Wireless Sensor Networks (WSN) have drawn the attention of many researchers as well as general users in recent years. Since WSN has a wide range of applications, including environmental monitoring, medical applications, and surveillance, their usage is not limited. As energy is a major constraint in WSN, it is necessary to employ techniques that reduce energy consumption in order to extend the network’s lifetime. Clustering, data aggregation, duty cycling, load balancing, and efficient routing are some of the techniques used to reduce energy consumption. In this paper, we discuss in details about clustering, its properties, the existing clustering protocols. The clustering protocols that support data aggregation will also be discussed. The paper concludes with considering the impact of clustering and data aggregation in WSN.
Simulated Annealing (SA), Tabu Search (TS), Genetic Algorithm (GA), and Ant Colony System (ACS) are four of the main algorithms for solving challenging problems of intelligent systems. In this paper, these four techniques and three novel hybrid combinations of them are proposed to mammogram segmentation. The novel hybrid algorithms consist of a Sequential TS-ACS, a Hybrid ACS/TS, and a Sequential ACS-TS algorithms. Initially the mammogram images are enhanced and Markov Random Field (MRF) is applied to label the image pixels, and the Maximizing a Posterior (MAP) is calculated for each pixel. To find out the optimum label, which minimizes the MAP estimate, the heuristic algorithms are applied. Statistical comparative analysis conclude that all of the three proposed novel techniques are significantly better than each of their nonhybrid competitors, and furthermore the Sequential ACS-TS provides the superior solution of all.
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