MSSI: Middleware for Unified Semantic and Syntactic Interoperability in IoT

2026-07-23

AI Summary

This work addresses the interoperability challenges in the Internet of Things (IoT) arising from syntactic and semantic heterogeneity among devices by proposing a publish-subscribe-based middleware that enables seamless communication between users and devices. The approach unifies syntactic transformation and semantic mapping through an innovative framework integrating a multilayer perceptron (MLP)-driven semantic mapping module, a custom-designed feature extraction algorithm, and comprehensive support for diverse data formats and semantic standards. Experimental evaluation in real-world scenarios demonstrates a semantic attribute recognition accuracy of 95.78%, confirming the system’s effectiveness and generalizability across heterogeneous IoT environments.

Abstract

With the growing demand of Internet of Things (IoT), there is a need for seamless and reliable communication between heterogeneous IoT devices and the cyber-world to ensure autonomous control over any application process. More specifically, seamless communication requires interoperability between heterogeneous devices (actors) having different semantics and data formats (syntaxes), while making it more challenging. In this paper, we propose a middleware solution for unified semantic and syntactic interoperability in the publisher-subscriber framework of IoT network. The proposed framework automatically translates the subscribers (users) compatible syntax and semantics of the receiver message from the publishers (IoT devices). First, we propose a novel method of syntax translation of messages, to solve the syntactic disparities between users and devices, while providing the information in the user requested syntax. Thereafter, a multilayer perceptron (MLP)-based semantic interoperability framework is proposed to translate the device information to the user requested semantics. Additionally, a novel algorithm is proposed for extracting raw and discriminative features, which are to be fitted to the MLP model as inputs. To show the effectiveness of the proposed middleware, we evaluate different parameters, while considering various publicly used data formats and semantic annotations of attributes to ensure the versatility of the proposed middleware in the practical scenario. The overall classification accuracy using MLP is $95.78$\% for determining the standard meaning of each attribute of the incoming message from the publisher to address the semantic interoperability problem in IoT.

Problem

• semantic interoperability
• syntactic interoperability
• heterogeneous IoT devices
• middleware
• publisher-subscriber framework

Innovation

• semantic interoperability
• syntactic interoperability
• middleware
• multilayer perceptron
• IoT