Multiobjective Deployment of Data Analysis Operations in Heterogeneous IoT Infrastructure

• Published in: IEEE transactions on industrial informatics Vol. 16; no. 11; pp. 7014 - 7024
• Main Authors: Jha, Devki Nandan, Michalak, Peter, Wen, Zhenyu, Ranjan, Rajiv, Watson, Paul
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.11.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytic hierarchical process (AHP); Analytic hierarchy process; Batteries; Cloud computing; Computational modeling; Data analysis; Decision analysis; Decision making; Hardware; Health care; Heterogeneity; Infrastructure; Internet of Things; Internet of Things (IoT); Medical services; Multiple criterion; Multiple objective analysis; Optimization; Platforms; Smart buildings; smart healthcare; Software; streaming data; User requirements
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2019.2961676

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.