Network Working Group J. Arkko
Internet-Draft H. Rissanen
Intended status: Informational S. Loreto
Expires: January 6, 2012 Z. Turanyi
O. Novo
Ericsson
July 5, 2011
Implementing Tiny COAP Sensors
draft-arkko-core-sleepy-sensors-01
Abstract
The authors are developing COAP and IPv6-based sensor networks for
environments where lightweight implementations, long battery
lifetimes, and minimal management burden are important. The memo
shows how different communication models supported by COAP affect
implementation complexity and energy consumption, far more so than
mere changes in message syntax. Our prototype implements a
multicast-based IPv6, UDP, COAP, and XML protocol stack in less than
50 assembler instructions. While this extremely minimal
implementation is suitable only for limited applications and makes a
number of assumptions, the general conclusions point to need for
further work in developing the COAP multicast and observation
frameworks.
Status of this Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
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Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
This Internet-Draft will expire on January 6, 2012.
Copyright Notice
Copyright (c) 2011 IETF Trust and the persons identified as the
document authors. All rights reserved.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3. Implementing Tiny COAP-Based Sensors . . . . . . . . . . . . . 5
3.1. Sleeping Nodes and Energy Use . . . . . . . . . . . . . . 6
3.2. Address Autoconfiguration . . . . . . . . . . . . . . . . 6
3.3. Using Multicast . . . . . . . . . . . . . . . . . . . . . 7
3.4. Using COAP . . . . . . . . . . . . . . . . . . . . . . . . 8
3.5. Power Usage Calculation . . . . . . . . . . . . . . . . . 9
3.6. Software Construction . . . . . . . . . . . . . . . . . . 10
3.7. UDP Checksums . . . . . . . . . . . . . . . . . . . . . . 11
3.8. Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 11
4. Choosing a Communication Model . . . . . . . . . . . . . . . . 12
4.1. End-to-End Communication and Intermediaries . . . . . . . 13
4.2. COAP Messaging . . . . . . . . . . . . . . . . . . . . . . 15
4.2.1. Client Model . . . . . . . . . . . . . . . . . . . . . 15
4.2.2. Server Model . . . . . . . . . . . . . . . . . . . . . 17
4.2.3. Observer Model . . . . . . . . . . . . . . . . . . . . 18
4.3. Resources and Data Formats . . . . . . . . . . . . . . . . 21
4.4. Configuration . . . . . . . . . . . . . . . . . . . . . . 22
5. Security Considerations . . . . . . . . . . . . . . . . . . . 23
6. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . 23
7. References . . . . . . . . . . . . . . . . . . . . . . . . . . 24
7.1. Normative References . . . . . . . . . . . . . . . . . . . 24
7.2. Informative References . . . . . . . . . . . . . . . . . . 25
Appendix A. Acknowledgments . . . . . . . . . . . . . . . . . . . 26
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 26
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1. Introduction
The authors are developing COAP [I-D.ietf-core-coap] and IPv6-based
[RFC2460] sensor networks for home, building, and other consumer
environments. These environments demand solutions where the sensors
are physically small, inexpensive, have long battery lifetimes, and
require minimal amount of management effort. Our prototype sensor
implementation requires no configuration and implements a multicast-
based IPv6, UDP, COAP, and XML protocol stack and an application in
very small amount of code.
Small devices are naturally preferred in most applications, but for
some applications small enough size is a critical concern, for
instance, to make devices embedded in our clothing practical, to fit
within the space available in buildings or everyday objects, or to
ensure that the devices do not cause a visual distraction. Another
key concern is device and battery lifetime. Sufficient battery
lifetime in an application with a large number of devices can be
surprisingly long. A home with hundred devices with ten year battery
lifetimes will result in a battery change operation every month.
The practical challenge is to increase battery lifetimes of small
devices by several orders of magnitude, and to enable pinhead size
devices connected to the Internet. These are not unattainable goals,
as legacy sensor networking technology can in some cases reach these
goals. For instance, networked 1-Wire temperature sensors are the
size of a packaged transistor. Our aim is to replicate this model or
even improve it for IP-based sensors.
Another challenge is to ensure that COAP-based networks are
interoperable in a multi-vendor environment. For instance, it is
important that proxies and servers can perform all the necessary
tasks without being programmed to support a sensor node manufactured
by a particular vendor, or perhaps even without being programmed to
support a particular class of a sensor.
This memo describes implementation experiences, open questions that
we have encountered, and areas where COAP makes it difficult to make
very low power implementations. The memo discusses implementation
techniques that are useful in these environments and what is needed
for fully interoperable solutions based on COAP.
The goals for our work are described in Section 2. Before we can
dwell into the high-level networking design choices, we highlight
some of the implications of detailed implementation strategies
through an example. Section 3 discusses our specific implementation
strategies, and describes our experiences with these choices. This
example is an extreme case, an attempt to minimize as much as
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possible for a limited set of applications. However, some general
conclusions can still be seen. The more general discussion of the
different high-level approaches to communications models can be found
in Section 4. Different communication models supported by COAP
affect implementation complexity and energy consumption, far more so
than mere changes in message syntax. The required configuration
effort is also directly affected by the choice of the communications
model. Finally, the concluding recommendations point to need for
further work in developing COAP and its multicast and observation
frameworks [I-D.ietf-core-coap] [I-D.ietf-core-observe], as discussed
in Section 6.
