In-network aggregation trade-offs for data collection in wireless sensor networks

Abstract

This paper explores in-network aggregation as a power-efficient mechanism for collecting data in wireless sensor networks. In particular, we focus on sensor network scenarios where a large number of nodes produce data periodically. Such communication model is typical of monitoring applications, an important application domain sensor networks target. The main idea behind in-network aggregation is that, rather than sending individual data items from sensors to sinks, multiple data items are aggregated as they are forwarded by the sensor network. Through simulations, we evaluate the performance of different in-network aggregation algorithms, including our own cascading timers, in terms of the trade-offs between energy efficiency, data accuracy and freshness. Our results show that timing, i.e., how long a node waits to receive data from its children (downstream nodes in respect to the information sink) before forwarding data onto the next hop (toward the sink) plays a crucial role in the performance of aggregation algorithms for applications that generate data periodically. By carefully selecting when to aggregate and forward data, cascading timers achieves considerable energy savings while maintaining data freshness and accuracy. We also study in-network aggregation's cost-efficiency using simple mathematical models.

Delay Efficient Distributed Data Aggregation Algorithm in Wireless Sensor Networks

“…Therefore, such redundant messages would increase the average energy consumption of network. [8] The main idea in the data gathering scheme is each sensor should have expanded the same amount of energy in each data gathering round and it is the energy efficient if it maximized the functionality of the network. This idea is captured by the network lifetime which quantifies the energy efficiency of the network.…”

Collaborative Scheduling in Highly Dynamic Environments Using Error Inference

“…It allows networked nodes to reduce their transmitting and sensing power while preserving sensing quality. A common technique to reduce power is local data control, e.g., reducing the duty cycle (the active duration) of sensors by turning them off while using a prediction model to estimate the actual data at each sensor node [20], [21]. In general, a higher duty cycle leads to a better prediction accuracy, but consumes more energy.…”

“…Data aggregation approaches have been widely acclaimed as useful techniques to reduce communication overhead in sensor networks [21], [30]. However, there has been little cooperation between sensor nodes.…”

Uncertainty-aware Wireless Sensor Networks

“…We can save significant amount of energy by proper selection of data aggregation and forwarding intervals. There are trade-offs among network life time, data accuracy, data freshness and latency (Solis and Obraczka, 2003).…”