Optimization of the lifetime of the battery within
wireless sensor networks (WSNs) is challenging due to
communication infrastructure. Subsequently, minimizing the
amount of power required for data collection and processing to
serve the intended purposes has become an open research
problem. Conventional and compressive sensing-based (CS) query
processing being the candidates to perform these tasks, require a
comparative analysis in the current WSN application context. In this
paper. Simulations have been carried out to compare the
performance of conventional and compressive sensing-based (CS)
query processing with respect to energy efficiency, sensing reliability
and normalized estimation error within WSN. A significant
reduction in the computational complexity reaching 70% is noticed
using CS compared to conventional query processing algorithms.
Moreover, it is observed that up to 90% sensing reliability can be
achieved with CS compared to existing query processing. Hence, the
reduction in computational complexity has not compromised the
sensing reliability with an observed reduction in the normalized
estimation error.