Minimizing Maximum Age of Service in Virtualized Green IoT Networks
This project addresses the challenge of embedding and scheduling applications in solar-powered green IoT networks, with the goal of minimizing the maximum Age of Service (AoS) — a freshness metric indicating the delay between data generation and service completion.
Objectives
The research focuses on virtualized, computation-enabled IoT infrastructures powered by renewable energy (solar). The applications are modeled as Directed Acyclic Graphs (DAGs) with Virtual Network Functions (VNFs) that must be executed under fluctuating energy and computational constraints.
Key Contributions
Mixed Integer Linear Programming (MILP) Formulation
- Proposed the first MILP model to jointly optimize:
- Device selection and sampling time
- DAG request embedding decision
- Energy consumption at devices, gateways, and servers
- Objective: minimize the maximum AoS across all DAG requests.
Heuristic and Predictive Control Solutions
- Developed GreedyOL, a fast heuristic that embeds DAGs based on current AoS.
- Proposed RHCOP, a Receding Horizon Control Optimization framework:
- Utilizes Gaussian Mixture Models (GMMs) to predict solar energy arrivals and wireless channel gains.
- Enables real-time scheduling using only causal (non-future) information.
Results & Insights
- RHCOP achieves a 1.07× and GreedyOL a 1.13× min-max AoS compared to optimal MILP.
- More gateways and servers reduce AoS due to enhanced redundancy and flexibility.
- Equal numbers of VNF-Cs (collection) and VNF-Ps (processing) yield optimal freshness.
Broader Impact
The proposed system lays groundwork for energy-aware, delay-sensitive IoT applications, especially in remote or energy-constrained environments. The results provide insights into the tradeoffs between computation freshness, resource allocation, and green network deployment strategies.
📄 [IEEE Transactions on Services Computing Submission] — Coming Soon