Aggregation in sensor networks: an energy–accuracy trade-off

Abstract

-Wireless ad hoc sensor networks (WASNs) are in need of the study of useful applications that will help the researchers view them as distributed physically coupled systems, a collective that estimates the physical environment, and not just energylimited ad hoc networks. We develop this perspective using a large and interesting class of WASN applications called aggregation applications. In particular, we consider the challenging periodic aggregation problem where the WASN provides the user with periodic estimates of the environment, as opposed to simpler and previously studied snapshot aggregation problems. In periodic aggregation our approach allows the spatial-temporal correlation among values sensed at the various nodes to be exploited towards energy-efficient estimation of the aggregated value of interest. Our approach also creates a system level energy vs. accuracy knob whereby the more the estimation error that the user can tolerate, the less is the energy consumed. We present a distributed estimation algorithm that can be applied to explore the energy-accuracy subspace for a sub-class of periodic aggregation problems, and present extensive simulation results that validate our approach. The resulting algorithm, apart from being more flexible in the energy-accuracy subspace and more robust, can also bring considerable energy savings for a typical accuracy requirement (five -fold decrease in energy consumption for 5% estimation error) compared to repeated snapshot aggregations.

Fault Tolerant Evaluation of Continuous Selection Queries over Sensor Data

“…Node s forwards a sequence of tuples to its parent P s during a round. It numbers these sequentially, e.g., (1,2,3,4,5,6). The highest sequence number is labeled n max = 6.…”

“…If m = 0 (second case) then the message cannot be delivered, as there are no more retries left. Finally, for the third case: if the message is successfully forwarded by one hop, then it must clear k -1 hops with m retries; 4 otherwise it must clear all k hops with one less (m -1) number of retries. Now, consider the expected number of messages for m tries:…”

“…Increasingly, researchers are realizing that sensors are more than passive beacons, but can perform useful work, to conserve their own resources and to meet application goals. Sensors can be smart in terms of data acquisition [16], processing before transmission [11], and propagation across the network [4]. Pushing intelligent decision-making and data manipulation to sensors has many benefits:…”

Centralized Lifetime Maximizing Tree For Wireless Sensor Networks

“…A node at one specific time may be granted more access to the network than all other nodes if the program objective is still satisfied. For this reason, network resources are shared but it is not necessary that they may be equally distributed as long as the application performance is not degraded Since sensors are being densely deployed in WSNs, the detection of a the [4]. In this paper, we focus on constructing a data aggregation tree among any given set of source nodes.…”

Joint Sensor Selection and Data Routing in Sensor Networks

“…The closest model to ours is described in [3]; however, in [3] the objective is to detect the occurrence of an event in an energy-efficient manner rather than to continuously collect the ambient conditions of the area. More recently, Boulis et al [1] and Kalpakis et al [6], considered the energy-accuracy trade off in sensor networks when the network is continuously monitored. Boulis et al, investigated the periodic aggregation and estimation problem when the observed data is time correlated.…”