Exact Algorithms for the Minimum Load Spanning Tree Problem

In a minimum load spanning tree (MLST) problem, we are given an undirected graph and nondecreasing load functions for nodes defined on nodes' degrees in a spanning tree, and the objective is to find a spanning tree that minimizes the maximum load among all nodes. We propose the first O*(2(n)) time exact algorithm for the MLST problem, where n is the number of nodes and O* ignores polynomial factor. The algorithm is obtained by repeatedly querying whether a candidate objective value is feasible, where each query can be formulated as a bounded degree spanning tree problem (BDST). We propose a novel solution to BDST by extending an inclusion-exclusion based algorithm. To further enhance the time efficiency of the previous algorithm, we then propose a faster algorithm by generalizing the concept of branching walks. In addition, for the purpose of comparison, we give the first mixed integer linear programming formulation for MLST. In numerical analysis, we consider various load functions on a randomly generated network. The results verify the effectiveness of the proposed algorithms. Summary of Contribution: Minimum load spanning tree (MLST) plays an important role in various applications such as wireless sensor networks (WSNs). In many applications of WSNs, we often need to collect data from all sensors to some specified sink. In this paper, we propose the first exact algorithms for the MLST problem. Besides having theoretical guarantees, our algorithms have extraordinarily good performance in practice. We believe that our results make significant contributions to the field of graph theory, internet of things, and WSNs.

Automated summary

Minimum load spanning tree (MLST) problem plays a significant role in various applications, such as wireless sensor networks (WSNs). In this paper, the first exact algorithms for the MLST problem are proposed, which not only have theoretical guarantees but also exhibit extraordinary performance in practice. The results of the proposed algorithms contribute significantly to the fields of graph theory, internet of things, and WSNs.