Sensor nodes constituting Wireless Sensor Networks (WSN) are often battery-

operated and have limited resources. To save energy, nodes sleep most of the

time, and wake up periodically to handle communication. Such radio duty cycling

poses a basic trade-off between energy and latency.

In previous work, we have shown that opportunistic routing is an efficient way

to achieve low-latency yet energy efficient data collection in WSN (ORW [3]).

In this paper, we extend this approach to the context of low-power IP networks,

where nodes need to be addressed individually and where traffic patterns are

irregular. We present ORPL, an opportunistic extension of RPL, the stan-

dard, state-of-the-art routing protocol for low-power IP networks. We discuss

our preliminary results obtained with Contiki in a 137-node testbed

Duquennoy, Simon

Landsiedel, Olaf

English

Swedish Institute of Computer Science Publications Database

Poster Abstract: Opportunistic RPL
Simon Duquennoy1 and Olaf Landsiedel2
1 Swedish Institute of Computer Science (SICS), simonduq@sics.se
2 Chalmers University of Technology, olafl@chalmers.se
1 Introduction
Sensor nodes constituting Wireless Sensor Networks (WSN) are often battery-
operated and have limited resources. To save energy, nodes sleep most of the
time, and wake up periodically to handle communication. Such radio duty cycling
poses a basic trade-off between energy and latency.
In previous work, we have shown that opportunistic routing is an efficient way
to achieve low-latency yet energy efficient data collection in WSN (ORW [3]).
In this paper, we extend this approach to the context of low-power IP networks,
where nodes need to be addressed individually and where traffic patterns are
irregular. We present ORPL, an opportunistic extension of RPL [5], the stan-
dard, state-of-the-art routing protocol for low-power IP networks. We discuss
our preliminary results obtained with Contiki in a 137-node testbed.
2 Overview of ORPL
ORPL routes data traffic following a DODAG (Destination-Oriented Directed
Acyclic Graph) in an opportunistic fashion. Like ORW [3], it relies on anycast
over a low-power listening MAC. The selection of the next hop is done during
the transmission: The first neighbor that, (1) wakes up, (2) successfully receives
the packet and (3) offers sufficient routing progress, acknowledges the packet
and forwards it.
ORPL performs any-to-any routing in a similar fashion as RPL, following
a two step process. The data is first routed up in the DODAG until finding a
common ancestor, then it is routed down to the destination. Routing upwards
is done exactly as in ORW, along a gradient. Routing downwards, however,
cannot rely only on the routing metric. Merely routing away from the root is
not enough to ensure the destination will be reached. Because ORPL relies on
anycast and opportunistic routing, we opt for routing-table free solution. Instead,
each node maintains a set of nodes that are below them in the DODAG – their
sub-DODAG. This information allows to decide whether a node is on the way
to the destination or not. We use Bloom filters to represent this set of reachable
nodes in a compact way and with fixed size, which enables efficient propagation
through the network.
2 Simon Duquennoy and Olaf Landsiedel
3 Design
This section describes the basic mechanisms of ORPL.
Routing Decision In ORPL, nodes anycast packets instead of electing a next hop
and unicasting to it. Nodes receiving a packet choose whether to forwards it or
not, and send an acknowledgment only in case they act as next hop. The routing
decision at node N for a packet sent by S with destination D is as follows: If
the packet is marked as going up in the DODAG (this information is included
in RPL’s IPv6 extension headers), then N forwards it iff RankN < RankS .
If the packet is marked as going down in the DODAG, then N forwards it iff
RankN > RankS ∧D ∈ SubDODAGN
Bloom Filters In ORPL, nodes need to know the set of nodes composing their
sub-DODAG. To enable storing this set in memory-constrained devices and prop-
agating it within size-restricted network frames, we resort to Bloom filters [1].
Each node populates its Bloom filter with its direct children, and propagate the
filters through the RPL DIO messages (Trickle beacons). A node receiving a DIO
from a node already in its Bloom filter merges the newly received filter with its
own, by simply bitwise-ORing the filters.
Bloom filters are subject to false positives, meaning that sometimes a node
believes the destination is in its sub-DODAG while it is not. When such situation
is suspected (non-acked anycast), the node stores the packet ID in a blacklist, and
sends the packet back to the previous hop. The previous hop will anycast again,
looking for another path. If repeating this process recursively is not enough
to reach the destination, the fallback behavior is to perform a network-wide
multicast. This is inspired by CBFR [4], which extends CTP with downwards
routing through network-wide multicast.
Link Quality Estimation ORPL requires to maintain link quality estimates of
every reachable neighbor to decide whether to include nodes and merge Bloom
filters. As ORPL targets scenarios with any-to-any, irregular traffic, we need a
to maintain link quality even in the absence of traffic. In our current design, we
us acknowledged broadcast to achieve efficient link estimation. With low-power-
listening, broadcast are sent repeatedly for exactly one wakeup period. Nodes
receiving a broadcast send a 802.15.4 link-layer acknowledgement extended with
