ROLL D. Popa
Internet-Draft J. Jetcheva
Intended status: Standards Track Itron
Expires: January 26, 2012 N. Dejean
Elster SAS
R. Salazar
Landis+Gyr
J. Hui
Cisco
July 25, 2011
Applicability Statement for the Routing Protocol for Low Power and Lossy
Networks (RPL) in AMI Networks
draft-ietf-roll-applicability-ami-01
Abstract
This document discusses the applicability of RPL in Advanced Metering
Infrastructure (AMI) networks.
Status of this Memo
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. Electric Metering . . . . . . . . . . . . . . . . . . . . 3
1.2. Gas and Water Metering . . . . . . . . . . . . . . . . . . 4
1.3. Routing Protocol for LLNs (RPL) . . . . . . . . . . . . . 4
1.4. Requirements Language . . . . . . . . . . . . . . . . . . 5
2. Deployment Scenarios . . . . . . . . . . . . . . . . . . . . . 5
2.1. Network Topology . . . . . . . . . . . . . . . . . . . . . 5
2.2. Traffic Characteristics . . . . . . . . . . . . . . . . . 6
2.2.1. Meter Data Management . . . . . . . . . . . . . . . . 6
2.2.2. Distribution Automation . . . . . . . . . . . . . . . 7
2.2.3. Emerging Applications . . . . . . . . . . . . . . . . 7
3. Using RPL to Meet Functional Requirements . . . . . . . . . . 7
4. RPL Profile . . . . . . . . . . . . . . . . . . . . . . . . . 8
4.1. RPL Features . . . . . . . . . . . . . . . . . . . . . . . 8
4.1.1. RPL Instances . . . . . . . . . . . . . . . . . . . . 8
4.1.2. Storing vs. Non-Storing Mode . . . . . . . . . . . . . 8
4.1.3. DAO Policy . . . . . . . . . . . . . . . . . . . . . . 8
4.1.4. Path Metrics . . . . . . . . . . . . . . . . . . . . . 9
4.1.5. Objective Function . . . . . . . . . . . . . . . . . . 9
4.1.6. DODAG Repair . . . . . . . . . . . . . . . . . . . . . 9
4.1.7. Multicast . . . . . . . . . . . . . . . . . . . . . . 10
4.1.8. Security . . . . . . . . . . . . . . . . . . . . . . . 10
4.2. RPL Options . . . . . . . . . . . . . . . . . . . . . . . 10
4.3. Recommended Configuration Defaults and Ranges . . . . . . 10
5. Manageability Considerations . . . . . . . . . . . . . . . . . 11
6. Security Considerations . . . . . . . . . . . . . . . . . . . 11
7. Other Related Protocols . . . . . . . . . . . . . . . . . . . 12
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 12
9. Security Considerations . . . . . . . . . . . . . . . . . . . 12
10. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 12
11. References . . . . . . . . . . . . . . . . . . . . . . . . . . 12
11.1. Informative References . . . . . . . . . . . . . . . . . . 12
11.2. Normative References . . . . . . . . . . . . . . . . . . . 13
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 13
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1. Introduction
Advanced Metering Infrastructure (AMI) systems enable the
measurement, configuration, and control of energy, gas and water
consumption and distribution, through two-way scheduled, on
exception, and on-demand communication.
AMI networks are composed of millions of endpoints, including meters,
distribution automation elements, and home area network devices.
They are typically inter-connected using some combination of wireless
technologies and power-line communications, along with backhaul
network providing connectivity to "command-and-control" management
software applications at the utility company back office.
1.1. Electric Metering
In many deployments, in addition to measuring energy consumption, the
electric meter network plays a central role in the Smart Grid since
it enables the utility company to control and query the electric
meters themselves and also since it can serve as a backhaul for all
other devices in the Smart Grid, e.g., water and gas meters,
distribution automation and home area network devices. Electric
meters may also be used as sensors to monitor electric grid quality
and to support applications such as Electric Vehicle charging.
Electric meter networks are composed of millions of smart meters (or
nodes), each of which is resource-constrained in terms of processing
power, storage capabilities, and communication bandwidth, due to a
combination of factors including Federal Communications Commission
(FCC) or other continents' regulations on spectrum use, American
National Standards Institute (ANSI) standards or other continents'
regulation on meter behavior and performance, on heat emissions
within the meter, form factor and cost considerations. This results
in a compromise between range and throughput, with effective link
throughput of tens to a few hundred kilobits per second per link, a
potentially significant portion of which is taken up by protocol and
encryption overhead when strong security measures are in place.
