DocumentCode :
1759730
Title :
Practical Data Prediction for Real-World Wireless Sensor Networks
Author :
Raza, Usman ; Camerra, Alessandro ; Murphy, Amy L. ; Palpanas, Themis ; Picco, Gian Pietro
Author_Institution :
Bruno Kessler Found., Trento, Italy
Volume :
27
Issue :
8
fYear :
2015
fDate :
Aug. 1 2015
Firstpage :
2231
Lastpage :
2244
Abstract :
Data prediction is proposed in wireless sensor networks (WSNs) to extend the system lifetime by enabling the sink to determine the data sampled, within some accuracy bounds, with only minimal communication from source nodes. Several theoretical studies clearly demonstrate the tremendous potential of this approach, able to suppress the vast majority of data reports at the source nodes. Nevertheless, the techniques employed are relatively complex, and their feasibility on resource-scarce WSN devices is often not ascertained. More generally, the literature lacks reports from real-world deployments, quantifying the overall system-wide lifetime improvements determined by the interplay of data prediction with the underlying network. These two aspects, feasibility and system-wide gains, are key in determining the practical usefulness of data prediction in real-world WSN applications. In this paper, we describe derivative-based prediction (DBP), a novel data prediction technique much simpler than those found in the literature. Evaluation with real data sets from diverse WSN deployments shows that DBP often performs better than the competition, with data suppression rates up to 99 percent and good prediction accuracy. However, experiments with a real WSN in a road tunnel show that, when the network stack is taken into consideration, DBP only triples lifetime-a remarkable result per se, but a far cry from the data suppression rates above. To fully achieve the energy savings enabled by data prediction, the data and network layers must be jointly optimized. In our testbed experiments, a simple tuning of the MAC and routing stack, taking into account the operation of DBP, yields a remarkable seven-fold lifetime improvement w.r.t. the mainstream periodic reporting.
Keywords :
data analysis; resource allocation; telecommunication computing; wireless sensor networks; DBP; MAC; data prediction technique; data sampling; data suppression rates; derivative-based prediction; energy savings; network stack; real-world WSN applications; real-world wireless sensor networks; resource-scarce WSN devices; routing stack; system-wide gains; system-wide lifetime improvements; Computational modeling; Data models; Predictive models; Wireless sensor networks; Wireless sensor networks; data prediction; energy efficiency; network protocols; time series forecasting;
fLanguage :
English
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1041-4347
Type :
jour
DOI :
10.1109/TKDE.2015.2411594
Filename :
7056557
Link To Document :
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