Multi-channel Scheduling Algorithm Research In Data Aggregation Network Based On The Vertex-Coloring

In recent years, Data aggregation Networks which are a newly emerging network technology with huge development potential have been widely applied in many fields of the national economy. A key challenge in multi-channel data aggregation networks is to minimize network conflicts and optimize network data gathering performance with scheduling on channel and TDMA time slot. This paper focuses on this problem and proposes a multi-channel TDMA scheduling algorithm based on Gibbs Sampling. Quantities of comparison experiments under different network parameters demonstrate the excellent performance of the new algorithm.The main problem studied in this paper and its major innovations are summarized as follows.(1) Firstly, the paper models multi-channel scheduling in data aggregation network based on graph coloring theory. Based on the routing tree of wireless network, we use an interference graph to describe interference relationships between communication links in the network, namely, coloring relationship of nodes corresponds to communication conflict relationship of communication link. On this basis, we define the interference field, interference degree and communication conflict, and then we formulate the multi-channel scheduling in data aggregation network in order to minimize network communication conflict.(2) Secondly, we propose a annealing Gibbs Samper Node Coloring Algorithm (GSNC) to solve the problem. We prove that the coloring is a Markov random field problem, then according to the optimization objective, we design an energy function to calculate the selection probability, and we introduce an annealing strategy and we propose a annealing Gibbs Sampler Node Coloring Algorithm. Quantities of comparison experiments under different network parameters demonstrate the algorithm can solve multi-channel TDMA scheduling problem and minimize network communication conflict and scheduling length.The paper has a certain reference value to improve the theoretical level and the function of data aggregation networks.