Autoregressive integrated moving average model–based secure data aggregation for wireless sensor networks

Hongtao Song, Shanshan Sui, Qilong Han, Hui Zhang and Zaiqiang Yang

International Journal of Distributed Sensor Networks, 2020, vol. 16, issue 3, 1550147720912958

Abstract: Nodes in a wireless sensor network are normally constrained by hardware and environmental conditions and face challenges of reduced computing capabilities and system security vulnerabilities. This fact calls for special requirements for network protocol design, security assessment models, and energy-efficient algorithms. Data aggregation is an effective energy conservation technique, which removes redundant information from the data aggregated from neighbor sensor nodes. How to further improve the effectiveness of data aggregation plays an important role in improving data collection accuracy and reducing the overall network energy consumption. Unfortunately, sensor nodes are normally deployed in an open environment and thus are subject to various attacks conducted by adversaries. Consequently, data aggregation brings new challenges to wireless sensor network security. In this article, we propose a novel secure data aggregation solution based on autoregressive integrated moving average model, a time series analysis technique, to prevent private data from being learned by adversaries. We leverage the autoregressive integrated moving average model to predict the data volume in sensor nodes, and update and synchronize the model as needed. The experimental results demonstrate that our model provides accurate predictions and that, compared with competing methods, our solution achieves better security, lower computation and communication costs, and better flexibility.

Keywords: Wireless sensor networks; secure; data aggregation; autoregressive integrated moving average; Internet of Things (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:16:y:2020:i:3:p:1550147720912958

DOI: 10.1177/1550147720912958

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