[802-LMSC] FWD: IEEE CS Standards Activities Board Newsletter - Issue #5
Dear WG/TAG Chairs
Please forward to your groups.
--
James Gilb
IEEE 802 LMSC Chair
AK6AI, Amateur Extra
Date: Oct 1, 2026, 10:13 AM
From: bkirk@xxxxxxxxxxxx
To: sab@xxxxxxxxxxxx
Subject: IEEE CS Standards Activities Board Newsletter - Issue #5
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> IEEE Computer Society Standards Newsletter
> Issue #5 <https://www.computer.org/?source=email>
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> The IEEE Computer Society Standards Activity Newsletter
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> Issue 5 – October 2026
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> Welcometo the fifth IEEE Computer Society Standards Activity newsletter, which coversthe newly approved PARs from the September NesCom meeting. As per last time, pleasecan all Standards Committee Chairs/Vice-Chairs cascade this to the membership,so that we can improve the communication and awareness across our differentworking groups. If there is anything that is of interest, then please eithercontact the WG leads, the standards committee chairs or myself, so that we canput you in contact as needed. Note that in the tables below, PARs which areentity based rather than individual are explicitly called out.
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> Thankyou for all your efforts in developing new standards – together we can be evenmore successful.
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> DarrenGalpin
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> 2026SAB VP for Standards Activities
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> IEEE Computer Society Standards <https://www.computer.org/volunteering/boards-and-committees/standards-activities/home>>
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> New PARs
> Artificial Intelligence Standards Committee
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> Project Number
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> Project Title
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> Scope
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> Purpose
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> P4135
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> (Entity)
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> Standard for Technical Requirements of Artificial Intelligence-Based Multi-Modal Data Annotation for Electrical Power Grid Substation Equipment
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> This standard specifies the technical requirements for Artificial Intelligence (AI)-based multi-modal data annotation as applied to electrical power grid substation equipment. It applies to the multi-modal datasets development for AI applications in the substation domain, covering dataset management, annotation task management, annotation of multiple modalities, intelligent annotation methodologies, and data delivery.
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> P4213
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> Standard for Observability of Artificial Intelligence Systems
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> This standard defines a unified observability framework that helps organizations to instrument, monitor, measure, and validate the operational behavior of Artificial Intelligence (AI) systems with the rigor required for production-grade reliability and safety. The standard defines observability requirements, terminology, telemetry models, and measurement frameworks for AI systems throughout their operational lifecycle. It applies to AI-native systems and hybrid systems that integrate AI capabilities into traditional software, cloud-native, distributed, edge, and cyber-physical environments.
>
> To support the development of safe, dependable, and accountable AI systems in critical operational environments, the standard covers
>
> • model observability, including accuracy, drift, hallucination rates, safety evaluation metrics, and performance indicators;
> • inference and serving observability and dependencies, including latency distribution, throughput, token usage, request failures, and service level indicators;
> • agent and workflow observability, including tool-call tracing, agentic workflow and planning transparency, memory utilization, and multi-agent execution;
> • data and retrieval-augmented generation observability, including retrieval quality, embedding performance, vector database behavior, and context utilization; and
> • observability of supporting platforms and infrastructure, including compute accelerators, memory, storage, networking, and orchestration components that directly affect AI system behavior, performance, reliability, or safety.
>
> This standard applies to AI systems deployed across cloud, on-premises, edge, and hybrid environments. It does not replace general-purpose software or infrastructure observability standards but specifies the AI-specific telemetry and measurements required from those layers.
>
> This standard addresses limitations in general-purpose software, platform, and infrastructure observability practices when applied to the unique operational characteristics of AI workloads.
>
> By standardizing observability taxonomies, telemetry models, service level definitions, and measurement frameworks specifically for AI systems—including both AI-native deployments and hybrid systems that embed AI capabilities within traditional software environments—this standard helps enable consistent, auditable, and comparable observability practices across diverse AI deployment architectures.
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> P4166
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>
> (Entity)
>
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> Standard for Data Requirements of Large Language Models in Agriculture
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>
> This standard specifies data related requirements for the entire life cycle of large language models specialized for the agricultural domain. The standard focuses on the collection, preprocessing, storage, transmission, sharing, and destruction of agricultural data. It clarifies data quality indicators, format specifications, compatibility requirements, security measures, and privacy protection guidelines. This standard applies to agricultural farming.
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> P4167
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>
> (Entity)
>
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> Standard for High-Quality Artificial Intelligence (AI) Datasets for Rail Transit Equipment Manufacturing System
>
>
> This standard establishes a unified framework and technical requirements for the development, management, and quality assurance of high-quality Artificial Intelligence (AI) datasets for rail transit equipment manufacturing. The standard addresses domain-specific characteristics of industrial datasets that are not fully covered by general-purpose AI dataset standards. The standard defines processes, key technical requirements, inputs and outputs, and requirements for quality control and analysis of data, data planning, data acquisition, data processing, dataset construction, validation, and evaluation.