Electric meters are often interconnected into multi-hop mesh
networks, each of which is connected to a backhaul network leading to
the utility network through a network aggregation point (NAP). These
kinds of networks increase coverage and reduce installation cost,
time and complexity, as well as operational costs, as compared to
single-hop wireless networks, relying on a wireline or cellular
backhaul. Each electric meter mesh typically has on the order of
several thousand wireless endpoints, with densities varying based on
the area and the terrain. Apartment buildings in urban centers may
have hundreds of meters in close proximity, whereas rural areas may
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have sparse node distributions and include nodes that only have one
or two network neighbors. Paths in the mesh between a network device
and the nearest aggregation point may be composed of several hops or
even tens of hops.
1.2. Gas and Water Metering
While electric meters typically consume electricity from the same
electric feed that they are monitoring, gas and water meters
typically run on a modest source of stored energy (i.e. batteries).
In some scenarios, gas and water meters are integrated into the same
AMI network as the electric meters and may operate as network
endpoints (rather than routers) in order to prolong their own
lifetime. In other scenarios, such meters may not have the luxury of
relying on a powered routing infrastructure but must communicate
through other energy-constrained devices (i.e., through other gas and
water meters) to reach a NAP. In some cases, battery-powered meters
need to communicate directly with a sparsely deployed network
infrastructure, requiring them to use high transmit power levels (and
thus more energy) in order to achieve the necessary range to reach
the infrastructure. In all of these types of networks, the routing
protocol must operate with energy consumption in mind.
RPL is designed to operate in energy-constrained environments and
includes energy-saving mechanisms (e.g. Trickle timers) and energy-
aware metrics. Its ability to support multiple different metrics and
constraints at the same time enables it to run efficiently in
heterogeneous networks composed of nodes and links with vastly
different characteristics. [I-D.ietf-roll-routing-metrics].
1.3. Routing Protocol for LLNs (RPL)
RPL provides routing functionality for mesh networks composed of a
large number of resource-constrained devices, interconnected by low
power and lossy links, and communicating with the external network
infrastructure through a common aggregation point (e.g., a border
router).
RPL builds a Directed Acyclic Graph (DAG) routing structure rooted at
the aggregation point, ensures loop-free routing, and provides
support for alternate routes, as well as, for a wide range of routing
metrics and policies.
This note describes the applicability of RPL (as defined in
[I-D.ietf-roll-rpl]) to AMI deployments. RPL was designed to meet
the following application requirements:
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o Routing Requirements for Urban Low-Power and Lossy Networks
[RFC5548].
o Industrial Routing Requirements in Low-Power and Lossy Networks
[RFC5673].
o Home Automation Routing Requirements in Low-Power and Lossy
Networks [RFC5826].
o Building Automation Routing Requirements in Low-Power and Lossy
Networks [RFC5867].
The Routing Requirements for Urban Low-Power and Lossy Networks are
applicable to AMI networks as well.
The terminology used in this document is defined in
[I-D.ietf-roll-terminology].
1.4. Requirements Language
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [RFC2119].
2. Deployment Scenarios
2.1. Network Topology
AMI networks are composed of millions of endpoints distributed across
both urban and rural environments. Such endpoints include electric,
gas, and water meters, distribution automation elements, and home
area network devices. Devices in the network communicate directly
with other devices in close proximity using a variety of low-power
and/or lossy link technologies that are both wired and wireless (e.g.
IEEE 802.15.4, IEEE P1901.2, and WiFi). In addition to serving as
sources and destinations of packets, many network elements typically
also forward packets to reduce the need for dedicated network
infrastructure and the associated deployment and operational costs.
In a typical AMI deployment, groups of meters within physical
proximity form routing domains, each in the order of a 1,000 to
10,000 meters. These routing domains are connected to the larger IP
infrastructure through one or more LLN Border Routers (LBRs), which
provide Wide Area Network (WAN) connectivity through various
traditional network technologies, e.g., Ethernet, Cellular, private
WAN.
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Powered from the main line, electric meters have less energy
constraints than battery powered devices and can afford the
additional resources required to route packets. In mixed
environments, electric meters provide the routing topology while gas
and water meters operate as leaf nodes. However, in the absence of a
co-located electric meter network, gas and water meters must either
connect directly to the larger IP network infrastructure or form
their own routing topology, albeit with energy consumption in mind.
