdated security threatens patient safety, compliance, and reputation. Don’t let vulnerabilities and ransomware lead to catastrophic losses.

For Wearable Tech Innovators (Graphene/Fitness/Medical): Ignoring these risks can lead to health hazards, data theft, recalls, lawsuits, and lasting brand damage.

For Edge AI and Private 5G Security Leaders : The rise of 5G, edge computing, and AI expands vulnerabilities like network flaws and APIs beyond traditional defenses. Without prompt action, adversaries can hijack networks, manipulate edge AI, and disrupt critical operations undetected.

Areas Covered in the Session:

• Advanced Applications in IoT, Wearables, and Edge Computing : IoT-assisted wearable sensor systems in healthcare integrate architectures, wireless communication methods, and applications for patient monitoring and telehealth. Key focus areas include device interoperability using standardized protocols like ZigBee and Wi-Fi for seamless data exchange, and sensor design tradeoffs-such as size, power efficiency, and accuracy-highlighted through comparisons of consumer and medical-grade devices
• Secure Development and Validation of Medical IoT and Graphene Wearables: A detailed review of medical wearable security covers end-to-end encryption, multifactor authentication, and hardware trust anchors (TPM, TEEs, SGX). It highlights regulatory compliance (FDA, HIPAA, GDPR, CE, ISO), emphasizing documentation, clinical validation, and audit trails. Secure manufacturing and validation of advanced materials like graphene, along with ethical oversight and regular security assessments, are vital to ensure patient safety
• Edge AI and Private Wireless (5G): Security Challenges and Opportunities: Edge AI deployment in healthcare and IoT offers real-time processing, reduced cloud reliance, and better patient outcomes. However, it presents security risks like data transmission vulnerabilities, diagnostic data manipulation, and device authentication challenges. Best practices include secure protocols, anomaly detection, layered security, and thorough validation of edge AI operations

Who Will Benefit:

• Product Managers
• Systems Engineering Professionals
• Validation Architects
• Project Managers