Roll D. Popa
Internet-Draft M. Gillmore
Intended status: Standards Track Itron, Inc
Expires: December 31, 2013 L. Toutain
Telecom Bretagne
J. Hui
Cisco
R. Ruben
Landis+Gyr
K. Monden
Hitachi, Ltd., Yokohama Research Laboratory
July 2013
Applicability Statement for the Routing Protocol for Low Power and Lossy
Networks (RPL) in AMI Networks
draft-ietf-roll-applicability-ami-07
Abstract
This document discusses the applicability of RPL in Advanced Metering
Infrastructure (AMI) networks.
Status of this Memo
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provisions of BCP 78 and BCP 79.
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This Internet-Draft will expire on December 31, 2013.
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provided without warranty as described in the Simplified BSD License.
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. Requirements Language . . . . . . . . . . . . . . . . . . 3
1.2. Required Reading . . . . . . . . . . . . . . . . . . . . . 3
1.3. Out of scope requirements . . . . . . . . . . . . . . . . 3
2. Routing Protocol for LLNs (RPL) . . . . . . . . . . . . . . . 3
3. Advanced Metering Infrastructure Description . . . . . . . . . 4
3.1. Electric Metering . . . . . . . . . . . . . . . . . . . . 4
3.2. Gas and Water metering . . . . . . . . . . . . . . . . . . 5
4. Deployment Scenario . . . . . . . . . . . . . . . . . . . . . 6
4.1. General Considerations on Network Topology . . . . . . . . 6
4.1.1. Networks of Electric Meters . . . . . . . . . . . . . 6
4.1.2. Networks of Gas/Water Meters . . . . . . . . . . . . . 7
5. Smart Grid Traffic Description . . . . . . . . . . . . . . . . 7
5.1. Traffic Characteristics . . . . . . . . . . . . . . . . . 7
5.1.1. Smart Metering Data Traffic . . . . . . . . . . . . . 7
5.1.2. Distribution Automation Traffic . . . . . . . . . . . 8
5.1.3. Emerging Applications . . . . . . . . . . . . . . . . 8
6. Description of Smart Grid Communication Paradigm . . . . . . . 9
6.1. Source-sink (SS) communication paradigm . . . . . . . . . 9
6.2. Publish-subscribe (PS, or pub/sub) communication paradigm 9
6.3. Peer-to-peer (P2P) communication paradigm . . . . . . . . 9
6.4. Peer-to-multipeer (P2MP) communication paradigm . . . . . 9
6.5. Additional considerations: Duocast and N-cast . . . . . . 9
6.6. RPL applicability per communication paradigm . . . . . . . 9
7. Layer 2 applicability. . . . . . . . . . . . . . . . . . . . . 9
7.1. Wireless technology . . . . . . . . . . . . . . . . . . . 9
7.2. PowerLine Communication (PLC) technology . . . . . . . . . 9
8. Using RPL to Meet Functional Requirements . . . . . . . . . . 9
9. RPL Profile . . . . . . . . . . . . . . . . . . . . . . . . . 10
9.1. RPL Features . . . . . . . . . . . . . . . . . . . . . . . 10
9.1.1. RPL Instances . . . . . . . . . . . . . . . . . . . . 10
9.1.2. Storing vs. Non-Storing Mode . . . . . . . . . . . . . 11
9.1.3. DAO Policy . . . . . . . . . . . . . . . . . . . . . . 11
9.1.4. Path Metrics . . . . . . . . . . . . . . . . . . . . . 11
9.1.5. Objective Function . . . . . . . . . . . . . . . . . . 12
9.1.6. DODAG Repair . . . . . . . . . . . . . . . . . . . . . 12
9.1.7. Multicast . . . . . . . . . . . . . . . . . . . . . . 12
9.1.8. Security . . . . . . . . . . . . . . . . . . . . . . . 13
9.1.9. P2P communications . . . . . . . . . . . . . . . . . . 13
9.2. Description of Layer-two features . . . . . . . . . . . . 13
9.2.1. IEEE 802.15.4e MAC sub-layer features . . . . . . . . 13
9.2.2. IEEE P1901.2 MAC sub-layer features . . . . . . . . . 13
9.2.3. Security features provided by MAC sub-layer. . . . . . 13
9.2.3.1. IEEE 802.15.4e . . . . . . . . . . . . . . . . . . 13
9.2.3.2. IEEE P1901.2 . . . . . . . . . . . . . . . . . . . 13
9.2.4. MLE and other things . . . . . . . . . . . . . . . . . 13
9.3. 6LowPAN Options . . . . . . . . . . . . . . . . . . . . . 13
9.4. Recommended Configuration Defaults and Ranges . . . . . . 14
9.4.1. Trickle Parameters . . . . . . . . . . . . . . . . . . 14
9.4.2. Other Parameters . . . . . . . . . . . . . . . . . . . 15
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10. Manageability Considerations . . . . . . . . . . . . . . . . . 15
11. Security Considerations . . . . . . . . . . . . . . . . . . . 16
11.1. Security Considerations during initial deployment . . . . 16