Meter networks may also serve as transit networks for other types of
devices, including distribution automation elements (e.g., sensors
and actuators), and in-home devices. These other devices may utilize
a different link-layer technology than the one used in the meter
network.
2.2. Traffic Characteristics
2.2.1. Meter Data Management
Meter Data Management (MDM) applications typically require every
smart meter to communicate with a few head-end servers deployed in a
utility data center. As a result, all smart metering traffic
typically goes through the LBRs, with the vast majority of traffic
flowing from smart meter devices to the head-end servers, i.e., in a
Multipoint-to-Point (MP2P) fashion.
Smart meters may generate traffic according to a schedule (e.g.,
periodic meter reads), in response to on-demand queries (e.g., on-
demand meter reads), or in response to events (e.g., power outages,
leak detections). Such traffic is typically unicast since it is sent
to a single head-end server.
Head-end servers generate traffic to configure smart metering devices
or initiate queries, and use unicast and multicast to efficiently
communicate with a single device (i.e., Point-to-Point (P2P)
communication) or groups of devices respectively (i.e., Point-to-
Multipoint (P2MP) communication). The head-end server may send a
single small packet at a time (e.g., a meter read request, or small
configuration change) or many consecutive large packets (e.g., a
firmware upgrade across one or even thousands of devices).
While smart metering applications typically do not have hard real-
time constraints, they are often subject to stringent latency and
reliability service level agreements.
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2.2.2. Distribution Automation
Distribution Automation (DA) applications typically involve a small
number of devices that communicate with each other in a Point-to-
Point (P2P) fashion. The DA devices may or may not be in close
physical proximity.
DA applications typically have more stringent latency requirements
than MDM applications.
2.2.3. Emerging Applications
There are a number of emerging applications such as electric vehicle
charging. These applications may require P2P communication and may
eventually have more stringent latency requirements than MDM
applications.
3. Using RPL to Meet Functional Requirements
The functional requirements for most AMI deployments are similar to
those listed in [RFC5548]:
o The routing protocol MUST be capable of supporting the
organization of a large number of nodes into regions containing on
the order of 10^2 to 10^4 nodes each.
o The routing protocol MUST provide mechanisms to support
configuration of the routing protocol itself.
o The routing protocol SHOULD support and utilize the large number
of highly directed flows to a few head-end servers to handle
scalability.
o The routing protocol MUST dynamically compute and select effective
routes composed of low-power and lossy links. Local network
dynamics SHOULD NOT impact the entire network. The routing
protocol MUST compute multiple paths when possible.
o The routing protocol MUST support multicast and anycast
addressing. The routing protocol SHOULD support formation and
identification of groups of field devices in the network.
RPL supports:
o Large-scale networks characterized by highly directed traffic
flows between each smart meter and the head-end servers in the
utility network. To this end, RPL builds a Directed Acyclic Graph
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(DAG) rooted at each LBR.
o Zero-touch configuration. This is done through in-band methods
for configuring RPL variables using DIO messages.
o The use of links with time-varying quality characteristics. This
is accomplished by allowing the use of metrics that effectively
capture the quality of a path (e.g., Expected Transmission Count
(ETX)) and by limiting the impact of changing local conditions by
discovering and maintaining multiple DAG parents, and by using
local repair mechanisms when DAG links break.
4. RPL Profile
This section outlines a RPL profile for a representative AMI
deployment.
4.1. RPL Features
4.1.1. RPL Instances
RPL operation is defined for a single RPL instance. However,
multiple RPL instances can be supported in multi-service networks
where different applications may require the use of different routing
metrics and constraints, e.g., a network carrying both MDM and DA
traffic.
4.1.2. Storing vs. Non-Storing Mode
In most scenarios, electric meters are powered by the electric grid
they are monitoring and are not energy-constrained. Instead, the
capabilities of an electric meter are primarily determined by cost.
As a result, different AMI deployments can vary significantly in
terms of the memory, computation, and communication trade-offs that
they embody. For this reason, the use of RPL storing or non-storing
mode SHOULD be deployment specific.
When meters are memory constrained and cannot adequately store route
tables to support downward routing, non-storing mode is preferred.
However, when nodes are capable of adequately storing such routing
tables, storing mode can lead to reduced overhead and shorter route
repair latency.
4.1.3. DAO Policy
Two-way communication is a requirement in AMI systems. As a result,
nodes SHOULD send DAO messages to establish downward paths from the
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root to themselves.
