Title: DAO-R: integrating data aggregation and offloading in sensor networks via data replication

Authors: Basil Alhakami; Bin Tang; Jianchao Han; Mohsen Beheshti

Addresses: Computer Science Department, California State University Dominguez Hills, Carson, 90747, CA, USA ' Computer Science Department, California State University Dominguez Hills, Carson, 90747, CA, USA ' Computer Science Department, California State University Dominguez Hills, Carson, 90747, CA, USA ' Computer Science Department, California State University Dominguez Hills, Carson, 90747, CA, USA

Abstract: When sensor network applications are deployed in an inaccessible or inhospitable region, or under extreme weather, it is not viable to install a long-term base station in the field to collect data. The generated sensory data is therefore stored inside the network and waiting to be uploaded. When more data is generated than available storage spaces in the entire network can possibly store, and uploading opportunities have not arrived, data loss becomes inevitable. We refer to this problem as overall storage overflow in sensor networks. To solve this problem, we propose a unified framework and design two energy-efficient data replication algorithms to integrate data aggregation and data offloading. We refer to our approach as DAO-R. We give a sufficient condition under which DAO-R is solved optimally. Via extensive simulations, we show that DAO-R outperforms the existing research by around 30% in terms of energy consumption under different network parameters.

Keywords: data aggregation; data offloading; overall storage overflow; sensor networks; energy efficiency.

DOI: 10.1504/IJSNET.2019.097810

International Journal of Sensor Networks, 2019 Vol.29 No.2, pp.134 - 146

Accepted: 15 Jul 2018
Published online: 11 Feb 2019 *

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