2. Goals
The main focus of this draft is sensors that are deployed in large
quantities and have specific physical requirements. There are
similar issues with other nodes such as servers and proxies, but in
general these nodes have better access to power and other resources,
and typically can also be more easily configured by humans.
As discussed in the introduction, for sensors the overall
requirements revolve around minimizing physical size, cost,
management effort and maximizing battery lifetimes. More
specifically, we believe the following goals are key in achieving
fulfilling these requirements:
o Natural support for sleeping nodes. There are many aspects to
power usage in small devices, but we believe this one is the most
significant one in terms of minimizing power usage. Many of the
other aspects are either dictated by the environment (such as
choice of radio technology in a given network) or have a
relatively small impact (small variations in message size, for
instance).
o Communication models that fit the problem at hand. It is
essential that the small nodes can engage in communication
exchanges that suit their needs. Having to employ multiple
roundtrips, wait for nodes they have no control over, and so on
can have a large negative effect on the amount of power that the
node has to spend.
o Good design from user perspective. It is obviously undesirable to
require a lot of per-device configuration effort when deploying a
large set of small devices. In addition, direct configuration
efforts with the device itself may be problematic, given that
there is no room for any type of a user interface. For instance,
some of the legacy sensor devices in existing networks are just a
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few millimeters across. It is natural that some information needs
to be configured, but configuration should be minimized and
whatever configuration is necessary should take place in nodes
that have the necessary user interfaces and capabilities.
3. Implementing Tiny COAP-Based Sensors
We have implemented prototypes of small sensors and a sensor gateway
to pass the information onwards. The main target of these
implementations is temperature, humidity, and other measurements in
home environments. Our focus is primarily in sensing. Actuators and
other more complex functions are outside the scope of our analysis.
Our prototype sensor implementation requires no configuration and
simply runs based on its own identity burned in the hardware. The
complete functionality requires only a small amount of code. Our
prototype platform uses a 32-bit processor architecture and the
hardware provides an underlying capability to send a link layer
frame. In this platform, our implementation is under 50 assembler
instructions (see Note 1). The implementation consists of a
Ethernet, IPv6, UDP, COAP, and XML protocol stack and the sensor
application. The sensor application is based on values provided by
an A/D converter; any analog value can be measured.
Note 1: This contains everything necessary for using an A/D
converter, constructing a complete Ethernet frame along with the
higher level protocol fields, and asking the link layer to send
it. It excludes platform specific initializations of the link
layer, actual emitting of bits on the wire, and putting the device
to sleep and waking it up. The complexity of these tasks varies
highly between platforms and link layer technologies. For
instance, one some platforms and with Ethernet, sending a frame
out is merely a question of setting some registers and asking the
hardware to send a packet. Of course, with different link layers
and platforms an implementation might be have to be arbitrarily
complex to support the intricacies of the link layer in question.
Even this size of the implementation is not the absolute minimum.
One quarter of the code in our implementation relates to specific
initializations required for the A/D converter that we used. Another
quarter relates to binary to decimal conversions on the chosen XML-
based payload. On a different platform and with binary data, 25
instructions would be achievable.
The following subsections outline the design choices that were taken
to create the small implementation we have.
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3.1. Sleeping Nodes and Energy Use
As discussed earlier, choosing the right communication model is what
drives a good design from a power conservation perspective.
In our implementation, we chose to use a send-only model where the
device only sends messages, but never receives one. This model can
be applied in specialized applications under some assumptions that
will be discussed further later in this memo. In our case, the
sensor will periodically take a reading and send a COAP message to
the network with that reading. In order to eliminate potential
waiting periods where the device has to stay on, we needed to
eliminate the following:
o DHCP request - response process [RFC2131].
o Router Discovery process [RFC4861].
o Duplicate Address Detection process [RFC4862].
o Acting as a COAP server [I-D.ietf-core-coap].
o Waiting for COAP observation subscriptions
[I-D.ietf-core-observe].
3.2. Address Autoconfiguration
Eliminating DHCP is easy, as we can simply use IPv6 and stateless
address autoconfiguration. Eliminating router discovery is harder,
however. To avoid having to wait for a Router Advertisement to carry
a prefix, we chose to employ a link-local source adress. These
addresses can be constructed from the well-known prefix FE80::0 and a
link layer hardware address burned to the hardware [RFC4862].
Eliminating Duplicate Address Detection is a matter of choice. We
chose to behave as if DupAddrDetectTransmits had been set to zero, in
other words not performing any Duplicate Address Detection. It may
be debatable whether this is a violation of [RFC4862], but it is
certainly against its spirit. This choice seems to be the right
technical action, however, on a number of grounds:
o As the node is not receiving any packets, nor sending Neighbor
Advertisement messages, any effects of possible duplication would
be limited to some additional traffic in the network. No other
traffic would be impacted. Application-level collection of sensor
information can proceed even in this situation.
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o [RFC4862] requires that upon detecting a duplicated address,
"autoconfiguration stops and manual configuration of the interface
is required". However, it is obvious that no such action is
possible on a small device. The device has no user interface.
The only interface that the device has is the network, and if the
network cannot be brought up, there's very little that can be
done. As a result, the ability to not stop in a duplication case
may actually be better than what is recommended by [RFC4862].
o These devices are manufactured with hardware identities that are
expected to be unique. There are obviously no guarantees about
this succeeding in all cases, but non-unique identifiers would
represent a major failure of the manufacturing process.
