دانلود رایگان مقاله مکانیزم جمع آوری کلان داده مقیاس پذیر حفاظت از حریم خصوصی

Abstract

As the massive sensor data generated by large-scale Wireless Sensor Networks (WSNs) recently become an indispensable part of ‘Big Data’, the collection, storage, transmission and analysis of the big sensor data attract considerable attention from researchers. Targeting the privacy requirements of large-scale WSNs and focusing on the energy-efficient collection of big sensor data, a Scalable Privacy-preserving Big Data Aggregation (Sca-PBDA) method is proposed in this paper. Firstly, according to the pre-established gradient topology structure, sensor nodes in the network are divided into clusters. Secondly, sensor data is modified by each node according to the privacy-preserving configuration message received from the sink. Subsequently, intra- and inter-cluster data aggregation is employed during the big sensor data reporting phase to reduce energy consumption. Lastly, aggregated results are recovered by the sink to complete the privacy-preserving big data aggregation. Simulation results validate the efficacy and scalability of Sca-PBDA and show that the big sensor data generated by large-scale WSNs is efficiently aggregated to reduce network resource consumption and the sensor data privacy is effectively protected to meet the ever-growing application requirements.

8. Conclusion

Targeting the privacy requirements of large-scale WSNs and focusing on the energy-efficient collection of big sensor data, ScaPBDA is proposed in this paper. Firstly, a gradient-based equal network clustering method is proposed to reasonably determine the network topology, according to the estimated node energy consumption. With the proposed clustering method, the identical number of CH and CMs support the uniform privacy-preserving configuration and further inter-cluster data aggregation process, which meets the scalability requirements of the big sensor data collected by large-scale WSNs. Secondly, a scalable privacy-preserving data aggregation method is further designed to provide the simple privacy-preserving data configuration and scalable intra- and inter-cluster data aggregation. Especially, the effectively protected big sensor data is parallel aggregated at each CH, and relay CHs also perform aggregation operations on the received privacy-preserving aggregation data to further reduce the resource consumption. Lastly, aggregated results are recovered by the sink to complete the privacy-preserving big data aggregation. Simulation results validate the efficacy and scalability of Sca-PBDA and show that the big sensor data generated by large-scale WSNs is efficiently aggregated to reduce the network resource consumption and the sensor data privacy is effectively protected to meet the ever-growing application requirements. Most importantly, the proposed Sca-PBDA gives inspiring ideas of collecting and processing the big sensor data, where the energy-efficient parallel aggregating of big sensor data and the scalable privacy-preserving method for large-scale WSNs is of great reference value.