Optimization of data aggregation process in HWSN using adaptive clustering protocols

Abstract

The Wireless sensor networks (WSNs) are intended for use in applications where energy is constrained and therefore, the longevity of the network is an essential aspect to the design. From WSN protocols and data aggregation techniques which reduce the numbers of redundant data packets, save energy and make the network lifespan longer, have led to the increased importance of heterogeneous WSNs; Consequently, there is the need for efficient data gathering protocols that minimize energy consumption, congest time and enhance network lifetime of the HWSNs, This study focuses particularly on clustering algorithms for data collection in HWSNs. Clustering makes it easy to organize the sensor nodes and the operation of the main cluster involves collecting information from the nodes and delivering it to the sink node. Furthermore, this review goes further to explore other types of clustering methods such as LEACH, TEEN and SEP to determine their ability to improve data aggregate performance These reveal the special characteristics of HWSNs and the challenges faced; the network impact is shown and factors that affect the aggregate efficiency such as data collection, clustering and head selection, and routing are identified Advanced techniques like hierarchical clustering, Machine learning and Fuzzy logic based are also discussed as possible solutions Finally the review presents static issues such as security, reliability, and power and overviews major conclusions and future research agenda. The research contributes to the development of more effective ideas to further improve the network based on the assessment of WSN performance and the longevity of the wireless sensor network The research emerges as a handy tool for researchers and professionals in the field, most especially in data aggregation for HWSNs.