Energy-Aware Data Collection in Sensor Networks: A Localized Selective Sampling Approach
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One of the most prominent and comprehensive ways of data collection in sensor networks is to periodically extract raw sensor readings. This way of data collection enables complex analysis of data, which may not be possible with in-network aggregation or query processing. However, this flexibility in data analysis comes at the cost of power consumption. In this paper, we introduce selective sampling for energy-efficient periodic data collection in sensor networks. The main idea behind selective sampling is to use a dynamically changing subset of nodes as samplers such that the sensor readings of sampler nodes are directly collected, whereas the values of non-sampler nodes are predicted through the use of probabilistic models that are locally and periodically constructed in an in-network manner. Selective sampling can be effectively used to increase the network lifetime while keeping quality of the collected data high, in scenarios where either the spatial density of the network deployment is superfluous relative to the required spatial resolution for data analysis or certain amount of data quality can be traded off in order to decrease the overall power consumption of the network. Our selective sampling approach consists of three main mechanisms. First, sensing-driven cluster construction is used to create clusters within the network such that nodes with close sensor readings are assigned to the same clusters. Second, correlation-based sampler selection and model derivation is used to determine the sampler nodes and to calculate the parameters of probabilistic models that capture the spatial and temporal correlations among sensor readings. Last, selective data collection and model-based prediction is used to minimize the number of messages used to extract data from the network. A unique feature of our selective sampling mechanisms is the use of localized schemes, as opposed to the protocols requiring global information, to select and dynamically refine the subset of sensor nodes serving as samplers and the modelbased value prediction for non-sampler nodes. Such runtime adaptations create a data collection schedule which is self-optimizing in response to changes in energy levels of nodes and environmental dynamics.
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