Elimination of Duplicate Address Detection also eliminates the need
for the node to implement Multicast Listener Detection (MLD) protocol
[RFC2710] [RFC3810]. This is because it now no longer needs to
listen for messages to the solicited node multicast address, so there
is no need to send out MLD messages.
3.3. Using Multicast
To further eliminate configuration or protocol exchanges for
discovery, we chose to employ a multicast model where the sensor
sends COAP POST requests to a well-known multicast address. While
the type of sensors targeted here send information very infrequently,
one of our goals was to ensure that the architecture would scale to
more frequent information distribution and far larger groups of
sensors. As a result, it was important to ensure that the multicast
messages do not lead to multicast storms or unnecessary waking up
many nodes due to frequent messages.
We chose to employ an interest-based generated multicast group
address. These addresses are similar to those used in IPv6 Neighbor
Discovery [RFC4861] for sending messages to solicited node addresses
(FF02:0:0:0:0:1:FFXX:XXXX) [RFC4291]. The idea is that some bits
from the object of interest are reflected in the multicast address,
making it statistically likely that someone interested in a specific
object only has to receive packets relating to that object, and not
all packets.
We employ FF02:0:0:0:1:FEXX:XXXX, where XX:XXXX is a 6-byte value
representing the type of sensor. (This address is currently reserved
by IANA, but could be allocated for this purpose if needed.) The
sensor type represents a classification of different sensor to types.
For instance, we could let 00:0001 stand for temperature sensors.
Each temperature sensor would send information to the multicast
address FF02:0:0:0:1:FE00:0001, and only those devices that are
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interested in temperature measurements would subscribe to this
multicast group. Techniques such as MLD/IGMP snooping can be used in
the network to ensure that multicast messages are physically
transmitted only in those parts of the network that actually care
about those messages [RFC4541]. In practice, this would mean that in
a star topology network with a large number of sensors and a few
central nodes, none of the sensors would receive any messages from
each other.
Finally, randomization of actual transmission times for the periodic
transmissions ensures that transmissions from different sensors are
not synchronized.
When sensors send multicast messages with link-local source and
destination addresses, all communication is confined to a single
network. We expect that there is a node in the network that listens
to the multicast messages, collects the data from them, and is
capable of relaying the information to other parties. Such a node
might store the latest information related to each sensor, and allow
other nodes in the Internet to query the latest information on a per-
sensor or an aggregate basis.
3.4. Using COAP
Our implementation uses non-confirmable requests at the messaging
layer of COAP, and sends a POST message that carries an XML payload
for a well-known URI. The implementation sends a message and does
not wait for a message at this layer. We have used a gateway to
store the information received from the sensors, making the gateway
act as a server, storing everything posted to it. The stored
information can be fetched from the gateway, for instance, with a
COAP or HTTP GET.
Per Section 2.8.2 of [I-D.ietf-core-coap], POST methods normally
generate a response at the request/response layer. If the server
sends a response, the sensor is already asleep and will not respond
to Neighbor Discovery messages or receive the actual message. The
message is therefore lost, but it is fine in our case given that the
information was already stored in the server.
Reliable transmission is achieved through assuming a sufficiently
high periodic transmission rate to account for randomly occurring
message loss.
There are several areas of concern with the above arrangements,
discussed further in Section 3.8 and Section 4.2.
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3.5. Power Usage Calculation
Our communication model is now complete. Its effectiveness can be
calculated by determining what fraction of time the device would have
to be awake. Lets assume periodic messages once per minute, a 10
Mbit/s link layer interface, and a CPU running at 1 Mhz. With the
given link layer, sending one message takes theoretically 100
microseconds. Constructing the message takes 50 instructions and if
we for simplicity assume that each instruction takes two clock
cycles, the CPU needs to run for an additional 100 microseconds.
Since our device is only sending messages, it only has to wake up to
send the message. Ratio of sleeping versus being awake is now 200
microseconds versus 60 seconds, i.e., 300.000. Even if we assume
that it takes an additional 800 microseconds to power the device up
and let the A/D converter stabilize, the ratio is still 1000
microseconds vs. 60 seconds, i.e., 60.000.
We can compare this to some other possible implementations. A node
that stays awake and participates in Neighbor Discovery, Duplicate
Address Detection, and ARP processes would consume 60.000 times more
energy. One could assume that listening is less power consuming than
sending, however. On some link layers today this ratio can be as
high as listening consuming 2.500 time less power, though practical
implementations (talk vs. standby times) seem to be more in the range
of a 100-fold difference. If we assume an optimistic 1.000 time
difference, our implementation would still consume 60 times less
energy than one that stays on all the time.
Another possible implementation is that a node stays awake for a
short period of time to listen for possible messages. Some COAP
implementations do this to enable discovery and observe subscriptions
to work. If we assume one second awake time during one minute, then
the power consumption difference to our implementation is somewhere
between 1.000 and 2 times, depending on whether send/receive power
requirements differences are factored in.
While these comparisons have produced wildly different numbers, it is
clear that our implementation strategy is far superior to the
simplistic always-on model. The situation is less clear with the
comparison to the periodically listening approach, but even there it
is clear that not listening consumes less energy than listening.
While the actual numbers depend highly on the characteristics of the
link layer, even with the most optimistic assumptions for the
alternative approach it uses twice as much energy. This may not
sound like a significant difference, but if it means a ten year
battery lifetime instead of five year battery lifetime, it can make
or break a business case for building some types of sensors.
