WEB OF THINGS ARCHITECTURES AND INTEROPERABILITY CHALLENGES IN SMART ENVIRONMENTS

Abstract

The Web of Things (WoT) aims to achieve interoperability and integration of IoT devices with varying data models. Heterogeneous IoT devices bring diverse protocols and semantics to smart environments. The ongoing scaling and turnover of devices makes interoperability fragile. This paper aims to alleviate these problems with the introduction of the Dynamic Mapping Optimization Framework (DMOF). This framework focuses on the continuous refinement of device-to-ontology mappings. This is achieved through attribute standardization, summation-based semantic similarity, weighted correlation, and loss-guided optimization. DMOF aims to improve on the limitation’s static middleware translation. The framework aims to reduce semantic drift and minimize recalibration due to changes in the device such as new added capabilities, frequent changes in the device, or the introduction of new devices. DMOF aims to achieve this through real-time mapping changes. DMOF is the only of the reviewed frameworks to maintain an updated ontology-backed knowledge layer. This enables the framework to provide constant alignment with the interpretational device properties that differ across vendors and platforms. The level of computational overhead is also controlled in the process. This evaluation aims to assess the operational viability and level of defence interoperability of DMOF. The evaluation of DMOF’s operations is compared against the most cited interoperability frameworks: ontology-centric frameworks, semantic integration models, context-aware middleware, edge-enabled interoperability, and AI-optimized WoT. The evaluation proved the operational viability of DMOF,netting 91% operational viability as well as 92% scalability, and 90% interoperability. This was achieved while the framework only required 85 ms latency on average. The framework also maintained 95% in security, 93% in privacy and 91% operational viability under dynamic on the defensive interoperability challenge. Moreover, with regard to DMOF, system efficacy has been made possible with the following metrics: impact efficiency 89%, system throughput rate 80 Mbps, fault tolerance 88%, impact efficiency on QoS 92%, consistency on data 90%, and resource utility 91%, proving across the board networking, resilience, and resource efficiency. The data shows that to be effective and dependable with respect to interoperability on a smart system at scale, loss-driven semantic mapping has to be dynamic.