ROLL D. Popa
Internet-Draft J. Jetcheva
Intended status: Standards Track Itron
Expires: January 26, 2012 N. Dejean
Elster
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-00
Abstract
This document discusses the applicability of RPL in Advanced Metering
Infrastructure (AMI) networks.
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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. Storing vs. Non-Storing Mode . . . . . . . . . . . . . 8
4.1.2. DAO Policy . . . . . . . . . . . . . . . . . . . . . . 8
4.1.3. Path Metrics . . . . . . . . . . . . . . . . . . . . . 8
4.1.4. Objective Function . . . . . . . . . . . . . . . . . . 9
4.1.5. DODAG Repair . . . . . . . . . . . . . . . . . . . . . 9
4.1.6. Security . . . . . . . . . . . . . . . . . . . . . . . 10
4.2. RPL Options . . . . . . . . . . . . . . . . . . . . . . . 10
4.3. Recommended Configuration Defaults and Ranges . . . . . . 10
5. Other Related Protocols . . . . . . . . . . . . . . . . . . . 10
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 11
7. Security Considerations . . . . . . . . . . . . . . . . . . . 11
8. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 11
9. References . . . . . . . . . . . . . . . . . . . . . . . . . . 11
9.1. Informative References . . . . . . . . . . . . . . . . . . 11
9.2. Normative References . . . . . . . . . . . . . . . . . . . 12
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 12
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1. Introduction
Advanced Metering Infrastructure (AMI) systems measure, collect, and
analyze energy consumption information. An AMI system enables two-
way communication with electricity, water, gas, and/or heat meters.
The communication may be scheduled, on exception, or on-demand.
AMI networks are composed of millions of endpoints, including meters,
distribution automation elements, and home area network devices,
typically inter-connected using some combination of wireless
technologies and power-line communications, along with a wired or
wireless 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, including 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 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) node.
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 wired or cellular
backhaul. Each electric meter mesh typically has in the order of
several thousand wireless endpoints, with densities varying based on
the area and the terrain, with apartment buildings in urban centers
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having possibly hundreds of meters in close proximity, and rural
areas having sparse node distributions, including nodes that only
have one or two network neighbors. Mesh deployments can exhibit tens
of hops between a network device and the nearest aggregation point.
1.2. Gas and Water Metering
While electric meters can 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 certain scenarios, gas and water meters are integrated with
electric meters in the same AMI network. In this scenario, gas and
water meters typically do not route messages or operate as hosts to
prolong their lifetime.
In other scenarios, however, gas and water meters do not have the
luxury of communicating with a powered routing infrastructure.
Instead, they must communicate through other battery powered devices
(i.e. through other gas and water meters) to reach a NAP.
Alternative scenarios also include water and/or gas meters
communicating directly to a sparsely deployed network infrastructure,
requiring increased transmit power levels for increased range that
significantly impacts energy consumption and battery lifetime. For
such networks, the routing protocol must configure routes with energy
consumption in mind. The NAPs, however, are typically mains powered
as in AMI networks with electric meters.
RPL is designed to operate in energy-constrained environments and
includes energy-saving mechanisms (e.g. Trickle timers) and energy-
aware metrics. By supporting a number of different metrics and
constraints, RPL is also designed to support networks composed of
nodes that have 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. Constrained devices within the same network
typically communicate through a common aggregation point (e.g., a
border router). RPL builds a Directed Acyclic Graph (DAG) routing
structure rooted at the aggregation point. It ensures loop-free
routing, support for alternate routes, and a wide range of routing
metrics and policies.
This note describes the applicability of RPL 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 is
most applicable to AMI networks.
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 in-home
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). Network elements may not only
source and sink packets, but must also forward packets to reduce the
need for dedicated routers and associated deployment costs.
In a typical AMI deployment, groups of meters within physical
proximity form routing domains. The size of each group in a typical
AMI deployment can be from 1000 to 10000 or 15000 meters
Powered from the main line electric meters have less energy
constraints than battery powered devices and can afford the
additional resources required for routing packets. In mixed
environments, electric meters provide the routing topology while gas
and water meters operate as leaves. However, in networks that cannot
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afford a powered infrastructure, gas and water meters must either
talk directly to a network infrastructure or form their own routing
topology, albeit with energy consumption in mind.
Each meter routing domain is connected to a larger IP infrastructure
through one or more LLN Border Routers (LBRs). The LBRs provide Wide
Area Network (WAN) connectivity through more traditional links (e.g.
Ethernet, Cellular, Private WAN) or other wireless technologies.