>
>
> This standard is needed to help improving dataset consistency, interoperability, and reusability. Thereby, the standard supports the efficient development and deployment of trustworthy Artificial Intelligence (AI) applications.
>
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> P4165
>
>
> (Entity)
>
>
> Standard for Evaluation Methods of Agriculture Large Language Models
>
>
> This standard specifies evaluation principles, procedures, core indicators, and specific evaluation methods for Artificial Intelligence (AI) large language models used for agriculture. It covers evaluation contents including performance, accuracy, scenario adaptability, data security, privacy protection, and scalability of agricultural large language models, and clarifies technical requirements for evaluation implementation and criteria for result judgment. This standard applies to agricultural farming.
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>
>
>
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> P4182
>
>
> (Entity)
>
>
> Standard for Federated Machine Learning Application in Trusted Data Space
>
>
> This standard provides a common framework for deploying federated machine learning within trusted data space. It defines a reference architecture as well as functional and non-functional requirements that enable interoperable and privacy-preserving data collaboration across organizations. Federated machine learning and trusted data spaces enable the standard's basic functions through their intrinsic capabilities. The standard also specifies essential non-functional requirements, most notably those concerning privacy and security requirements for this converged application.
>
>
> By establishing clear specifications, this standard aims to reduce integration complexity and promote large-scale adoption of federated machine learning in regulated data-sharing environments.
>
>
> P4211
>
>
> Standard for Operational Lifecycle Management and Governance of Generative Artificial Intelligence Systems in Production Environments
>
>
> This standard provides a unified operational framework for organizations deploying and operating Generative Artificial Intelligence (GenAI) systems in production environments.
>
> It establishes common requirements and operational guidance across the following operational domains:
>
> (1) Deployment and Release Management;
> (2) Evaluation and Validation;
> (3) Runtime Monitoring and Observability;
> (4) Security Operations;
> (5) Operational Safety Controls;
> (6) Incident Management;
> (7) Change Management; and
> (8) Lifecycle Governance.
>
> The standard applies to GenAI systems that may utilize foundation models, including large language models (LLMs), small language models (SLMs), multimodal models, diffusion models, and future foundation-model architectures. This standard does not define requirements for model training, model architecture, foundation model development, AI ethics frameworks, enterprise AI governance programs, or regulatory compliance frameworks.
>
> This standard is needed because no consensus standard currently provides an integrated operational lifecycle and governance framework specifically for GenAI systems in production environments. This gap leaves organizations without common practices for consistently, safely, and auditably deploying, monitoring, securing, and governing GenAI systems, which present distinct operational challenges including non-deterministic behavior, behavioral drift, and security risks.
>
>
>
>
>
>
>
> Artificial Intelligence Standards Committee
>
> Project Number
>
>
> Project Title
>
>
> Scope
>
>
> Purpose
>
>
> P4135
>
>
> (Entity)
>
>
> Standard for Technical Requirements of Artificial Intelligence-Based Multi-Modal Data Annotation for Electrical Power Grid Substation Equipment
>
>
> This standard specifies the technical requirements for Artificial Intelligence (AI)-based multi-modal data annotation as applied to electrical power grid substation equipment. It applies to the multi-modal datasets development for AI applications in the substation domain, covering dataset management, annotation task management, annotation of multiple modalities, intelligent annotation methodologies, and data delivery.
>
>
>
>
>
> P4213
>
>
> Standard for Observability of Artificial Intelligence Systems
>
>
> This standard defines a unified observability framework that helps organizations to instrument, monitor, measure, and validate the operational behavior of Artificial Intelligence (AI) systems with the rigor required for production-grade reliability and safety. The standard defines observability requirements, terminology, telemetry models, and measurement frameworks for AI systems throughout their operational lifecycle. It applies to AI-native systems and hybrid systems that integrate AI capabilities into traditional software, cloud-native, distributed, edge, and cyber-physical environments.
>
> To support the development of safe, dependable, and accountable AI systems in critical operational environments, the standard covers
>
> • model observability, including accuracy, drift, hallucination rates, safety evaluation metrics, and performance indicators;
> • inference and serving observability and dependencies, including latency distribution, throughput, token usage, request failures, and service level indicators;
> • agent and workflow observability, including tool-call tracing, agentic workflow and planning transparency, memory utilization, and multi-agent execution;
> • data and retrieval-augmented generation observability, including retrieval quality, embedding performance, vector database behavior, and context utilization; and
> • observability of supporting platforms and infrastructure, including compute accelerators, memory, storage, networking, and orchestration components that directly affect AI system behavior, performance, reliability, or safety.
>
> This standard applies to AI systems deployed across cloud, on-premises, edge, and hybrid environments. It does not replace general-purpose software or infrastructure observability standards but specifies the AI-specific telemetry and measurements required from those layers.
>
> This standard addresses limitations in general-purpose software, platform, and infrastructure observability practices when applied to the unique operational characteristics of AI workloads.
