IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering (TKDE) is an archival journal published monthly designed to inform researchers, developers, managers, strategic planners, users, and others interested in state-of-the-art and state-of-the-practice activities in the knowledge and data engineering area. Read the full scope of TKDE
IEEE Transactions on Knowledge and Data Engineering (TKDE) has moved to the OnlinePlus publication model starting with 2013 issues!
From the August 2015 issue
Practical Data Prediction for Real-World Wireless Sensor Networks
By Usman Raza, Alessandro Camerra, Amy L. Murphy, Themis Palpanas, and Gian Pietro Picco
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.
Editorials and Announcements
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- TKDE celebrates its 25th Anniversary. Editor-in-Chief Jian Pei says, "We are celebrating the 25th Anniversary of TKDE. Since its first issue in March 1989, TKDE has published 2,981 articles, and another 220 articles in the early access portal. With 898 submissions and 79 accepted articles in 2012, TKDE is now the premier journal in the broad and general fields of data management, data mining, and knowledge engineering. We thank all the authors, reviewers, and readers for their continuing support to TKDE. As always, we are eager to hear your ideas and suggestions, and will do our best to meet your expectations. With all your passions, contributions, and supports, TKDE is embracing the new era of big data and big data analytics. Happy birthday to TKDE!"
- Editorial (August 2015)
- State of the Journal Editorial (January 2015)
- Editorial: State of the Transactions (January 2014)
- Editorial (August 2013)
- New EIC Editorial (February 2013)
- Outgoing EIC Editorial (February 2013)
- State of the Journal (February 2012)
- EIC Editorial (January 2011)
- Special Section on the International Conference on Data Engineering (July 2015)
- Special Section on the International Conference on Data Engineering (June 2014)
- Special Section on the 27th International Conference on Data Engineering (ICDE 2011)(Oct 2012)
- Special Section on Keyword Search on Structured Data (Dec 2011)
- Cloud Data Management (Sept 2011)
- Special Section on the 26th International Conference on Data Engineering (Aug 2011)
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