Study on Edge-Based Optimization Architectures for Autonomous Climate Control and Secure Sensor Telemetry in Smart Homes

2026-10-07T08:52:41+00:00

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.

SteadyPath: Ensuring Stability in Autonomous Industrial Maneuvering

2026-09-11T09:54:13+00:00

Autonomous industrial vehicles, such as automated guided vehicles (AGVs) and autonomous forklifts, operate in confined, dynamic environments where collision-free motion alone is not a sufficient safety criterion. A maneuver that successfully avoids an obstacle can still leave the vehicle in an unstable or uncontrollable configuration, jeopardizing the next stage of motion. This article presents SteadyPath, a stability-focused navigation framework that integrates environment state acquisition, decision-making, sampling/graph-based motion planning, and terminal-constrained Model Predictive Control (MPC) within a closed feedback loop. Unlike conventional planners that only certify path feasibility, SteadyPath explicitly constrains the terminal state of the prediction horizon to a safe, controllable region, ensuring that the vehicle exits every avoidance maneuver in a state from which safe continuation is supported. The framework is developed from an expanded survey that combines the original five motivating studies with recent IEEE journal literature on autonomous-mobile-robot path planning, terminally constrained safe navigation, smooth trajectory optimization, and robust MPC. The proposed architecture, mathematical formulation, intended simulation environment, test scenarios, and stability-oriented evaluation metrics are described. The study is presented as a design and methodology contribution; quantitative performance results are not included and are reserved for future experimental validation.

Journal of Computer Based Parallel Programming

Study on Edge-Based Optimization Architectures for Autonomous Climate Control and Secure Sensor Telemetry in Smart Homes

SteadyPath: Ensuring Stability in Autonomous Industrial Maneuvering