The Internet of Things (IoT) is revolutionizing the way industries operate by enabling seamless connectivity between devices, systems, and sensors. From improving healthcare and transportation to advancing smart city infrastructure, IoT facilitates real-time data exchange, automation, and smarter decision-making. Yet, as this interconnected network expands, it also exposes critical vulnerabilities, such as cybersecurity risks and data breaches, which threaten the safety, privacy, and reliability of these systems.
Leading the charge in addressing these challenges is Manvitha Gali, a pioneer in IoT network security research. She has developed innovative solutions such as the Distributed Deep Meta learning-driven Task Offloading (DDMTO) framework, which optimizes IoT security and efficiency, and a blockchain-based IoT-SOA for secure and trustworthy service architectures. Her research tackles key areas like task offloading, drone network privacy, and advanced threat detection.
Manvitha's expertise stems from over seven years of hands-on experience with real-time IoT communications, particularly through Verizon's ThingSpace applications. Her work focuses on improving data processing between edge and cloud environments, designing scalable drone communication models, and leveraging blockchain for enhanced trust and reliability.
Currently serving as a Technical Lead at HCL America while contracting with Verizon, Manvitha leads critical IoT projects centered on automation and security. Her academic pursuits, including a PhD and collaborations with domain experts, have further strengthened her ability to bridge research and real-world applications. Through her dedication to solving IoT's most pressing challenges, Manvitha is shaping a future where IoT systems are secure, efficient, and resilient.
Solving IoT Challenges
Rooted in years of hands-on work in real-time IoT communications, particularly through her experience with Verizon's ThingSpace applications, Manvitha's journey into IoT network security reflects her deep technical expertise. Over the last seven years, she has addressed challenges like system performance, secure data transmission, and large-scale device connectivity. "This real-time technical experience inspired me to explore critical challenges in the field," she explains. Her research focuses on enhancing data processing efficiency between edge and cloud environments, designing scalable communication models for drones in critical applications like surveillance, and exploring blockchain-enabled IoT architectures to improve trust, reliability, and fault tolerance.
Collaboration plays a pivotal role in advancing her research, particularly in multidisciplinary projects like the Brain Hemorrhage Detector Device, which was granted a patent by the UK Intellectual Property Office. Manvitha emphasizes the importance of teamwork, where clear roles and shared goals drive innovation. "My expertise in IoT security frameworks guided secure data handling, while collaborators from healthcare, AI, and hardware development contributed their specialized knowledge," she shares. This approach ensures challenges are tackled from multiple perspectives, creating solutions that are both innovative and practical.
By combining her industry experience with academic pursuits, including her PhD and her role as a Teaching Assistant at the University of Houston-Clear Lake, Manvitha brings a holistic perspective to IoT research. Her ability to align technical expertise with collaborative efforts has enabled her to develop solutions that bridge industries, solve real-world challenges, and advance critical technologies in IoT network security and beyond.
DDMTO: Smarter IoT Solutions
The Distributed Deep Meta Learning-Driven Task Offloading (DDMTO) framework, developed by Manvitha, offers an intelligent solution for managing tasks in complex cloud environments, especially within smart city systems. The framework determines whether tasks generated by IoT devices should be processed on local edge servers or in the cloud, depending on available processing power and network capacity. "This makes the system faster and more efficient," Manvitha explains, as it reduces latency by processing data closer to its source, ensuring quicker responses and optimal resource usage.
What sets DDMTO apart is its ability to learn from past task management experiences through meta-learning. This capability allows the system to adapt and improve its decision-making over time, even when new tasks arise or system conditions change. In applications like traffic management, public safety, and environmental monitoring, the framework effectively balances workloads between edge and cloud systems. By minimizing delays and maximizing resource efficiency, DDMTO ensures that critical city services remain uninterrupted while keeping operational costs low and performance high.
Securing Drone Networks with AI
Critical security vulnerabilities in IoT-enabled drone networks, such as data breaches, eavesdropping, and system infiltration, were identified by Manvitha, stemming from the continuous data exchange that drones rely on for communication. These vulnerabilities make drone networks prime targets for cyberattacks. To address these risks, Manvitha developed a machine learning-based privacy and security framework that combines logistic regression (LR) and random forest algorithms to detect potential threats in real time. "By applying these advanced AI techniques, the system can quickly identify unusual patterns, prevent unauthorized access, and secure communication channels between drones," she explains.