>
> By standardizing observability taxonomies, telemetry models, service level definitions, and measurement frameworks specifically for AI systems—including both AI-native deployments and hybrid systems that embed AI capabilities within traditional software environments—this standard helps enable consistent, auditable, and comparable observability practices across diverse AI deployment architectures.
>
> P4166
>
>
> (Entity)
>
>
> Standard for Data Requirements of Large Language Models in Agriculture
>
>
> This standard specifies data related requirements for the entire life cycle of large language models specialized for the agricultural domain. The standard focuses on the collection, preprocessing, storage, transmission, sharing, and destruction of agricultural data. It clarifies data quality indicators, format specifications, compatibility requirements, security measures, and privacy protection guidelines. This standard applies to agricultural farming.
>
>
>
>
>
> P4167
>
>
> (Entity)
>
>
> Standard for High-Quality Artificial Intelligence (AI) Datasets for Rail Transit Equipment Manufacturing System
>
>
> This standard establishes a unified framework and technical requirements for the development, management, and quality assurance of high-quality Artificial Intelligence (AI) datasets for rail transit equipment manufacturing. The standard addresses domain-specific characteristics of industrial datasets that are not fully covered by general-purpose AI dataset standards. The standard defines processes, key technical requirements, inputs and outputs, and requirements for quality control and analysis of data, data planning, data acquisition, data processing, dataset construction, validation, and evaluation.
>
>
> This standard is needed to help improving dataset consistency, interoperability, and reusability. Thereby, the standard supports the efficient development and deployment of trustworthy Artificial Intelligence (AI) applications.
>
>
> P4165
>
>
> (Entity)
>
>
> Standard for Evaluation Methods of Agriculture Large Language Models
>
>
> This standard specifies evaluation principles, procedures, core indicators, and specific evaluation methods for Artificial Intelligence (AI) large language models used for agriculture. It covers evaluation contents including performance, accuracy, scenario adaptability, data security, privacy protection, and scalability of agricultural large language models, and clarifies technical requirements for evaluation implementation and criteria for result judgment. This standard applies to agricultural farming.
>
>
>
>
>
> P4182
>
>
> (Entity)
>
>
> Standard for Federated Machine Learning Application in Trusted Data Space
>
>
> This standard provides a common framework for deploying federated machine learning within trusted data space. It defines a reference architecture as well as functional and non-functional requirements that enable interoperable and privacy-preserving data collaboration across organizations. Federated machine learning and trusted data spaces enable the standard's basic functions through their intrinsic capabilities. The standard also specifies essential non-functional requirements, most notably those concerning privacy and security requirements for this converged application.
>
>
> By establishing clear specifications, this standard aims to reduce integration complexity and promote large-scale adoption of federated machine learning in regulated data-sharing environments.
>
>
> P4211
>
>
> Standard for Operational Lifecycle Management and Governance of Generative Artificial Intelligence Systems in Production Environments
>
>
> This standard provides a unified operational framework for organizations deploying and operating Generative Artificial Intelligence (GenAI) systems in production environments.
>
> It establishes common requirements and operational guidance across the following operational domains:
>
> (1) Deployment and Release Management;
> (2) Evaluation and Validation;
> (3) Runtime Monitoring and Observability;
> (4) Security Operations;
> (5) Operational Safety Controls;
> (6) Incident Management;
> (7) Change Management; and
> (8) Lifecycle Governance.
>
> The standard applies to GenAI systems that may utilize foundation models, including large language models (LLMs), small language models (SLMs), multimodal models, diffusion models, and future foundation-model architectures. This standard does not define requirements for model training, model architecture, foundation model development, AI ethics frameworks, enterprise AI governance programs, or regulatory compliance frameworks.
>
> This standard is needed because no consensus standard currently provides an integrated operational lifecycle and governance framework specifically for GenAI systems in production environments. This gap leaves organizations without common practices for consistently, safely, and auditably deploying, monitoring, securing, and governing GenAI systems, which present distinct operational challenges including non-deterministic behavior, behavioral drift, and security risks.
>
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>
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>
>
>
> Blockchain and Distributed Ledgers
>
> Project Number
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> Project Title
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>
> Scope
>
>
> Purpose
>
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> P3260.04
>
>
> (Entity)
>
>
> Recommended Practice for Unmanned Aerial Vehicles (UAV)-based Live-line X-ray Inspection of Transmission Lines rated 35kV ac and above and Data Evidence Preservation
>
>
> This recommended practice specifies requirements for Distributed Ledger Technology (DLT)-based on-chain recording, evidence preservation, traceability, and cryptographic verification of inspection data throughout all stages of Unmanned Aerial Vehicle (UAV)-based live-line X-ray inspection of tension clamps and splicing sleeves on Alternating Current (AC) overhead power transmission lines rated 35 kV and above, covering inspection equipment, environmental and operating conditions, field procedures, defect evaluation, and evidence reporting.
>
>
> This recommend practice helps to address the current lack of consistent operation workflows for Unmanned Aerial Vehicle (UAV)-based live-line X-ray inspection across equipment manufacturers, electric utilities, inspection organizations, and regulatory authorities. It also helps overcoming that incomparable inspection results and non-standardized mechanisms for evidence preservation and reporting hinder the increasing use of live non-destructive testing (NDT) power line transmission.