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3.6. Software Construction
When memory and processing power is at a premium, the detailed
software design approach needs to suit the platform that the software
runs on. That being said, for simple send-only applications we have
found that a packet template-based approach works well. In this
approach an image of the message sent by the application is burned
into the read-only memory of the device, as a part of the overall
software image. When the device powers up, the message image is
copied to random access memory, necessary changes are applied, and
the underlying link layer hardware or the CPU emits it on the
outgoing interface bit by bit.
In case of COAP and simple sensors that output a numerical value
transmitted in an XML [W3C.REC-xml-19980210] or JSON [RFC4627]
payload, only the following changes are applicable:
o 16-bit COAP Message ID field (see Section 3.1 in
[I-D.ietf-core-coap]). This field should be set to a random
value, a rarely repeating value. We have found that using a
suitably shifted value of a real-time clock is the most convenient
way to generate a good value for this field. On many small
platforms, a real-time clock can be kept counting with a very
small amount of power. Note that it does not matter what value
the real-time clock is initially initialized to; the only thing
that matters for the Message ID field is that it keeps changing.
If a sensor sends a value every minute, shifting a seconds-from-
epoch counter by five bit positions is a good way to generate a
unique value.
Note that using a different value may not actually be required,
though it is certainly helpful for understanding network traces
and debugging. According to Section 4.1 of [I-D.ietf-core-coap],
Message IDs only have to be unique within RESPONSE_TIMEOUT *
RESPONSE_RANDOM_FACTOR * (2 ^ MAX_RETRANSMIT - 1) or 45 seconds,
so a sensor sending messages every minute would be allowed to send
them with the same Message ID.
o The actual sensor reading. In both XML and JSON, values can be
padded with leading zeros or spaces, so the overall size of the
packet can be kept the same in all circumstances. This greatly
simplifies the construction of the packet.
Note that binary or hexadecimal formats would make this even
simpler, but the savings are in the order of few instructions; the
difference is not big. Of course, a message that carries a text
is longer than a pure binary message. However, the format is not
so important as is avoiding including a lot of extraneous
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information. Some XML schemas can be problematic. We advocate
simplicity and restraint in XML schema design for sensor data.
o 16-bit UDP checksum field. For computing this field, see
Section 3.7.
Note this small set of changes is only applicable when it can be
assumed that both source and destination IP addresses are known
beforehand.
3.7. UDP Checksums
Both IPv4 and IPv6 have some form of mandatory checksums, either in
the IP header (IPv4) or as part of upper layer protocols such as UDP
(IPv6). Computing the checksum is not difficult, but requires
looping through all the 16-bit words in a packet. Fortunately, for a
simple application the checksum calculation is actually very simple.
Following the algorithm in [RFC1624], there is no need to calculate
the checksum for the entire packet. The checksum can be precomputed
on the packet template with zero words filled in for the variable
parts. Lets call this precomputed checksum value C. Let NC be its
negation, i.e.,
NC = ~C
Once the actual values are filled in the packet, the true checksum C'
needs to be calculated as follows:
T = NC + W1 + W2 + ... + Wn
C' = ~(T + (T >> 16))
where T is a temporary variable and Wi, i = 0, 1, ..., n are the
words that got changed from the template. Naturally, this approach
makes sense only when the number of changed words is small. We have
found that suitable placement of spaces and string values in an XML
object, for instance, is helpful in aligning the changed parts to
word boundaries, and in sensor implementations n = 3.
3.8. Evaluation
This type of an implementation is obviously an extreme example. This
level of optimization may not be needed in all cases. Nevertheless,
it is interesting to see that COAP can be used in such small
implementations.
In general, our implementation satisfies the requirements set for the
special environment that it was designed for: power usage is
minimized, individual sensor devices do not require configuration,
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existing legacy networks can migrate to general-purpose IP-based
networks, and all the necessary information can be passed in the
messages.
That being said, there are also some issues with this implementation
approach. The first issue is that information delivery frequency is
hardwired into the sensors. The chosen frequency may be sufficient
for a given application, but the same sensors cannot be used by
another application that would require a faster delivery of
measurements.
Related but more serious concern is that reliability is achieved
through randomized message intervals and multiple transmissions; it
is considered unlikely that a very large number of messages in
sequence are lost from the same sensor. The message transmission
frequency needs to be set high enough to accommodate some packet
loss. There is no way to actively request retransmission. We
believe that this is a small problem in well-designed networks and
for most applications that are not real-time critical, such as home,
weather, maintenance, and energy monitoring. However, this approach
may not be suitable for real-time or safety-critical applications.
The third and obvious limitation is that there is an assumption of a
network node in the same network that is capable of storing
information. We believe that there is little that can be done about
this assumption; it is fundamental for the nature of low-power
devices that they have to be able to sleep periodically, and there
are very few other options beyond implementing a time-shifting device
such as a cache. The location of the cache node could be outside the
sensor network in some other designs, however.
4. Choosing a Communication Model
COAP is a specialized web transfer protocol designed to be used in
various ways. The communication model of COAP is flexible and the
application developer has to decide the best way to use it. This
involves
o deciding which parties are in server/client roles,
o determining whether to use end-to-end communication or employ
intermediary nodes,
o deciding whether to use base COAP operations or the observation
framework,
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o deciding whether a discovery process is required,
o specifying how COAP maps to lower layers, including choice of
source and destination addresses, and
o agreeing about commonly understood methods, resource identifiers
and data representation.
Note that the number of these choices alone makes it hard to achieve
interoperability, as we should strive for application
interoperability at the semantic level [arkko.iab], rather than mere
ability to transport correctly formed COAP packets.
