Wireless sensor network (WSN) is used to sense the environment, collect the data, and further transmit it to the base station (BS) for analysis. A synchronized tree-based approach is an efficient approach to aggregate data from various sensor nodes in a WSN environment. However, achieving energy efficiency in such a tree formation is challenging. In this research work, an algorithm named fuzzy attribute-based joint integrated scheduling and tree formation (FAJIT) technique for tree formation and parent node selection using fuzzy logic in a heterogeneous network is proposed. FAJIT mainly focuses on addressing the parent node selection problem in the heterogeneous network for aggregating different types of data packets to improve energy efficiency. The selection of parent nodes is performed based on the candidate nodes with the minimum number of dynamic neighbors. Fuzzy logic is applied in the case of an equal number of dynamic neighbors. In the proposed technique, fuzzy logic is first applied to WSN, and then min–max normalization is used to retrieve normalized weights (membership values) for the given edges of the graph. This membership value is used to denote the degree to which an element belongs to a set. Therefore, the node with the minimum sum of all weights is considered as the parent node. The result of FAJIT is compared with the distributed algorithm for Integrated tree Construction and data Aggregation (DICA) on various parameters: average schedule length, energy consumption data interval, the total number of transmission slots, control overhead, and energy consumption in the control phase. The results demonstrate that the proposed algorithm is better in terms of energy efficiency.

A hybrid approach combining genetic algorithm(GA) and K-means algorithm, called KGA is proposed in this paper for design of clustering protocol with energy efficiency for non-homogeneous wireless sensor network. The problem of optimal clustering can be considered as a problem for searching for an optimal number of clusters in a big search space such that WSN metrics are optimized. In the proposed protocol, distance between clusters, distance within clusters and a number of cluster heads are employed to search for optimal number of clusters and cluster heads. Maximization of energy saving and lifetime of a network are the two important metrics. The KGA is designed with a hybrid approach to population initialization scheme and objective function. The superiority of the protocol over other heuristic and meta-heuristic techniques is extensively demonstrated on several parameters: energy efficiency, network life time and throughput

Persistent buccopharyngeal membrane is an extremely rare clinical entity that can be diagnosed easily by simple examination of the oral cavity and confirmed by a lateral contrast x-ray. The abnormality can be easily corrected surgically.