>
>
> P3225.05
>
>
> (Entity)
>
>
> Standard for Reference Architecture of the Internet of Agents (IoA)
>
>
> This standard specifies a reference architecture for the Internet of Agents (IoA). It covers user and functional views and defines technical requirements and a framework for IoA. This standard applies to the architecture design, software development, system integration, deployment, operation, testing, and evaluation of IoA-based platforms, systems, and application services.
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>
>
>
>
> P3272.03
>
>
> (Entity)
>
>
> Standard for Stable Coin-Based Micropayments
>
>
> This standard establishes basic definitions for micropayments and specifies a technical architecture for stablecoin-based micropayment systems. The standard is applicable to complex scenarios. It covers transaction workflows, communication interfaces, message specifications, clearing ledgers, security and risk control, adaptation for Artificial Intelligence (AI) agents and embodied intelligence, regulatory compliance, and other relevant contents.
>
>
>
>
>
> P3225.04
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>
> (Entity)
>
>
> Standard for Reference Architecture of Distributed Swarm Intelligence
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>
> This standard defines the business model as well as the user and functional views of distributed swarm intelligence enabled by blockchain technology. It specifies requirements for decentralized identification, peer‑to‑peer (P2P) communication networks, control of heterogeneous agents and embodied‑intelligence systems, cross‑domain task scheduling enabled by smart contracts, stablecoin‑based payment, governance, compliance, and related functions.
>
>
> This proposal will not focus on Artificial Intelligence (AI)‑related large language models (LLMs) and agents themselves. Instead, it intends to provide a solution for addressing interaction problems in certain specific industries or business scenarios, especially for agents/robots belonging to different organizations and regions across the world. Blockchain technology may be a good choice to resolve cross‑border related issues, such as decentralized identity authentication, data and privacy protection, task assignment via smart contract, on-chain proof‑of‑work, and reduced costs for stablecoin‑based payments.
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>
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> Cybersecurity & Privacy Standards Committee
>
> Project Number
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> Project Title
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> Scope
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>
> Purpose
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> P3409.1
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>
> Recommended Practice for Zero Trust Security Governance
>
>
> This recommended practice provides security guidance for implementing IEEE Std 3409 governance. The guidance considers risk, feasibility, effectiveness, and economic impact on the entity operation.
>
>
>
>
>
> P3409.2
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> Recommended Practice for Zero Trust Security Visibility and Analytics
>
>
> This recommended practice provides security guidance for implementing IEEE Std 3409 visibility and analytics.
>
>
> .
>
>
>
>
>
>
>
> Data Compression Standards Committee
>
> Project Number
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> Project Title
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>
> Scope
>
>
> Purpose
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> P1857.17
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>
> Standard for Security Video and Audio Coding
>
>
> This standard specifies the structure, syntax, semantics, parsing process, and decoding process of coded bitstreams for security video and audio coding.
>
> The standard defines an integrated coding framework that combines audio and video compression with bitstream-level security and other application-oriented coding functions. It includes scene coding, temporal and spatial scalable video coding, encryption, authentication, privacy-preserving coding, and intelligent information coding for visual semantic information.
>
> The standard specifies mechanisms for integrating security functions with the coded bitstream, including signaling and data structures for encryption, authentication, security parameters, digital signatures, and related security information.
>
> The standard applies to audio and video services in smart city, transportation, campus, consumer, residential, enterprise, and other security-related applications, and supports the transmission, storage, retrieval, intelligent analysis, and cloud-edge collaborative processing of coded audio and video content.
>
> The purpose of this standard is to provide a unified video and audio coding framework that integrates efficient media compression with bitstream-level security, privacy protection, and intelligent information coding. By incorporating encryption, authentication, digital signatures, and related security mechanisms into the coded media structure, the standard enables secure, trustworthy, privacy-preserving, and interoperable audio and video services across a broad range of applications.
>
>
>
>
> Digital Trust Standards Committee
>
> Project Number
>
>
> Project Title
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>
> Scope
>
>
> Purpose
>
>
> P4172
>
>
> (Entity)
>
>
> Standard for Trust-Oriented Data Classification, Grading, and Governance for Electric Power Data Applications
>
>
> This standard defines principles, frameworks, and methods for trust-oriented classification, grading, and governance of electric power data. It establishes a structured taxonomy of electric power data across business domains including generation, transmission, distribution, consumption, and trading, and provides multi-dimensional grading criteria based on data sensitivity, disclosure risk, and operational impact.
> This standard further specifies a trust-oriented governance baseline for authorized data operations, trusted data sharing, and cross-organizational data circulation within the power sector. This standard applies to power utilities, grid operators, energy trading platforms, distributed energy resource aggregators, and other entities engaged in power data management and exchange.