Nevertheless, the main focus of this memo is to determine the power
efficiency implications for the different communications models, and
to identify areas where COAP limits this efficiency. The rest of
this section is structured as follows. Section 4.1 discusses which
nodes are involved. Section 4.2 discusses the specific COAP
messaging alternatives. Section 4.3 discusses resources and data
formats. Section 4.4 discusses configuration issues.
4.1. End-to-End Communication and Intermediaries
In most applications, user interactions and information requests can
come at any time. Some form of an intermediary that can buffer such
requests between a possibly sleeping device and the end user seems
therefore useful to provide "time-shifting" capability. Similarly,
an intermediary can be useful to reduce the number of transactions
that one has to do with the low-power device to a minimum; the
intermediary can answer on behalf of the device should a large number
of information requests be placed.
In its simplest form, the intermediary is a part of the application
server. For instance, a web-based application server is capable of
serving web clients at any time, but will only place a periodic
request to the sensor in order to take a reading. There are
virtually no downsides to this arrangement, and it is generally
recommended practice.
What is perhaps more controversial and interesting is the placement
of intermediaries elsewhere, such as requiring an intermediary in the
same network as the sensor devices are in. In our example
implementation, such an intermediary was used for both time-shifting
purposes and to bridge the gap between addressing domains, as the
sensor was only capable of sending messages to nearby devices with
link-local multicast addressing. For obvious reasons, sending
traffic to well-known multicast groups works only on the local scale.
Other possible reasons for using a local intermediary include
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protocol conversion and providing TCP-based congestion control for
traffic passing through the Internet. Where mechanisms for dealing
with packet loss are limited, such as in the case of our
implementation, an intermediary can also shield the sensors from
having to deal with networks that have not been engineered for this
purpose.
There are also downsides to having to place a local intermediary.
The obvious downside is that such a device must now exist in the
local network.
The use of COAP intermediaries is not fully specified, however. Some
of the issues we have encountered include:
o COAP defines the roles for clients, servers, caches, and proxies,
but while the specification allows an intermediary to act as
server that stores all information sent to it, it is by no means
specified as something that all implementations should do. The
desirable behavior from the point of low-power sensors would be
that the local server would store the information from every POST
sent to it for a period of time specified in the Max-Age option
[I-D.ietf-core-coap], and then be able provide access to the
information using GET and HTTP/COAP. It would be useful to define
such a new server role, along with specifying the necessary
security and operational conditions for this practice.
o If designed badly, the intermediary may also limit the type of
communications it can relay. For instance, a gateway that is only
built for a particular types of sensors might only accept very
specific COAP messages. In particular, intermediaries need to
support any type of resource identifiers and data formats.
Further discussion of this can be found in Section 4.3.
o In several CoAP applications the user is interested in the latest
value of a resource, but historical values are also interesting in
several use cases, e.g. tracking the movements of a truck during
the day. Thus, the information stored in the cache/gateway should
not expire. Even if a new value is received every minute, old
values should be accessible and new value should not overwrite the
old value. For this kind of cases, schemas for representing also
historical values of the sensor would be useful for
interoperability. Of course, simple schemas are easy to implement
even if there did not exist any standards or recommendations, but
again, there will not be interoperability.
o If the information is such that it should expire after some time,
Max-Age option can be used as defined [I-D.ietf-core-coap].
However, [I-D.ietf-core-coap] discusses Max-Age option only in the
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context of responses. In the multicast use case (sensor the one
sending requests), Max-Age option would be needed to be supported
in requests, too.
o Multicast requests, particiularly GETs, might be forwarded by
several proxies and possibly even to further multicast addresses,
causing a storm of messages. The COAP specification does not
describe when the forwarding of multicast requests is appropriate
and when it is not.
4.2. COAP Messaging
The interaction model of COAP is similar to the client/server model
in HTTP. A sensor can act either as a client that sends requests
containing updated measurement information to a server, or as a
server that responds to requests from others. If the sensor is a
server, it can either employ the basic communication model from
[I-D.ietf-core-coap] or use the observation framework
[I-D.ietf-core-observe]. This section looks at the energy efficiency
implications of these models.
It is important to make this analysis not merely based on the data
transmission phase, but also based on what discovery actions and
related signaling may be necessary.
4.2.1. Client Model
In this model, a sensor acts as a client that periodically sends POST
requests containing updated measurement information to a server.
This is the model that we used in our example implementation.
User or Sensor
Intermediary (Client)
(Server) .
| .
| .
| wake-up
| |
| NON/POST |
| content |
+<--------------------|
| |
| power-down
| .
| NON/RSP .
|----------------/ .
| .
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Figure 1. Send-only client model
In its simplest form, this model can be reduced to sending a single
message per observation period, however this comes at the cost of:
o Limited support for reliable transmission. Messages may arrive
out of order and they may go missing without notice. While
periodic retransmissions do provide a statistical likelihood that
the transmission eventually succeeds, they do not guarantee it.
o Possible spurious diagnostic or other problems caused by not being
able to receive the REST level response to the POST message that
the server will send (see Section 3.4).
Both of these problems can be addressed by forcing the device to wait
for a response, incurring the cost of having to be awake for 1 RTT
for each observation period. Using the assumptions from Section 3.5
and a 2 ms RTT for a local intermediary to respond, the power usage
of this model would be either two times more or 0.2% more, depending,
again, on whether the send/receive power differences are factored in.