the receiver’s address. The sender counts the received ACKs to estimate link
quality.
Networks Dynamics and Inconsistency Detection Low-power networks are dy-
namic, often resulting in varying logical topology. To enable forgetting that given
nodes were in one’s sub-DODAG in the past, we use aging Bloom filters. We use
a simple approach where two filters are maintained, one active, and one warmup.
The filters are swapped and emptied periodically. ORPL inherits RPL’s datap-
ath validation mechanism to detect and repair loops. We extend this mechanism
with a systematic lookup of the packet source in the local Bloom filter. This
Poster Abstract: Opportunistic RPL 3
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Du
ty
 C
yc
le
 (%
)
0 20 40 60 80 100 120 140
node index
0.0
0.8
1.6
2.4
3.2
4.0
4.8
La
te
nc
y 
(s
)
0
1
2
3
4
5
6
Du
ty
 C
yc
le
 (%
)
0 20 40 60 80 100 120 140
node index
0
1
2
3
4
5
6
7
8
La
te
nc
y 
(s
)
Fig. 1: In both up-traffic (left) and down-traffic (right) experiments in Indriya,
the average duty cycle is kept below 2% and latency below 3 s. Downwards
routing is not as efficient as upwards but remains in the same order of magnitude.
allows detecting, for instance, cases where a node with lower rank (a parent) is
in one’s filter. When this happens, the Bloom filters are switched and the Trickle
reset in order to repair inconsistencies.
4 Preliminary results
We implement ORPL in Contiki, based on ContikiRPL and the ContikiMAC
duty cycling protocol (used with a wakeup interval of 500 ms). We run our
experiments (summarized in Figure 1) in the Indriya testbed [2] which has 137
Tmote sky spanning 3 floors of an office building. Our first experiment (up-
traffic) is a collect-only scenario, with an inter-packet interval of 8 min, used
as a baseline. Our second experiment (down-traffic) is a reverse-collect, where
the sink sends periodically data to every node in the network. The inter-packet
interval is 4 seconds, resulting in a load similar to that of the first experiment. To
isolate the effects of filter propagation and the adaptation to network dynamics,
we use a three steps scenario where (1) ranks are calculated in the network,
(2) bloom filters are propagated and (3) sink-to-nodes traffic is started. Each
experiment runs for 80 minutes.
In the up-traffic experiment, ORPL achieves a packet delivery ratio of 99%, a
network duty cycle of 0.69%, and an average latency of 1.75 s. The down-traffic
experiment obtains an average delivery ratio of 95%, a duty cycle of 1.74%
and latency of 2.57 s. These results are promising in terms of energy-latency
balance, keeping the average duty cycle below 2% and latency below 3 s in a
testbed with >100 addressable nodes within a hop count of 1–9. Note that in
both experiments, the energy expenditure is well balanced among nodes, which
is an important property for network lifetime.
Downwards routing in RPL (or ORPL) is by nature more challenging than
upwards routing, because the topology and link metrics are build in direction
of the sink. In RPL, upwards traffic is used to build and heal the topology
4 Simon Duquennoy and Olaf Landsiedel
throughout the lifetime of the network. The other reason behind the performance
difference between up and down traffic is the fact that the sink is not duty cycled.
Sending to the sink is therefore more energy efficient and fast than sending to
other nodes.
5 Conclusion and Future work
Our initial prototype implementation and testbed experiments demonstrate the
feasibility of our approach. There are a number of key questions we need to
address in our future work. First, we will look in more detail at Bloom filter
propagation, maintenance and aging. There is a difficult trade-off between ef-
ficiency and correctness of the topology. We want to investigate and evaluate
policies to recover from false positives when routing downwards. We may also
consider resetting the network in case the filters contain more false positives, and
changing the seed used for hash calculation, aiming at converging to a state with
fewer false positives. Finally, we plan to diversify our experiments across scenar-
ios with different node density, and compare ORPL to traditional any-to-any
routing in RPL.
Acknowledgments
This work was partly funded by the European Commission through CALIPSO
(contract number FP7-ICT-2011.1.3-288879). We’d like to thank the Communi-
cations and Internet Research Lab in Singapore for providing the Indriya testbed.
References
1. B. H. Bloom. Space/time trade-offs in hash coding with allowable errors. Commun.
ACM, 13(7):422–426, July 1970.
2. M. Doddavenkatappa, M. C. Chan, and A. Ananda. Indriya: A Low-Cost, 3D Wire-
less Sensor Network Testbed. In TridentCom: Proc. of the Int. ICST Conference on
Testbeds and Research Infrastructures for the Development of Networks and Com-
munities, 2011.
3. O. Landsiedel, E. Ghadimi, S. Duquennoy, and M. Johansson. Low Power, Low
Delay: Opportunistic Routing meets Duty Cycling. In Proceedings of the Inter-
national Conference on Information Processing in Sensor Networks (ACM IPSN
2012), Beijing, China, Apr. 2012.
4. A. Reinhardt, O. Morar, S. Santini, S. Zo¨ller, and R. Steinmetz. Cbfr: Bloom filter
routing with gradual forgetting for tree-structured wireless sensor networks with
mobile nodes. In Proceedings of the IEEE International Symposium on a World of
Wireless Mobile and Multimedia Networks (WoWMoM 2012), San Francisco, CA,
USA, June 2012.
5. T. Winter (Ed.), P. Thubert (Ed.), and RPL Author Team. RPL: IPv6 Routing
Protocol for Low power and Lossy Networks. RFC 6550.


