An Experimental Study of the Effectiveness of Clustered AGgregation ( CAG ) Leveraging Spatial and Temporal Correlations in Wireless Sensor Networks

Sensed data in Wireless Sensor Networks (WSN) reflect the spatial and temporal correlations of physical attributes existing intrinsically in the environment. In our previous work, we proposed CAG (Clustered AGgregation) which exploits this spatial correlation to trade-off accuracy for efficiency during in-network aggregation. In this paper, we present the updated CAG algorithm that forms clusters of nodes sensing similar values within a given threshold (spatial correlation), and these clusters remain unchanged as long as the sensor values stay within a threshold over time (temporal correlation). With CAG, only one sensor reading per cluster is transmitted whereas with Tiny AGgregation (TAG) all the nodes in the network transmit the sensor readings. Thus, CAG provides energy efficient and approximate aggregation results where the error is bounded by a user-provided threshold. In this paper we extend our study in five directions: First, we design CAG for two modes of operations (interactive and streaming) to enable CAG to be used in different environments and for different purposes. Interactive mode is appropriate for dynamic and ad-hoc queries, whereas the streaming mode is appropriate for continuous queries. Second, we propose a fixed range clustering method which makes the performance of our system independent of the magnitude of sensor readings and the network topology. Third, using mica2 motes, we perform a largescale measurement of real environmental data (temperature and light, both indoor and outdoor) and the wireless radio reliability, which were used for both analytical modeling and simulation experiments. Fourth, we model the spatially correlated data using the properties of our real world measurements. Fifth, we investigate the effectiveness of CAG that exploits the temporal as well as spatial correlations using both the measured and modeled data. Our experimental result shows that CAG in the interactive mode, with the user-provided error threshold of 10%, can save 50% of energy over TAG with only 4% inaccuracy in the result. The streaming mode of CAG can save even more energy (up to 73.21%) over the interactive mode when data shows high temporal correlation. CAG is the first system that leverages spatial and temporal correlations to improve energy efficiency of in-network aggregation. This study analytically and empirically validates CAG’s effectiveness.

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