User or Sensor
Intermediary (Client)
(Server) .
| .
| .
| wake-up
| |
| CON/POST |
| content |
+<--------------------|
| |
| ACK/RSP |
|-------------------->|
| |
| power-down
| .
| .
Figure 2. Send-and-confirm client model
(Interestingly, a similar model could be implemented even with HTTP.
With TCP, one additional roundtrip and one additional message would
be necessary to start the communications. This model would be
roughly twice as power hungry as the COAP alternative. Note at least
in the implementation strategy that was used in our example
implementation, the format differences between COAP and HTTP would
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make little difference for implementation complexity, as messages are
created based on pre-filled packet templates. Supporting TCP would
require some complexity, however.)
In addition, there is an added factor, having to discover the right
peer to send messages to. In our example implementation this was
simply a well-known multicast address, in which case no additional
power is spent. The downside is that this can easily be done only
with local multicast, necessitating the existence of suitable
intermediary in the same network. Alternatively, the sensor could
run a discovery phase at installation time to find the addresses of
the peers wishing to receive the information. This discovery would
have to repeated in order to account for changes and new equipment.
Nevertheless, if discovery is run once a day and uses the same amount
of power as sending one data observation, the increased power
requirements are in the order of 0.1%, i.e., negligible.
The COAP specification has also a few more detailed issues around the
use of the client model:
o Section 4.2 of [I-D.ietf-core-coap] indicates that multiple
transmissions can be used to increase the reliability of non-
confirmable requests. However, no rules are given about how many
repetions can be made or how quickly they can follow each other.
The specification also does not say if the rules are the same for
unicast and multicast.
o Section 4.4 of [I-D.ietf-core-coap] does not explain the
congestion control rules for multicast requests. There is an
informative reference to another draft, but even that draft does
not specify the behavior for multicast.
4.2.2. Server Model
In the basic server model as defined in [I-D.ietf-core-coap], the
sensor waits for requests from a client. The power requirements for
this model have been analyzed in Section 3.5 and are substantially
higher than in any other model, even if one takes into account that
listening is less power intensive than sending.
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User or Sensor
Intermediary (Server)
(Client)
| |
| CON/GET |
+-------------------->|
| |
| ACK/RSP |
| content |
|<--------------------+
| |
Figure 3. Server model.
There may be an additional discovery exchange where the sensor
responds to requests sent for the well-known resources defined in
[I-D.ietf-core-link-format]. However, these additional exchanges do
not change power requirements significantly, as the sensor already
has to be awake at all times. A more relevant concern is perhaps
unwanted or accidental traffic to the sensor or one of the multicast
addresses it belongs to (such as all-nodes [RFC4291]). Such traffic
may have to be replied to or ICMP error messages may have to be sent,
consuming additional energy.
The server model is not recommended. Variations of the model may be
a little bit more efficient, however. For instance, a local server
could send multiple requests in an effort to randomly hit a period
when the sensor is powered up. However, such practices would still
generate a lot of traffic in the network, which might not be
desirable. For instance, if the network involves low-powered RPL
routers [I-D.ietf-roll-rpl], extra traffic would be harmful.
4.2.3. Observer Model
The observer model [I-D.ietf-core-observe] allows clients to decide
what information they want and servers to decide when to send that
information. The model involves an initial registration, followed by
the server sending periodic notifications. These notifications can
be timed appropriately, so that the sensor only needs to wake up at
suitable times.
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User or Sensor
Intermediary (Server)
(Client)
| |
| NON/GET |
| observe registration |
+---------------------->|
| |
| power-down
| .
| .
| .
| NON/RSP wake-up
| content |
|<----------------------+
| |
| power-down
| .
| .
| .
| NON/RSP wake-up
| content |
|<----------------------+
| |
| power-down
| .
| .
Figure 4. Observer model.
On the face of it, this is a very efficient model. Unfortunately,
one has to take into account the registration phase. For this model
to work, the sensor has to first be able to receive a registration
request, and later be able to receive further requests in case there
are changes or additional clients that want information. As a
result, a straightforward implementation of the observe framework
would appear to save no energy at all compared to the server model.
The sensor would still have to stay awake all the time. Again, this
model is not recommended.
Optimizations of the observer model are of course possible.
Transmitting multiple registration requests is less damaging than
transmitting multiple data requests, as the registration is only a
one-time event. Nevertheless, for interoperability, it would be
useful to understand what timelines and retransmission counts should
be followed by both servers and clients. For instance, a sensor
could assume that it has to be up one second out of every minute.
This would increase power consumption compared to the send-only model
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as described in Section 3.5. Users or intermediaries interested in
subscribing to the information from the sensor would on the average
have to re-transmit registration requests thirty times to randomly
hit the period that a particular sensor is awake.
Another possible optimization would be the definition of implicit
subscriptions where for some application a certain subscription would
always be assumed so that a sensor can start sending periodic
notifications immediately to a well-known address. With such a model
the notifications are carried as responses and an intermediary can
act as a COAP cache, avoiding most of the issues from the above
paragraphs.
