6LoWPAN Working Group E. Kim
Internet-Draft ETRI
Expires: May 17, 2008 N. Chevrollier
TNO
D. Kaspar
ETRI
JP. Vasseur
Cisco Systems, Inc
November 14, 2007
Design and Application Spaces for 6LoWPANs
draft-ekim-6lowpan-scenarios-01
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Abstract
This document investigates potential application scenarios and use
cases for low-power wireless personal area networks (LoWPANs).
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Design Space . . . . . . . . . . . . . . . . . . . . . . . . . 5
3. Application Scenarios . . . . . . . . . . . . . . . . . . . . 7
3.1. Industrial Monitoring . . . . . . . . . . . . . . . . . . 7
3.2. Structural Monitoring . . . . . . . . . . . . . . . . . . 9
3.3. Healthcare . . . . . . . . . . . . . . . . . . . . . . . . 11
3.4. Connected Home . . . . . . . . . . . . . . . . . . . . . . 12
3.5. Vehicle Telematics . . . . . . . . . . . . . . . . . . . . 14
3.6. Agricultural Monitoring . . . . . . . . . . . . . . . . . 15
4. Security Considerations . . . . . . . . . . . . . . . . . . . 18
5. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 19
6. References . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 21
Intellectual Property and Copyright Statements . . . . . . . . . . 22
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1. Introduction
LoWPANs are inexpensive, low-performance, wireless communication
networks, and are formed by devices complying with the IEEE 802.15.4
standard [1]. Their typical characteristics can be summarized as
follows:
o Low power: depending on country regulations and used frequency
band, the maximum transmit power levels can be up to 1000 mW [1].
However, typical wireless radios for LoWPANs are battery-operated
and consume between 10 mW and 20 mW [2].
o Short range: The Personal Operating Space (POS) defined by IEEE
802.15.4 implies a range of 10 meters. For real implementations,
the range of LoWPAN radios is typically measured in tens of
meters, but can go far beyond that in non line-of-sight situations
[2].
o Low bit rate: the IEEE 802.15.4 standard defines a maximum over-
the-air rate of 250 kb/s, as well as lower data rates of 40 kb/s
and 20 kb/s for each of the currently defined physical layers (2.4
GHz, 915 MHz and 868 MHz, respectively).
o Small memory capacity: common RAM sizes for LoWPAN devices consist
of a few kilobytes, such as 4 KB.
o Limited processing capability: current LoWPAN nodes usually have
8-bit processors with clock rates around 10 MHz.
The IEEE 802.15.4 standard distinguishes between two types of nodes,
reduced-function devices (RFDs) and full-function devices (FFDs).
Through their inability to transmit MAC layer beacons, RFDs can only
communicate with FFDs in a resulting "master/slave" star topology.
FFDs are able to communicate with peer FFDs and with RFDs in the
aforementioned relation. FFDs can therefore assume arbitrary network
topologies, such as multi-hop meshes.
LoWPANs do not necessarily comprise of sensor nodes only, but may
also consist of actuators. For instance, in an agricultural
environment, sensor nodes might detect low soil humidity and then
send commands to activate the sprinkler system.
A LoWPAN network can be seen as a network of small star-networks,
each consisting of a single FFD connected to zero or more RFDs. The
FFDs themselves act as packet forwarders or routers and connect the
entire LoWPAN in a multi-hop fashion. A LoWPAN domain is defined by
the number of devices controlled by the LoWPAN coordinator. Each
LoWPAN has a single coordinator, which must be of FFD type and it is
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responsible for address allocation. One can note that a LoWPAN
coordinator is responsible for a single LoWPAN.
O X
| |
O --- O --- X O: FFD
/ \ \ X: RFD
X X X
Figure 1: A simple LoWPAN
Furthermore, communication to corresponding nodes outside of the
LoWPAN is becoming increasingly important. The distinction between
RFDs and FFDs and the introduction of additional functional elements,
such as gateways or border routers, increase the complexity on how
basic network functionality (e.g., routing and mobility) can be
designed for LoWPANs.
After describing the characteristics of a LoWPAN, this draft provides
a list of use cases and market domains that may benefit and motivate
the work currently done in the 6LoWPAN WG.
