A Hierarchical Energy Conservation Framework (HECF) of Wireless Sensor Networks by Temporal Association Rule Mining for Smart Buildings

The challenge of extending the sensor's energy consumption is a key research issue in Wireless Sensor Networks (WSNs). Recently, association rule mining has proven to be a potential candidate to prolong the lifetime of sensor nodes in WSN. However, temporal correlations of the contextual values are not taken into account which is useful for the sensors to conserve their energy. Similarly, association rules mining at different tiers of the network has not been considered to reduce the number of transmission messages, by avoiding redundant data which is the major cause of the energy drain of sensors. In this paper, a novel Hierarchical Energy Conservation Framework (HECF) is proposed which aims to conserver's energy at each layer of a network by using the Hierarchical Temporal Association Rule Mining in multistory buildings. In hierarchal setup, each floor of the building can conserve energy locally at the local sink and conserve entire network energy at the global sink by using temporal association rule mining at different tiers of the network. The HECF is ideal for large multistory buildings where energy conservation is a major issue along with effective monitoring and system performance. The result shows that HECF outperformed other classical energy conversation methods such as LEACH-C and RR-ScheduleBuffer in terms of energy consumption. It extends 16% network lifetime, also 20% less number of messages during data transmission, which is a remarkable improvement for sensors energy conservation. (c) 2022 Published by Elsevier B.V. on behalf of Faculty of Computers and Information, Cairo University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).