11.2. Security Considerations during incremental deployment . . 16
12. Other Related Protocols . . . . . . . . . . . . . . . . . . . 16
13. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 17
14. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 17
15. References . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 17
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 a backhaul network providing
connectivity to "command-and-control" management software
applications at the utility company back office.
1.1. 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 [RFC2119].
1.2. Required Reading
TBD
1.3. Out of scope requirements
This should list other documents (if any) which deal with situations
where things are not in scope for this document. (For instance, the
AMI document tries to cover both line-powered urban metering
networks, and energy-constrained metering networks, and also tries to
deal with rural requirements. This should be three or four
documents, so this section should list the limits of what this
document covers)
2. Routing Protocol for LLNs (RPL)
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RPL provides routing functionality for mesh networks that can scale
up to thousands of resource-constrained devices, interconnected by
low power and lossy links, and communicating with the external
network infrastructure through a common aggregation point(s) (e.g., a
LBR). RPL builds a Directed Acyclic Graph (DAG) routing structure
rooted at the LBR, ensures loop-free routing, and provides support
for alternate routes, as well as, for a wide range of routing metrics
and policies. RPL was designed to operate in energy-constrained
environments and includes energy-saving mechanisms (e.g., Trickle
timers) and energy- aware metrics. RPL's 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 [RFC6551]. This document
describes the applicability of RPL (as defined in [RFC6550]) to AMI
deployments. RPL was designed to meet the following application
requirements:
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].
3. Advanced Metering Infrastructure Description
3.1. Electric Metering
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In many deployments, in addition to measuring energy consumption, the
electric meter network plays a central role in the Smart Grid since
the device enables the utility company to control and query the
electric meters themselves and 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 with up to 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. These
constraints result 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 company network through a network aggregation
point, e.g., an LBR (LLN Border Router).
3.2. 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 (e.g., batteries).
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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, however, such meters may not have the
luxury of relying on a fully powered AMI routing infrastructure but
must communicate through a dedicated infrastructure to reach a LBR.
This infrastructure can be either powered by the electricity grid, by
battery-based devices, or ones relying on alternative sources of
energy (e.g., solar power).
4. Deployment Scenario
4.1. General Considerations on 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 wireless and wired
(e.g., IEEE 802.15.4, IEEE 802.15.4(g+e), IEEE P1901.2, and IEEE
802.11). In addition to serving as sources and destinations of
packets, many network elements typically also forward packets and
thus form a mesh topology.
4.1.1. Networks of Electric Meters
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. Thus, each electric meter mesh typically has several
thousand wireless endpoints, with densities varying based on the area
and the terrain. For example, apartment buildings in urban centers
may have hundreds of meters in close proximity, whereas rural areas
may have sparse node distributions and include nodes that only have a
small number of network neighbors. Each routing domain is connected
to the larger IP infrastructure through one or more LBRs, which
provide Wide Area Network (WAN) connectivity through various
traditional network technologies, e.g., Ethernet, cellular, private
WAN. Paths in the mesh between a network node and the nearest LBR
may be composed of several hops or even several tens of hops.
