| description abstract | Abstract. Recent advances in wireless networking technologies, the reliable application of fault diagnostic and prognostic techniques, and rapid advances in machine learning algorithms have led to widespread use and benefits for Internet-of-Things applications. These advances have also made it possible for devices, previously used for monitoring at enterprise levels, to be applied to monitoring at the individual equipment level. Devices such as networked sensors and radio frequency identification (RFID) tags are increasingly used to detect, track, and monitor equipment in a variety of applications. While the benefits are quite promising, there are significant challenges in the adoption and deployment of such networked sensors in terms of design, operation, and economic justification. Selecting sensors with appropriate specifications is imperative for capturing high-quality information. This article presents a goal programming approach that utilizes sensor manufacturers' datasheets to specify and select a heterogeneous set of sensors. The usefulness of the proposed goal programming approach is demonstrated through its ability to concurrently handle the specification and selection of heterogeneous sensors in intricate systems. This differs from prior approaches, which treat sensor selection and specification disjointly during system design. The main contribution of this study is the development of a mathematical model to support flexible design needs and distinguishes itself from previous approaches in the field, and the use of dynamic specification bounds allowing users to explore design trade-offs and adjust system targets as needed. As a result, the proposed goal programming approach enables a more thorough and integrated design strategy for selecting sensors for intricate equipment. | |