and RPL Author Team. RPL: IPv6 Routing Protocol for Low power and Lossy Networks.

Cbfr: Bloom filter routing with gradual forgetting for tree-structured wireless sensor networks with mobile nodes.

Indriya: A Low-Cost, 3D Wireless Sensor Network Testbed.

Low Power, Low Delay: Opportunistic Routing meets Duty Cycling.

Space/time trade-offs in hash coding with allowable errors.

Poster Abstract: Opportunistic RPL

Sensor nodes constituting Wireless Sensor Networks (WSN) are often battery- operated and have limited resources. To save energy, nodes sleep most of the time, and wake up periodically to handle communication. Such radio duty cycling poses a basic trade-off between energy and latency. In previous work, we have shown that opportunistic routing is an efficient way to achieve low-latency yet energy efficient data collection in WSN (ORW [3]). In this paper, we extend this approach to the context of low-power IP networks, where nodes need to be addressed individually and where traffic patterns are irregular. We present ORPL, an opportunistic extension of RPL, the stan- dard, state-of-the-art routing protocol for low-power IP networks. We discuss our preliminary results obtained with Contiki in a 137-node testbed.CALIPS

Digitala Vetenskapliga Arkivet - Academic Archive On-line

Software institutes' Online Digital Archive

Simon Duquennoy

Olaf Landsiedel

CiteSeerX

Sensor nodes constituting Wireless Sensor Networks (WSN) are often battery-operated and have limited resources. To save energy, nodes sleep most of the time, and wake up periodically to handle communication. Such radio duty cycling poses a basic trade-off between energy and latency. In previous work, we have shown that opportunistic routing is an efficient way to achieve low-latency yet energy efficient data collection in WSN. In this paper, we extend this approach to the context of low-power IP networks, where nodes need to be addressed individually and where traffic patterns are irregular. We present ORPL, an opportunistic extension of RPL, the standard, state-of-the-art routing protocol for low-power IP networks. We discuss our preliminary results obtained with Contiki in a 137-node testbed

