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Modified CSMA/CA protocol for real-time data fusion applications based on clustered WSN

2021; Elsevier BV; Volume: 196; Linguagem: Inglês

10.1016/j.comnet.2021.108243

1872-7069

Achour Achroufene, Mourad Chelik, Nassima Bouadem,

Water Quality Monitoring Technologies

The large amount of data generated by the Internet of Things (IoT) is exploited by the data fusion process to improve the quality of services (QoS) requested by the user. Data fusion systems typically collect and fuse data from various sensor nodes to achieve better data quality (more precision, accuracy, or global view) on the field of interest. For the specific case of the wireless sensor network (WSN) based on cluster-tree topology, the data transmission between nodes and the intermediate data fusion node (DFN) depends primarily on the channel access often managed by the Carrier-Sens Multiple Access with Collision Avoidance (CSMA/CA) protocol. In this article, a modified CSMA/CA is proposed to meet the requirements of the WSN real-time data fusion applications and, in particular, to ensure better management of the transmission channel. It takes into account a transmission time-delay, schedules the retransmission dynamically, and eliminates data packets scheduled beyond the predefined time-delay, thereby releasing the channel to the benefit of other nodes. Moreover, before proceeding with the fusion at the DFN level, it optimizes the probability of receiving data from each node in the specified time-delay. The proposed solution is implemented under the Contiki operating system; it aims to collect data from all nodes within a given time-delay. The results of simulations and experiments show increasing in the data received within time-delay, collisions reduction, and saving energy in the WSN. Moreover, two real-time data fusion applications, namely mobile localization and majority decision-making, are also simulated to support the found results. Compared to the use of CSMA/CA as implemented in Contiki, using the proposed modified CSMA/CA in both data fusion applications improves QoS.