@Article{2019.100000132,
AUTHOR = {Isma Farah Siddiqui, Scott Uk-Jin Lee, Asad Abbas},
TITLE = {A Novel Knowledge-Based Battery Drain Reducer for Smart Meters},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {1},
PAGES = {107--119},
URL = {http://www.techscience.com/iasc/v26n1/39847},
ISSN = {2326-005X},
ABSTRACT = {The issue of battery drainage in the gigantic smart meters network such as 
semantic-aware IoT-enabled smart meter has become a serious concern in the 
smart grid framework. The grid core migrates existing tabular datasets i.e., 
Relational data to semantic-aware tuples in its Resource Description Framework 
(RDF) format, for effective integration among multiple components to work 
aligned with IoT. For this purpose, WWW Consortium (W3C) recommends two 
specifications as mapping languages. However, both specifications use entire 
RDB schema to generate data transformation mapping patterns and results 
large quantity of unnecessary transformation. As a result, smart meters use 
huge computing resources, maximum energy capacity and come across battery 
drain problems. This paper proposes a novel semantic-aware battery drain 
optimization strategy ‘SPARQL Auto R2RML Mapping (SARM)’ that generates 
custom RDF patterns with precise metadata and avoids use of full schema 
along with optimized usage of network resources through (i) selective metadata 
migration, and (ii) optimal battery usage. The proposed approach effectively 
increases battery life with a balanced proportion of energy consumption and 
reduces meter load congestion which happens to be another vital reason of 
battery drain problem. The presented knowledge-based battery drain 
prevention strategy is evaluated over an RDB dataset using three types of 
SPARQL queries; Basic, Nested and Join. Furthermore, the R2RML processors 
evaluated SARM over the most recent Berlin SPARQL Benchmark datasets which 
depicts that SARM is efficient 40.4% in mapping generation time and 10.46% in 
average planning time than default RDB2RDF transformations. Finally, SARM 
significantly improves total execution time of RDB2RDF migration with an 
efficiency of 8.82% and conserves battery drain by 18.5% over the smart grid 
data cluster.},
DOI = {10.31209/2019.100000132}
}