Draft: draft-irtf-t2trg-iot-edge

Commentary: ( Can wait until after RGLC ) 
 Looks good to me , thanks .
The Industry IoT Consortium (IIC, formerly OpenFog) defines fog
computing as "a horizontal, system-level architecture that
distributes computing, storage, control and networking functions
closer to the users along a cloud-to-thing continuum" OpenFog.
The Industry IoT Consortium (IIC, now incorporating what was
formerly OpenFog) defines fog computing as "a horizontal, system-
level architecture that distributes computing, storage, control
and networking functions closer to the users along a cloud-to-
thing continuum" OpenFog.
The use of edge computing in a smart factory can reduce the cost of
network and storage resources by reducing the communication load to
the central data center or server.  It is also possible to improve
process efficiency and facility asset productivity through the real-
time prediction of failures, and to reduce the cost of failure
through preliminary measures.  In the existing manufacturing field,
production facilities are manually run according to a program entered
in advance, but edge computing in a smart factory enables tailoring
solutions by analyzing data at each production facility and machine
level.  Digital twins Jones of IoT devices have been use jointly with
edge computing in industrial IoT scenarios Chen.
The use of edge computing in a smart factory can reduce the cost of
network and storage resources by reducing the communication load to
the central data center or server.  It is also possible to improve
process efficiency and facility asset productivity through the real-
time prediction of failures, and to reduce the cost of failure
through preliminary measures.  In the existing manufacturing field,
production facilities are manually run according to a program entered
in advance, but edge computing in a smart factory enables tailoring
solutions by analyzing data at each production facility and machine
level.  Digital twins Jones of IoT devices have been used jointly
with edge computing in industrial IoT scenarios Chen.
In future smart city scenarios, the Smart Grid will be critical in
ensuring highly available/efficient energy control in city-wide
electricity management.  Edge computing is expected to play a
significant role in those systems to improve transmission efficiency
of electricity; to react and restore power after a disturbance; to
reduce operation costs and reuse renewable energy effectively, since
these operations involve local decision-making.  In addition, edge
computing can help to monitor power generation and power demand, and
making local electrical energy storage decisions in the smart grid
system.
In future smart city scenarios, the Smart Grid will be critical in
ensuring highly available/efficient energy control in city-wide
electricity management.  Edge computing is expected to play a
significant role in those systems to improve transmission efficiency
of electricity; to react to, and restore power after, a disturbance;
to reduce operation costs and reuse renewable energy effectively,
since these operations involve local decision-making.  In addition,
edge computing can help to monitor power generation and power demand,
and making local electrical energy storage decisions in the smart
grid system.
Safety is critical on a construction site.  Every year, many
construction workers lose their lives due to falls, collisions,
electric shocks, and other accidents.  Therefore, solutions have been
developed in order to improve construction site safety, including
real-time identification of workers, monitoring of equipment
location, and predictive accident prevention.  To deploy these
solutions, many cameras and IoT sensors were installed on
construction sites, that measure noise, vibration, gas concentration,
etc.  Typically, data generated from these measurements has been
collected in an on-site gateway and sent to a remote cloud server for
storage and analysis.  Thus, an inspector can check the information
stored on the cloud server to investigate an incident.  However, this
approach can be expensive, due to transmission costs, e.g., of video
streams over an LTE connection, and due to usage fees of private
cloud services such as Amazon Web Services.
Safety is critical on a construction site.  Every year, many
construction workers lose their lives due to falls, collisions,
electric shocks, and other accidents.  Therefore, solutions have been
developed in order to improve construction site safety, including
real-time identification of workers, monitoring of equipment
location, and predictive accident prevention.  To deploy these
solutions, many cameras and IoT sensors were installed on
construction sites, measuring noise, vibration, gas concentration,
etc.  Typically, data generated from these measurements has been
collected in an on-site gateway and sent to a remote cloud server for
storage and analysis.  Thus, an inspector can check the information
stored on the cloud server to investigate an incident.  However, this
approach can be expensive, due to transmission costs, e.g., of video
streams over an LTE connection, and due to usage fees of private
cloud services such as Amazon Web Services.
Additionally, oneM2M recently studied several use cases related to
edge computing, including: smart factories, smart transportation, an
accident notification service, a high-precision road map service, a
vulnerable road user service and a vehicular data service.  These use
cases are documented in oneM2M-TR0001, oneM2M-TR0018 and oneM2M-
TR0026.  Edge computing related requirements raised through the
analysis of these use cases are captured in oneM2M-TS0002.
oneM2M recently studied several use cases related to edge computing,
including: smart factories, smart transportation, an accident
notification service, a high-precision road map service, a vulnerable
road user service and a vehicular data service.  These use cases are
documented in oneM2M-TR0001, oneM2M-TR0018 and oneM2M-TR0026.  Edge
computing related requirements raised through the analysis of these
use cases are captured in oneM2M-TS0002.
