An optimal congestion aware data aggregation and target object tracking approaches for wireless sensor networks

Abstract

Wireless sensor network (wsn) is an infrastructure-less network. it consists of number of spatially dispersed sensor nodes to observe the physical or environmental conditions such as temperature, sound, vibration, pressure, motion or pollutants. due to effective infrastructure, the wsn is widely utilized for performing different kinds of sensing applications. in healthcare application, wsn plays an essential role for directing the health status of people. here, the security becomes major issue due to the occurrence of attacks in wsn. it is necessary to protect the sensitive information of the patient. healthcare data transmission and the performance of target object detection in wsn is enhanced by providing the privacy and security of the patient data. existing research works have been developed to perform the target tracking in wsn by performing the energy efficient routing with the avoidance of malicious sensor nodes. however, increase of packet delivery ratio is not achieved with the reduction of time consumption while performing routing and data aggregation in wsn. this affects the effectiveness in the detection of target objects. in order to address these limitations, this research work introduces three different techniques to perform optimal routing and secured data aggregation to locate the target objects in wsn. the major objective of the proposed trust assisted global and greedy congestion-aware data aggregation (tag-gcda) method is to perform the secured data delivery with maximum packet delivery ratio and less communication overhead. in the proposed tag-gcda method, the trustworthiness reinforced neighbor selection (trns) model is introduced with the aim of detecting the specific neighbor sensor node cohesively with less communication overhead. followed by, the proposed tag-gcda method designs the global cost-based energy conservation (gcec) model newline