>
>
>
>
> P4169
>
>
> (Entity)
>
>
> Standard for Security and Trust Evaluation of Lightweight Secure Communication for Electric Power Internet of Things (IoT) Terminals
>
>
> This standard specifies security capability requirements, trusted evaluation criteria, and evaluation methods for lightweight secure communication used by electric power Internet of Things (IoT) terminals. It defines the capabilities and evaluation requirements related to terminal identity authentication, lightweight encrypted communication, key and credential management, secure session establishment, communication integrity and confidentiality, trust evidence collection, trustworthiness verification, and security capability classification.
> This standard applies to resource-constrained electric power IoT terminals used in power generation, transmission, distribution, consumption, distributed energy resources, metering, sensing, and edge-control scenarios. It supports the establishment, maintenance, and verification of trust among Power IoT terminals, edge nodes, management platforms, and related digital entities during communication interactions in electric power environments.
>
>
>
>
> P4180
>
>
> (Entity)
>
>
> Standard for Requirements for Token Lifecycle Trustworthiness in Large Language Model Inference
>
>
> This standard specifies requirements for evaluating the metering of tokens for Large Language Model (LLM) inference. The metering requirements address the full token lifecycle and align with Artificial Intelligence (AI) cloud service token engineering capability requirements on production, flow orchestration, application integration, and governance.
> The standard defines metrics for source trustworthiness, transmission integrity, processing consistency, and usage traceability. It also specifies lifecycle management requirements for token production, distribution, consumption, and retirement.
> The standard applies to token providers, cloud service operators, and enterprise consumers in public, private, and hybrid cloud environments.
>
>
>
>
>
>
> LAN/MAN(802.1) Standards Committee
>
> Project Number
>
>
> Project Title
>
>
> Scope
>
>
> Purpose
>
>
> P802.1EJ
>
>
> Standard for Local and Metropolitan Area Networks--Backward Notification
>
>
> This standard specifies procedures, protocols, and managed objects for Backward Notification (BN) to signal data center network conditions toward the source end station. This standard supports Medium Access Control (MAC) bridges, Customer Virtual Local Area Network (C-VLAN) bridges and Service Virtual Local Area Network (S-VLAN) bridges and end stations in data center networks. The BN mechanism conveys information for congestion management, path rebalancing, and failure recovery. This standard specifies how BNs are triggered and constructed, and a service interface for exposing the information to entities above the data link layer. This standard supports managed objects and a YANG data model for backward notification.
>
>
>
>
>
> P802.1DGei
>
>
> (Amendment)
>
>
> IEEE Standard for Local and Metropolitan Area Networks—Time-Sensitive Networking Profile for Automotive In-Vehicle Ethernet Communications Amendment: Automotive Time Synchronization
>
>
> This amendment selects IEEE 802.1AS time synchronization options, defaults, and parameter values and provides configuration guidelines and constraints for automotive in-vehicle bridged IEEE 802.3 Ethernet networks.
>
>
> This standard provides profiles for designers and implementers of automotive IEEE 802.3 Ethernet networks that support a wide range of in-vehicle applications.
>
>
> P802.1EH
>
>
> Standard for Local and Metropolitan Area Networks--Telemetry Tagging of Data Frames
>
>
> This standard specifies protocols, procedures, and managed objects to support forward signaling of telemetry in dedicated tags added to data frames. This standard supports Medium Access Control (MAC) bridges, Customer Virtual Local Area Network (C-VLAN) bridges, Service Virtual Local Area Network (S-VLAN) bridges, and end stations. Telemetry in conjunction with IEEE Std 802.1AE MAC Security is also supported. This standard specifies a service interface to enable higher layer protocols to initiate and retrieve telemetry from the data link layer. This standard supports managed objects and a YANG data model for initiating and recording telemetry.
>
>
> This standard provides a simple, high-performance method to provide information about the congestion state of the bottleneck hop along the path of the data frame within a flow. The collection of fine-grained bottleneck information can be important for network protocols and network performance analysis.
>
>
> P60802
>
>
> (Revision)
>
>
> IEC/IEEE International Standard Time-Sensitive Networking Profile for Industrial Automation
>
>
> This document defines time-sensitive networking profiles for industrial automation. The profiles select features, options, configurations, defaults, protocols, and procedures of bridges, end stations, and Local Area Networks (LANs) to build industrial automation networks. This document also specifies YANG modules defining read-only information available online and offline as a digital data sheet. This document also specifies YANG modules for remote procedure calls and actions to address requirements arising from industrial automation networks.
>
>
>
>
>
> P802.1CM
>
>
> (Revision)
>
>
> Standard for Local and Metropolitan Area Networks -- Time-Sensitive Networking for Fronthaul
>
>
> This standard defines profiles that select features, options, configurations, defaults, protocols and procedures of bridges, stations, and local area networks that are necessary to build networks that are capable of transporting fronthaul streams, which are time-sensitive.
>
>
> The purpose of this standard is to specify defaults and profiles that enable the transport of time-sensitive fronthaul streams in Ethernet bridged networks.
