The Clustered AGgregation (CAG) technique leveraging spatial and temporal correlations in wireless sensor networks

Abstract

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.

Energy Optimisation Schemes for Wireless Sensor Network

“…Two more recent weight-based protocols were proposed in [41,42]. In [41] [DistributedWeightBased Energy-Efficient Hierarchical Clustering (DWEHC)] a corresponding distributed algorithm is given, which aims at high energy efficiency by generating balanced cluster sizes and optimizing the intracluster topology.…”

“…Similarly, in [42] (Topology Adaptive Spatial ClusteringTASC), the authors propose another distributed algorithm that partitions the network into a set of locally isotropic, nonover-lapping clusters without prior knowledge of the number of clusters, cluster size, and node coordinates. This is achieved by deriving a set of weights that include distance, connec-tivity, and density information within the locality of each node.…”

Adaptive Data Quality for Persistent Queries in Sensor Networks

“…Approximate Query Processing: Since performing in-network aggregation [16] by distributing the computation through out the network can be quite expensive, approximate querying techniques were designed to provide estimates of answers. Clustered Aggregation (CAG) [22] creates clusters where nodes with highly correlated data form a group, and only the cluster head is involved in transmitting data. CAG's emphasis is network structure maintenance while ours is to adapt the approximation technique based on the dynamics of the network.…”

“…Approximate query processing typically models the data at a base station and periodically updates the stored model using values from the network [3]. Other approaches form spatially correlated groups, and one node from every group participates in the query [22]. This results in fewer message transmissions and removes the need to perform computation at every node.…”

“…Some approximation algorithms cluster nodes with highly correlated sensor values [22]. This requires a priori knowledge of the range of data values.…”