When IoT services are deployed at home, personal information can be
learned from detected usage data.  For example, one can extract
information about employment, family status, age, and income by
analyzing smart meter data ENERGY.  Policy-makers started to provide
frameworks that limit the usage of personal data and put strict
requirements on data controllers and processors.  However, data
stored indefinitely in the Cloud also increases the risk of data
leakage, for instance, through attacks on rich targets.
When IoT services are deployed at home, personal information can be
learned from detected usage data.  For example, one can extract
information about employment, family status, age, and income by
analyzing smart meter data ENERGY.  Policy-makers started to provide
frameworks that limit the usage of personal data and put strict
requirements on data controllers and processors.  Data stored
indefinitely in the Cloud also increases the risk of data leakage,
for instance, through attacks on rich targets.
In this section, we first look at the current state of IoT edge
computing sec-overview, and then define a general system model sec-
model.  This provides context for IoT edge computing functions, which
are listed in sec-components.
In this section, we first look at the current state of IoT edge
computing (sec-overview), and then define a general system model
(sec-model).  This provides context for IoT edge computing functions,
which are listed in sec-components.
Physical or virtual IoT gateways can host application programs, which
are typically built using an SDK to access local services through a
programmatic API.  Edge cloud system operators host their customers'
applications VMs or containers on servers located in or near access
networks, which can implement local edge services.  For example,
mobile networks can provide edge services for radio network
information, location, and bandwidth management.
Physical or virtual IoT gateways can host application programs, which
are typically built using an SDK to access local services through a
programmatic API.  Edge cloud system operators host their customers'
application VMs or containers on servers located in or near access
networks, which can implement local edge services.  For example,
mobile networks can provide edge services for radio network
information, location, and bandwidth management.
Life cycle management of services and applications on physical IoT
gateways is often cloud-based.  Edge cloud management platforms and
products (such as StarlingX, Akraino Edge Stack, Mobile EdgeX) adapt
cloud management technologies (e.g., Kubernetes) to the edge cloud,
i.e., to smaller, distributed computing devices running outside a
controlled data center.  Services and application life-cycle is
typically using an NFV-like management and orchestration model.
Life cycle management of services and applications on physical IoT
gateways is often cloud-based.  Edge cloud management platforms and
products (such as StarlingX, Akraino Edge Stack, Mobile EdgeX) adapt
cloud management technologies (e.g., Kubernetes) to the edge cloud,
i.e., to smaller, distributed computing devices running outside a
controlled data center.  Service and application life-cycle is
typically using an NFV-like management and orchestration model.
Adapting cloud management platforms to the edge, to account for
its distributed nature, e.g., using Conflict-free Replicated Data
Types (CRDT) Jeffery, heterogeneity and customization, e.g., using
intent-based management mechanisms Cao, and limited resources.
A core function of IoT edge computing is to enable local computation
on a node at the network edge, e.g. processing input data from
sensors, making local decisions, preprocessing data, offloading
computation on behalf of a device, service, or user.  Related
functions include orchestrating computation (in a centralized or
distributed manner) and managing applications lifecycle.  Support for
in-network computation may vary in terms of capability, e.g.,
computing nodes can host virtual machines, software containers,
software actors or unikernels able to run stateful or stateless code,
or a rules engine providing an API to register actions in response to
conditions such as IoT device ID, sensor values to check, thresholds,
etc.
A core function of IoT edge computing is to enable local computation
on a node at the network edge, e.g. processing input data from
sensors, making local decisions, preprocessing data, offloading
computation on behalf of a device, service, or user.  Related
functions include orchestrating computation (in a centralized or
distributed manner) and managing application lifecycles.  Support for
in-network computation may vary in terms of capability, e.g.,
computing nodes can host virtual machines, software containers,
software actors or unikernels able to run stateful or stateless code,
or a rules engine providing an API to register actions in response to
conditions such as IoT device ID, sensor values to check, thresholds,
etc.
Defining, managing, and verifying Service Level Agreements (SLA)
for edge computing systems.  Pricing is a related challenge.
An edge cloud may provide a northbound data plane or management plane
interface to a remote network, e.g., a cloud, home or enterprise
network.  This interface does not exist in standalone (local-only)
scenarios.  To support such an interface when it exists, an edge
computing component needs to expose an API, deal with authentication
and authorization, support secure communication.
An edge cloud may provide a northbound data plane or management plane
interface to a remote network, e.g., a cloud, home or enterprise
network.  This interface does not exist in standalone (local-only)
scenarios.  To support such an interface when it exists, an edge
computing component needs to expose an API, deal with authentication
and authorization, and support secure communication.