>
>
> P802.3dv
>
>
> (Amendment)
>
>
> IEEE Standard for Ethernet Amendment: Physical Layers and Management Parameters for 400 Gb/s, 800 Gb/s, and 1.6 Tb/s Electrical and Single-Mode Fiber (SMF) Optical Interfaces Based on 400 Gb/s/lane Signaling
>
>
> The scope of the project is to specify modifications of IEEE Std 802.3 to add Physical Layer specifications and Management Parameters for 400 Gb/s, 800 Gb/s and 1.6 Tb/s using 400 Gb/s/lane signaling for electrical interconnects and single-mode fiber optical interconnects with reaches up to 500 meters.
>
>
>
>
>
> P802.3dw
>
>
> (Amendment)
>
>
> Standard for Ethernet Amendment: Fault Managed Power (FMP)
>
>
> This document specifies optional Fault Managed Power (FMP) delivery on Ethernet cabling. It also specifies additions and modifications to Physical Layer specifications, including the reconciliation sublayer, and management parameters that support FMP.
>
>
>
>
>
>
>
>
>
>
> Microprocessor Standards Committee
>
> Project Number
>
>
> Project Title
>
>
> Scope
>
>
> Purpose
>
>
> P1722c
>
>
> (Amendment)
>
>
> IEEE Standard for a Transport Protocol for Time-Sensitive Applications in Bridged Local Area Networks Amendment: New and Extended Streaming Formats
>
>
> This amendment specifies extensions to IEEE Std 1722-2025 to add support for streaming sensor data such as camera and radar data, support for encapsulation of industry-wide deployed display interfaces, and support for a Remote Control Protocol (RCP). This amendment enhances support for remote Local Interconnect Network (LIN) read requests and the semantic for Stream ID and Byte Bus ID overcoming limitations by Annex L to achieve interoperability between vendors. This amendment adds an annex that describes how to calculate the SRP bandwidth for each of the stream types, improves AES-SIV support, and adds cryptographic algorithm options: ed25519 (RFC8032) and X25519 (RFC 7748) to clauses 16 and 17. This amendment addresses errors and omissions in the description of existing functionality.
>
>
> This standard facilitates interoperability between end stations that transport time-sensitive media across LANs providing time synchronization, latency, and bandwidth services by defining additional packet format protocols, synchronization mechanisms, and diagnostic counters.
>
>
>
>
> Software & Systems Engineering Standards Committee
>
> Project Number
>
>
> Project Title
>
>
> Scope
>
>
> Project Number
>
>
> P42020 (Revision)
>
>
> ISO/IEC/IEEE International Standard — Enterprise, systems and software — Architecture processes
>
>
> This document establishes a set of process descriptions for the governance and management of a collection of architectures and the architecting of entities. This document establishes a set of process descriptions for the conceptualization, evaluation and elaboration of an architecture
> and the architecting of an entity. This document also establishes an enablement process description that provides support to these other architecture processes. These processes are associated with the architecture practice applicable to various entities such as enterprises, business components, capability areas, mission areas, missions, systems, systems of systems, families of systems, infrastructure, products, services, product lines, hardware, software applications and technologies.
> These processes are intended for:
> • participants in architecting and engineering efforts;
> • organizations and projects adopting architecture practice in their architecting and engineering efforts;
> • developers of architectures, architecture designs, solution designs, architecture descriptions, reference architectures, reference architecture descriptions and reference models;
> • stakeholders using architectures as the baseline for their activities;
> • providers of architecture-related standards, guidelines, training, education and evaluation instruments used for assessment of architecture practices and architecting work products;
> • developers of architecture description languages, architecture modelling languages, architecture frameworks, architecture development and modelling methodologies, and architecture-related tools and technologies.
>
> The application areas of this document include the following: Artificial Intelligence (AI), machine learning (ML), Internet of Things (IoT), cloud computing, big data, smart cities, smart manufacturing, cybersecurity, digital twin, telecommunications, aerospace, defense, banking, finance, insurance, energy, automotive, hospitality, healthcare, supply chain, transportation, manufacturing, agriculture, production and infrastructure.
> This document addresses generic architecture-related terms and definitions and does not address domain-specific terms and definitions. This document does not address concepts and principles related to architecture competencies.
>
>
>
>
> P42030 (Revision)
>
>
> ISO/IEC/IEEE International Standard - Enterprise, systems and software -- Architecture evaluation framework
>
>
> This document specifies the means to organize and record architecture evaluations for enterprise, systems and software fields of application.
>
> The aim of this document is to enable architecture evaluations that are used to:
> a) validate that architectures address the concerns of stakeholders;
> b) assess the quality of architectures with respect to their intended purpose;
> c) assess the value of architectures to their stakeholders;
> d) determine whether architecture entities address their intended purpose;
> e) provide architecture-related knowledge and information about entities;
> f) get architecture-related knowledge and information about entities;
> g) assess progress towards achieving architecture objectives;
> h) clarify understanding of problem space and of stakeholder needs and expectations;
> i) identify risks and opportunities associated with architectures and related entities; and
> i) support decision making where architectures are involved.
