Active Neighbor Exploitation for Fast Data Aggregation in IoT Sensor Networks

Abstract

Fast data aggregation is crucial to facilitate critical Internet of Things (IoT) services as it collects all sensory data under restricted volume and time using in-network computation. The minimum latency data aggregation scheduling problem has attracted a lot of attention for the last decade. Existing approaches for the problem schedule all data transmissions based on an aggregation tree which was typically constructed without secondary interference awareness. Such interference causes numerous delay when scheduling a transmission from a node to its parent in the tree. When a sensor is active at multiple timeslots in a working period, the secondary interference can simply be omitted by scheduling a node to transmit its data to an active neighbor instead of its parent. This paper presents an Active Neighbor EXploitation (ANEX) approach which enables sensor nodes to switch their parents by discovering active neighbors for possible connectivity, regardless of the receivers established in the tree. Furthermore, the scheme prioritizes scheduling nodes with the fewest unscheduled active neighbors in order to execute more concurrent transmissions. We evaluate ANEX by theoretical analysis and extensive simulations with various scenarios. The results show that ANEX outstandingly achieves faster aggregation than the state-of-the-art approach by up to 86% while having an equivalent time complexity.