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2. Design Space
From [3], this section describes the potential dimensions that could
be used to describe the design space of wireless sensor networks in
the context of the 6LoWPAN WG. Note that the design space is already
limited by the characteristics of a 6LoWPAN (e.g., low-power, short
range, low-bit rate) as described in [4]. The possible dimensions
for scenario categorization used in this draft are described as
follows:
o Deployment: In a LoWPAN, sensor nodes can be scattered randomly or
deployed in an organized manner. The deployment can occur at
once, or as an iterative process. The selected type of deployment
has an impact on node density and location. This feature affects
how to organize - manually or automatically - the sensor network,
and how to allocate addresses in the network.
o Mobility: Inherent to the wireless characteristics of LoWPANs,
sensor nodes could move or be moved around. Mobility can be an
induced factor (e.g., sensors in an automobile), hence not
predictable, or a controlled characteristic (e.g., pre-planned
movement in a supply chain).
o Network Size: The network size takes into account nodes that
provide the intended network capability (i.e., FFD). The number
of nodes involved in a LoWPAN could be small (10 nodes), moderate
(several 100s), or large (over a 1000).
o Power Source: Whether the sensor nodes are battery-powered or
mains-powered influence the network design. A hybrid solution is
also possible where only part of the network (e.g., FFDs) is
mains-powered.
o Security Level: sensor networks may carry sensitive information
and require high-level of security support where the availability,
integrity, and confidentiality of the information are primordial.
This high level of security may be needed in case of structural
monitoring of key infrastructures or heath monitoring of patients.
o Routing: The routing factor highlights the number of hops that has
to be traversed to reach the edge of the network or a destination
node within it. A single hop may be needed for simple star-
topologies or a multi-hop communication scheme for more elaborate
topologies, such as meshes or trees. From previous work on
LoWPANs from academia and industry, various routing mechanisms
have been introduced, such as data-centric, event-driven, address-
centric, localization-based, or geographical routing. We do not
use such a fine granularity in our draft but rather use topologies
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and single/multi-hop communication when referring to the routing
categorization.
o Connectivity: Nodes within a LoWPAN are considered "always
connected" when there is a network connection between any two
given nodes. However, due to external factors (e.g., extreme
environment, mobility) or programmed disconnections (e.g.,
sleeping mode), the network connectivity can be "intermittent"
(i.e., regular disconnection) to "sporadic" (i.e., almost always
disconnected network).
o Quality of Service (QoS): for mission-critical applications,
support of QoS is mandatory in resource-constrained LoWPAN.
o Traffic Pattern: several traffic partners may be used in sensor
networks. To name a few, Point-to-Multi-Point(P2MP), Multi-Point-
to-Point (MP2P) and Point-to-Point (P2P).
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3. Application Scenarios
This section lists a fundamental set of LoWPAN application scenarios
in terms of system design. A complete list of practical use cases is
not the goal of this draft. The intention is to define a minimal set
of LoWPAN configurations to which any other scenario can be
abstracted to. Also, the characteristics of the scenarios described
in this section do not reflect the characteristics that every LoWPAN
must have in a particular environment (e.g., healthcare).
3.1. Industrial Monitoring
Sensor network applications for industrial monitoring can be
associated with a broad range of methods to increase productivity,
energy efficiency, and safety of industrial operations in engineering
facilities and manufacturing plants. Many companies currently use
time-consuming and expensive manual monitoring to predict failures
and schedule maintenance or replacement to avoid costly manufacturing
downtime. Deploying wireless sensor networks, which can be installed
inexpensively and provide more frequent and more reliable data, can
reduce equipment downtime and eliminate costly manual equipment
monitoring. Additionally, data analysis can be pushed into the
network, eliminating the need for manual data transfer and analysis.
Industrial monitoring can be largely split into following application
fields:
o Process Monitoring and Control: combining advanced energy metering
and sub-metering technologies with wireless sensor networking in
order to optimize factory operations, reduce peak demand, and
ultimately lower costs for energy.
o Machine Surveillance: ensuring product quality and efficient and
safe equipment operation. Critical equipment parameters such as
vibration, temperature, and electrical signature are analyzed for
abnormalities that are suggestive of impending equipment failure
(see Section 3.2).
o Supply Chain Management and Asset Tracking: with the retail
industry being legally responsible for the quality of sold goods,
early detection of inadequate storage conditions with respect to
temperature will reduce risk and cost to remove products from the
sales channel. Examples include container shipping, product
identification, cargo monitoring, distribution and logistics.
o Storage Monitoring: sensory systems designed to prevent releases
of regulated substances to ground water, surface water and soil.
