Pragmatic Distance in IoT Devices

Abstract

Stand-alone devices perform their functions without human intervention. These types of devices would benefit from capabilities that allow collaboration among the devices in order to maximize service availability, as well as dynamic adaptation of the devices to user requirements in order to minimize resource usage. For example, when detecting a malfunction in one of the devices, the system should find devices with similar functionality to recover the overall service by functionality substitution. Additionally, for example, in the context of a user with hearing loss, the system should utilize devices with visual feedback functionality and disable audio feedback functionality to conserve resources. In this paper, we describe the implementation of a method to detect similar functionalities between heterogeneous devices, denoted Lightweight Machine to Machine Resource Pragmatic Distance (LwRPD). We define that devices are pragmatically close if they can be substituted with each other in order to perform a specific function in a particular context. We implement the method for enabling the aforementioned capabilities in specific use cases, using resources from real-life devices, to demonstrate the benefits. We also compare LwRPD with a multi-agent pragmatic similarity metric, Fuzzy logic and Levenshtein distance techniques, demonstrating LwRPD superiority over those techniques.