Sensed data in Wireless Sensor Networks (WSN) reflect the spatial and temporal correlations of physical attributes existing intrinsically in the environment. In this article, we present the Clustered AGgregation (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 with small and often negligible and bounded error. In this article we extend our initial work in CAG in five directions: First, we investigate the effectiveness of CAG that exploits the temporal as well as spatial correlations using both the measured and modeled data. Second, we design CAG for two modes of operation (interactive and streaming) to enable CAG to be used in different environments and for different purposes. Interactive mode provides mechanisms for one-shot queries, whereas the streaming mode provides those for continuous queries. Third, we propose a fixed range clustering method, which makes the performance of our system independent of the magnitude of the sensor readings and the network topology. Fourth, using mica2 motes, we perform a large-scale 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. Fifth, we model the spatially correlated data using the properties of our real world measurements. Our experimental results show that when we compute the average of sensor readings in the network using the CAG interactive mode with the user-provided error threshold of, 20%, we can save 68.25% of transmissions over TAG with only 2.46% inaccuracy in the result. The streaming mode of CAG can save even more transmissions (up to 70.24% in our experiments) over TAG, when data shows high spatial and temporal correlations. We expect these results to hold in reality, because we used the mica2 radio profile and empirical datasets for our simulation study. 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.

In wireless sensor networks (WSN), monitoring applications use in-network aggregation to minimize energy overhead by reducing the number of transmissions between the nodes. We note that nearby sensor nodes monitoring an environmental feature (e.g., temperature or brightness) typically register similar values. In this paper, we propose clustered aggregation (CAG), which is a mechanism that reduces the number of transmissions and provides approximate results to aggregate queries by utilizing the spatial correlation of sensor data. The result is guaranteed to be within a user-provided error-tolerance threshold. While a query is disseminated to the network, CAG forms clusters of nodes sensing similar values. Subsequently, only one value per cluster is transmitted up the aggregation tree. We use mathematical models and simulations with synthetic and empirical data to evaluate the efficiency-correctness tradeoff of CAG. Our simulation shows that with highly correlated sensor reading and 10% error threshold, CAG can save the communication overhead by as much as 70.9% over TAG while incurring a modest 1.7% error in result.