Task Offloading and Approximate Computing in Solar Powered IoT Networks
Task Offloading and Approximate Computing in Solar Powered IoT Networks
This research proposes a novel framework for minimizing the total energy consumption of solar-powered IoT networks through task offloading and approximate computing. Devices can choose between local execution (exact or approximate) or offloading tasks to a solar-powered edge server.
Core Objectives
- Reduce energy usage by allowing approximate task execution when tolerable errors are acceptable.
- Leverage digital twins (DTs) to estimate future energy availability and channel conditions.
- Optimize offloading decisions and resource allocation across time slots and channels.
Technical Highlights
MILP Formulation
- Designed the first MILP to jointly optimize:
- Task offloading decisions
- Approximate vs. exact execution
- Channel allocation
- Virtual machine (VM) assignment
- Captures constraints on energy arrivals, CPU cycles, approximation error bounds, and VM capacity.
DT-Assisted Receding Horizon Control (DT-RHC)
- Introduced a DT-based control algorithm using:
- Gaussian Mixture Models (GMMs) to predict energy and channel gain
- Sliding-window MILP optimization for dynamic scheduling
- Achieves energy usage within 1.62× of MILP optimal while requiring only causal (past) data
Results & Evaluation
- DT-RHC significantly outperforms random strategies across metrics such as:
- Energy consumption vs. number of devices
- Impact of approximation ratios
- Task completion within extended time horizons
- Simulations conducted in Python + Gurobi over 100×100 m² deployments using realistic solar input and wireless models.
Conclusion
This study demonstrates the viability of integrating approximate computing and intelligent offloading in renewable-powered IoT environments. It provides a robust foundation for future distributed optimization and adaptive energy-aware network control.