6LoWPAN Working Group E. Kim
Internet-Draft ETRI
Expires: August 28, 2008 N. Chevrollier
TNO
D. Kaspar
Simula Research Laboratory
JP. Vasseur
Cisco Systems, Inc
February 25, 2008
Design and Application Spaces for 6LoWPANs
draft-ekim-6lowpan-scenarios-02
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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 . . . . . . . . . . . . . . . . . . 10
3.3. Healthcare . . . . . . . . . . . . . . . . . . . . . . . . 12
3.4. Connected Home . . . . . . . . . . . . . . . . . . . . . . 14
3.5. Vehicle Telematics . . . . . . . . . . . . . . . . . . . . 16
3.6. Agricultural Monitoring . . . . . . . . . . . . . . . . . 17
4. Security Considerations . . . . . . . . . . . . . . . . . . . 20
5. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 21
6. References . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 23
Intellectual Property and Copyright Statements . . . . . . . . . . 24
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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 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. A LoWPAN coordinator is
responsible for a single LoWPAN.
O X
| | C: Coordinator
C --- O --- O --- X O: FFD
/ \ \ X: RFD
X X X
Figure 1: Example of 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
Inspired by [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. The design space is
already limited by the unique 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
they may be 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 influences 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 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 infrastructure or health 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 from
"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 LoWPANs.
o Traffic Pattern: several traffic patterns 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 to schedule maintenance or replacements in order 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
functionality can be placed into the network, eliminating the need
for manual data transfer and analysis.
Industrial monitoring can be largely split into the 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.
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
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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.
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.
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
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,
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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 at centimeter precision by transponders
under the asphalt.
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
systems for storage facilities or other infrastructure, such as
pipelines.
[Example]: Storage Monitoring (Hospital Storage Rooms)
In a hospital, maintenance of the right temperature in storage rooms
is very critical. Red blood cells need to be stored at 2 to 6
degrees Celsius, blood platelets at 20 to 24 C, and blood plasma
below -18 C. For anti-cancer medicine, maintaining a humidity of 45%
to 55% is required. Storage rooms have temperature sensors and
humidity sensors every 25m to 100m, based on the floor plan and the
location of shelves, as indoor obstacles distort the radio signals.
At each blood pack a sensor node can be installed to track the
temperature during delivery. In this case, highly dense networks
must be managed.
All nodes are statically deployed and manually configured with either
a single- or multi-hop connection to the coordinator. FFD and RFD
nodes are configured based on the topology.
All sensor nodes do not move unless the blood packs or a container of
block packs is moved. Moving nodes get connected by locigal
attachment to a new sink node. Placement of sink nodes differs
between various service scenarios.
The network configuration and routing tables are not changed in the
storage room unless node failure occurs.
This type of application works based on both periodic and event-
driven notifications. Periodic data is used for monitoring the right
temperature and humidity in the storage rooms. The data over or
under a pre-defined threshold is meaningful to report. Blood cannot
be used if it is exposed to the wrong environment for about 30
minutes. Thus, event-driven data sensed on abnormal occurrences is
time-critical and requires secure and reliable transmission.
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Due to the time-critical sensing data, reliable and secure data
transmission is highly important.
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: business-critical. Secure and reliable
transmission must be guaranteed. An extra key mechanism can be
used.
o Routing: single- to multi-hop. Routing tables are merely changed
after configuration, except in the asset tracking case. Node
failure or indoor obstacles will cause the changes.
o Connectivity: always on for crucial processes, otherwise
intermittent
o QoS: important for time-critical event-driven data
o Traffic Pattern: P2P (actuator control), MP2P (data collection)
o Other Issues: Sensor network management
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]: Bridge Safety Monitoring
A 1000m long bridge with 10 pillars is described. Each pillar and
the bridge body contain 5 sensors to measure the water level, and 5
vibration sensors are used to monitor its structural health. The
sensor nodes are deployed to have 100m line-of-sight distance from
each other. All nodes are placed statically 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 tables are changed only in case of node failure. Except
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from the pillars, there are no special obstacles of attenuation to
the sensor signals, but careful configuration is needed to prevent
signal interference between sensors.
The network configuration and routing tables are changed only in case
of node failure. On the top part of each pillar, an "infrastructure"
FFD sink node is placed to collect the sensed data. The FFD is the
LoWPAN coordinator of the sensors for each pillar which can be either
FFDs or RFDs.
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. The data gathering
entity can be programmed to trigger actuators installed in the
infrastructure, when a certain threshold value has been reached.
This type of application works based on both periodic and 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 requires secure and reliable
transmission. For energy conservation, 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 medium or large size sensor networks
to monitor a building or for instance the safety status of highways
and tunnels. Larger networks of the same kind still have similar
characteristics such as static nodes, manual deployment, and mostly
star (or multi-level of star) topologies, and periodic 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 (dozens of nodes) to large, low density
o Power Source: mostly battery powered (except LoWPAN coordinators)
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o Security Level: safety-critical. Secure transmission must be
guaranteed. Only authenticated users should be able to access and
handle the data. Lightweight key mechanisms can be used.
o Routing: star-topology (potentially hierarchical) In case of
hierarchical case, reorganization of routing tree may be the
issue. However, routing table may merely be changed after
configuration. Node failure or indoor obstacles will cause the
changes.
o Connectivity: always connected or intermittent by sleeping mode
scheduling.
o QoS: Emergency notification (fire, over-threshold vibrations,
water level, etc) is required to have priority of delivery and
must be transmitted in a highly reliable manner.
o Traffic Pattern: MP2P (data collection), P2P (localized querying)
o Other Issues: accurate sensing and reliable transmission are
important. In addition, sensor status reports may be needed to
maintain a reliable monitoring system.