Powered from the main line, electric meters have less energy
constraints than battery powered devices, such as gas and water
meters, and can afford the additional resources required to route
packets. In mixed environments, electric meters can provide the
routing topology while gas and water meters can operate as leaf
nodes. Electric 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. The routing protocol operating in
networks with the topology characteristics described above needs to
be able to scale with network size and number of forwarding hops, and
have the ability to handle a wide range of network densities.
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4.1.2. Networks of Gas/Water Meters
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 rely on a dedicated routing infrastructure.
Deploying such infrastructures is a challenging task as the routing
devices can sometimes only be placed in specific locations and thus
do not always have access to a continuous energy source. Battery-
operated or energy-harvesting (e.g., equipped with solar panels)
routers are thus often used in these kinds of scenarios. Due to the
expected lifetime (10 to 20 years) of such networks and their
reliance on alternative sources of energy, energy consumption needs
to be taken into account when designing and deploying them. There
are a number of challenging trade-offs and considerations that exist
in that respect. One such consideration is that managing a higher
number of meters per router leads to increased energy consumption.
However, increasing the number of routers in the network and thus
reducing the number of meters managed by each router increases
deployment and maintenance costs. At the same time, the use of a
sparser routing infrastructure necessitates the use of higher
transmit power levels at nodes in the network, which causes increased
energy consumption. The deployment and operational needs of energy-
constrained network infrastructure require the use of routing
mechanisms that take into account energy consumption, minimize energy
use and prolong network lifetime.
5. Smart Grid Traffic Description
5.1. Traffic Characteristics
5.1.1. Smart Metering Data Traffic
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In current AMI deployments, metering applications typically require
all smart meters to communicate with a few head-end servers, deployed
in the utility company data center. Head-end servers generate data
traffic to configure smart metering devices or initiate queries, and
use unicast and multicast to efficiently communicate with a single
device 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 to the meters (e.g., a meter read request, a small
configuration change, service switch command) or a series of large
packets (e.g., a firmware upgrade across one or even thousands of
devices). The frequency of large file transfers, e.g., firmware
upgrade of all metering devices, is typically much lower than the
frequency of sending configuration messages or queries. Each smart
meter generates Smart Metering Data (SMD) 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 some local
event (e.g., power outage, leak detection). Such traffic is
typically destined to a single head-end server. The bulk of the SMD
traffic tends to be directed towards the LBR, both in terms of bytes
(since reports are typically much larger than queries) and in terms
of number of packets, e.g., some reports have to be split into
multiple packets due to packet size limitations, periodic reports can
be sent without requiring a query to be sent for each one first,
unsolicited events like alarms and outage notifications are only
generated by the meters and sent towards the LBR. The SMD traffic is
thus highly asymmetric, where the majority of the traffic volume
generated by the smart meters typically goes through the LBRs, and is
directed from the smart meter devices to the head-end servers, in a
Multipoint-to-Point (MP2P) fashion. Current SMD traffic patterns are
fairly uniform and well-understood. The traffic generated by the
head-end server and destined to metering devices is dominated by
periodic meter reads, while traffic generated by the metering devices
is typically uniformly spread over some periodic read time-window.
Smart metering applications typically do not have hard real-time
constraints, but they are often subject to bounded latency and
stringent reliability service level agreements. From a routing
perspective, SMD applications require efficient P2MP communication
between the devices in the network and one or more LBRs. In
addition, timely loop resolution and broken link repair are needed to
meet latency requirements. Finally, the availability of redundant
paths is important for increasing network reliability.
5.1.2. Distribution Automation Traffic
Distribution Automation (DA) applications typically involve a small
number of devices that communicate with each other in a Point-to-
Point (P2P) fashion, and may or may not be in close physical
proximity. DA applications typically have more stringent latency
requirements than SMD applications.
5.1.3. Emerging Applications
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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 SMD
applications.