This application field may also include theft/tampering prevention
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systems for storage facilities or other infrastructure, such as
pipelines.
Example 1: Process Monitoring and Control
Manufacturing plants and engineering facilities, such as product
assembly lines and engine rooms, can be drastically optimized using
wireless sensor technology in order to ensure product quality,
control energy consumption, avoid machine downtimes, and increase
operation safety. In industrial settings, sensors such as vibration
detectors can be used to continuously monitor equipment and predict
equipment failure and to detect the need for maintenance, with far
greater precision. This allows companies to avoid costly equipment
failures or shutdowns of production lines and therefore increase
their productivity.
Greater access to process parameters gives engineers better
visibility and ultimately better decision making power. Various
sensor measurements, such as gas pressure, the flow of liquids and
gases, room temperature and humidity, or tank charging levels may be
used together with controllers and actuators to improve a plant's
productivity in a continuous self-controlling loop, in which
instruments can be upgraded, calibrated, and reconfigured as needed
via the wireless channel.
A plant's monitoring boundary often does not cover the entire
facility but only those areas considered critical to the process.
Easy to install wireless connectivity extends this line to include
peripheral areas and process measurements that were previously
infeasible or impractical to reach with wired connections.
Example 2: Supply Chain Management and Asset Tracking
Global supply chain and transportation applications increasingly
require real-time sensor and location information about their
supplies and assets. Wireless sensor networks meet these
requirements efficiently with low installation and management costs,
providing benefits such as reduced inventory, increased asset
utilization, and precise location tracking of containers, goods, and
mobile equipment. Clients can be provided with an early warning of
possible chain ruptures, for example by using call centers or
conveniently accessing comprehensive on-line reports and data
management systems. Such reports could include monitoring of current
states, the history of goods with critical conservation conditions,
and in critical areas the monitoring status of oil containers, or
verification of chemical gas substance concentration.
For instance, thousands of cargo ships loaded with millions of
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containers are sailing the oceans today. However, supply and demand
are not equally distributed around the world, which results in high
costs for shipping empty containers. Sophisticated IT systems try to
circumnavigate this problem and precision planning is critical in any
case: the customer always expects containers to arrive just in time.
Wireless sensor networks have a great potential of making this
growing market even more efficient by allowing more reliable tracking
and identification of containers, and cargo monitoring for hazardous
freight detection or identification of illegal shipment.
Also, the process of loading and unloading can be implemented more
efficiently. For example, after a crane operator has lifted a
container from the deck, its content is identified and taken to the
corresponding warehouse -- on a driverless truck whose movements are
controlled within centimeters by transponders under the asphalt.
Dominant parameters in industrial monitoring scenarios:
o Deployment: pre-planned, manually attached
o Mobility: no (except for the asset tracking case)
o Network Size: medium to large size, high node density
o Power Source: all battery-operated
o Security Level: high
o Routing: multi-hop, highly event-driven
o Connectivity: always on for crucial processes, otherwise
intermitted
o QoS: important
o Traffic Pattern: P2P (actuator control), MP2P (data collection)
3.2. Structural Monitoring
Intelligent monitoring in facility management can make safety checks
and periodic monitoring of the architecture status highly efficient.
Mains powered nodes can be included in the design phase of a
construction or battery-equipped nodes can be added afterwards.
Example: A 1000m long bridge with 10 pillars. Each pillar contains 5
sensors to measure the water level, and 5 vibration sensors to
monitor its structural health. On the top part of each pillar, an
"infrastructure" FFD sink node is placed to collect the sensor data.
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The FFD is the LoWPAN coordinator of the sensors for each pillar
which can be FFD or RFD. All nodes are static, and manually
configured with a single-hop connection to the coordinator. All
sensor nodes do not move while the service is provided. The network
configuration and routing table are changed only in case of node
failure. Except from the pillar itself, there are no special
obstacles of attenuation to the sensor signals, but careful
configuration is needed to prevent signal interference between
sensors.
