Maximum-Quality Tree Construction for Deadline-Constrained Aggregation in WSNs

TL;DR

This paper introduces a novel approach for constructing optimal aggregation trees in deadline-constrained wireless sensor networks, significantly improving data aggregation quality through distributed algorithms despite NP-hard complexity.

Contribution

It formulates the tree construction as a combinatorial optimization problem, employs Markov approximation for near-optimal solutions, and proposes efficient distributed algorithms with improved convergence.

Findings

Proposed algorithms outperform existing methods in aggregation quality.

Markov approximation effectively finds near-optimal trees.

New initial tree construction accelerates convergence.

Abstract

In deadline-constrained wireless sensor networks (WSNs), quality of aggregation (QoA) is determined by the number of participating nodes in the data aggregation process. The previous studies have attempted to propose optimal scheduling algorithms to obtain the maximum QoA assuming a fixed underlying aggregation tree. However, there exists no prior work to address the issue of constructing optimal aggregation tree in deadline-constraints WSNs. The structure of underlying aggregation tree is important since our analysis demonstrates that the ratio between the maximum achievable QoAs of different trees could be as large as O(2^D), where D is the deadline. This paper casts a combinatorial optimization problem to address optimal tree construction for deadline-constrained data aggregation in WSNs. While the problem is proved to be NP-hard, we employ the recently proposed Markov approximation framework and devise two distributed algorithms with different computation overheads to find close-to-optimal solutions with bounded approximation gap. To further improve the convergence of the proposed Markov-based algorithms, we devise another initial tree construction algorithm with low computational complexity. Our extensive experiments for a set randomly-generated scenarios demonstrate that the proposed algorithms outperforms the existing alternative methods by obtaining better quality of aggregations.