An improved CSMA/CA algorithm based on WSNs of the drug control system

• Published in: Cluster computing Vol. 20; no. 2; pp. 1345 - 1357
• Main Authors: Luo, Zhenjun, Zhong, Luo, Miao, Yongfei, Zhang, Kaisong, Wu, Beiping
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-06-2017; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Automation; Bayesian analysis; Cloud computing; Computer Communication Networks; Computer Science; Computer simulation; Control systems; Efficiency; Energy consumption; Information sources; Internet of Things; Neural networks; Nodes; Operating Systems; Packets (communication); Processor Architectures; Radio frequency identification; Routing (telecommunications); Sensors; Wireless sensor networks; WSNs; Artificial Neural Network; CSMA/CA; Energy efficient; Bayesian valuation
• ISSN: 1386-7857; 1573-7543
• DOI: 10.1007/s10586-017-0828-1

A new improved CSMA/CA algorithm for wireless sensor networks (WSNs) is proposed in this paper to save energy and prolong the life cycle of WSNs. This algorithm is combined with the artificial neural network and Bayesian algorithm according to the practical applications of the drug control system of the Internet of Things. The algorithm is divided into two parts: first, the artificial neural network algorithm is used to estimate the data of WSNs, the results are the reference for the conversion of routing node frequency; second, by using the Bayesian formula, valuation method, and the CSMA/CA’s collision detection mechanism, the algorithm adjusts the frequency of the routing node and the relevant node frequency to establish the normal communication of packets sent by nodes and the aggregation node packets. In this way, it will reduce the collision detection and the back off time and avoid data packet duplication. The simulation tool-NS2 is used to configure an appropriate simulation scene for the experiment, which analyses and compares the received packet rate, the overall energy consumption of the network, and so on. The results demonstrate that the proposed algorithm ensures high energy efficiency and balanced energy consumption. Therefore the results show that the improved algorithm increases the efficiency, so that the network has the function of intelligent learning.