A logical entity of data gathering can lie with each LoWPAN
coordinator. Communication schedules must be set up between leaf
nodes and their LoWPAN coordinator, to efficiently gather the
different types of sensed data. Each data packet may include meta-
information about its data, or the type of sensors could be encoded
in its address during the address allocation. This type of
application works based on event-driven notifications. The data over
or under a pre-defined threshold is meaningful to report. The event-
driven data sensed on abnormal occurrences is time-critical and
required secure and reliable transmission. For energy saving, all
sensors could have periodic and long sleep modes but wake up on
certain events.
The LoWPAN coordinators can play the role of a gateway, so that a
third party with internet access can check the status of the specific
pillar. Due to the contents of the data, only authenticated users
should be allowed to access the data.
This use case can be extended to a medium size sensor network to
monitor a building for instance. They still have similar
characteristics such as static nodes, manually deployed networks, and
mostly star (or multi-level of star) topology, and event-driven real-
time data gathering.
Dominant parameters in structural monitoring applications:
o Deployment: static, organized, pre-planned
o Mobility: none
o Network Size: small to medium size (dozens to hundreds of nodes),
low density
o Power Source: mostly battery powered (except LoWPAN coordinators)
o Security Level: high (safety-critical), authentication required
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o Routing: star-topology (potentially hierarchical)
o Connectivity: always connected (periodic/intermittent beacon mode)
o QoS: medium to high
o Traffic Pattern: MP2P (data collection), P2P (localized querying)
X X X
\ | /
X --- O --- X
/ | \ O: LoWPAN coordinator (FFD)
X X X X: FFD or RFD
Figure 2: A LoWPAN with a simple star topology.
3.3. Healthcare
LoWPANs are envisioned to be heavily used in healthcare environments.
They would ease the deployment of new services by getting rid of
cumbersome wires and ease the patient care and life in hospitals and
for home care. In this environment, delay or lost information may be
a matter of life or death.
Example 1: a small number (e.g., less than 10) of sensors are
deployed on a patient body for medical surveillance. They monitor
vital signs such as heart beats (electrocardiogram-ECG) or blood
pressure, and provide localization information. The patient is able
to move in his room or within the hospital. The collected data is
sent to sinks placed onto hospital's walls. Sinks in the hospital's
walls are mains powered. Devices carried by the patient run on
battery. Localization-based services are provided in this scenario.
Furthermore, the stringent requirements of medical applications imply
highly reliable communications over a robust network.
Example 2: Healthcare at home and tele-assistance. Various systems
ranging from a simple wearable remote controls for tele-assistance or
intermediate systems with wearable sensors monitoring various metrics
to more complex systems studying life dynamics can be supported by
the LoWPAN. In this latter category, a large amount of data from
various sensors can be collected: movement pattern observation, check
that medicines have been taken, objects tracking and so on. A
example of such deployment is described in [6] using the concept of
Personal Networks.
Dominant parameters in healthcare applications:
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o Deployment: pre-planned
o Mobility: moderate (patient's mobility)
o Network Size: small
o Power Source: hybrid
o Security Level: Encryption is required
o Routing: multihop, star topology on patients body
o Connectivity: always on
o QoS: high level of support (life and death implication)
o Traffic Pattern: MP2P/P2MP (data collection), P2P (local
diagnostic)
+-------+
| Sinks | (in hospital walls)
+-------+
|
+-----------+
| O | (on patient's body)
| /|\ |
| X X X | O: FFD
+-----------+ X: RFD (could be replaced with FFDs)
Figure 4: A mobile star-shaped LoWPAN.
3.4. Connected Home
The "Connected" Home or "Smart" home is with no doubt an area where
LoWPANs can be used to support an increasing number of services:
o Home safety/security
o Home Automation and Control
o Healthcare (see above section)
o Smart appliances
The LoWPAN network typically comprises a few dozen and probably in a
near future a few hundreds of nodes of various nature: sensors,
actuators and objects.
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Example:
In terms of home safety and security, the LoWPAN is made of motion,
audio, door/window sensors, video cameras to which additional sensors
can be added for security (gas, water, CO, Radon, smoke detection).
The LoWPAN typically comprises a few dozen of nodes forming a ad-hoc
network with multi-hop routing since the nodes may not be in direct
range. In its most simple form all nodes are static and communicate
with a central control module but more sophisticated scenarios may
also involve inter-device communication. For example, a motion/
presence sensor may send a multicast message to a group of lights to
be switched on, a video camera will be activated sending a video
stream to a gateway that can be received on a cell phone.