In addition, we have found a few more specific issues with the
observer model:
o There is no well-defined termination period. The consumer of the
information can observe that information is still flowing to it as
expected. However, when non-confirmable messages are used, the
sensor sending the notifications has no knowledge if the receiver
is still even in the network. As a result, a simple
implementation that keeps sending information until an explicit
unsubscription is not desirable, as the sensor may have to send
more messages than is necessary.
o Section 3.2 of [I-D.ietf-core-observe] specifies that a
registration request from the same source address but a different
port is considered a new, additional request. This can be
problematic if the client reboots and assigns a different port
number for its communication with the server.
o Section 3.3 of [I-D.ietf-core-observe] makes it optional for a
server to terminate the observation request when a GET request is
sent without the Observe option. This makes it hard for a client
to indicate to the server that it is no longer interested in the
resource.
o Section 3.3 of [I-D.ietf-core-observe] specifies that a
subscription can be terminated using a RST message. This makes it
impossible to know if the receiver rejects a confirmable
notification because some context was missing or because the
receiver wants to terminate the subscription.
o Section 3.3 of [I-D.ietf-core-observe] specifies that a timeout
when sending a notification may be used to terminate a
subscription. This seems like a drastic action for situations
where it is important that the listener gets the information. For
instance, using the observe model with a fire alarm would probably
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not be a good idea if a temporary network problem could suddenly
terminate a subscription.
o A server may receive more subscription messages than it can
handle. The base specification defines an error code (5.03
Service Unavailable) that could perhaps be used to reply in such
situations, but it would be better if the behavior was explicitly
specified and if it used a separate error code to make it clearer
what the issue is.
4.3. Resources and Data Formats
The choice of resource identifiers (URIs) and data formats is
important to achieve semantic interoperability between a sensor and
an application using it. It is not enough to transport some data for
some object, the parties involved in the application have to
understand that the information comes from, say, a particular
temperature sensor and that the information contains a temperature
value encoded in a particular way.
The choice of URIs is clear as far as COAP transport is concerned in
the server model. Here the Link Format [I-D.ietf-core-link-format]
can be used by clients to find out what URIs exist. Nevertheless,
there are two remaining concerns:
o The authors of this memo found it desirable to implement a new URI
type to represent device identities, such as MAC addresses or
1-wire device identifiers. While UUIDs [RFC4122] can also be used
for this purpose, they are more complex for no additional value
from the point of view of our application. UUIDs are required to
contain a time component, which would cause both additional
implementation complexity, as well as make it more difficult to
correlate identifiers from a manufacturer's list or printed on the
outside of the sensor to the ones actually sent in the network.
(Such correlation is often required in order to configure the
real-world location of various sensors.). The new URI type is
simply of the form "device:ID", where ID is the hardware address
associated with the device. Such an URI could have uses not only
in sensor networks, but also in cataloging network equipment, etc.
o While the Link Format provides a way to determine what resources
exist, the semantics of those resources and data formats still
require standardization. Some work regarding such standardization
is ongoing, e.g. in ZigBee IP Smart Energy 2.0 Profile, but it
remains to be seen how much work is needed overall. This problem
might become even more real when sensors from particular
application areas, such as electrical cars or lightning, are being
implemented. Without any common schemas or data models no
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interoperability can be provided.
o It is also important to care about the size and complexity of the
data models developed for low-power applications. Even if moving
from HTTP to COAP and some form of compression saves some number
of bytes, complicated XML models can easily consume the savings
and more. The authors have found [I-D.jennings-senml] a workable,
simple model.
In addition, in the client model it becomes important that the server
(local intermediary) is capable of storing information about any
resource when it receives a POST request. This is not necessarily
the case. First, it is unclear what resource identifiers the client
should use, particularly when multicast is used. Our example
implementation employed a well-known URI "/publish" and placed the
identity of the device sending the request inside the payload part of
the request along with the sensor readings. But it is not clear that
this is the best approach, and furthermore, such an approach has not
been standardized so it may not work with all devices. As an
example, in one of the COAP stacks that we tried, it is only possible
to generate resources by a user under a root resource called
"storage". This requirement makes it incompatible with other
implementations we tried.
4.4. Configuration
One overriding concern in networks with large number of sensors is
configuration effort. In addition, the sensors are typically
deployed in homes and other environments where the necessary skills
for installation and operational tasks cannot be assumed. As a
result, it is important that at installation of individual sensors
leads to little or no configuration effort. Furthermore, given the
small physical size and lack of user interfaces, it is essential that
any configuration be doable on other devices on behalf of the
sensors.
A good model for configuration is that the sensors are fully factory-
configured with respect to their identities and capable of operating
autonomously in any IP network with suitable network interfaces.
Typically, some configuration information is required but this can be
provided as additional information associated with a particular
sensor identity, and configured in the application server or
intermediary. For instance, the physical location of a sensor can be
configured in this manner.
From the point of view of the COAP protocol and its communication
model, this means that the sensors should operate as much as possible
based on autoconfigured addresses, well-known destinations and/or
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resource discovery [I-D.ietf-core-link-format]
[I-D.shelby-core-resource-directory]. COAP should also allow
configuration and passing of additional information in
intermediaries.
5. Security Considerations
Support for authentication of sensors, integrity of messages sent by
sensors, or protection of the data objects carried by the messages
would be useful in some environments, while physical security and
link-layer protection may be sufficient in others. Mechanisms for
these security mechanisms are for further study.
6. Conclusions
This memo has analyzed the power requirements for sensor applications
through an example implementation that runs on absolute minimum power
and through an analysis of various different more general
communications models.
The general conclusion is that the chosen communications model and
overall system and network architecture is far more important for low
power usage than details of the message formats. Much of the work in
COAP has focused on the latter rather than the former. Even the
difference between COAP and HTTP transactions is small compared to
the difference between choosing the optimal and worst communications
model.
