Learning Automata-Based Data Aggregation Tree Construction Framework for Cyber-Physical Systems

Jiankang Ren, Guowei Wu, Xiaoyan Su, Guanghai Cui, Feng Xia, Mohammad S. Obaidat

Abstract: A high degree of energy efficiency and real-time for data transmission is required in cyber-physical systems (CPSs). Data aggregation is an efficient technique to conserve energy by reducing the amount of transmission data. To optimize real-time communication under constraints of power consumption and data aggregation performance of each node in CPS, this paper presents a learning automata (LA)-based degree-bounded bottleneck data aggregation tree (DBBDAT) construction framework to minimize the maximum delay on data aggregation trees with bounded degree, which is an NP-hard problem. We model the network of CPS as a connected weighted and directed graph to form a network of LA. Degree-bounded data aggregation trees are constructed first by the action selection of each automaton. Then, the action vector of each automaton is updated by linear reward-inaction learning algorithm, and at last DBBDAT is constructed based on a threshold. Simulation results show that our approach significantly outperforms integer linear programming (ILP)-based method in terms of time complexity. Compared with ILP-based method, it can obtain an optimal solution or a suboptimal solution with guaranteed approximation ratios, and can control the tradeoff between accuracy and cost by choosing appropriate learning rate and threshold. Its distributed implementation is simple and it can efficiently solve the problem for the sparse graph in practice.

External IDs:dblp:journals/sj/RenWSCXO18