The Home automation and control system LoWPAN offers a wide range of
services: local or remote access from the Internet (via a secured
gateway) to monitor the home (temperature, humidity, activation of
remote video surveillance, status of the doors (locked),...) but also
for home control (activate the air conditioning/heating, door locks,
sprinkler systems, ...). Fairly sophisticated systems can also
optimize the level of energy consumption thanks to a wide range of
input from various sensors connected to the LoWPAN: light sensors,
presence detection, temperature, ... in order to control electric
window shades, chillers, air flow control, air conditioning and
heating with the objective to optimize energy consumption.
Ergonomics in Connected Home is key and the LoWPAN must be self-
managed and easy to install. Traffic patterns may greatly vary
depending on the applicability and so does the level of reliability
and QoS expected from the LoWPAN. Humidity sensing is typically not
critical and requires no immediate action whereas tele-assistance or
gas leak detection is critical and requires a high degree of
reliability. Furthermore, although some actions may not involve
critical data, still the response time and network delays must be on
the order of a few hundreds of milliseconds to preserve the user
experience (e.g. use a remote control to switch a light on). A
minority of nodes are mobile (with slow motion). Connected Home
LowPAN usually do not require multi-topology or QoS routing and
fairly simple QoS mechanisms must be supported by the LoWPAN (the
number of Class of Services is usually limited).
Dominant parameters for home automation applications:
o Deployment: multi-hop topologies
o Mobility: small degree of mobility
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o Network Size: medium number of nodes, potentially high density
o Power Source: mix of battery and AC powered devices
o Security Level: authentication and encryption required
o Routing: no requirement for multi-topology or QoS routing
o Connectivity: intermittent (usage-dependent sleep modes)
o QoS: support of limited QoS (small number of Class of Service)
o Traffic Pattern: P2P (inter-device), P2MP and MP2P (polling)
3.5. Vehicle Telematics
LoWPANs play an important role in intelligent transportation systems.
Incorporated in roads or/and, they contribute to the improvement of
safety of transporting systems. Through traffic or air-quality
monitoring, they increase the possibilities in terms of traffic flow
optimization and help reducing road congestion.
Example: Scattered sensors are included in roads during their
construction for motion monitoring. When a car passes on top of
these sensors, the possibility is then given to track the trajectory
and velocity of the car for safety purpose. The lifetime of sensors
devices incorporated into roads is expected to be as long as the life
time of the roads (10 years). Multihop communication is possible
between sensors, and the network should be able to cope with the
deterioration over time of the node density due to power failure.
Sinks place at the road side are mains powered, sensor nodes in the
roads run on battery. Power savings schemes might disconnect
intermittently sensors nodes. A rough estimate of 4 sensors per
square meter is needed. Other applications may involved car-to-car
communications.
Dominant parameters in vehicle telematics applications:
o Deployment: Scattered, pre-planned
o Mobility: High
o Network Size: large
o Power Source: Mostly battery powered
o Security Level: low
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o Routing: Multi-hop
o Connectivity: Intermittent
o QoS: support of limited QoS
o Traffic Pattern: mostly Multi-Point-to-Point (MP2P)
+-------+
| Sinks | (at the road side)
+-------+
-------|------------------------------
|
O --- O --- O ----- O +---|---+
/ \ | | X-O-X | (cars)
O O --- O +---|---+ O: FFD
X: RFD
--------------------------------------
Figure 5: Multi-hop LoWPAN combined with mobile star LoWPAN.
3.6. Agricultural Monitoring
Accurate temporal and spatial monitoring can significantly increase
agricultural productivity. Due to natural limitations, such as a
farmers' inability to check the crop at all times of day or
inadequate measurement tools, luck often plays a too large role in
the success of harvests. Using a network of strategically placed
sensors, indicators such as temperature, humidity, soil condition,
can be automatically monitored without labor intensive field
measurements. For example, sensor networks could provide precise
information about crops in real time, enabling businesses to reduce
water, energy, and pesticide usage and enhancing environment
protection.