>
> The entity being evaluated can be of several kinds, as illustrated in the following examples: enterprise, organization, solution, system, subsystem, business, data (as a data element or data structure), application, information technology (as a collection), mission, product, service, software item, hardware item, etc. The kind of entity can also be a product line, family of systems, system of systems, etc. It also spans the variety of applications that utilize digital technology such as mobile, cloud, big data, robotics, Internet of Things (IoT), web, desktop, embedded systems, and so on.
>
> The generic Architecture Evaluation (AE) framework specified in this document can be used in support of the Architecture Evaluation process defined in ISO/IEC/IEEE 42020. Specific frameworks can be derived from this generic framework, which can provide a mapping to the system life cycle processes in ISO/IEC/IEEE 15288 or to the software life cycle processes in ISO/IEC/IEEE 12207.
>
>
>
>
> P24774 (Revision)
>
>
> ISO/IEC/IEEE 24774 - Systems and software engineering - Life cycle management--Specification for process description
>
>
> This document provides a specification for describing processes. This document gives requirements and recommendations for the description of processes by identifying elements and rules for their formulation.
> This document also provides a specification for describing process views.
> This document explains how conformance related to processes and process views can be defined, when processes and process views are described in accordance with this document.
> This document does not describe how processes are composed or otherwise aggregated into larger frameworks, process reference models or life cycle models. Nor does the document cover how to assess or evaluate the performance of a process, or the output (products) of a process.
> NOTE: Two prominent International Standards in process description for software and system engineering are ISO/IEC/IEEE 12207 and ISO/IEC/IEEE 15288. These two standards have very similar process models. The information items associated with their process definitions are given in ISO/IEC/IEEE 15289. Other documents provide further characterization of a single life cycle process by elaborating the process elements and levying specific requirements on the performance of the process.
> This document is applicable when processes or process views are described for various uses by any party, organization or standard relating to systems and software engineering processes.
>
>
>
>
>
>
> Standards Activities Board Standards Committee
>
> Project Number
>
>
> Project Title
>
>
> Scope
>
>
> Purpose
>
>
> P4135
>
>
> (Entity)
>
>
> Standard for Technical Requirements of Artificial Intelligence-Based Multi-Modal Data Annotation for Electrical Power Grid Substation Equipment
>
>
> This standard specifies the technical requirements for Artificial Intelligence (AI)-based multi-modal data annotation as applied to electrical power grid substation equipment. It applies to the multi-modal datasets development for AI applications in the substation domain, covering dataset management, annotation task management, annotation of multiple modalities, intelligent annotation methodologies, and data delivery.
>
>
>
>
>
> P4213
>
>
> Standard for Observability of Artificial Intelligence Systems
>
>
> This standard defines a unified observability framework that helps organizations to instrument, monitor, measure, and validate the operational behavior of Artificial Intelligence (AI) systems with the rigor required for production-grade reliability and safety. The standard defines observability requirements, terminology, telemetry models, and measurement frameworks for AI systems throughout their operational lifecycle. It applies to AI-native systems and hybrid systems that integrate AI capabilities into traditional software, cloud-native, distributed, edge, and cyber-physical environments.
>
> To support the development of safe, dependable, and accountable AI systems in critical operational environments, the standard covers
>
> • model observability, including accuracy, drift, hallucination rates, safety evaluation metrics, and performance indicators;
> • inference and serving observability and dependencies, including latency distribution, throughput, token usage, request failures, and service level indicators;
> • agent and workflow observability, including tool-call tracing, agentic workflow and planning transparency, memory utilization, and multi-agent execution;
> • data and retrieval-augmented generation observability, including retrieval quality, embedding performance, vector database behavior, and context utilization; and
> • observability of supporting platforms and infrastructure, including compute accelerators, memory, storage, networking, and orchestration components that directly affect AI system behavior, performance, reliability, or safety.
>
> This standard applies to AI systems deployed across cloud, on-premises, edge, and hybrid environments. It does not replace general-purpose software or infrastructure observability standards but specifies the AI-specific telemetry and measurements required from those layers.
>
> This standard addresses limitations in general-purpose software, platform, and infrastructure observability practices when applied to the unique operational characteristics of AI workloads.
>
> By standardizing observability taxonomies, telemetry models, service level definitions, and measurement frameworks specifically for AI systems—including both AI-native deployments and hybrid systems that embed AI capabilities within traditional software environments—this standard helps enable consistent, auditable, and comparable observability practices across diverse AI deployment architectures.
>
> P4166
>
>
> (Entity)
>
>
> Standard for Data Requirements of Large Language Models in Agriculture
>
>
> This standard specifies data related requirements for the entire life cycle of large language models specialized for the agricultural domain. The standard focuses on the collection, preprocessing, storage, transmission, sharing, and destruction of agricultural data. It clarifies data quality indicators, format specifications, compatibility requirements, security measures, and privacy protection guidelines. This standard applies to agricultural farming.
