Title: Task classification-aware data aggregation scheduling algorithm in wireless sensor networks

Authors: Hongsen Zou; Liang Li; Chen Ao; Puning Zhang; Ning Li; Zheng Wang

Addresses: State Grid Ningxia Electric Power Co. Ltd., Ningxia 750011, China ' State Grid Key Laboratory of Power Industrial Chip Design and Analysis Technology, Beijing Smart-Chip Microelectronics Technology Co., Ltd., Beijing 100192, China ' State Grid Key Laboratory of Power Industrial Chip Design and Analysis Technology, Beijing Smart-Chip Microelectronics Technology Co., Ltd., Beijing 100192, China ' School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China ' State Grid Ningxia Electric Power Company Maintenance Company, Ningxia 750011, China ' State Grid Key Laboratory of Power Industrial Chip Design and Analysis Technology, Beijing Smart-Chip Microelectronics Technology Co., Ltd., Beijing 100192, China

Abstract: In order to minimise the delay of data aggregation scheduling, a task classification aware data aggregation scheduling algorithm is proposed. Through the multi-power and multi-channel approach of sensor nodes, maximum independent sets are used to construct network topology structure based on data aggregation backbone tree. According to the scheduling priority, the data aggregation scheduling within clusters is achieved by approximating the greedy algorithm. Besides, combined with sparse coefficient, sensing task type reduces the amount of data transmission, and then the level of cluster head nodes in the network is used to achieve data aggregation scheduling between clusters. Numerical results show that the proposed algorithm can reduce cluster heads data traffic and energy consumption, while shortening the data aggregation delay and enhancing the network survivability.

Keywords: wireless sensor network; WSN; data aggregation; task classification; sparse coefficient; delay minimisation.

DOI: 10.1504/IJMNDI.2019.105319

International Journal of Mobile Network Design and Innovation, 2019 Vol.9 No.2, pp.106 - 117

Received: 17 Jan 2019
Accepted: 21 Jul 2019
Published online: 24 Feb 2020 *

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