Data-driven Energy Conservation in Wireless Sensor Networks: A Survey
Keywords:
WSN, Similarity Metrics, Euclidean Distance, Cosine similarity, Pearson correlation coefficientAbstract
Wireless Sensor Network (WSN) consists of large number of sensor nodes deployed at physical location for sensing & monitoring some physical phenomenon like temperature, humidity, pressure, soil moisture etc. Energy Conservation is one of the prime challenges in WSN owing to the limited battery power and unattended deployment of sensor nodes. There are various techniques motivated towards conserving energy in order to increase overall lifetime of the network. Various schemes and protocols like energy efficient media access control and Routing protocols, Data cycling techniques, Power Management, Sleep Management, Datadriven techniques etc. are used for energy conservation. This paper mainly focuses on exploitation of data-driven techniques in which the sensed data is statistically analyzed in order to find a clue which can save energy requirements. Spatial and temporal correlation between sensor node data can be exploited to propose and efficient data-acquisition technique. The inter-node data association can be measured by calculating the most commonly used metrics for measuring degree of similarity. Finally, data-driven energy conservation technique can be applied if high correlation between inter-node observations is identified.
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