Orchestrating Data Collection and Computation in Green IoT Networks

2026-05-21

AI Summary

This study addresses the joint scheduling of data sensing and computation tasks in energy-harvesting green Internet of Things (IoT) systems under maximum Age of Service (AoS) constraints. It jointly optimizes device sampling instants, task offloading decisions, and energy consumption across the end-edge-cloud continuum, with the objective of minimizing the maximum AoS. To this end, the work presents the first mixed-integer linear programming (MILP) formulation tailored to this scenario. Furthermore, an efficient embedded algorithm is developed by integrating receding horizon control (RHC) with a greedy AoS-aware strategy. Experimental results demonstrate that the proposed method achieves a maximum AoS only 1.07× and 1.13× higher than the optimal MILP solution, respectively, thereby offering a compelling balance between timeliness, practicality, and scalability.

Abstract

Future Internet of things (IoT) networks will host applications that involve data collection and computation tasks on one or more servers. To this end, this paper proposes the first mixed integer linear program (MILP) to schedule and embed applications on energy harvesting nodes, where it optimizes (i) the sampling time of devices, (ii) whether to run an application, and (iii) the energy usage of devices, gateways and servers. To ensure applications are run often, we adopt the maximum age of service (AoS) metric, and set the MILP's objective to minimize the maximum AoS or min-max AoS of applications. This paper also proposes two novel solutions: (i) a receding horizon control (RHC) based method, and (ii) a solution that greedily embeds applications according to their AoS. The results show that the min-max AoS of RHC and greedy approach is respectively 1.07x and 1.13x higher than MILP.

Problem

• Green IoT
• data collection
• computation scheduling
• energy harvesting
• age of service

Innovation

• Mixed Integer Linear Program (MILP)
• Age of Service (AoS)
• Receding Horizon Control (RHC)
• Energy Harvesting IoT
• Application Embedding