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]: Healthcare at a Hospital
A small number (e.g., less than 10) of sensors are deployed on a
patient's 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
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placed onto the hospital's walls. These sinks are mains-powered.
Devices carried by the patients 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 by Tele-Assistance
Various systems ranging from simple wearable remote controls for
tele-assistance or intermediate systems with wearable sensors
monitoring various metrics to more complex systems for 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, checks that medicaments have been taken, object
tracking, and more. An example of such a deployment is described in
[6] using the concept of Personal Networks.
An old citizen who lives alone wears one to few wearable sensors to
measure heartbeat, pulse rate, etc. Dozens of sensor nodes are
densely installed at home for movement detection. A LoWPAN home
gateway will send the sensing data to the connected healthcare
center. Portable base stations with LCDs may be used to check the
data at home, as well. The different roles of devices have different
duty-cycles, which affect node management.
Multipath interference may often occur due to the patients' mobility
at home, where there are many walls and obstacles. Even during
sleeping, the change of the body position will affect the radio
propagation.
Data is gathered both periodically and event-driven. In this
application, event-driven data can be very time-critical. Thus,
real-time and reliable transmission must be guaranteed.
Privacy also becomes an issue in this case, as the sensing data is
very personal data. In addition, different data will be provided to
the hospital system than what is given to a patient's family members.
Role-based access control is needed to support such services, thus
support of authorization and authentication is important here.
Dominant parameters in healthcare applications:
o Deployment: pre-planned
o Mobility: moderate (patient's mobility)
o Network Size: small, high node density
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o Power Source: hybrid
o Security Level: Data privacy and security must be provided.
Encryption is required. Role based access control is required to
be support by proper authentication mechanism
o Routing: multihop for homecare devices, star topology on patients
body. Multipath interference due to walls and obstacles at home
must be considered.
o Connectivity: always on
o QoS: high level of support (life and death implication), role-
based
o Traffic Pattern: MP2P/P2MP (data collection), P2P (local
diagnostic)
o Other issues: Plug-and-play configuration is required for mainly
non-technical end-users. Real-time data acquisition and analysis
are important. Efficient data management is needed for various
devices which have different duty-cycles, and for role-based data
control. Reliability and robustness of the network are also
essential.
+-------+
| 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
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o Healthcare (see above section)
o Smart appliances and home entertainment systems
In home environments LoWPAN networks typically comprise a few dozen
and probably in the near future a few hundreds of nodes of various
nature: sensors, actuators and connected objects.
[Example]: Home Automation
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 Homes 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).
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Dominant parameters for home automation applications:
o Deployment: multi-hop topologies
o Mobility: small degree of mobility
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 and/or, 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]: Telematics
Scattered sensors are included in roads during their construction for
motion monitoring. When a car passes over of these sensors, the
possibility is then given to track the trajectory and velocity of the
car for safety purposes. The lifetime of sensor 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 placed at the road
side are mains-powered, sensor nodes in the roads run on battery.
Power savings schemes might intermittently disconnect sensors nodes.
A rough estimate of 4 sensors per square meter is needed. Other
applications may involve car-to-car communication for increased road
safety.
Dominant parameters in vehicle telematics applications:
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o Deployment: scattered, pre-planned
o Mobility: high
o Network Size: large
o Power Source: mostly battery powered
o Security Level: low
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. The sensing data can be used to find optimal
environments for the plants. In addition, the data on the planting
condition can be saved by sensor tags, which can be used in supply
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chain management.
[Example]: Automated Vineyard
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
with 8 parcels of land, 10 sensors are placed within each parcel 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 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
LoWPAN, 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: pre-planned
The sensor nodes are installed outdoors or in a greenhouse with
high exposure to water, soil, dust, in dynamic environments of
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moving people and machinery, with growing crop and foliage.
Sensor nodes can be deployed in a pre-defined manner, considering
the harsh environment.
o Mobility: all static
o Network Size: medium to large, low to medium density
o Power Source: all nodes are battery-powered, except the sink
o Security Level: business-critical. Light-weight security or a
global key management can be used depending on the business
purpose.
o Routing: mesh topology with local star connections. Routing table
is merely changed after configuration. Node failure or indoor
obstacles will cause the changes.
o Connectivity: intermittent (many sleeping nodes)
o QoS: not critical
o Traffic Pattern: Mainly MP2P/P2MP. P2P for Gateway communication
or actuator triggering.
o Other issues: Time synchronization among sensors are required, but
the traffic interval may not be frequent (e.g. once in 30 minutes
to 1 hour).
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
To be defined.
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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. 802.15.4-2006", 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, RFC4919",
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
Simula Research Laboratory
Martin Linges v 17
Snaroya 1367
Norway
Phone: +47-4748-9307
Email: dokaspar.ietf@gmail.com
JP Vasseur
Cisco Systems, Inc
1414 Massachusetts Avenue
Boxborough MA 01719
USA
Phone:
Email: jpv@cisco.com
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