Quantum Particle Swarm Optimization and Compressive Sensing-Based Clustering Protocol for Wireless Sensor Networks

Abstract

Wireless sensor networks play important role to build various smart systems such as health, medical, military, etc. A wireless sensor network contains tiny sensor nodes to sense information of given environment. But these sensor networks are battery constrained. Therefore, become dead after certain period. Also, the batteries of these sensor nodes are not rechargeable and even not replaceable. Therefore, conserving the energy of these sensor nodes become more challenging. Many researchers have developed various protocols to reduce the energy consumption. But it is still defined as an open area of research. Therefore, in this paper, we have designed a novel quantum particle swarm optimization and compressive sensing-based clustering protocol. Extensive experiments show that the proposed protocol indicates better energy conservation as compared to the competitive protocols.

Ensemble Learning Approach for Effective Software Development Effort Estimation with Future Ranking

“…Investigated several data sets and obtained encouraging findings for software development effort assessment. When estimating software maintenance effort using particle swarm optimization, (Singh, C., et al, 2019) proposed a successful swarm intelligence-based method. Undefined constraints, product quality, massive organizational involvement, overestimation, and underestimation are some of the major problems with current methods, despite the fact that the standard techniques of SDEE are readily available, as explained in the preceding section.…”