The framework has been rigorously tested on complex datasets, demonstrating significant improvements in speed, accuracy, reliability, and stability. It strengthens overall security by proactively identifying and mitigating threats, enabling drones to operate safely and efficiently. This solution supports critical applications such as GPS navigation, surveillance, and other IoT-driven services, ensuring that drone networks remain both reliable and resilient against evolving cyber threats.
Building Trust with Blockchain
Manvitha highlights how blockchain and smart contracts enhance trust and security in IoT-enabled Service-Oriented Architecture (IoT-SOA) by ensuring transparency and reliability. Blockchain functions as a tamper-proof digital ledger that records all service interactions, making it nearly impossible to alter or manipulate data. "This builds trust because everyone involved can see a clear history of service activities," Manvitha explains, fostering accountability in IoT service transactions.
Smart contracts further strengthen the system by automating service agreements and enforcing preset rules. "If there's a disagreement, the smart contract checks the recorded data and resolves the issue based on the agreed terms," she adds, eliminating the need for manual conflict resolution and ensuring fairness. By encrypting and securely transmitting service details, the system protects sensitive information from unauthorized access. Together, blockchain's secure record-keeping and smart contracts' automated enforcement make IoT-SOA a trustworthy, efficient, and reliable solution, addressing the limitations of traditional service systems while improving the overall security of IoT services.
Innovative Threat Detection
The hierarchical threat detection framework developed by Manvitha provides a powerful solution for identifying malware in 5G-enabled IoT networks, achieving an impressive 97.01% accuracy. The model integrates multiple layers of analysis, combining byte features, PE structure features, and mining operation execution features to provide comprehensive malware detection. "Its key innovation lies in combining threat intelligence insights with multi-layered detection, enhancing its adaptability to emerging threats," Manvitha explains. Notably, the framework outperformed baseline methods by 6.13%, proving its reliability and effectiveness in real-world scenarios.
Looking ahead, Manvitha envisions the framework evolving alongside the growing complexity of IoT security challenges. With the rise of advanced cyber threats, the system's adaptability will be strengthened through real-time monitoring and adaptive learning. She also emphasizes the importance of integrating with global threat intelligence networks, which will ensure the framework remains scalable, responsive, and future-proof. By combining innovation with adaptability, this solution positions itself as a critical defense mechanism for safeguarding IoT ecosystems against evolving malware attacks.
Making Smart Cities Safer
As smart cities grow and rely on interconnected devices for managing services like traffic, power, water, and healthcare, ensuring strong IoT security becomes critical to prevent cyberattacks that could disrupt essential operations. Manvitha emphasizes, "Without proper security, hackers could target smart city systems, causing issues like power outages, traffic jams, or even delays in emergency response." Since even minor vulnerabilities can have significant ripple effects in interconnected systems, safeguarding these environments is paramount.
To address these risks, Manvitha's IoT frameworks integrate secure communication protocols, data encryption, and real-time monitoring to detect and prevent potential threats. She highlights her Drones Network Framework, which includes robust security measures to prevent unauthorized access and ensure safe drone operations in applications such as emergency deliveries and surveillance. Additionally, her Hybrid Task Offload Framework securely manages data sharing between cloud systems and devices, ensuring that sensitive information remains protected during processing. By adopting a security-first approach, Manvitha's work creates IoT systems that are not only efficient and scalable but also resilient against cyber threats, enabling smart cities to operate smoothly and reliably.
Turning Ideas into Real-World Solutions
To ensure her innovative frameworks, such as DDMTO and IoT-SOA, remain both practical and scalable, Manvitha focuses on addressing real-world challenges and rigorously validating her solutions through research and testing. She begins by identifying key technological hurdles, such as managing tasks between cloud and edge systems or ensuring trust and security in IoT services, which allows her to design solutions that effectively solve real-world problems.
Scalability is a core consideration in her work. For instance, the DDMTO framework efficiently distributes tasks across cloud and edge devices, adapting seamlessly to changing workloads, while the IoT-SOA framework leverages blockchain to integrate new services and devices without compromising system performance. "By running tests and studying the results," Manvitha explains, "I refine the designs to ensure they perform well in real-world environments." Through simulations, experiments, and data analysis, her research-driven approach ensures her frameworks are not only innovative but also ready to be applied at scale, bridging the gap between academic research and industry needs.
As IoT systems expand, Manvitha emphasizes the need for continuous advancements in security to protect critical infrastructures from growing vulnerabilities. Her vision prioritizes secure communication protocols, data encryption, and real-time monitoring to create efficient, resilient ecosystems capable of addressing emerging challenges. Beyond her technical contributions, Manvitha calls for collaboration between industry and academia to ensure IoT systems remain secure and adaptable against future threats.
