An Energy-Efficient Data Collection Framework for Wireless Sensor Networks by Exploiting Spatiotemporal Correlation

Abstract

Limited energy supply is one of the major constraints in wireless sensor networks. A feasible strategy is to aggressively reduce the spatial sampling rate of sensors, i.e., the density of the measure points in a field. By properly scheduling, we want to retain the high fidelity of data collection. In this paper, we propose a data collection method that is based on a careful analysis of the surveillance data reported by the sensors. By exploring the spatial correlation of sensing data, we dynamically partition the sensor nodes into clusters so that the sensors in the same cluster have similar surveillance time series. They can share the workload of data collection in the future since their future readings may likely be similar. Furthermore, during a short time period, a sensor may report similar readings. Such a correlation in the data reported from the same sensor is called temporal correlation, which can be explored to further save energy. We develop a generic framework to address several important technical challenges, including how to partition the sensors into clusters, how to dynamically maintain the clusters in response to environmental changes, how to schedule the sensors in a cluster, how to explore temporal correlation, and how to restore the data in the sink with high fidelity. We conduct an extensive empirical study to test our method using both a real test bed system and a large-scale synthetic dataset.

Modern Clustering Techniques in Wireless Sensor Networks

“…where mt i and mt j are the magnitudes of the values of n i and n j D ij < D Th (10) where D ij is the distance between n i and n j and D Th is the distance threshold.…”

“…The performance of the proposed ADCA algorithm has been compared against a contemporary clustering algorithm named energy-efficient distributed clustering (EEDC) algorithm [10]. Number of sensor nodes has been varied from 20 to 100 in a simulation area of 500 Â 500 m 2 .…”

Distributed Cluster Based Routing Technique with Multiple Sinks for Wireless Sensor Networks

“…The performance of DCBR technique is compared with EEDC (Energy Efficient Dynamic Clustering) technique (Liu et al, 2007). The performance is evaluated considering the following metrics.…”

A calculus for information-driven networks

“…Particularly, the spatial and temporal correlations of information have been studied and utilized in network scheduling and resource saving in wireless sensor networks [16], [18], [20]; information redundancy has been exploited to help load balancing and improve fault tolerance in peer-to-peer content sharing systems [2], [12]. Regarding the third challenge, to the best of our knowledge, the only attempts to accommodate information processing in performance modeling are the work in [8], [19].…”

“…For instance, in a sensor network, sensor nodes may be configured to check information redundancy [16], [18], and as such they only care about the amount of information arrivals instead of the amount of packets. This motivates our following definitions:…”