Interoperability Challenges:

In the context of SOC and IoT, interoperability remains a fundamental challenge due to the diverse nature of devices, protocols, and service interfaces. AI techniques offer promising solutions by enabling systems to autonomously understand and adapt to varying data formats, communication protocols, and service requirements. Machine learning algorithms, for instance, can analyze data from disparate IoT devices, identify patterns, and facilitate seamless integration within SOC frameworks. Additionally, natural language processing (NLP) and semantic technologies can standardize and interpret service descriptions, thereby enhancing service discovery and composition across heterogeneous environments.

Scalability Solutions:

Scalability is another critical concern as IoT deployments continue to expand exponentially, generating massive volumes of data that SOC frameworks must efficiently process and manage. AI-driven approaches such as edge computing and distributed learning enable decentralized decision-making and data processing, reducing latency and enhancing scalability. By leveraging AI algorithms for predictive analytics and resource allocation, SOC architectures can dynamically scale services based on real-time demand, optimizing performance without compromising efficiency.

Research Focus Areas:

The workshop encourages exploration of novel AI-driven methodologies and technologies aimed at improving interoperability and scalability in SOC and IoT environments. Key research areas include but are not limited to:

• AI Integration in Service-Oriented Systems
• Semantic based IoT solution for Interoperability
• Smart Solutions in service-oriented computing
• BIG Data Management in Service-Oriented IoT
• AI enabled Sustainable IoT Solutions
• Context-Aware and AI technologies for Services and IoT computing
• Autonomous Systems in Service-Oriented Environments
• Big Data Analytics for Machine Learning in Service-Oriented Systems
• Data Preprocessing Techniques for Machine Learning in IoT
• Scalable Data Management for Machine Learning Applications
• AI-Driven Service Orchestration and Composition
• AI-Enhanced Service Discovery and Recommendation Systems
• AI-Based Service-Level Agreement (SLA) Management
• AI-Driven Adaptive and Self-Healing Systems
• AI for IoT and Edge Computing Integration
• AI-Enabled Security and Privacy in SOC
• AI for Quality of Service (QoS) Optimization
• AI-Driven Analytics and Decision Support Systems
• Semantic description of Virtual Network Function
• Intent-based service computing
• AI as a service (AIaaS)

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