Abstract
The Internet of Things (IoT) has evolved significantly with advances in gathering data that can be extracted to provide knowledge and facilitate decision-making processes. Currently, IoT data analytics encountered challenges such as growing data volumes collected by IoT devices and fast response requirements for time-sensitive applications in which traditional Cloud-based solution is unable to meet due to bandwidth and high latency limitations. In this paper, we develop a distributed analytics framework for fog-enabled IoT systems aiming to avoid raw data movement and reduce latency. The distributed framework leverages the computational capacities of all the participants such as edge devices and fog nodes and allows them to obtain the global optimal solution locally. To further enhance the privacy of data holders in the system, a privacy-preserving protocol is proposed using cryptographic schemes. Security analysis was conducted and it verified that exact private information about any edge device’s raw data would not be inferred by an honest-but-curious neighbor in the proposed secure protocol. In addition, the accuracy of solution is unaffected in the secure protocol comparing to the proposed distributed algorithm without encryption. We further conducted experiments on three case studies: seismic imaging, diabetes progression prediction, and Enron email classification. On seismic imaging problem, the proposed algorithm can be up to one order of magnitude faster than the benchmarks in reaching the optimal solution. The evaluation results validate the effectiveness of the proposed methodology and demonstrate its potential to be a promising solution for data analytics in fog-enabled IoT systems.
Highlights
The Internet of Things (IoT) is a system of interconnected devices and networks in which information can be gathered from the surrounding environment
Fog computing is emerging as an alternative to the traditional Cloud-based solution by pushing processing and analytics near to where the data are generated [2]
(2) We further develop a privacy-preserving secure protocol based on the proposed distributed algorithm with homomorphic encryption
Summary
The Internet of Things (IoT) is a system of interconnected devices and networks in which information can be gathered from the surrounding environment. Fog computing is emerging as an alternative to the traditional Cloud-based solution by pushing processing and analytics near to where the data are generated [2] It adds another middle fog layer between IoT devices and Cloud containing fog nodes that are placed close to edge devices (see Figure 1). The raw data are not transferred, the private information about their raw data can be obtained from their computed quantity such as gradients or model parameters as these are computed based on its local data [5,6] To address this concern, a privacy-preserving data analytics scheme becomes imperative for adopting data-driven solutions in various fields in practice. The experiment results demonstrate that the proposed secure protocol achieves data privacy and outperforms the benchmarks in terms of convergence speed We believe this is an important addition to existing infrastructures for efficient and secure data analytics in fog-enabled IoT systems.
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