Example: In a vineyard with medium to large geographical size, a
number of 50 to 100 FFDs nodes are manually deployed in order to
provide full signal coverage over the study area. These FFD nodes
support a multi-hop routing scheme to enable data forwarding to a
sink node at the edge of the vineyard. An additional number of 100
to 1000 (possibly different) specialized RFD sensors (i.e., humidity,
temperature, soil condition, sunlight) are attached to the FFDs in
local wireless star topologies, periodically reporting measurements
to the associated master FFD. For example, in a 20-acres vineyard, 8
parcels of land with each 10 sensors to provide readings on
temperature and soil moisture. Each of the 8 parcels contains 1 FFD
sink to collect the sensor data. 10 intermediate FFD "routers" are
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used to connect the sinks to the main gateway.
Sensor nodes may send event-driven notifications when readings exceed
certain thresholds, such as low soil humidity; which may
automatically trigger a water sprinkler in the local environment.
For increased energy efficiency, all sensors are in periodic sleep
state. However, FDD nodes need to be aware of sudden events from
RFDs. Their sleep periods should therefore be set to shorter
intervals. Communication schedules must be set up between RFDs and
FFDs and global time synchronization is needed to account for clock
drift.
Sensor localization is important for geographical routing, for
pinning down where an event occurred, and for combining gathered data
with their actual position. Using manual deployment, device
addresses can be used. For randomly deployed nodes, a localization
algorithm needs to be applied.
There might be various types of sensor devices deployed in a single
WPAN, each providing raw data with different semantics. Thus, an
additional method is required to correctly interpret sensor readings.
Each data packet may include meta-information about its data, or a
type of a sensor could be encoded in its address during address
allocation.
Dominant parameters in agricultural monitoring:
o Deployment: randomly scattered with pre-planned coordinators
o Mobility: all static
o Network Size: medium to large, low to medium density
o Power Source: all nodes are battery-powered, except sink
o Security Level: high (business-critical)
o Routing: mesh topology with local star connections
o Connectivity: intermittent (many sleeping nodes)
o QoS: no
o Traffic Pattern: MP2P/P2MP and P2P
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X X X X X X X X X X X X
+---------+ \|/ \|/ \|/ \|/
| Gateway | ---- O ---- O ---- O ---- O
+---------+ /|\ /|\ /|\ /|\ X: RFD
X X X X X X X X X X X X O: FFD
Figure 3: An aligned multi-hop LoWPAN.
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4. Security Considerations
TBD.
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5. Acknowledgements
Thanks to David Cypher for giving more insight on the IEEE 802.15.4
standard and to Irene Fernandez for her review and valuable comments.
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6. References
[1] IEEE Computer Society, "IEEE Std. 802.15.4-2003", October 2003.
[2] Bulusu, N. and S. Jha, "Wireless Sensor Networks", July 2005.
[3] Roemer, K. and F. Mattern, "The Design Space of Wireless Sensor
Networks", December 2004.
[4] Kushalnagar, N., Montenegro, G., and C. Schumacher, "6LoWPAN:
Overview, Assumptions, Problem Statement and Goals,
draft-ietf-6LoWPAN-problem-08 (work in progress)",
February 2007.
[5] Culler, D. and J. Hui, "6lowPAN Tutorial: IP on IEEE 802.15.4
Low Power Wireless Networks", May 2007.
[6] den Hartog, F., Schmidt, J., and A. de Vries, "On the potential
of personal networks for hospitals", May 2006.
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Authors' Addresses
Eunsook Kim
ETRI
161 Gajeong-dong
Yuseong-gu
Daejeon 305-700
Korea
Phone: +82-42-860-6124
Email: eunah.ietf@gmail.com
Nicolas G. Chevrollier
TNO
Brassersplein 2
P.O. Box 5050
Delft 2600
The Netherlands
Phone: +31-15-285-7354
Email: nicolas.chevrollier@tno.nl
Dominik Kaspar
ETRI
161 Gajeong-dong
Yuseong-gu
Daejeon 305-700
Korea
Phone: +82-42-860-1072
Email: dokaspar.ietf@gmail.com
JP Vasseur
Cisco Systems, Inc
1414 Massachusetts Avenue
Boxborough MA 01719
USA
Phone:
Email: jpv@cisco.com
Kim, et al. Expires May 17, 2008 [Page 21]
Internet-Draft 6LoWPAN Design and Applications November 2007
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Kim, et al. Expires May 17, 2008 [Page 22]