Chalmers Research

RISE – Research Institutes of Sweden

Chalmers Publication Library

Poster Abstract: Opportunistic RPL
Simon Duquennoy1 and Olaf Landsiedel2
1 Swedish Institute of Computer Science (SICS), simonduq@sics.se
2 Chalmers University of Technology, olafl@chalmers.se
1 Introduction
Sensor nodes constituting Wireless Sensor Networks (WSN) are often battery-
operated and have limited resources. To save energy, nodes sleep most of the
time, and wake up periodically to handle communication. Such radio duty cycling
poses a basic trade-o↵ between energy and latency.
In previous work, we have shown that opportunistic routing is an e cient way
to achieve low-latency yet energy e cient data collection in WSN (ORW [3]).
In this paper, we extend this approach to the context of low-power IP networks,
where nodes need to be addressed individually and where tra c patterns are
irregular. We present ORPL, an opportunistic extension of RPL [5], the stan-
dard, state-of-the-art routing protocol for low-power IP networks. We discuss
our preliminary results obtained with Contiki in a 137-node testbed.
2 Overview of ORPL
ORPL routes data tra c following a DODAG (Destination-Oriented Directed
Acyclic Graph) in an opportunistic fashion. Like ORW [3], it relies on anycast
over a low-power listening MAC. The selection of the next hop is done during
the transmission: The first neighbor that, (1) wakes up, (2) successfully receives
the packet and (3) o↵ers su cient routing progress, acknowledges the packet
and forwards it.
ORPL performs any-to-any routing in a similar fashion as RPL, following
a two step process. The data is first routed up in the DODAG until finding a
common ancestor, then it is routed down to the destination. Routing upwards
is done exactly as in ORW, along a gradient. Routing downwards, however,
cannot rely only on the routing metric. Merely routing away from the root is
not enough to ensure the destination will be reached. Because ORPL relies on
anycast and opportunistic routing, we opt for routing-table free solution. Instead,
each node maintains a set of nodes that are below them in the DODAG – their
sub-DODAG. This information allows to decide whether a node is on the way
to the destination or not. We use Bloom filters to represent this set of reachable
nodes in a compact way and with fixed size, which enables e cient propagation
through the network.
2 Simon Duquennoy and Olaf Landsiedel
3 Design
This section describes the basic mechanisms of ORPL.
Routing Decision In ORPL, nodes anycast packets instead of electing a next hop
and unicasting to it. Nodes receiving a packet choose whether to forwards it or
not, and send an acknowledgment only in case they act as next hop. The routing
decision at node N for a packet sent by S with destination D is as follows: If
the packet is marked as going up in the DODAG (this information is included
in RPL’s IPv6 extension headers), then N forwards it i↵ RankN < RankS .
If the packet is marked as going down in the DODAG, then N forwards it i↵
RankN > RankS ^D 2 SubDODAGN
Bloom Filters In ORPL, nodes need to know the set of nodes composing their
sub-DODAG. To enable storing this set in memory-constrained devices and prop-
agating it within size-restricted network frames, we resort to Bloom filters [1].
Each node populates its Bloom filter with its direct children, and propagate the
filters through the RPL DIO messages (Trickle beacons). A node receiving a DIO
from a node already in its Bloom filter merges the newly received filter with its
own, by simply bitwise-ORing the filters.
Bloom filters are subject to false positives, meaning that sometimes a node
believes the destination is in its sub-DODAG while it is not. When such situation
is suspected (non-acked anycast), the node stores the packet ID in a blacklist, and
sends the packet back to the previous hop. The previous hop will anycast again,
looking for another path. If repeating this process recursively is not enough
to reach the destination, the fallback behavior is to perform a network-wide
multicast. This is inspired by CBFR [4], which extends CTP with downwards
routing through network-wide multicast.
Link Quality Estimation ORPL requires to maintain link quality estimates of
every reachable neighbor to decide whether to include nodes and merge Bloom
filters. As ORPL targets scenarios with any-to-any, irregular tra c, we need a
to maintain link quality even in the absence of tra c. In our current design, we
us acknowledged broadcast to achieve e cient link estimation. With low-power-
listening, broadcast are sent repeatedly for exactly one wakeup period. Nodes
receiving a broadcast send a 802.15.4 link-layer acknowledgement extended with
the receiver’s address. The sender counts the received ACKs to estimate link
quality.
Networks Dynamics and Inconsistency Detection Low-power networks are dy-
namic, often resulting in varying logical topology. To enable forgetting that given
nodes were in one’s sub-DODAG in the past, we use aging Bloom filters. We use
a simple approach where two filters are maintained, one active, and one warmup.