The meter networks may also serve as transit networks for other
devices, including battery powered gas and water meters, distribution
automation elements (i.e. distribution sensors and actuators), and
in-home devices. These other devices may utilize a different link-
layer technology than the one used in the metering 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 flows through the LBRs. In general, the vast majority of
traffic flows from smart meter devices to the head-end servers with
limited traffic flowing from head-end servers to smart meter devices.
In RPL terminology, this traffic flow is also referred to as
Multipoint-to-point Traffic (MP2P).
Smart meters may generate traffic according to a schedule (e.g. meter
read reporting), in response to on-demand queries (e.g. on-demand
meter read), or in response to events (e.g. power outages or leak
detections). Such traffic is typically unicast since it is sent to a
single head-end server.
Head-end servers may generate traffic to configure smart metering
devices or initiate queries. Head-end servers generate both unicast
and multicast traffic to efficiently communicate with a single device
or groups of devices. In RPL terminology, this traffic flow is also
referred to as Point-to-Multipoint Traffic (P2MP). 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 large packets in
sequence (e.g. a firmware upgrade across one or 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. Some applications also have
stringent latency requirements to function properly.
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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 (e.g. Electric Vehicle
charging) that may involve P2P communication as well. These
applications 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 direct 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 efficiently supports scalability and highly directed traffic
flows between every smart meter and the few head-end servers by
building a Directed Acyclic Graph (DAG) rooted at each LBR.
RPL supports zero-touch configuration by providing in-band methods
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for configuring RPL variables using DIO messages.
RPL supports time-varying link qualities by allowing the use of
metrics that effectively characterize the quality of a path (e.g.
Estimated Transmission Count (ETX)). RPL limits the impact of
changing local conditions by discovering and maintaining multiple DAG
parents and providing a local repair mechanism when all parents have
been dropped.
4. RPL Profile
This section outlines a RPL profile for most representative AMI
deployments.
4.1. RPL Features
4.1.1. Storing vs. Non-Storing Mode
In most scenarios, electric meters can utilize the power they are
monitoring for their own processing and computation and are not as
constrained in energy consumption. Instead, the capabilities of an
electric meter are primarily constrained by cost. As a result,
different AMI deployments can vary significantly in terms of the
memory, computational, and communication trade-offs that were made
for their devices. 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 shorter paths and reduce channel
utilization near the root.
4.1.2. DAO Policy
Two-way communication is required in AMI systems. As a result,
electric meters SHOULD send DAO messages to establish downward paths
back to themselves.
4.1.3. 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
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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 metrics may be vendor-specific or defined at a later time into
companion RFCs.
4.1.4. 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 but also from the metrics in use in the
network. Two objective functions for RPL have been defined:
o OF0 which does not deal with any metric,
o MRHOF which deals with a single metric.
Both of them define the selection of a preferred parent and backup
parents. Note that these Objective Functions do not support multiple
metrics that might be required in heterogeneous networks (i.e.
networks composed of devices with varying energy constraints). While
RPL provides the flexibility to support additional metrics, a new
Objective Function MAY be specified to properly handle additional
metrics.
4.1.5. DODAG Repair
To effectively handle time-varying link characteristics, AMI
deployments SHOULD utilize the local repair mechanisms in RPL.
The first mechanism for local repair when a node loses its parents is
to detach from a DODAG then re-attach to the same or 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.
The second mechanism is a limit on how much a node can increase its
rank within a given DODAG Version. Setting the DAGMaxRankIncrease to
a non-zero value enables this local repair mechanism. Setting
DAGMaxRankIncrease to a value less than infinity limits the cost of
count-to-infinity scenarios when they occur.
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The third mechanism is loop detection, enabled by including the rank
value of a node in packets forwarded towards the root in RPL Packet
Information [I-D.ietf-6man-rpl-option]. Note that loop detection is
not needed when sending packets using strict source routing.
4.1.6. 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, giving a Trickle Imin of 1
minute or more. For low-energy consumption operations, such
networks SHOULD set DIOIntervalMin 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. For low-energy
consumption operations, such networks SHOULD set
DIOIntervalDoublings to a value that gives a Trickle Imax of 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, giving 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. Other Related Protocols
This document contains no other related protocols.
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6. IANA Considerations
This memo includes no request to IANA.
7. Security Considerations
This memo includes no security considerations.
8. Acknowledgements
The authors would like to acknowledge the review, feedback, and
comments from Dominique Barthel.
9. References
9.1. 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
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.
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[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.
9.2. 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
Email: daniel.popa@itron.com
Jorjeta Jetcheva
Itron
2111 N Molter Rd.
Liberty Lake, WA
USA
Phone: +408 688 1428
Email: jorjeta.jetcheva@itron.com
Nicolas Dejean
Elster
Email: nicolas.dejean@coronis.com
Ruben Salazar
Landis+Gyr
Email: ruben.salazar@landisgyr.com
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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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