4.1.4. Path Metrics
Smart metering deployments utilize link technologies that can exhibit
significant packet loss. To characterize a path over such link
technologies, AMI deployments can use the Expected Transmission Count
(ETX) metric as defined in[I-D.ietf-roll-routing-metrics].
For water- and gas-only networks that cannot rely on a powered
infrastructure, energy constraints may require simpler metrics that
do not require as much energy to compute. In particular, hop count
and link quality level may be more suitable in such deployments.
Other possible metrics to use may be vendor-specific or defined at a
later time in companion RFCs.
4.1.5. Objective Function
RPL relies on an Objective Function for selecting parents and
computing path costs and rank. This objective function is decoupled
from the core RPL mechanisms and also from the metrics in use in the
network. Two basic objective functions for RPL have been defined at
the time of this writing, OF0 and MRHOF, both of which define the
selection of a preferred parent and backup parents, and are suitable
for a basic AMI deployment. Neither of these supports multiple
metrics that might be required in heterogeneous networks (i.e.
networks composed of devices with different energy constraints). A
new objective function can be defined to meet this requirement.
4.1.6. DODAG Repair
To effectively handle time-varying link characteristics and
availability, AMI deployments SHOULD utilize the local repair
mechanisms in RPL.
The first mechanism for local repair when a node loses connectivity
to its parents is to detach from a DODAG then re-attach to the same
or to a different DODAG at a later time. While detached, a node
advertises an infinite rank value so that its children can select a
different parent. This process is known as poisoning and described
in Section 8.2.2.5 of [I-D.ietf-roll-rpl]. While RPL provides an
option to form a local DODAG, doing so in AMI deployments is of
little benefit since AMI applications typically communicate through a
LBR. After the detached node has made sufficient effort to send
notification to its children that it is detached, the node can rejoin
the same DODAG with a higher rank value. Note that when joining a
different DODAG, the node need not perform poisoning.
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The second local repair mechanism controls how much a node can
increase its rank within a given DODAG Version. Setting the
DAGMaxRankIncrease to a non-zero value enables this mechanism, and
setting it to a value of less than infinity limits the cost of count-
to-infinity scenarios when they occur.
The third local repair mechanism enables loop detection, and is
implemented by including the rank value of the transmitting node in
packets forwarded towards the root (in the packet's RPL Packet
Information option [I-D.ietf-6man-rpl-option]). Note that loop
detection is not needed when sending packets using strict source
routing.
4.1.7. Multicast
RPL defines multicast support for its storing mode of operation. The
DODAG structure built for unicast packet dissemination is used for
multicast distribution as well. In particular, multicast forwarding
state creation is done through DAO messages with multicast target
options sent along the DODAG towards the root. Thereafter nodes with
forwarding state for a particular group forward multicast packets
along the DODAG by copying them to all children from which they have
received a DAO with a multicast target option for the group.
Multicast support for RPL in non-storing mode will be defined in
companion RFCs.
4.1.8. Security
AMI deployments operate in areas that do not provide any physical
security. For this reason, the link technologies used within AMI
deployments typically provide security mechanisms to ensure
confidentiality, integrity, and freshness. As a result, AMI
deployments may not need to implement RPL's security mechanisms and
could rely on link-layer security features.
4.2. RPL Options
4.3. Recommended Configuration Defaults and Ranges
o AMI deployments can involve densities of hundreds of devices
within communication range. As a result, such networks SHOULD set
the DIOIntervalMin to 16 or more, resulting in a Trickle Imin of 1
minute or more. In networks with low-energy consumption
requirements, DIOIntervalMin SHOULD be set to a higher value.
o AMI deployments SHOULD set DIOIntervalDoublings to a value that
gives a Trickle Imax of 2 hours or more. In networks with low-
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energy consumption requirements, DIOIntervalDoublings SHOULD be
set to a value that results in a Trickle Imax of several (e.g., 2)
days.
o AMI deployments SHOULD set DIORedundancyConstant to a value of 10
or more.
o AMI deployments SHOULD set MinHopRankIncrease to 256, resulting in
8 bits of resolution (e.g. for the ETX metric).
o To enable local repair, AMI deployments SHOULD set MaxRankIncrease
to a value that allows a device to move a small number of hops
away from the root. With a MinHopRankIncrease of 256, a
MaxRankIncrease of 1024 would allow a device to move up to 4 hops
away.
