Semantics-Empowered Communication for Networked Intelligent Systems (2007.11579v3)

Abstract: Wireless connectivity has traditionally been regarded as an opaque data pipe carrying messages, whose context-dependent meaning and effectiveness have been ignored. Nevertheless, in emerging cyber-physical and autonomous networked systems, acquiring, processing, and sending excessive amounts of distributed real-time data, which ends up being stale or useless to the end user, will cause communication bottlenecks, increased latency, and safety issues. We envision a communication paradigm shift, which makes the Semantics of Information, i.e., the significance and the usefulness of messages with respect to the goal of data exchange, the underpinning of the entire communication process. This entails a goal-oriented unification of information generation, transmission, and usage, by taking into account process dynamics, signal sparsity, data correlation, and semantic information attributes. We apply this structurally new, synergetic approach to a communication scenario where the destination is tasked with real-time source reconstruction for the purpose of remote actuation. Capitalizing on semantics-empowered sampling and communication policies, we show significant reduction in both reconstruction error and cost of actuation error, as well as in the number of uninformative samples generated.

• The paper proposes a novel semantics-empowered communication model that integrates data significance at microscopic, mesoscopic, and macroscopic scales.
• It demonstrates via a Markovian two-state example that semantics-aware sampling minimizes uninformative samples and reduces reconstruction and actuation costs.
• The approach addresses inefficiencies in traditional networks by aligning data transmission with application-specific relevance to enhance system performance.

Semantics-Empowered Communication for Networked Intelligent Systems

The paper "Semantics-Empowered Communication for Networked Intelligent Systems" by Marios Kountouris and Nikolaos Pappas presents a novel approach to communication in future networked systems. The authors propose a paradigm shift, focusing on the semantics of information to enhance communication processes for networked intelligent systems. Traditional communication has been largely content-agnostic, focusing on reliability over noisy channels without considering the significance of transmitted data. As technology generates vast amounts of data, this approach results in bottlenecks and inefficiencies. The proposed semantics-empowered communication aims to address these issues by integrating information significance into the communication processes.

Key Concepts and Model

The authors propose assessing and integrating the semantic value of data at microscopic, mesoscopic, and macroscopic scales.

• Microscopic Scale: At the source, semantics refers to the importance of events or measurements, which can be incorporated into information measures like entropy. The relevance of transmitted information is reflective of their context and utility.
• Mesoscopic Scale: At the link level, innate attributes (such as freshness and precision) and contextual attributes (such as timeliness) of information are incorporated to determine their semantic value. A composite function assesses these attributes to determine the usefulness of the data concerning application-specific goals.
• Macroscopic Scale: At the system level, the focus is on effective distortion and synchronization between the transmitted and reconstructed data. This aims to minimize the time and error gap between the real-world system’s state and its digital representation.

The authors also introduce a semantics-empowered communication model which unifies data generation, transmission, and reconstruction based on semantic relevance. The process begins with intelligent sampling by devices and culminates in goal-oriented data interpretation by the receiver.

Numerical Results and Illustration

The authors present an illustrative example of a Markovian two-state scenario, comparing the performance of various sampling and transmission policies. These include uniform sampling, age-aware, semantics-aware, and end-to-end (E2E) semantics policies. The E2E semantics approach was shown to minimize uninformative samples and significantly reduce reconstruction and actuation cost errors, especially in scenarios of low source variability and poor channel quality.

Implications and Future Challenges

The paper suggests that semantics-aware communication has significant potential to improve efficiency in communication networks, reducing redundant data transmission and improving the timeliness and relevance of communicated information. Moving forward, several challenges need addressing, such as defining concrete semantic metrics, ensuring efficient semantics-aware multiple access, and developing goal-oriented resource orchestration techniques. Additionally, integrating these concepts within multi-objective stochastic optimization frameworks poses further complexity.

The implications of this research are profound, offering a potential blueprint for future communication networks that align with the demands of autonomous and intelligent systems. By focusing on the semantic value of information, communication can be optimized to meet the needs of applications more effectively, potentially transforming design principles across wireless communication, information theory, and signal processing landscapes.