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.
Highlights
As a distributed sensor network, wireless sensor networks (WSNs) have been widely used in many different domains, such as environmental monitoring, intelligent transportation, medical care, and military affairs
We introduce the autoregressive integrated moving average (ARIMA) model, a technique for time series analysis, to data aggregation, and propose an improved data aggregation scheme, which offers high security, low computational and communication costs, high accuracy, and better flexibility
To evaluate the performance of the method proposed in this article, we compare it with exponential smoothing data aggregation (ESDA), tiny aggregation (TAG), cluster-based private data aggregation (CPDA), and rotation-based privacy-preserving data aggregation (RPDA)
Summary
As a distributed sensor network, wireless sensor networks (WSNs) have been widely used in many different domains, such as environmental monitoring, intelligent transportation, medical care, and military affairs. The cluster head performs data aggregation processing as needed This scheme is based on the semi-honest model, that is, the participating nodes follow the protocol completely, but they will attempt to obtain the private data of other participating nodes. If we set thdown to a small value, the acceptable error of the model will be smaller In this case, sensor nodes need to update the model frequently and communicate with the cluster head, which results in additional computation and communication overhead. Considering the limited storage in sensor nodes, each node only stores the latest W data, where W .S. If the time gap of data acquisition in the network is small, each sensor needs to acquire additional memory space to store the latest S prediction values for error calculation.
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