Open AccessModel of dynamic clustering‐based energy‐efficient data filtering for mobile RFID networks
Author(s) -
Vo Viet Minh Nhat,
Le Van Hoa
Publication year - 2021
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.2020-0009
Subject(s) - cluster analysis , computer science , base station , energy consumption , cluster (spacecraft) , energy (signal processing) , task (project management) , efficient energy use , radio frequency identification , real time computing , data mining , computer network , distributed computing , engineering , artificial intelligence , electrical engineering , statistics , mathematics , computer security , systems engineering
Data filtering is an essential task for improving the energy efficiency of radio‐frequency identification (RFID) networks. Among various energy‐efficient approaches, clustering‐based data filtering is considered to be the most effective solution because data from cluster members can be filtered at cluster heads before being sent to base stations. However, this approach quickly depletes the energy of cluster heads. Furthermore, most previous studies have assumed that readers are fixed and interrogate mobile tags in a workspace. However, there are several applications in which readers are mobile and interrogate fixed tags in a specific area. This article proposes a model for dynamic clustering‐based data filtering (DCDF) in mobile RFID networks, where mobile readers are re‐clustered periodically and the cluster head role is rotated among the members of each cluster. Simulation results show that DCDF is effective in terms of balancing energy consumption among readers and prolonging the lifetime of the mobile RFID networks.