Speeding Up Exact Algorithms for Maximizing Lifetime of WSNs Using Multiple Cores

Abstract

Maximizing the lifetime of wireless sensor networks is NP-hard, and existing exact algorithms run in exponential time. These algorithms implicitly use only one CPU core. In this work, we propose to use multiple CPU cores to speed up the computation. The key is to decompose the problem into independent subproblems and then solve them on different cores simultaneously. We propose three decomposition approaches. Two of them are based on the notion that a tree does not contain cycles, and the third is based on the notion that, in any tree, a node has at most one parent. Simulations on an 8-core desktop computer show that our approach can speed up existing algorithms significantly.

Performance evaluation of spatial fuzzy C-means clustering algorithm on GPU for image segmentation

“…Many scholars worked on different technologies to speed up their software applications using regular multi-core CPUs [ 15 , 23 ]. Other works have operated on the multiprocessors as a CPUs cluster and have implemented some clustering algorithms such as Fuzzy C-means (FCM) [ 11 ], Spatial Fuzzy C-means (SFCM) [ 18 ] and Gaussian Kernel-based FCM with spatial bias correction [ 34 ].…”