The Syntactic-Semantic Internet:Engineering Infrastructures for Autonomous Systems

The Internet has evolved through successive architectural abstractions that enabled unprecedented scale, interoperability, and innovation. Packet-based networking enabled the reliable transport of bits; cloud-native systems enabled the orchestration of distributed computation. Today, the emergence of autonomous, learning-based systems introduces a new architectural challenge: intelligence is increasingly embedded directly into network control, computation, and decision-making, yet the Internet lacks a structural foundation for representing and exchanging meaning. In this paper, we argue that cognition alone: pattern recognition, prediction, and optimization, is insufficient for the next generation of networked systems. As autonomous agents act across safety-critical and socio-technical domains, systems must not only compute and communicate, but also comprehend intent, context, and consequence. We introduce the concept of a Semantic Layer: a new architectural stratum that treats meaning as a first-class construct, enabling interpretive alignment, semantic accountability, and intelligible autonomous behavior. We show that this evolution leads naturally to a Syntactic-Semantic Internet. The syntactic stack continues to transport bits, packets, and workloads with speed and reliability, while a parallel semantic stack transports meaning, grounding, and consequence. We describe the structure of this semantic stack-semantic communication, a semantic substrate, and an emerging Agentic Web, and draw explicit architectural parallels to TCP/IP and the World Wide Web. Finally, we examine current industry efforts, identify critical architectural gaps, and outline the engineering challenges required to make semantic interoperability a global, interoperable infrastructure.

Edge AI Inference in Heterogeneous Constrained Computing: Feasibility and Opportunities

The network edge's role in Artificial Intelligence (AI) inference processing is rapidly expanding, driven by a plethora of applications seeking computational advantages. These applications strive for data-driven efficiency, leveraging robust AI capabilities and prioritizing real-time responsiveness. However, as demand grows, so does system complexity. The proliferation of AI inference accelerators showcases innovation but also underscores challenges, particularly the varied software and hardware configurations of these devices. This diversity, while advantageous for certain tasks, introduces hurdles in device integration and coordination. In this paper, our objectives are three-fold. Firstly, we outline the requirements and components of a framework that accommodates hardware diversity. Next, we assess the impact of device heterogeneity on AI inference performance, identifying strategies to optimize outcomes without compromising service quality. Lastly, we shed light on the prevailing challenges and opportunities in this domain, offering insights for both the research community and industry stakeholders.

Intelligent Reflecting Surfaces for Enhanced NOMA-based Visible Light Communications

The emerging intelligent reflecting surface (IRS) technology introduces the potential of controlled light propagation in visible light communication (VLC) systems. This concept opens the door for new applications in which the channel itself can be altered to achieve specific key performance indicators. In this paper, for the first time in the open literature, we investigate the role that IRSs can play in enhancing the link reliability in VLC systems employing non-orthogonal multiple access (NOMA). We propose a framework for the joint optimisation of the NOMA and IRS parameters and show that it provides significant enhancements in link reliability. The enhancement is even more pronounced when the VLC channel is subject to blockage and random device orientation.