FLECHA : um mecanismo preditivo para eleição de coletores semânticos em grupos de nós sensores sem fio

Abstract

Technological advances in microelectronics and wireless communications have enabled a fast development of the so-called Low-Power and Lossy Networks (LLN), including Wireless Sensor Networks (WSN), which are comprised of several sensing embedded nodes. Nowadays, these small-size nodes play a key role as a sensing and communication infrastructure for the smart-x applications, such as smart environment, smart metering, smart agriculture, and green smart cities. Although there are interesting energy-harvesting solutions in outdoor and low power applications, most of the sensor nodes are battery-operated, making energy efficiency a critical goal. The semantic clustering is a technique based on similarity data for forming the semantic cluster. This technique has become an efficient solution for increasing the WSN lifetime compared to traditional clustering, however, there is still an excessive power expenditure in the collector nodes (leaders of the semantic cluster) due to the accumulation of inherent functions in it . Our approach aims to show a solution to reduce power consumption of WSN through a predictive election of semantic collectors using the ARIMA method. The FLECHA extends decentralized approach of DSENSE, which make it self-adaptive through a collaborative process. Our results show that the predictive election of semantic collectors increases the WSN lifetime in relation to reactive methods of election for cluster leaders.