Quality Aware Data Aggregation Trees in Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) are key enablers for IoT and pervasive computing paradigm. While devices are being seamlessly enabled with connection and communication capabilities, exploring techniques to quantify and improve quality has gathered significance. This work explores quality of a Data Aggregation Tree (DAT) in sensor networks. DATs are building blocks for data collection in WSNs. In this work Quality of Experience (QoE) and Quality of Service (QoS) of DATs is evaluated using data aggregation ratio $$\alpha $$ and generated data $$\delta $$ respectively. An algorithm Quality Aware Data Aggregation Tree (QADAT) to construct a quality aware DAT is proposed. QADAT adapts the DAT to network and user expectation dynamics. Simulation results show the effectiveness of the proposed algorithm and demonstrates quality awareness through DAT adaptability.