Study on Edge-Based Optimization Architectures for Autonomous Climate Control and Secure Sensor Telemetry in Smart Homes
Keywords:
Artificial intelligence, Data analytics, Optimization, Privacy, Security, Smart homeAbstract
Thermostats, occupancy sensors, and smart meters flood residential networks with continuous telemetry. This immense data volume creates a distinct advantage. Raw digital noise can be converted into automated climate control, predictive appliance maintenance, and strict perimeter security. This paper examines the strict mechanics of residential data analytics, detailing the collection, preprocessing, and secure routing of heterogeneous sensor feeds. Algorithms must replace manual operation. Machine learning models adjust HVAC compressors based on real-time kitchen occupancy, while AI agents track voltage irregularities to predict refrigerator failure. Scaled deployment faces immediate roadblocks. Proprietary communication protocols isolate hardware. Black-box algorithms alienate users by obscuring their decision logic. Furthermore, broadcasting a family's daily habits to external servers creates a severe privacy vulnerability. To solve these systemic failures, this paper proposes a unified, high-volume control framework. The architecture relies on edge-based distributed cache storage and rapid indoor wireless communication. By integrating hybrid particle swarm optimization, the system processes thousands of localized data points instantly. These forces fragmented hardware to operate as a single, highly efficient intelligent shelter without exposing occupant telemetry.
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