Multiobjective Deployment of Data Analysis Operations in Heterogeneous IoT Infrastructure
Multiobjective Deployment of Data Analysis Operations in Heterogeneous IoT Infrastructure
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DOI：
10.1109/tii.2019.2961676
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发表时间：
2020-11
影响因子：
12.3
通讯作者：
D. N. Jha;Peter Michalák;Z. Wen;R. Ranjan;P. Watson
中科院分区：
文献类型：
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作者：
D. N. Jha;Peter Michalák;Z. Wen;R. Ranjan;P. Watson
物联网（IoT）技术的发展为智能医疗、智能建筑和智能农业等领域的应用带来了许多新的机遇。这些应用程序通常必须将从传感器数据中提取价值所需的计算分布在物联网基础设施平台上（例如，传感器、电话、现场网关和云）。由于上述平台的异构性，这对物联网应用开发人员来说可能是非常具有挑战性的，潜在的非功能性需求（例如，电池功率、延迟和成本）以及相关的部署标准，这是不可能手动解决的。为了应对上述挑战，我们开发了PATH2iot框架，将复杂的物联网应用程序分解为自包含的微操作。根据部署标准，PATH2iot自动将微操作集分布在物联网基础设施平台上，同时尊重它们的运行时数据和控制流依赖性。在我们之前的工作中，我们已经展示了如何使用PATH2iot来优化医疗可穿戴设备的电池寿命。在这篇文章中，我们描述了一项新的研究，显着扩展了PATH2iot，它引入了一个启发式模型，能够根据多个冲突的非功能需求和选择标准（用户偏好）做出最佳部署决策。它通过利用一种著名的多标准决策方法，称为层次分析法（AHP）。部署模型的适用性基于真实世界的数字医疗分析用例进行了验证。结果表明，我们的模型是能够找到最佳的部署解决方案，为不同的用户偏好。
The growth of Internet of Things (IoT) technology brings many new opportunities for applications in areas including smart healthcare, smart buildings, and smart agriculture. These applications must normally distribute the computations, required for extracting value from sensor data, over the IoT infrastructure platforms (e.g., sensors, phones, field-gateways, and clouds). This can be very challenging for IoT application developers due to the heterogeneity of the aforementioned platforms, potentially conflicting nonfunctional requirements (e.g., battery power, latency, and cost), and related deployment criteria, which is impossible to resolve manually. To address the above challenges, we have developed the PATH2iot framework that decomposes a complex IoT application into self-contained micro-operations. Based on the deployment criteria, PATH2iot automatically distributes the set of micro-operations across IoT infrastructure platforms, while respecting their run-time data and control flow dependencies. In our previous work, we have shown how to use the PATH2iot to optimize the battery life of a healthcare wearable. In this article, we describe a new research that significantly extends PATH2iot, which introduces a heuristic model capable of making optimal deployment decisions based on multiple conflicting nonfunctional requirements and selection criteria (user preferences). It does so by leveraging a well-known multicriteria decision-making method called the analytic hierarchical processes (AHP). The applicability of the deployment model is validated based on a real-world digital healthcare analytics use case. The results show that our model is able to find the optimal deployment solution for different user preferences.