A Lightweight and Privacy-Friendly Data Aggregation Scheme against Abnormal Data

Zhang, Jianhong; Han, Haoting

doi:10.3390/s22041452

Cited by 7 publications

(2 citation statements)

References 28 publications

(63 reference statements)

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“…In the scheme, the sensing area is partitioned into many cells of a grid, and the treelike path is established by using the minimum spanning tree algorithm. Zhang et al [18] proposed a lightweight and privacy-friendly data aggregation scheme against abnormal data, in which the valid data can correctly be aggregated, but abnormal data will be filtered out during the aggregation process. Data aggregation emphasizes the task allocation of data reduction and reconstruction at the physical level.…”

Section: Data Aggregationmentioning

confidence: 99%

Autonomous Internet of Things (IoT) Data Reduction Based on Adaptive Threshold

Zhang,

Na,

Zhang

2023

Sensors

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With the development of intelligent IoT applications, vast amounts of data are generated by various volume sensors. These sensor data need to be reduced at the sensor and then reconstructed later to save bandwidth and energy. As the reduced data increase, the reconstructed data become less accurate. Usually, the trade-off between reduction rate and reconstruction accuracy is controlled by the reduction threshold, which is calculated by experiments based on historical data. Considering the dynamic nature of IoT, a fixed threshold cannot balance the reduction rate with the reconstruction accuracy adaptively. Aiming to dynamically balance the reduction rate with the reconstruction accuracy, an autonomous IoT data reduction method based on an adaptive threshold is proposed. During data reduction, concept drift detection is performed to capture IoT dynamic changes and trigger threshold adjustment. During data reconstruction, a data trend is added to improve reconstruction accuracy. The effectiveness of the proposed method is demonstrated by comparing the proposed method with the basic Kalman filtering algorithm, LMS algorithm, and PIP algorithm on stationary and nonstationary datasets. Compared with not applying the adaptive threshold, on average, there is an 11.7% improvement in accuracy for the same reduction rate or a 17.3% improvement in reduction rate for the same accuracy.

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Section: Data Aggregationmentioning

confidence: 99%

Autonomous Internet of Things (IoT) Data Reduction Based on Adaptive Threshold

Zhang,

Na,

Zhang

2023

Sensors

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“…Especially in the case of micro-grids, storage management is critical and cannot be accomplished without the aid of accurate short-term load forecasts for load shifting and balancing operations [ 9 , 10 ]. Moreover, the grid extension and the increasing exploitation of smart meters affect the efficient operation of the grid, leading to a complex and multifaceted framework [ 11 , 12 ]. As regards the distribution network on the substation level, load forecasting up to one day ahead, could be a valuable asset in the grid’s optimization tasks [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Model Ensemble for Mixed Power Load Forecasting across Multiple Time Horizons

Γιαμαρέλος

Papadimitrakis

Stogiannos

et al. 2023

Sensors

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The increasing penetration of renewable energy sources tends to redirect the power systems community’s interest from the traditional power grid model towards the smart grid framework. During this transition, load forecasting for various time horizons constitutes an essential electric utility task in network planning, operation, and management. This paper presents a novel mixed power-load forecasting scheme for multiple prediction horizons ranging from 15 min to 24 h ahead. The proposed approach makes use of a pool of models trained by several machine-learning methods with different characteristics, namely neural networks, linear regression, support vector regression, random forests, and sparse regression. The final prediction values are calculated using an online decision mechanism based on weighting the individual models according to their past performance. The proposed scheme is evaluated on real electrical load data sensed from a high voltage/medium voltage substation and is shown to be highly effective, as it results in R2 coefficient values ranging from 0.99 to 0.79 for prediction horizons ranging from 15 min to 24 h ahead, respectively. The method is compared to several state-of-the-art machine-learning approaches, as well as a different ensemble method, producing highly competitive results in terms of prediction accuracy.

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