IEEE

This recommended practice specifies how the elements and attributes defined in the Atom Syndication Format (Atom) relate to the components of the conceptual model for resource aggregation defined in IEEE Std 1484.13.1(tm)-2012.

This guide will provide a framework that outlines technologies and the processes for planning the evolution of a smart city. Smart Cities and related solutions require technology standards and a cohesive process planning framework for the use of the internet of things to ensure interoperable, agile, and scalable solutions that are able to be implemented and maintained in a sustainable manner. This framework provides a methodology for municipalities and technology integrators to use as a tool to plan for innovative and technology solutions for smart cities.

This Standard describes a data model to support the interchange of agreed upon data elements and their values between a learning-related content object and a runtime service (RTS) used to support learning management. This Standard does not specify the means of communication between a content object and an RTS nor how any component of a learning environment shall behave in response to receiving data in the form specified. This Standard is based on a related data model defined in the "Computer Managed Instruction (CMI) Guidelines For Interoperability," version 3.4, defined by the Aviation Industry CBT Committee (AICC). To balance the need to support existing implementations with the need to make technical corrections and support emerging practice, this Standard selectively includes those data elements from the CMI specification that are commonly implemented; renames some data elements taken from the CMI specification to clarify their intended meaning; modifies the data types of data elements taken from the CMI specification to reflect ISO standard data types and internationalization requirements; removes some organizational structures used in the CMI specification to group data elements that are specific to the AICC community of practice and not generally applicable; and introduces some data elements not present in the CMI specification to correct known technical defects in data elements taken from that specification.

Augmented Reality (AR) promises to provide significant boosts in operational efficiency by making information available to employees needing task support in context in real time. To support according implementations of AR training systems,this document proposes an overarching integrated conceptual model that describes interactions between the physical world, the user, and digital information, the context for AR-assisted learning and other parameters of the environment. It defines two data models and their binding to XML and JSON for representing learning activities (also known as employee tasks and procedures) and the learning environment in which these tasks are performed (also known as the workplace). The interoperability specification and standard is presented in support of an open market where interchangeable component products provide alternatives to monolithic Augmented Reality-assisted learning systems. Moreover, it facilitates the creation of experience repositories and online marketplaces for Augmented Reality-enabled learning content. Specific attention was given to reuse and repurposing of existing learning content and catering to ‘mixed' experiences combining real world learner guidance with the consumption (or production) of traditional contents such as instructional video material or learning apps and widgets.

Specified in this recommended practice are how the elements and attributes defined in the Open Archives Initiative Object Reuse and Exchange (OAI-ORE) Abstract Model and expressed in the OAI-ORE Resource Map Implementation in RDF/XML relate to the components of the conceptual model for resource aggregation defined in IEEE Std 1484.13.1(TM)-2012.

A map data representation of environments of a mobile robot performing a navigation task is specified in this standard. It provides data models and data formats for two-dimensional (2D) metric and topological maps.

Self-management of health, with decision-making processes(cognitive and robotics)

Discusses Standards Roadmaps for Future Networks, including 5G/6G, as well as in the area of Autonomic/Autonomous Networking (ANs) Standards. The Emerging Industry Requirement for Standardization of a Blueprint for Common Operational Principles for Autonomic/Autonomous Networks (COPAAN) is presented in relation to the Autonomic/Autonomous Networking (ANs) paradigm. The connection of COPAAN and Robotics is illustrated.

The document describes Gaps in Standards for Autonomic/Autonomous Networking (ANs), including Self-Organizing Systems and Networks. The Emerging Industry Requirement for Standardization of a Blueprint for Common Operational Principles for Autonomic/Autonomous Networks (COPAAN) is presented in more detail in relation to the Autonomic/Autonomous Networking (ANs) paradigm. The Model of Interfaces of an AN that call for COPAAN Standard Development is presented. The connection of COPAAN and Robotics is illustrated. Systems Optimization, Traffic Variance, Control Variance, Service Variance, Confluence, Dependency, Complex Systems, Self-Organizing Networks, Self-X, Autonomics, Autonomic Management & Control (AMC), Emergence

This standard extends the IEEE 1873-2015™ Standard for Robot Map Data Representation from two-dimensional (2D) maps to three-dimensional (3D) maps.

This Guide for Verification of Autonomous Systems enables the user to define a customized process for verification of their autonomous system based on their available resources. It documents best practices across all levels of abstraction within a given system.

"Nudges" as exhibited by robotic, intelligent or autonomous systems are defined as overt or hidden suggestions or manipulations designed to influence the behavior or emotions of a user. This standard establishes a delineation of typical nudges (currently in use or that could be created). It contains concepts, functions and benefits necessary to establish and ensure ethically driven methodologies for the design of the robotic, intelligent and autonomous systems that incorporate them.