ROLL D. Popa
Internet-Draft J. Jetcheva
Intended status: Standards Track Itron
Expires: November 2, 2012 N. Dejean
Elster SAS
R. Salazar
Landis+Gyr
J. Hui
Cisco
K. Monden
Hitachi, Ltd., Yokohama Research
Laboratory
May 1, 2012
Applicability Statement for the Routing Protocol for Low Power and Lossy
Networks (RPL) in AMI Networks
draft-ietf-roll-applicability-ami-06
Abstract
This document discusses the applicability of RPL in Advanced Metering
Infrastructure (AMI) networks.
Status of this Memo
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Copyright (c) 2012 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. Electric Metering . . . . . . . . . . . . . . . . . . . . 3
1.2. Gas and Water Metering . . . . . . . . . . . . . . . . . . 3
1.3. Routing Protocol for LLNs (RPL) . . . . . . . . . . . . . 4
1.4. Requirements Language . . . . . . . . . . . . . . . . . . 5
2. Deployment Scenarios . . . . . . . . . . . . . . . . . . . . . 5
2.1. Network Topology . . . . . . . . . . . . . . . . . . . . . 5
2.1.1. Electric Meter Network . . . . . . . . . . . . . . . . 5
2.1.2. Energy-Constrained Network Infrastructure . . . . . . 6
2.2. Traffic Characteristics . . . . . . . . . . . . . . . . . 6
2.2.1. Smart Metering Data . . . . . . . . . . . . . . . . . 7
2.2.2. Distribution Automation Communication . . . . . . . . 8
2.2.3. Emerging Applications . . . . . . . . . . . . . . . . 8
3. Using RPL to Meet Functional Requirements . . . . . . . . . . 8
4. RPL Profile . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.1. RPL Features . . . . . . . . . . . . . . . . . . . . . . . 9
4.1.1. RPL Instances . . . . . . . . . . . . . . . . . . . . 9
4.1.2. Storing vs. Non-Storing Mode . . . . . . . . . . . . . 9
4.1.3. DAO Policy . . . . . . . . . . . . . . . . . . . . . . 10
4.1.4. Path Metrics . . . . . . . . . . . . . . . . . . . . . 10
4.1.5. Objective Function . . . . . . . . . . . . . . . . . . 10
4.1.6. DODAG Repair . . . . . . . . . . . . . . . . . . . . . 11
4.1.7. Multicast . . . . . . . . . . . . . . . . . . . . . . 11
4.1.8. Security . . . . . . . . . . . . . . . . . . . . . . . 12
4.1.9. P2P communications . . . . . . . . . . . . . . . . . . 12
4.2. Recommended Configuration Defaults and Ranges . . . . . . 12
4.2.1. Trickle Parameters . . . . . . . . . . . . . . . . . . 12
4.2.2. Other Parameters . . . . . . . . . . . . . . . . . . . 13
5. Manageability Considerations . . . . . . . . . . . . . . . . . 14
6. Security Considerations . . . . . . . . . . . . . . . . . . . 14
7. Other Related Protocols . . . . . . . . . . . . . . . . . . . 15
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 15
9. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 15
10. References . . . . . . . . . . . . . . . . . . . . . . . . . . 15
10.1. Informative References . . . . . . . . . . . . . . . . . . 15
10.2. Normative References . . . . . . . . . . . . . . . . . . . 16
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 16
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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 a 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. 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).
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
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typically run on a modest source of stored energy (e.g., 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, 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).
1.3. Routing Protocol for LLNs (RPL)
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 desgined 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].
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:
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].
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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 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.
2.1.1. Electric Meter Network
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
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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.
2.1.2. Energy-Constrained Network Infrastructure
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 continous 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.
2.2. Traffic Characteristics
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2.2.1. Smart Metering Data
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
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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.
2.2.2. Distribution Automation Communication
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.
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 SMD
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:
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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
(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 SDM and DA
traffic.
4.1.2. 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
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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.
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
root to themselves.
4.1.4. 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[I-D.ietf-roll-routing-metrics].
For water- and gas-only 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.
The usage of additional metrics specifically designed for such
networks may be defined in companion RFCs, e.g.,
[I-D.ietf-roll-routing-metrics] .
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 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.
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4.1.6. 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
[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. 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.
4.1.7. Multicast
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.
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4.1.8. 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.
4.1.9. P2P communications
Distribution Automation and other emerging applications may require
efficient P2P communications. Basic P2P capabilities are already
defined in the RPL RFC [I-D.ietf-roll-rpl]. Additional mechanisms
for efficient P2P communication are being developed in companion
RFCs.
4.2. Recommended Configuration Defaults and Ranges
4.2.1. 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 DIORefundancyConstant 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
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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.
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.
4.2.2. 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.
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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 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.
6. 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,
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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.
7. Other Related Protocols
This document contains no other related protocols.
8. IANA Considerations
This memo includes no request to IANA.
9. Acknowledgements
The authors would like to acknowledge the review, feedback, and
comments of Jari Arkko, Dominique Barthel, Cedric Chauvenet, Yuichi
Igarashi, Philip Levis, and JP Vasseur.
10. References
10.1. 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-06 (work in progress),
December 2011.
[I-D.ietf-roll-p2p-rpl]
Goyal, M., Baccelli, E., Philipp, M., Brandt, A., and J.
Martocci, "Reactive Discovery of Point-to-Point Routes in
Low Power and Lossy Networks", draft-ietf-roll-p2p-rpl-09
(work in progress), March 2012.
[I-D.ietf-roll-routing-metrics]
Barthel, D., Vasseur, J., Pister, K., Kim, M., and N.
Dejean, "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]
Brandt, A., Vasseur, J., Hui, J., Pister, K., Thubert, P.,
Levis, P., Struik, R., Kelsey, R., Clausen, T., and T.
Winter, "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-06 (work in
progress), September 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.
[RFC6206] Levis, P., Clausen, T., Hui, J., Gnawali, O., and J. Ko,
"The Trickle Algorithm", RFC 6206, March 2011.
10.2. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, March 1997.
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Authors' Addresses
Daniel Popa
Itron
52 Rue Camille Desmoulins
92448 Issy les Moulineaux
France
Email: daniel.popa@itron.com
Jorjeta Jetcheva
Itron
2111 N Molter Rd.
Liberty Lake, WA 99019
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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Kazuya Monden
Hitachi, Ltd., Yokohama Research Laboratory
292, Yoshida-cho, Totsuka-ku, Yokohama-shi
Kanagawa-ken 244-0817
Japan
Phone: +81-45-860-3083
Email: kazuya.monden.vw@hitachi.com
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