Abstract
Problem statement: Nowadays sensors are very essential for today life to monitor environment where human cannot get involved very often. Wireless Sensor Networks (WSN) are used in many real world applications like environmental monitoring, traffic control, trajectory monitoring. It is more challenging for sensor network to sense and collect a large amount of data which are continuous over time, which in turn need to be forwarded to sink for further decision making process. Clustering of sensory data act as a nucleus job of data mining. A clustering in WSN involves selecting cluster heads and assigning cluster members(sensors) to it for efficient data relay. The contraints in power supply, limited communication, bandwidh, storage resoures are the major challenges in WSN facing today. Conclusion: Proposed study presents K-Means Data Relay (K-MDR) clustering algorithm for grouping sensor nodes there by reducing number of nodes transmitting data to sink node, it reduces the communication overhead and in this manner increase the network performance. Furthermore Conserve and Observe Modes (COM) algorithm reduces the number of nodes within the cluster there by without compromising the coverage face major challenges such as limited communication bandwidth, constraints in power supply and storage resources region of it. The contribution of K-MDR is to reduce power consumption finally the simulation experimental results show that the time efficiency of the algorithm is achieved.
Highlights
Advances in wireless communications made to cultivate tiny hardware components as multifunctional and intelligent sensor nodes with major advantage of low-Power and low-cost.Usually it communicate in short range distances over a radio frequency channel and these devices are small in size.the componenets of these tiny nodes are sensing, processing and communicating data, realize the objectives of wireless sensor networks (Taherkordi et al, 2008)
A large number of integrated sensor nodes from the Wireless Sensor Network which are densely deployed either inside the observable fact or very close to it
In data mining grouping a simliar data is known as clustering which is a preparatory step for future data analysis
Summary
Advances in wireless communications made to cultivate tiny hardware components as multifunctional and intelligent sensor nodes with major advantage of low-Power and low-cost.Usually it communicate in short range distances over a radio frequency channel and these devices are small in size.the componenets of these tiny nodes are sensing, processing and communicating data, realize the objectives of wireless sensor networks (Taherkordi et al, 2008). The distributed environment faces many challenges in data analysis because of privacy and limited bandwidth Fig. 1: Cluster -based Sensor network (Silva et al, 2005). A wireless sensor network is made up of energy efficient sensors in a distributed environment These challenges should be addressed while designing a data clustering algorithms and the sensor constraints in communication and computation are considered. In our proposed approach the goal is to develop an algorithm that clusters the the sensor nodes for a data relay without compromising coverage area. Method would swallow a considerable amount of energy of the individual sensor in the network Another algorithm (COM) would render its function to make the lifetime of the entire network to be enhanced without comprising coverage area within its cluster by minimizing number of nodes contributing to sense and forward data. 10: f(i) ← j where chj∈ Qi stay in observe mode in the communication round
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 callDisclaimer: 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.
