Ravi Gupta

Ravi Gupta, Shabista Haider

Smart home lighting systems consume 15-20% of residential energy but lack adaptive intelligence to optimize for user comfort and energy efficiency simultaneously. We present BitRL-Light, a novel framework combining 1-bit quantized Large Language Models (LLMs) with Deep Q-Network (DQN) reinforcement learning for real-time smart home lighting control on edge devices. Our approach deploys a 1-bit quantized Llama-3.2-1B model on Raspberry Pi hardware, achieving 71.4 times energy reduction compared to full-precision models while maintaining intelligent control capabilities. Through multi-objective reinforcement learning, BitRL-Light learns optimal lighting policies from user feedback, balancing energy consumption, comfort, and circadian alignment. Experimental results demonstrate 32% energy savings compared to rule-based systems, with inference latency under 200ms on Raspberry Pi 4 and 95% user satisfaction. The system processes natural language commands via Google Home/IFTTT integration and learns from implicit feedback through manual overrides. Our comparative analysis shows 1-bit models achieve 5.07 times speedup over 2-bit alternatives on ARM processors while maintaining 92% task accuracy. This work establishes a practical framework for deploying adaptive AI on resource-constrained IoT devices, enabling intelligent home automation without cloud dependencies.

Ravi Gupta, Guneet Bhatia

Addressing educational inequity in Sub-Saharan Africa, this research presents an autonomous agent-orchestrated framework for decentralized, culturally adaptive educational content generation on edge devices. The system leverages four specialized agents that work together to generate contextually appropriate educational content. Experimental validation on platforms including Raspberry Pi 4B and NVIDIA Jetson Nano demonstrates significant performance achievements. InkubaLM on Jetson Nano achieved a Time-To-First-Token (TTFT) of 129 ms, an average inter-token latency of 33 ms, and a throughput of 45.2 tokens per second while consuming 8.4 W. On Raspberry Pi 4B, InkubaLM also led with 326 ms TTFT and 15.9 tokens per second at 5.8 W power consumption. The framework consistently delivered high multilingual quality, averaging a BLEU score of 0.688, cultural relevance of 4.4/5, and fluency of 4.2/5 across tested African languages. Through potential partnerships with active community organizations including African Youth & Community Organization (AYCO) and Florida Africa Foundation, this research aims to establish a practical foundation for accessible, localized, and sustainable AI-driven education in resource-constrained environments. Keeping focus on long-term viability and cultural appropriateness, it contributes to United Nations SDGs 4, 9, and 10. Index Terms - Multi-Agent Systems, Edge AI Computing, Educational Technology, African Languages, Rural Education, Sustainable Development, UN SDG.