>
>
>
>
>
> P4167
>
>
> (Entity)
>
>
> Standard for High-Quality Artificial Intelligence (AI) Datasets for Rail Transit Equipment Manufacturing System
>
>
> This standard establishes a unified framework and technical requirements for the development, management, and quality assurance of high-quality Artificial Intelligence (AI) datasets for rail transit equipment manufacturing. The standard addresses domain-specific characteristics of industrial datasets that are not fully covered by general-purpose AI dataset standards. The standard defines processes, key technical requirements, inputs and outputs, and requirements for quality control and analysis of data, data planning, data acquisition, data processing, dataset construction, validation, and evaluation.
>
>
> This standard is needed to help improving dataset consistency, interoperability, and reusability. Thereby, the standard supports the efficient development and deployment of trustworthy Artificial Intelligence (AI) applications.
>
>
> P4165
>
>
> (Entity)
>
>
> Standard for Evaluation Methods of Agriculture Large Language Models
>
>
> This standard specifies evaluation principles, procedures, core indicators, and specific evaluation methods for Artificial Intelligence (AI) large language models used for agriculture. It covers evaluation contents including performance, accuracy, scenario adaptability, data security, privacy protection, and scalability of agricultural large language models, and clarifies technical requirements for evaluation implementation and criteria for result judgment. This standard applies to agricultural farming.
>
>
>
>
>
>
>
> Completed Standards
>
> Standard Number
>
>
> Committee
>
>
> Project Title
>
>
> 3348
>
>
> Artificial Intelligence Standards Committee
>
>
> IEEE Approved Draft Recommended Practice for the Framework and Evaluation Methods of Two-Dimensional (2D) Real-Person Digital Clothes Model Generation System Based on Artificial Intelligence
>
>
> 3398
>
>
> Artificial Intelligence Standards Committee
>
>
> IEEE Approved Draft Recommended Practice for Generative Pre-trained Transformer Empowered Software Engineering Life Cycle
>
>
> 3376
>
>
> Artificial Intelligence Standards Committee
>
>
> IEEE Approved Draft Recommended Practice for Evaluating Artificial Intelligence Generated Content
>
>
> 3462
>
>
> Artificial Intelligence Standards Committee
>
>
> IEEE Approved Draft Recommended Practice for Using Safety by Design in Generative Models to Prioritize Child Safety
>
>
> 3241.03
>
>
> Blockchain and Distributed Ledgers Standards Committee
>
>
> IEEE Approved Draft Standard for Carbon Accounting Utilizing Blockchain
>
>
> 3241.02
>
>
> Blockchain and Distributed Ledgers Standards Committee
>
>
> IEEE Approved Draft Standard for Blockchain-Based Carbon Trading Data Format Specification
>
>
> 3477
>
>
> Cybersecurity & Privacy Standards Committee
>
>
> IEEE Approved Draft Standard for Evaluation Method for Anonymization Effectiveness in Data Sharing
>
>
> 3729
>
>
> Cybersecurity & Privacy Standards Committee
>
>
> IEEE Approved Draft Standard for Security Requirements of Trusted Data Space
>
>
> 3730
>
>
> Cybersecurity & Privacy Standards Committee
>
>
> IEEE Approved Draft Standard for Functional Requirements of Trusted Data Space
>
>
> 3184.3
>
>
> Data Compression Standards Committee
>
>
> IEEE Approved Draft Standard for Software-in-the-Loop Simulation Testing of Planning and Control Modules in Autonomous Driving Systems
>
>
> 3161.4
>
>
> Data Compression Standards Committee
>
>
> IEEE Approved Draft Standard for Cloud Subsystem of Digital Retina Systems
>
>
> 3161.8
>
>
> Data Compression Standards Committee
>
>
> IEEE Approved Draft Standard for Security and Privacy Protection of Digital Retina Systems
>
>
> 3366.3
>
>
> Data Compression Standards Committee
>
>
> IEEE Approved Draft Standard for Gaussian Splats Compression
>
>
> 2807.8
>
>
> Knowledge Engineering Standards Committee
>
>
> IEEE Approved Draft Standard for Knowledge Exchange Protocol with Knowledge Fusion Among Knowledge Graphs
>
>
> 60802
>
>
> LAN/MAN (802.1) Standards Committee
>
>
> IEC/IEEE International Standard Time-Sensitive Networking Profile for Industrial Automation
>
>
> 802.1AB
>
>
> LAN/MAN (802.1) Standards Committee
>
>
> IEEE Approved Draft Standard for Local and metropolitan area networks - Station and Media Access Control Connectivity Discovery
>
>
> 802.1AC
>
>
> LAN/MAN (802.1) Standards Committee
>
>
> IEEE Approved Draft Standard for Local and metropolitan area networks -- Media Access Control (MAC) Service Definition
>
>
>
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> Best,
>
> Brian Kirk
> Senior Technology Initiatives & Strategic Programs Manager
> CS Strategy and Governance
> IEEE Computer Society
> 10662 Los Vaqueros Cir
> Los Alamitos, CA 90720
> 714.822.9270
> bkirk@xxxxxxxxxxxx
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