6. Description of Smart Grid Communication Paradigm
6.1. Source-sink (SS) communication paradigm
TBD
6.2. Publish-subscribe (PS, or pub/sub) communication paradigm
TBD
6.3. Peer-to-peer (P2P) communication paradigm
TBD
6.4. Peer-to-multipeer (P2MP) communication paradigm
TBD
6.5. Additional considerations: Duocast and N-cast
TBD
6.6. RPL applicability per communication paradigm
TBD
7. Layer 2 applicability.
7.1. Wireless technology
TODO: Describe features of IEEE 802.15.4g and 802.15.4e.
7.2. PowerLine Communication (PLC) technology
TODO: Describe features of IEEE P1901.2 standard.
8. 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.
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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 unicast
addressing. The routing protocol SHOULD support formation and
identification of groups of field devices in the network.
RPL supports the following features:
o Scalability: 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 (DAG) rooted at each LBR.
o Zero-touch configuration: This is done through in-band methods
for configuring RPL variables using DIO messages, and DIO message
options.
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.
9. RPL Profile
9.1. RPL Features
9.1.1. RPL Instances
RPL operation is defined for a single RPL instance. However,
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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 SDM and DA
traffic.
9.1.2. Storing vs. Non-Storing Mode
In most scenarios, electric meters are powered by the grid they are
monitoring and are not energy-constrained. Instead, electric meters
have hardware and communication capacity constraints that are
primarily determined by cost, and secondarily by power consumption.
As a result, different AMI deployments can vary significantly in
terms of memory size, computation power and communication
capabilities. 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 the route tables necessary to
support hop-by-hop routing, RPL non-storing mode SHOULD be preferred.
On the other hand, when nodes are capable of storing such routing
tables, the use of storing mode may lead to reduced overhead and
route repair latency. However, in high-density environments, storing
routes can be challenging because some nodes may have to maintain
routing information for a large number of descendents. When the
routing table size becomes challenging, it is RECOMMENDED that nodes
perform route aggregation, similarly to the approach taken by other
routing protocols, although the required set of mechanism may differ.
9.1.3. 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
root to themselves.
9.1.4. Path Metrics
Smart metering deployments utilize link technologies that may exhibit
significant packet loss and thus require routing metrics that take
packet loss into account. To characterize a path over such link
technologies, AMI deployments can use the Expected Transmission Count
(ETX) metric as defined in [RFC6551].
For water and gas meter networks that do not rely on powered
infrastructure, simpler metrics that require less energy to compute
would be more appropriate. In particular, a combination of hop count
and link quality can satisfy this requirement. As minimizing energy
consumption is critical in these types of networks, available node
energy should also be used in conjunction with these two metrics.
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The usage of additional metrics specifically designed for such
networks may be defined in companion RFCs, e.g., [RFC6551].
9.1.5. 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 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 AMI deployments. Neither of the currently defined objective
functions supports multiple metrics that might be required in
heterogeneous networks (e.g., networks composed of devices with
different energy constraints) or combination of metrics that might be
required for water- and gas-only networks. Additional objective
functions specifically designed for such networks may be defined in
companion RFCs.
9.1.6. DODAG Repair
To effectively handle time-varying link characteristics and
availability, AMI deployments SHOULD utilize the local repair
mechanisms in RPL. Local repair is triggered by broken link
detection and in storing mode by loop detection as well. The first
local repair mechanism consists of a node detaching from a DODAG and
then re-attaching 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 is described in Section 8.2.2.5 of [RFC6550].
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. The configured duration of the poisoning mechanism needs to
take into account the disconnection time applications running over
the network can tolerate. Note that when joining a different DODAG,
the node need not perform poisoning. The second local repair
mechanism controls how much a node can increase its rank within a
given DODAG Version (e.g., after detaching from the DODAG as a result
of broken link or loop detection). 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, thus controlling the duration of disconnection
applications may experience.
9.1.7. Multicast
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RPL defines multicast support for its storing mode of operation,
where 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.
9.1.8. Security
AMI deployments operate in areas that do not provide any physical
security. For this reason, the link layer, transport layer and
application layer technologies utilized within AMI networks typically
provide security mechanisms to ensure authentication,
confidentiality, integrity, and freshness. As a result, AMI
deployments may not need to implement RPL's security mechanisms and
could rely on link layer and higher layer security features.
