In the ever-growing universe of smart devices, the world faces a double-edged challenge: how to harness the power of ubiquitous computing without exhausting the planet’s energy resources. One woman at the frontier of this challenge is Dr. Damilola Alao, whose groundbreaking research is helping to reshape the foundations of how smart devices think, act, and conserve.
With a Ph.D. in Computer Science and Informatics from Oakland University, Dr. Alao has pioneered a series of frameworks that enable IoT (Internet of Things) devices to manage their energy more intelligently using predictive artificial intelligence. Rather than allowing devices to remain always-on and waste energy, her system leverages data forecasting and AI-powered scheduling to ensure devices only activate when needed-and precisely how they’re needed.
Central to this innovation is her use of the NSGA-III multi-objective optimization algorithm, which balances energy consumption with user service demands in real time. Her other proposed model predicts usage patterns via Vector AutoRegression (VAR), allowing networks to anticipate user behavior and prepare for resource allocation before requests arrive.
Dr. Alao’s predictive model applies Vector AutoRegression (VAR) to analyze historical service usage patterns. This allows devices to anticipate user needs even in the absence of direct input. Combined with NSGA-III optimization, systems can handle trade-offs between service latency, energy availability, and user satisfaction. Simulation results across multiple IoT environments showed the framework can complete over 99% of predicted service requests with a 95% reduction in idle energy consumption. In constrained testbeds, device lifespans were extended by over 60%-a breakthrough for long-term deployments such as agricultural sensors, smart cities, and emergency medical devices.
The results of her simulation studies are impressive: up to 99% of user requests fulfilled, 95% network lifetime extension, and an 11% increase in energy usage efficiency. These figures aren’t just theoretical-they represent a critical step toward achieving scalable, self-sustaining IoT environments in areas where energy and connectivity are scarce, such as in smart agriculture, disaster zones, and remote medical services.
Another hallmark of Dr. Alao’s research is her solution to the cold-start problem in edge computing-when new users with no behavioral history join the network. Her system builds adaptive user profiles on the fly, allowing
for accurate service provisioning even when historical data is limited or absent.
The significance of this work has earned her publications in respected venues, including the IEEE Transactions on Services Computing, and recognition at international conferences like the International Conference on IoT, where she received a Best Paper Award for her work on personalized service prediction models.
But Dr. Alao’s work extends beyond performance metrics. At its core, her research re-centers technology around the people who use it. Her user-centric approach allows service parameters like latency, privacy, and reliability to be adjusted to match individual needs-ensuring that IoT systems are not only smart but also ethical and inclusive.
From a national perspective, Dr. Alao’s work aligns with the U.S. Department of Energy’s goals to improve the energy efficiency of connected systems and supports the National Institute of Standards and Technology (NIST) efforts in defining resilient, predictive IoT architectures. Her framework is also a candidate for deployment in underserved rural health systems, bridging the digital divide with intelligent, self-managing technology. The ability to conserve energy while offering personalized services directly benefits both environmental goals and digital equity for marginalized communities.
As smart cities and intelligent infrastructure become mainstream, Dr. Alao’s vision is poised to play a transformative role. With AI-enhanced foresight and energy-aware logic, her work is not just about saving power-it’s about designing a digital world that adapts to its users and sustains itself along the way.
Published on December 6, 2025
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