In particular, we would like to draw attention to system-level
analysis to ensure that nodes can stay asleep for as long as
necessary. This is particularly important when designing power-
efficient data transmission models such as the observe framework. It
is not enough for the data transmission itself to be efficient if the
device needs to stay awake or communicate for other reasons
(Section 4.2.3). Several other more detailed observations about the
COAP specifications were also noted in Section 3.3, Section 4.1,
Section 4.2, Section 4.3, and Section 4.4.
The communication model is also not just about finding the most
efficient sequence of messages. It is very much also an
architectural decision. The authors believe that an information-
centric or delay-tolerant networking model is appropriate for
collecting information from sensor networks. These models allow
communications based on identities, support intermittent
connectivity, focus on data rather than the location of the data, and
have the natural ability for nodes to aggregate, store, and process
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data. Some of the tasks for ensuring that such models can be
employed with COAP include
o Definition of URI types suitable to be used in sensor networks.
o Accurate specification of multicast support.
o Specifications for intermediary behavior so that they can store
and process data from sensors.
o Further standardization of data formats and application semantics.
Finally, it should be noted that the conclusions in this memo should
not be interpreted to apply too widely. Actuators and other, non-
sensor low-power device implementations have likely very different
requirements and may require different solutions.
7. References
7.1. Normative References
[I-D.ietf-core-coap]
Shelby, Z., Hartke, K., Bormann, C., and B. Frank,
"Constrained Application Protocol (CoAP)",
draft-ietf-core-coap-06 (work in progress), May 2011.
[I-D.ietf-core-link-format]
Shelby, Z., "CoRE Link Format",
draft-ietf-core-link-format-05 (work in progress),
May 2011.
[I-D.ietf-core-observe]
Hartke, K. and Z. Shelby, "Observing Resources in CoAP",
draft-ietf-core-observe-02 (work in progress), March 2011.
[RFC1624] Rijsinghani, A., "Computation of the Internet Checksum via
Incremental Update", RFC 1624, May 1994.
[RFC2460] Deering, S. and R. Hinden, "Internet Protocol, Version 6
(IPv6) Specification", RFC 2460, December 1998.
[RFC4291] Hinden, R. and S. Deering, "IP Version 6 Addressing
Architecture", RFC 4291, February 2006.
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7.2. Informative References
[RFC2131] Droms, R., "Dynamic Host Configuration Protocol",
RFC 2131, March 1997.
[RFC2710] Deering, S., Fenner, W., and B. Haberman, "Multicast
Listener Discovery (MLD) for IPv6", RFC 2710,
October 1999.
[RFC3810] Vida, R. and L. Costa, "Multicast Listener Discovery
Version 2 (MLDv2) for IPv6", RFC 3810, June 2004.
[RFC4122] Leach, P., Mealling, M., and R. Salz, "A Universally
Unique IDentifier (UUID) URN Namespace", RFC 4122,
July 2005.
[RFC4541] Christensen, M., Kimball, K., and F. Solensky,
"Considerations for Internet Group Management Protocol
(IGMP) and Multicast Listener Discovery (MLD) Snooping
Switches", RFC 4541, May 2006.
[RFC4627] Crockford, D., "The application/json Media Type for
JavaScript Object Notation (JSON)", RFC 4627, July 2006.
[RFC4861] Narten, T., Nordmark, E., Simpson, W., and H. Soliman,
"Neighbor Discovery for IP version 6 (IPv6)", RFC 4861,
September 2007.
[RFC4862] Thomson, S., Narten, T., and T. Jinmei, "IPv6 Stateless
Address Autoconfiguration", RFC 4862, September 2007.
[W3C.REC-xml-19980210]
Sperberg-McQueen, C., Bray, T., and J. Paoli, "XML 1.0
Recommendation", World Wide Web Consortium
FirstEdition REC-xml-19980210, February 1998,
<http://www.w3.org/TR/1998/REC-xml-19980210>.
[I-D.shelby-core-resource-directory]
Shelby, Z. and S. Krco, "CoRE Resource Directory",
draft-shelby-core-resource-directory-00 (work in
progress), June 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.
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[I-D.jennings-senml]
Jennings, C., "Media Type for Sensor Markup Language
(SENML)", draft-jennings-senml-05 (work in progress),
March 2011.
[arkko.iab]
Arkko, J., "Interoperability Concerns in the Internet of
Things", Position paper at the IAB workshop on Smart
Objects , March 2011, <http://www.arkko.com/publications/
IAB-IOT-WS-Interoperability.pdf>.
Appendix A. Acknowledgments
The authors would like to thank to Magnus Westerlund, Ari Keranen,
Stig Venaas, Zach Shelby, Cullen Jennings, Vlasios Tsiatsis, Jan
Holler, Anders Eriksson, and Joel Halpern for their help and for
interesting discussions in this problem space.
Authors' Addresses
Jari Arkko
Ericsson
Jorvas 02420
Finland
Email: jari.arkko@piuha.net
Heidi-Maria Rissanen
Ericsson
Jorvas 02420
Finland
Email: heidi-maria.rissanen@ericsson.com
Salvatore Loreto
Ericsson
Jorvas 02420
Finland
Email: salvatore.loreto@ericsson.com
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Zoltan Turanyi
Ericsson
Irinyi Jozsef u. 4-20.
Budabest
Hungary
Email: zoltan.turanyi@ericsson.com
Oscar Novo
Ericsson
Jorvas 02420
Finland
Email: oscar.novo@ericsson.com
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