5. Manageability Considerations
Network manageability is a critical aspect of smart grid network
deployment and operation. With millions of devices participating in
the smart grid network, many requiring real-time reachability,
automatic configuration, and lightweight network health monitoring
and management, are crucial for achieving network availability and
efficient operation.
RPL enables automatic and consistent configuration of RPL routers
through parameters specified by the DODAG root and dissemintated
through DIO packets. The use of Trickle for scheduling DIO
transmissions ensures lightweight yet timely propagation of important
network and parameter updates.
RPL specifies a number of variables and events that can be tracked
for purposes of network fault and performance monitoring of RPL
routers. Depending on the memory and processing capabilities of each
smart grid device, various subsets of these can be employed in the
field.
The CoRE Working Group is developing lightweight resource management
mechanisms for LLNs that are applicable to smart grid RPL networks as
well.
6. Security Considerations
Smart grid networks are subject to stringent security requirements as
they are considered a critical national infrastructure component. At
the same time, since they are composed of large numbers of resource-
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constrained devices inter-connected with limited-throughput links,
many available security mechanisms are not practical for use in such
networks. As a result, the choice of security mechanisms is highly
dependent on the device and network capabilities characterizing a
particular deployment.
In contrast to other types of LLNs, in smart grid networks
centralized administrative control and access to a permanent secure
infrastructure is available. As a result link-layer security
mechanisms are typically in place and using RPL's secure mode is not
necessary. Smart grid networks are often secured at other layers as
well, including end-to-end at the application layer.
7. Other Related Protocols
This document contains no other related protocols.
8. IANA Considerations
This memo includes no request to IANA.
9. Security Considerations
This memo includes no security considerations.
10. Acknowledgements
The authors would like to acknowledge the review, feedback, and
comments from Dominique Barthel.
11. References
11.1. Informative References
[I-D.ietf-6man-rpl-option]
Hui, J. and J. Vasseur, "RPL Option for Carrying RPL
Information in Data-Plane Datagrams",
draft-ietf-6man-rpl-option-03 (work in progress),
March 2011.
[I-D.ietf-roll-routing-metrics]
Vasseur, J., Kim, M., Pister, K., Dejean, N., and D.
Barthel, "Routing Metrics used for Path Calculation in Low
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Power and Lossy Networks",
draft-ietf-roll-routing-metrics-19 (work in progress),
March 2011.
[I-D.ietf-roll-rpl]
Winter, T., Thubert, P., Brandt, A., Clausen, T., Hui, J.,
Kelsey, R., Levis, P., Pister, K., Struik, R., and J.
Vasseur, "RPL: IPv6 Routing Protocol for Low power and
Lossy Networks", draft-ietf-roll-rpl-19 (work in
progress), March 2011.
[I-D.ietf-roll-terminology]
Vasseur, J., "Terminology in Low power And Lossy
Networks", draft-ietf-roll-terminology-05 (work in
progress), March 2011.
[RFC5548] Dohler, M., Watteyne, T., Winter, T., and D. Barthel,
"Routing Requirements for Urban Low-Power and Lossy
Networks", RFC 5548, May 2009.
[RFC5673] Pister, K., Thubert, P., Dwars, S., and T. Phinney,
"Industrial Routing Requirements in Low-Power and Lossy
Networks", RFC 5673, October 2009.
[RFC5826] Brandt, A., Buron, J., and G. Porcu, "Home Automation
Routing Requirements in Low-Power and Lossy Networks",
RFC 5826, April 2010.
[RFC5867] Martocci, J., De Mil, P., Riou, N., and W. Vermeylen,
"Building Automation Routing Requirements in Low-Power and
Lossy Networks", RFC 5867, June 2010.
11.2. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, March 1997.
Authors' Addresses
Daniel Popa
Itron
52 rue Camille Desmoulins
Issy-les-Moulineaux, Cedex, 92448
France
Email: daniel.popa@itron.com
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Jorjeta Jetcheva
Itron
2111 N Molter Rd.
Liberty Lake, WA
USA
Email: jorjeta.jetcheva@itron.com
Nicolas Dejean
Elster SAS
Espace Concorde, 120 impasse JB Say
Perols, 34470
France
Email: nicolas.dejean@coronis.com
Ruben Salazar
Landis+Gyr
30000 Mill Creek Ave # 100
Alpharetta, GA 30022
Email: ruben.salazar@landisgyr.com
Jonathan W. Hui
Cisco
170 West Tasman Drive
San Jose, California 95134
USA
Phone: +408 424 1547
Email: jonhui@cisco.com
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