## 2 Background and Related Work
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Architecture** & **Use Cases** & **Pros** & **Cons** \\ \hline Application-specific & Tailored for particular use cases or industries [20; 21] & High optimization, reduced overhead, fine-tuned performance & Lack of flexibility \\ \hline Agent-based & Uses autonomous agents for decisionmaking [22; 23; 24] & Scalability, decentralised decisionmaking, fault tolerance & High complexity in managing agents \\ \hline VM (Virtual Machine)-based & Abstracts hardware layer using VMs [25; 26] & Hardware abstraction, ease of application deployment, scalability & Overheads and performance issues \\ \hline Database-oriented & Centralises IoT management & Structured data storage, management [27; 28; 29] & support for complex queries & Overheads, scalability issues, single points of failure \\ \hline Tuple spaces & Distributed shared memory concept [30; 31; 32] & Flexibility, synchronous/asynchronous communication & High complexity and data redundancy \\ \hline Service-oriented & Modular services for specific functions [33] & High modularity, service reuse, adaptability & Overheads, latency issues, complexity \\ \hline Message-oriented & Message-based device communication [34; 35] & Asynchronous communication, scalability & Overheads, message losses, sequencing issues \\ \hline Semantic IoT Middleware (SIM) & Comprehensive IoT ecosystems, healthcare, smart homes, industrial automation & Semantic data processing, AI-driven feedback, blockchain security & Complexity in semantic processing, initial setup overheads \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of commercial-grade IoT middleware solutions. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Product** & **Key Features** & **Scope for Innovation** \\ \hline AWS IoT Core [43] & Device communication security, device authentication, rules engine for routing device data, and integration with other AWS services & Better edge computing capabilities, enhanced support for heterogeneous devices, more robust data analytics tools built in \\ \hline FIWARE-IoT-Agent [44] & Combines IoT devices with the Orion Context Broker, supports multiple IoT protocols, provides service and device provisioning & Expands the set of supported protocols, tighter integration with non-FIWARE systems, enhanced security features \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of commercial-grade IoT middleware solutions. As the IoT landscape diversifies with the introduction of new devices and protocols, it is imperative to evolve existing platforms to ensure seamless interoperability and integration. In this context, [49] presents an innovative multilayer IoT middleware rooted in the concepts of knowledge graph, effectively addressing the challenge of connecting a heterogeneous array of IoT devices employing various communication protocols. Another work, [50], was proposed in this area, with a prime focus on resolving privacy and security issues, specifically concentrated on the message queuing telemetry transport (MQTT) protocol. Some of the recent works that have amalgamated the concept of semantic annotations in IoT middleware include GMSCA [51], S2 NetM [52], and SEDIA [53]. GMSCA is a generic IoT middleware for smart city applications that targets to address challenges regarding heterogeneity, service management, and security. However, the paper acknowledges that security is still a challenge that needs to be addressed in future work. S2 NetM introduces the concept of Semantic Social Network of Things Middleware, and the purpose of this study is to enhance semantic interoperability in Social Internet of Things (SIoT) systems by comprehending and using social relationships. The study acknowledges several challenges, such as security and standardisation in SIoT environments. On the other hand, SEDIA is a platform for integrating IoT data with semantic enrichment.