A distributed and self-organizing scheduling algorithm for energy-efficient data aggregation in wireless sensor networks

Abstract

Wireless sensor networks (WSNs) are increasingly being used to monitor various parameters in a wide range of environmental monitoring applications. In many instances, environmental scientists are interested in collecting raw data using long-running queries injected into a WSN for analyzing at a later stage, rather than injecting snap-shot queries containing data-reducing operators (e.g., MIN, MAX, AVG) that aggregate data. Collection of raw data poses a challenge to WSNs as very large amounts of data need to be transported through the network. This not only leads to high levels of energy consumption and thus diminished network lifetime but also results in poor data quality as much of the data may be lost due to the limited bandwidth of present-day sensor nodes. We alleviate this problem by allowing certain nodes in the network to aggregate data by taking advantage of spatial and temporal correlations of various physical parameters and thus eliminating the transmission of redundant data. In this article we present a distributed scheduling algorithm that decides when a particular node should perform this novel type of aggregation. The scheduling algorithm autonomously reassigns schedules when changes in network topology, due to failing or newly added nodes, are detected. Such changes in topology are detected using cross-layer information from the underlying MAC layer. We first present the theoretical performance bounds of our algorithm. We then present simulation results, which indicate a reduction in message transmissions of up to 85% and an increase in network lifetime of up to 92% when compared to collecting raw data. Our algorithm is also capable of completely eliminating dropped messages caused by buffer overflow.

An Energy-aware Iterative Sampling Framework for Data Gathering in Wireless Sensor Networks

“…Researchers have studied some kinds of frameworks for data gathering for WSNs [6][7][8][9][10][11][12][13][14][15][16][17][18]. The methods in [9, 11, and 17] are model-driven sampling frameworks, using an approach similar to data prediction.…”

“…The centralized methods in [3,8] waste a lot of energy in communication among nodes and are not good fits for large scale applications. In the studies [12,13], researchers studied an efficient-energy data collection method in connection with the scheduling policies of MAC. The author of the paper [12] defined a spatial correlation-based collaborative MAC protocol (CC-MAC) that regulated sensor node transmissions so as to minimize the number of reporting nodes.…”

Energy-driven distribution of signal processing applications across wireless sensor networks

“…In this approach, each wireless sensor node locally adjusts its transmission power and selects which neighbor nodes to communicate with according to information about sensor nodes within its local neighborhood. In [Chatterjea et al 2008], the authors present a distributed scheduling algorithm for managing data aggregation according to correlated information among network nodes so that the transmission of redundant data can be minimized. The algorithm is also able to adapt to network topology changes (i.e., events in which nodes are added to or removed from the network).…”

Experiencing wireless sensor network concepts in an undergraduate computer science curriculum

“…Well known examples include environmental monitoring, for example on the great barrier reef where water quality and temperature are checked using a Wireless Sensor Network (9). Applications in logistics, like the monitoring of storage conditions for spoilable goods (11).…”