Improving Lifetime in Wireless Sensor Networks by Energy Conserved Data Aggregation

Abstract

newline Wireless sensor networks (WSNs) are provided by deploying different sensor newlinenodes in a physical environment in order to monitor the target events where sensor newlinenodes are wirelessly connected. These sensor nodes are operated with a specific newlinetransmission range with restricted energy resources. Energy efficient data collections newlineat sink node and network lifetime improvement in WSN are the challenging tasks to newlineachieve. Sensor network is partitioned and reconstructed with the aim of achieving newlineless energy consumption and hence improve the performance on data transmission in newlineWSN. In order to enhance the network lifetime, spatial and temporal correlations are newlineconsidered while aggregating the data about events from the sensor nodes. newlineTraditional combine-skip-substitute method was introduced with the aim of newlineminimizing the data collection latency in WSN. However, sensor network with newlinemultiple mobile elements are not considered for efficient data collection. Maximum newlineamount shortest path technique was developed to improve network throughput and newlineminimize energy consumption in WSN using genetic algorithm and distributed newlineapproximate algorithm. Though, reconstructing the WSN and tradeoff between energy newlineconsumption and network lifetime are not considered. newlineConnectivity-based data collection algorithm was implemented in WSN in newlineorder to minimize the number of multi-hop communication and balance the energy newlineusage among sensor nodes. Though, packet delivery ratio is not enhanced for data newlinetransmission among the sensor nodes in WSN. Data Routing for In-Network newlineAggregation method was developed for improving data aggregation rate with the help newlineof efficient cluster formation in WSN. But, spatial and temporal correlations of newlineaggregated data are not taken for the construction of routing tree. newlineTraffic Reconnect Set-up Partitioning (TRSP) method is first proposed to newlinereduce the energy consumption while partitioning WSN and time taken to re-establish newlinethe connectivity. Initially, affected locations in the network are determined with the newlineaim of reconstructing the network to improve the performance. Inter partition node newlinegaps are then measured with the application of phantom partitioning concept. In newlineiii newlineproposed TRSP method, double cut technique is utilized for dividing the network into newlinetwo parts with maintained route path connectivity. Network partitioning with double newlinecut technique in WSN provides safe route path by reducing the energy consumption newlineof sensor nodes. Finally, centroid mean point collection is performed on the reconnected newlinepartition free WSN. Proposed TRSP method achieves re-establishment of newlineconnectivity by using centroid mean point collection in order to improve data newlinecollection efficiency. newlineEnergy-efficient Spanning-Tree and Spatial Association-based Data Collection newline(EST-SADC) is then developed with the aim of enhancing packet delivery ratio and newlinenetwork lifetime in WSN. Initially, traffic renovate partitioning is performed for data newlinecommunication over the neighboring sensor nodes. Phantom partitioning, double cut newlinetechnique and centroid mean point method are utilized in traffic renovate partitioning newlinefor reducing energy consumption. Then, Energy-efficient Spanning-Tree based newlineinitialization is carried out with traffic renovate partitioning for improving packet newlinedelivery ratio. Node having highest energy is assigned for the root node of spanning newlinetree whereas other nodes are linked based on shortest route path. Finally, Spatial newlineAssociation-based Data Collection is performed by using probability measure and newlinespatial associated value for improving network lifetime. newlineSpatio Temporal Correlated Buffered Data Aggregation (STC-BDA) method newlineis proposed in order to reduce the energy consumption of sensor nodes by partitioning newlinethe detected sensor events in WSN. Initially, end-to-end delay is reduced by using newlineSpatio-Temporal Data Point selection algorithm where the gathered data are newlinetransmitted in WSN with the help of meeting points. Then, Buffered Data newlineAggregation algorithm is implemented with the assignment of separate buffers to the newlinerespective information about events. These buffers are utilized for overcoming the newlinevulnerabilities present in data aggregation for WSN. Proposed STC-BDA method newlineinvestigates the data aggregation process in order to reduce the energy conservation newlineand therefore improve the network lifetime in WSN. newlineExperimental results confirm that, time taken to re-establish the connectivity is newlinereduced in proposed TRSP method 28% by using double cut technique which offers newlinebetter connectivity. Data collection efficiency is improved by 21% in proposed ESTiv newlineSADC method by utilizing centroid mean point collection and Spatial Associationbased newlineData Collection among sensor nodes. Proposed EST-SADC method helps for newlineimproving packet delivery ratio by 18% with the application of Energy-efficient newlineSpanning-Tree algorithm. Proposed STC-BDA method reduces energy consumption newlineup to 29% by utilizing Partition Spanning Tree construction for the sensor nodes in newlineWSN. Proposed STC-BDA method improves network lifetime by 19% with the newlineimplementation of Buffered Data Aggregation algorithm. End-to-end delay is also newlinereduced by 24% in proposed STC-BDA method by using Spatio-Temporal Data Point newlineselection algorithm for data collection.