9.1.9. P2P communications
Distribution Automation and other emerging applications may require
efficient P2P communications. Basic P2P capabilities are already
defined in the RPL [RFC6550]. Additional mechanisms for efficient
P2P communication are being developed in companion RFCs (see [I-D
.draft-ietf-roll-p2p-rpl-17]).
9.2. Description of Layer-two features
9.2.1. IEEE 802.15.4e MAC sub-layer features
TODO: describe IEEE 802.15.4e MAC features.
9.2.2. IEEE P1901.2 MAC sub-layer features
TODO: describe IEEE P1901.2 MAC features.
9.2.3. Security features provided by MAC sub-layer.
9.2.3.1. IEEE 802.15.4e
TODO: Describe 802.15.4e MAC security features.
9.2.3.2. IEEE P1901.2
TODO: Describe P1901.2 MAC security features.
9.2.4. MLE and other things
9.3. 6LowPAN Options
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TODO: Describe 6LowPAN options applicable to RPL profile.
9.4. Recommended Configuration Defaults and Ranges
9.4.1. Trickle Parameters
Trickle was designed to be density-aware and perform well in networks
characterized by a wide range of node densities. The combination of
DIO packet suppression and adaptive timers for sending updates allows
Trickle to perform well in both sparse and dense environments. Node
densities in AMI deployments can vary greatly, from nodes having only
one or a handful of neighbors to nodes having several hundred
neighbors. In high density environments, relatively low values for
Imin may cause a short period of congestion when an inconsistency is
detected and DIO updates are sent by a large number of neighboring
nodes nearly simultaneously. While the Trickle timer will
exponentially backoff, some time may elapse before the congestion
subsides. While some link layers employ contention mechanisms that
attempt to avoid congestion, relying solely on the link layer to
avoid congestion caused by a large number of DIO updates can result
in increased communication latency for other control and data traffic
in the network. To mitigate this kind of short-term congestion, this
document recommends a more conservative set of values for the Trickle
parameters than those specified in [RFC6206]. In particular,
DIOIntervalMin is set to a larger value to avoid periods of
congestion in dense environments, and DIORedundancyConstant is
parameterized accordingly as described below. These values are
appropriate for the timely distribution of DIO updates in both sparse
and dense scenarios while avoiding the short-term congestion that
might arise in dense scenarios. Because the actual link capacity
depends on the particular link technology used within an AMI
deployment, the Trickle parameters are specified in terms of the
link's maximum capacity for transmitting link-local multicast
messages. If the link can transmit m link-local multicast packets
per second on average, the expected time it takes to transmit a link-
local multicast packet is 1/m seconds.
DIOIntervalMin: AMI deployments SHOULD set DIOIntervalMin such that
the Trickle Imin is at least 50 times as long as it takes to
transmit a link-local multicast packet. This value is larger than
that recommended in [RFC6206] to avoid congestion in dense urban
deployments as described above. In energy-constrained deployments
(e.g., in water and gas battery-based routing infrastructure),
DIOIntervalMin MAY be set to a value resulting in a Trickle Imin
of several (e.g. 2) hours.
DIOIntervalDoublings: AMI deployments SHOULD set DIOIntervalDoublings
such that the Trickle Imax is at least 2 hours or more. For very
energy constrained deployments (e.g., water and gas battery-based
routing infrastructure), DIOIntervalDoublings MAY be set to a
value resulting in a Trickle Imax of several (e.g., 2) days.
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DIORedundancyConstant: AMI deployments SHOULD set
DIORedundancyConstant to a value of at least 10. This is due to
the larger chosen value for DIOIntervalMin and the proportional
relationship between Imin and k suggested in [RFC6206]. This
increase is intended to compensate for the increased communication
latency of DIO updates caused by the increase in the
DIOIntervalMin value, though the proportional relationship between
Imin and k suggested in [RFC6206] is not preserved. Instead,
DIORedundancyConstant is set to a lower value in order to reduce
the number of packet transmissions in dense environments.
9.4.2. Other Parameters
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.
