Hierarchical In-Network Data Aggregation with Quality Guarantees

Deligiannakis, Antonios; Kotidis, Yannis; Roussopoulos, Nick

doi:10.1007/978-3-540-24741-8_38

Cited by 127 publications

(142 citation statements)

References 14 publications

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“…We have developed a simulator for testing various types of aggregate queries [6]. For the experiments that we present in this paper, we fed the simulator with a trace of readings from sensors in the Intel Research, Berkeley lab, collecting light, humidity and temperature readings [9].…”

Section: Methodsmentioning

confidence: 99%

Robust management of outliers in sensor network aggregate queries

Kotidis

Vassalos

Deligiannakis

et al. 2007

Proceedings of the 6th ACM International Workshop on Data Engineering for Wireless and Mobile Access

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Sensor networks are increasingly applied for monitoring diverse environments and applications. Due to their unsupervised nature of operation and inexpensive hardware used, sensor nodes may furnish readings of rather poor quality. We thus need to devise techniques that can withstand "dirty" data during query processing. In this paper we introduce a robust aggregation framework that can detect and isolate spurious measurements from computed aggregate values. Such readings are not injected in the reported aggregate, in order not to obscure the outcome, but are still maintained and returned to the user/application, which may investigate them further and take appropriate decisions. In addition, our framework provides a form of positive feedback to the user by enhancing the result with a set of nodes that contain the most characteristic values out of those included in the aggregation process. We perform an extensive experimental evaluation of our framework using real traces of sensory data and demonstrate its utility to the monitoring of applications.

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Section: Methodsmentioning

confidence: 99%

Robust management of outliers in sensor network aggregate queries

Kotidis

Vassalos

Deligiannakis

et al. 2007

Proceedings of the 6th ACM International Workshop on Data Engineering for Wireless and Mobile Access

Self Cite

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“…This, in turn, results in significant energy savings, which extends the lifetime of the resource-constraint sensor nodes. [6][8] [20] study aggregation with respect to the specific underlying sensor network topology (e.g. tree-based, multiple path, or hybrid) that is used in order to transfer the partial aggregates.…”

Section: Related Workmentioning

confidence: 99%

Accuracy analysis of data aggregation for network monitoring

Sofra

Zerfos

et al. 2008

MILCOM 2008 - 2008 IEEE Military Communications Conference

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“…Unfortunately, for all the reasons discussed in [8], this approach wastes significant amounts of power if directly used in sensor networks. In addition, since sampling intervals are not fixed, updates arrive unpredictably, so all relaying nodes must always have their radios on.…”

Section: A Caching To Reduce Data Transmissionsmentioning

confidence: 99%

Stochastically Consistent Caching and Dynamic Duty Cycling for Erratic Sensor Sources

Zhu

Wang

Ravishankar

2006

Distributed Computing in Sensor Systems

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Abstract-We present a novel dynamic duty cycling scheme to maintain stochastic consistency for caches in sensor networks. To reduce transmissions, base stations often maintain caches for erratically changing sensor sources. Stochastic consistency guarantees the cache-source deviation is within a pre-specified bound with a certain confidence level. We model the erratic sources as Brownian motions, and adaptively predict the next cache update time based on the model. By piggybacking the next update time in each regular data packet, we can dynamically adjust the relaying nodes' duty cycles so that they are awake before the next update message arrives, and are sleeping otherwise. Through simulations on the ns-2 simulator, we show that our approach can achieve very high sourcecache fidelity with low power consumption on many reallife sensor data. On average, our approach consumes 4-5 times less power than GAF [1], and achieves 50% longer network lifetime.

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Hierarchical In-Network Data Aggregation with Quality Guarantees

Cited by 127 publications

References 14 publications

Robust management of outliers in sensor network aggregate queries

Robust management of outliers in sensor network aggregate queries

Accuracy analysis of data aggregation for network monitoring

Stochastically Consistent Caching and Dynamic Duty Cycling for Erratic Sensor Sources

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