Tradeoffs among Delay, Energy and Accuracy of Partial Data Aggregation in Wireless Sensor Networks

Abstract

Due to the Recent development in wireless technology, wireless sensor networks attract researchers’ attention because of their applicability in many fields for effective collection of sensing data with low cost. Wireless sensor networks have many applications; some of the applications are military application, environmental application and flood detection. For example, in an environmental application for forest fire detection, the sensor nodes sense the fire information, then transmit or relay the information to base station in a multi-hop way. In wireless sensor networks, energy saving is critical issue as sensor nodes are battery-powered. Here we propose, partial data aggregation as one of the energy saving technique. In this paper, we analyze the tradeoffs among communication delay, energy consumption, and data accuracy of the partial data aggregation technique and discuss the results. First, we analyze the partial data aggregation with Markovian chain; analytical result shows that, non-aggregation method suffers large energy consumption while full aggregation suffers long transmission delay. From the analysis results, we find that the proposed partial aggregation method WRP (Waterfalls Random partial aggregation) can trade off energy consumption and transmission delay. Thus, we discuss the tradeoffs among data accuracy, transmission delay and energy consumption with different criteria and parameters. The results show that we could control the significance of transmission delay, energy consumption and data accuracy by tradeoffs index (TOI). We also analyze the several applications of wireless sensor networks with different significance based on the TOI. From the observed results, we found that we could set the significance of transmission delay, energy consumption and data accuracy for different applications based on different criteria TOI. Thus, by evaluating and comparing the criteria with different data generation rate as well as aggregation factor, we get the least TOI value, which denotes the desired tradeoffs among them.