The world is more connected than ever. Industry reports estimate that over 15 billion Internet of Things (IoT) devices are currently active worldwide — from smart watches and home assistants to hospital monitors and industrial sensors — with projections suggesting the number could exceed 25 billion devices by 2030.
As these networks expand, so do security risks. Cybersecurity analysts report that IoT-related attacks continue to increase each year, often exploiting unsecured or poorly monitored devices.
In a recent study published in the Journal of Information Systems Engineering and Management, Sushma Babburi, an independent researcher based in the United States, proposes a new framework that integrates Blockchain and Artificial Intelligence (AI) to make IoT systems more secure, reliable, and scalable
We spoke with Babburi about her research and why building trust into connected systems is becoming increasingly critical.
Q: For readers who may not be technical, what problem are you trying to solve?
Sushma Babburi:
Today, billions of devices constantly collect and transmit data. That includes medical monitors, shipping trackers, traffic sensors, and industrial equipment.
The challenge is that as these networks grow, ensuring that every device remains trustworthy becomes more complex. If even one device is hacked or begins behaving abnormally, it can affect an entire system.
My research focuses on creating a framework that makes IoT systems secure, transparent, and scalable, so they can expand safely without increasing risk.
Q: Why are current IoT systems vulnerable?
Babburi:
Most IoT systems rely on centralized servers. That means all data flows through one core system. If that central point is compromised, overloaded, or misconfigured, the entire network can be affected.
In addition, many devices are installed and then assumed to be trustworthy indefinitely. But devices can malfunction or be compromised over time. Continuous evaluation is essential.
Q: How does blockchain improve security?
Babburi:
Blockchain functions like a secure digital ledger that records information in a way that cannot easily be altered.
Instead of storing device data in one place, blockchain distributes records across multiple network nodes. This makes the system tamper-resistant and transparent.
For industries where accuracy is critical — such as healthcare or logistics — ensuring that device data cannot be secretly modified is extremely important.
Q: Where does Artificial Intelligence come in?
Babburi:
While blockchain protects the integrity of the data, AI monitors device behavior.
In my framework, AI continuously evaluates how devices perform. It analyzes patterns such as response time, data consistency, and operational behavior.
Rather than assuming devices are permanently safe, the system constantly reassesses them.
Q: You mention “trust scoring.” What does that mean in simple terms?
Babburi:
It’s similar to a credit score — but for connected devices.
Each device receives a dynamic trust score based on how reliably it behaves. If it shows unusual patterns, the score decreases.
This allows the system to flag or isolate potentially compromised devices before they cause broader disruption.
Q: How does your research detect problems early?
Babburi:
The framework includes anomaly detection that identifies unusual behavior in real time.
For example, if a sensor suddenly transmits data outside its normal range, the system can detect that quickly.
Early detection is especially important in sectors like healthcare, industrial automation, and smart city infrastructure, where system reliability directly affects safety.
Q: Blockchain is sometimes criticized for scalability issues. How do you address that?
Babburi:
Scalability is a real concern as IoT networks grow into millions of devices.
The research evaluates transaction processing performance under simulated IoT workloads and explores optimizations such as hybrid consensus mechanisms and edge computing.
The objective is to maintain strong security while ensuring operational efficiency.
Q: Which industries could benefit most from this work?
According to Babburi, the framework is designed to be domain-agnostic and adaptable across industries.
Examples include:
- Healthcare: securing connected medical devices
- Logistics: protecting supply chain data
- Industrial systems: enabling predictive maintenance
- Smart cities: ensuring reliable sensor-driven infrastructure
As IoT adoption accelerates globally, scalable and trustworthy architectures are becoming foundational to digital infrastructure.
Q: What is the broader vision behind your research?
Sushma Babburi:
We are moving toward a world where connected devices influence real-time decisions — from patient monitoring to traffic management.
If those systems are not trustworthy, the consequences can be significant.
By combining decentralized security with intelligent monitoring, we can build IoT ecosystems that are resilient, scalable, and transparent.
That foundation of trust will be critical as digital infrastructure continues to expand.
Author Note
Sushma Babburi is an independent researcher specializing in Blockchain-enabled and AI-driven data systems. Her study, Integrating Blockchain and AI for Trusted and Scalable IoT Data Ecosystems, was published in the Journal of Information Systems Engineering and Management
