Energy and Interference-Aware Scheduling for Minimizing the Age of Aggregate Information in Multi-Hop IoT Networks

In the realm of complex IoT-based smart city advancements, the real-time reception, processing, and maintenance of up-to-date multi-sourced data is essential for ensuring efficient urban infrastructure operations and functionality. Beyond the typical Age of Information (AoI), such applications vociferate the urgent need for a new metric, capable of capturing and accounting for the age of the aggregated data; namely, the Age of Aggregated Information (AoAI). This paper addresses an AoAI minimization problem for mixed-paths IoT networks. This problem is formulated as a Mixed Integer Linear Program (MILP) that jointly considers data packet scheduling and routing as well as nodal energy and power constraints. To overcome this problem’s notable complexity, the Column Generation Algorithm (CGA) is used to break it down into a Relaxation Master Problem (RMP) and a Pricing Problem (PP) with the objective of identifying optimal scheduling and aggregation strategies. Experimental results demonstrate the potency of the proposed CGA-based algorithm in generating accurate sub-optimal solutions with no more than 1.06% deviation from their optimal counterparts; an outstanding result that existing algorithms have failed to achieve. The variations in AoAI and latency trends were compared and found to be in-line for fixed network configurations.