Effects Of Data Aggregation In Wireless Sensor Networks

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Data Aggregation in Wireless Sensor Networks Introduction
Wireless sensor networks are usually deployed in infrastructure-less environment with the sensor nodes themselves being battery powered (low powered) devices so, data aggregation is an attractive method to reduce the power requirements of the individual sensor nodes that sense, record and transmit the data about their surroundings. Data aggregation may refer to any number of methods that allow for the reduction of the volume of data that the sensor nodes must exchange by combining data from multiple sources and removing redundancies, thereby reducing the power consumption by the sensor nodes.
The main goal of data aggregation algorithms is to gather and aggregate data in an energy
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Some node like cluster head aggregate the data prompting in-network aggregation. Aggregation structures can be hierarchy based or backbone based or even structureless. Structure less protocols try to eliminate the energy consumption of maintaining structures and are preferred for small networks. For larger ad-hoc wireless sensor networks hierarchy based networks like tree-based, cluster-based aggregation is preferred.
The Tiny AGgregation(TAG), Energy Aware Data Aggregation (EADA) are examples of protocols based on Tree structure. Low Energy Adaptive Clustering Hierarchy (LEACH) is a well-known cluster based protocol. Both tree-based and cluster-based rely on passing and aggregating data at a higher-level node (cluster head/ root of branch) thereby exploiting the topology of the network. Theses protocols aggregate data from multiple sources at a node a layer higher in topology to the source node. Usually statistical sampling is done to remove the redundancies and improve the accuracy of generated data (that is sent) to the raw data.
Aggregation structures introduce latency to the network but reduce the amount and number of transmissions thereby saving the network capacity.
Aggregation Functions: Protocols based purely on how to aggregate data exists. These protocols rely on forecasting and/or compression of data to reduce the transmissions and hence improve the energy efficiency. However, the tradeoff here is that the
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