Abstract
Abstract: In wireless sensor networks, efficient and effective data aggregation algorithms can prolong the network lifecycle by reducing communication of redundant data and improve the security of the networks. Tradition data aggregation algorithms in wireless sensor networks mainly aim to improve the energy utilization, and ignore the security and lifecycle. In order to get a good trade-off between these requirements, we proposed a data aggregation algorithm based on constructing a data aggregation tree. After give a formalism description of the problem, we proposed a data aggregation tree constructing algorithm. By minimize the maximal energy consumption of nodes, the algorithm can prolong the lifecycle. In data aggregation scheduling algorithm, we select the number of communications carefully to get the trade-off between low weighted delay and high network lifecycle. The simulation experiments show that, the proposed data aggregation algorithm consumes less energy while aggregating data from sensor nodes, and thus can prolong the network lifecycle.
Highlights
The wireless sensor networks consist thousands of or even tens of thousands of different kinds of sensors are important computing platforms
In order to get a good trade-off between these requirements, we proposed a data aggregation algorithm based on constructing a data aggregation tree
After give a formalism description of the problem, we proposed a data aggregation tree constructing algorithm
Summary
The wireless sensor networks consist thousands of or even tens of thousands of different kinds of sensors are important computing platforms. The sensors in a wireless sensor network are deployed randomly in an area to monitor and collect data of the real world environment [1]. Researchers have proposed many data aggregation algorithms for data of wireless sensor networks. The main idea of these algorithms is that, decrease the collision probability of data in wireless channels by reducing the transmitting data, and improve the efficiency of data collecting; check the validity of sensor nodes by comparing data collected from adjacent nodes, discard the data from the disabled nodes to reinforcement the accuracy of data; and aggregate the data sensed from different kinds of sensors to bridge the gap between the high-level user requirements with the low-level raw sensed data [7]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Online and Biomedical Engineering (iJOE)
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