10. Manageability Considerations
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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 disseminated through DIO packets. The use of Trickle
for scheduling DIO transmissions ensures lightweight yet timely
propagation of important network and parameter updates and allows
network operators to choose the trade-off point they are comfortable
with respect to overhead vs. reliability and timeliness of network
updates. The metrics in use in the network along with the Trickle
Timer parameters used to control the frequency and redundancy of
network updates can be dynamically varied by the root during the
lifetime of the network. To that end, all DIO messages SHOULD
contain a Metric Container option for disseminating the metrics and
metric values used for DODAG setup. In addition, DIO messages SHOULD
contain a DODAG Configuration option for disseminating the Trickle
Timer parameters throughout the network. The possibility of
dynamically updating the metrics in use in the network as well as the
frequency of network updates allows deployment characteristics (e.g.,
network density) to be discovered during network bring-up and to be
used to tailor network parameters once the network is operational
rather than having to rely on precise pre- configuration. This also
allows the network parameters and the overall routing protocol
behavior to evolve during the lifetime of the network. 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.
11. Security Considerations
Smart grid networks are subject to stringent security requirements as
they are considered a critical infrastructure component. At the same
time, since they are composed of large numbers of resource-
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, transport-layer and/or application-layer security mechanisms
are typically in place and using RPL's secure mode is not necessary.
11.1. Security Considerations during initial deployment
11.2. Security Considerations during incremental deployment
12. Other Related Protocols
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13. IANA Considerations
This memo includes no request to IANA.
14. Acknowledgements
The authors would like to acknowledge the review, feedback, and
comments of Jari Arkko, Dominique Barthel, Cedric Chauvenet, Yuichi
Igarashi, Philip Levis, Jeorjeta Jetcheva, Nicolas Dejean, and JP
Vasseur.
15. References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, March 1997.
[RFC5548] Dohler, M., Watteyne, T., Winter, T. and D. Barthel,
"Routing Requirements for Urban Low-Power and Lossy
Networks", RFC 5548, May 2009.
[RFC6206] Levis, P., Clausen, T., Hui, J., Gnawali, O. and J. Ko,
"The Trickle Algorithm", RFC 6206, March 2011.
[RFC6550] Winter, T., Thubert, P., Brandt, A., Hui, J., Kelsey, R.,
Levis, P., Pister, K., Struik, R., Vasseur, JP. and R.
Alexander, "RPL: IPv6 Routing Protocol for Low-Power and
Lossy Networks", RFC 6550, March 2012.
[RFC6551] Vasseur, JP., Kim, M., Pister, K., Dejean, N. and D.
Barthel, "Routing Metrics Used for Path Calculation in
Low-Power and Lossy Networks", RFC 6551, March 2012.
[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.
[RFC5826] Brandt, A., Buron, J. and G. Porcu, "Home Automation
Routing Requirements in Low-Power and Lossy Networks", RFC
5826, April 2010.
[RFC5673] Pister, K., Thubert, P., Dwars, S. and T. Phinney,
"Industrial Routing Requirements in Low-Power and Lossy
Networks", RFC 5673, October 2009.
Authors' Addresses
Daniel Popa
Itron, Inc
52, rue Camille Desmoulins
Issy les Moulineaux, 92130
FR
Email: daniel.popa@itron.com
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Internet-Draft RPL Applicability for AMI July 2013
Matthew Gillmore
Itron, Inc
2111 N Molter Rd.
Liberty Lake, WA, 99019
USA
Email: matthew.gillmore@itron.com
Laurent Toutain
Telecom Bretagne
2 rue de la Chataigneraie
Cesson Sevigne, 35510
FR
Email: laurent.toutain@telecom-bretagne.eu
Jonathan Hui
Cisco
170 West Tasman Drive
San Jose, CA, 95134
USA
Email: johui@cisco.com
Ruben Salazar
Landys+Gyr
30000 Mill Creek Ave # 100
Alpharetta, GA, 30022
USA
Email: ruben.salazar@landisgyr.com
Kazuya Monden
Hitachi, Ltd., Yokohama Research Laboratory
292, Yoshida-cho, Totsuka-ku, Yokohama-shi
Kanagawa-ken , 244-0817
Japan
Email: kazuya.monden.vw@hitachi.com
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