The filters are swapped and emptied periodically. ORPL inherits RPL’s datap-
ath validation mechanism to detect and repair loops. We extend this mechanism
with a systematic lookup of the packet source in the local Bloom filter. This
Poster Abstract: Opportunistic RPL 3
Fig. 1: In both up-tra c (left) and down-tra c (right) experiments in Indriya,
the average duty cycle is kept below 2% and latency below 3 s. Downwards
routing is not as e cient as upwards but remains in the same order of magnitude.
allows detecting, for instance, cases where a node with lower rank (a parent) is
in one’s filter. When this happens, the Bloom filters are switched and the Trickle
reset in order to repair inconsistencies.
4 Preliminary results
We implement ORPL in Contiki, based on ContikiRPL and the ContikiMAC
duty cycling protocol (used with a wakeup interval of 500 ms). We run our
experiments (summarized in Figure 1) in the Indriya testbed [2] which has 137
Tmote sky spanning 3 floors of an o ce building. Our first experiment (up-
tra c) is a collect-only scenario, with an inter-packet interval of 8 min, used
as a baseline. Our second experiment (down-tra c) is a reverse-collect, where
the sink sends periodically data to every node in the network. The inter-packet
interval is 4 seconds, resulting in a load similar to that of the first experiment. To
isolate the e↵ects of filter propagation and the adaptation to network dynamics,
we use a three steps scenario where (1) ranks are calculated in the network,
(2) bloom filters are propagated and (3) sink-to-nodes tra c is started. Each
experiment runs for 80 minutes.
In the up-tra c experiment, ORPL achieves a packet delivery ratio of 99%, a
network duty cycle of 0.69%, and an average latency of 1.75 s. The down-tra c
experiment obtains an average delivery ratio of 95%, a duty cycle of 1.74%
and latency of 2.57 s. These results are promising in terms of energy-latency
balance, keeping the average duty cycle below 2% and latency below 3 s in a
testbed with >100 addressable nodes within a hop count of 1–9. Note that in
both experiments, the energy expenditure is well balanced among nodes, which
is an important property for network lifetime.
Downwards routing in RPL (or ORPL) is by nature more challenging than
upwards routing, because the topology and link metrics are build in direction
of the sink. In RPL, upwards tra c is used to build and heal the topology
4 Simon Duquennoy and Olaf Landsiedel
throughout the lifetime of the network. The other reason behind the performance
di↵erence between up and down tra c is the fact that the sink is not duty cycled.
Sending to the sink is therefore more energy e cient and fast than sending to
other nodes.
5 Conclusion and Future work
Our initial prototype implementation and testbed experiments demonstrate the
feasibility of our approach. There are a number of key questions we need to
address in our future work. First, we will look in more detail at Bloom filter
propagation, maintenance and aging. There is a di cult trade-o↵ between ef-
ficiency and correctness of the topology. We want to investigate and evaluate
policies to recover from false positives when routing downwards. We may also
consider resetting the network in case the filters contain more false positives, and
changing the seed used for hash calculation, aiming at converging to a state with
fewer false positives. Finally, we plan to diversify our experiments across scenar-
ios with di↵erent node density, and compare ORPL to traditional any-to-any
routing in RPL.
Acknowledgments
This work was partly funded by the European Commission through CALIPSO
(contract number FP7-ICT-2011.1.3-288879). We’d like to thank the Communi-
cations and Internet Research Lab in Singapore for providing the Indriya testbed.
References
1. B. H. Bloom. Space/time trade-o↵s in hash coding with allowable errors. Commun.
ACM, 13(7):422–426, July 1970.
2. M. Doddavenkatappa, M. C. Chan, and A. Ananda. Indriya: A Low-Cost, 3D Wire-
less Sensor Network Testbed. In TridentCom: Proc. of the Int. ICST Conference on
Testbeds and Research Infrastructures for the Development of Networks and Com-
munities, 2011.
3. O. Landsiedel, E. Ghadimi, S. Duquennoy, and M. Johansson. Low Power, Low
Delay: Opportunistic Routing meets Duty Cycling. In Proceedings of the Inter-
national Conference on Information Processing in Sensor Networks (ACM IPSN
2012), Beijing, China, Apr. 2012.
4. A. Reinhardt, O. Morar, S. Santini, S. Zo¨ller, and R. Steinmetz. Cbfr: Bloom filter
routing with gradual forgetting for tree-structured wireless sensor networks with
mobile nodes. In Proceedings of the IEEE International Symposium on a World of
Wireless Mobile and Multimedia Networks (WoWMoM 2012), San Francisco, CA,
USA, June 2012.
5. T. Winter (Ed.), P. Thubert (Ed.), and RPL Author Team. RPL: IPv6 Routing
Protocol for Low power and Lossy Networks. RFC 6550.


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Poster Abstract: Opportunistic RPL

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