Data Aggregation Scheduling in Wireless Sensor Networks under SINR

Abstract

Data aggregation is an essential operation in various applications of wireless sensor networks (WSNs), where sensor nodes sense information and forward the sensing data to a sink node via multi-hop wireless communications. Most existing data aggregation algorithms were based on protocol interference model, which were not practical in real schemes. In this paper, we propose efficient algorithms to decrease data aggregation delay under physical interference model, that is, the SINR (signal to interference plus noise ratio) model. Firstly, we apply previous distributed minimum spanning tree (MST) construction method to form a MST under SINR model. Secondly, we propose a link scheduling algorithm DALS by gird partition, and finally we combine MST and DALS into data aggregation algorithm DA with node states transition. We theoretically prove that the aggregation scheduling algorithm is θ-approximation with θ = K2Γ and obtain the latency of DA O(HΔ), where K is a positive integer depended on α, β and Γ = [Pmax(2√2+1)α/Nβ2(1—1/Kα)] is a constant. H is the height of MST and Δ is the maximum node degree in G(V, E).