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Semantic Filtering and Source Coding in Distributed Wireless Monitoring Systems

Pouya Agheli, Nikolaos Pappas, Marios Kountouris

TL;DR

This work develops a goal-oriented semantic framework for distributed wireless monitoring, where multiple sensors and monitors with heterogeneous goals exchange timely updates. It introduces a two-tier SoI model (microscopic meta-value and mesoscopic timeliness), two semantics-aware filtering schemes (fixed- and adaptive-length), and semantics-aware truncated error control, then derives optimal real-valued codeword lengths to maximize a weighted semantic utility. The results show that adaptive-length filtering and semantic encoding substantially improve timely, valuable updates, reducing channel blockage and retransmission overhead while preserving or enhancing semantic usefulness. Together, these contributions offer practical design insights for efficient, goal-driven status-update systems in multiuser wireless networks.

Abstract

The problem of goal-oriented semantic filtering and timely source coding in multiuser communication systems is considered here. We study a distributed monitoring system in which multiple information sources, each observing a physical process, provide status update packets to multiple monitors having heterogeneous goals. Two semantic filtering schemes are first proposed as a means to admit or drop arrival packets based on their goal-dependent importance, which is a function of the intrinsic and extrinsic attributes of information and the probability of occurrence of each realization. Admitted packets at each sensor are then encoded and transmitted over block-fading wireless channels so that served monitors can timely fulfill their goals. A truncated error control scheme is derived, which allows transmitters to drop or retransmit undelivered packets based on their significance. Then, we formulate the timely source encoding optimization problem and analytically derive the optimal codeword lengths assigned to the admitted packets which maximize a weighted sum of semantic utility functions for all pairs of communicating sensors and monitors. Our analytical and numerical results provide the optimal design parameters for different arrival rates and highlight the improvement in timely status update delivery using the proposed semantic filtering, source coding, and error control schemes.

Semantic Filtering and Source Coding in Distributed Wireless Monitoring Systems

TL;DR

This work develops a goal-oriented semantic framework for distributed wireless monitoring, where multiple sensors and monitors with heterogeneous goals exchange timely updates. It introduces a two-tier SoI model (microscopic meta-value and mesoscopic timeliness), two semantics-aware filtering schemes (fixed- and adaptive-length), and semantics-aware truncated error control, then derives optimal real-valued codeword lengths to maximize a weighted semantic utility. The results show that adaptive-length filtering and semantic encoding substantially improve timely, valuable updates, reducing channel blockage and retransmission overhead while preserving or enhancing semantic usefulness. Together, these contributions offer practical design insights for efficient, goal-driven status-update systems in multiuser wireless networks.

Abstract

The problem of goal-oriented semantic filtering and timely source coding in multiuser communication systems is considered here. We study a distributed monitoring system in which multiple information sources, each observing a physical process, provide status update packets to multiple monitors having heterogeneous goals. Two semantic filtering schemes are first proposed as a means to admit or drop arrival packets based on their goal-dependent importance, which is a function of the intrinsic and extrinsic attributes of information and the probability of occurrence of each realization. Admitted packets at each sensor are then encoded and transmitted over block-fading wireless channels so that served monitors can timely fulfill their goals. A truncated error control scheme is derived, which allows transmitters to drop or retransmit undelivered packets based on their significance. Then, we formulate the timely source encoding optimization problem and analytically derive the optimal codeword lengths assigned to the admitted packets which maximize a weighted sum of semantic utility functions for all pairs of communicating sensors and monitors. Our analytical and numerical results provide the optimal design parameters for different arrival rates and highlight the improvement in timely status update delivery using the proposed semantic filtering, source coding, and error control schemes.
Paper Structure (33 sections, 4 theorems, 42 equations, 12 figures, 2 tables, 1 algorithm)

This paper contains 33 sections, 4 theorems, 42 equations, 12 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Contributions
  4. System Model
  5. Packet Transmission
  6. Packet Reception
  7. Semantic Value Assessment of Update Packets
  8. Microscopic Scale
  9. Meta-value assignment
  10. Mesoscopic Scale
  11. Semantic Filtering and Channel Error Control
  12. Semantics-Aware Filtering
  13. Fixed-length filtering
  14. Adaptive-length filtering
  15. Acceptance probability analysis
  16. ...and 18 more sections

Key Result

Lemma 1

The threshold $\tau^{(k,m)}_{d}$ in sec4:eq1 is derived as where $\widehat{W}_d^{(k,m)}$ is an Erlang r.v. of order $d$ with rate $\lambda_kq_{l_k^{(m)}}$, and $\ell_{\rm max}$ indicates an upper bound to which the size of a codeword length converges. Notably, we reach $\tau^{(k,m)}_{d} = d+1$ if $\ell_{\rm max}\!\rightarrow\!\infty$.

Figures (12)

  • Figure 1: A goal-oriented, semantics-empowered distributed monitoring system.
  • Figure 2: The functional diagram of a communication link between the $k$-th SSM and the $m$-th MM.
  • Figure 3: Three conditions for packet drop via adaptive-length filtering.
  • Figure 4: Sample evolution for the EUT case, where (a) $\text{MM}_1$ receives updates from identical $\text{SSM}_1$ and $\text{SSM}_3$, and (b) $\text{MM}_2$ receives packets from $\text{SSM}_2$.
  • Figure 5: The interplay between the SoI, the arrival rate, and the admission size for the EUT, LUT, and RUT cases.
  • ...and 7 more figures

Theorems & Definitions (8)

  • Lemma 1
  • proof
  • Lemma 2
  • proof
  • Proposition 1
  • proof
  • Lemma 3
  • proof