Hassanzadeh, Navid

Hermans, Frederik

Landsiedel, Olaf

Rensfelt, Olof

Voigt, Thiemo

English

RISE – Research Institutes of Sweden

Do Sensor Networks need Mobile MAC Protocols?

Software institutes' Online Digital Archive

Do Sensor Networks need Mobile MAC
Protocols?
Navid Hassanzadeh1, Olaf Landsiedel2,4, Frederik Hermans3, Olof Rensfelt3
and Thiemo Voigt1
1 Swedish Institute of Computer Science (SICS), Kista, Sweden
2 Chalmers University of Technology, Sweden
3 Uppsala University, Sweden
4 KTH Royal Institute of Technology, Sweden
Abstract. There exists a number of MAC protocols targeted for mobile
scenarios. These include MMAC, MS-MAC and AM-MAC. These MAC
protocols have in common that they seem to be evaluated only in sim-
ulation. This might indicate that these MAC are either too complex to
use or they are not needed, at least for data collection, the major task of
sensor networks. In this paper we show that extending a traditional data
collection protocol with lightweight, carefully selected mechanisms is suf-
ficient to provide reliable data collection at low energy cost for mobile
sensor networks where both sinks and sources move.
1 Introduction
There are quite a few MAC layers specifically designed for mobile scenarios.
These include MMAC [1], MS-MAC [2] and AM-MAC [3]. All of these MAC
protocols are evaluated by simulation and it is unclear if implementations for
real sensor node hardware exist. Given the complexity of some of these protocols
and the absence of implemenations that work on real sensor nodes, we wonder
if these mobile MAC protocols are really needed. We investigate this question
for mobile scenarios where mobile sink nodes collect data from mobile data
sources. We target scenarios where data is collected in real-time. This is in
contrast to approaches that use mechanisms from delay-tolerant networking such
as ZebraNet [4] or RatPack [5] where data is collected for off-line analysis and
hence longer delays do not matter.
In this paper we advocate a different approach to tackle the challenging
issue of mobile data collection. Rather than designing a new MAC protocol, we
enhance an existing data collection protocol for static networks with lightweight
mechanisms to improve performance in mobile scenarios. Towards this end, we
modify the Contiki Collect protocol [6], a protocol similar to the Collection Tree
Protocol (CTP) [7] for Tiny OS, to make it more suitable for mobile scenarios.
We enhance Contiki Collect with mechanisms to detect and repair loops since
these occur more often in mobile than in static scenarios. Furthermore, we enable
nodes to quickly find new parents as nodes often move out of range in mobile
scenarios. We provide an implementation for the Contiki operating system that
we call Mobile Collect.
2 Hassanzadeh, Landsiedel, Hermans, Rensfelt, Voigt
We evaluate Mobile Collect by both simulation and in a testbed that includes
mobile robots. We also perform simulations with a random waypoint mobility
model. Our experiments show that Mobile Collect performs very well in such a
scenario. Even in scenarios with very high mobility (2 to 8 m/s), Mobile Collect
still achieves a delivery rate of 70% and more at a low energy consumption of
10 mJ per received packet. Note that, for example, the simulations in MMAC
were performed with an average speed of only 0.1 m/s [1]. Given these results,
we conclude that in many scenarios with mobile nodes, there is no eminent need
for new MAC protocols specifically designed for mobility.
2 Design and Implementation
As mentioned above we enhance Contiki Collect with two types of mechanisms.
First, we enable nodes to more quickly find new parents as in mobile scenarios
nodes often move out of range. Second, we need mechanisms to detect and repair
loops since these occur more often in mobile than in static scenarios. Addition-
ally, we integrate routing beacons into the receiver initiated MAC-layer.
MAC Layer Beaconing: Topology dynamics triggered by node mobility
require nodes to frequently announce their presence and routing metrics to neigh-
boring nodes. To avoid additional cost from this beaconing, Mobile Collect in-
tegrates its beacons into the MAC layer: In each probe of its receiver initiated
MAC [8] a node also announces its current routing metric. As these probes are
transmitted on each wakeup of our duty-cycled nodes, Mobile Collect achieves
a high rate of routing beacons with essentially no additional cost.
Parent Switch on Timeout: In mobile scenarios, we argue that a routing
timeout indicates that the target node has disappeared from the communication
range of the sending node. Thus, instead of punishing the timed-out route by
slightly increasing its routing metric ETX, as done commonly in routing proto-
cols, we increase the ETX to the maximum value which enforces a parent switch.
Hence, the source node disconnects from its parent and (1) connects to alternate
parents in its neighbor table, or (2) if no alternative parents are available it uses
beaconing as fall-back to discover new parents.
Avoiding Routing Loops: The dynamic topology caused by the mobility
of nodes and the agile parent change in Mobile Collect increase the risk of routing
loops. Mobile Collect extends Contiki Collect by enabling a node to track the
parents of all its neighbors. Thus, nodes in Mobile Collect announce the IDs of
their parents in their routing beacons. This allows us to implement classic loop
suppression mechanisms such as triangle suppression [9].
Our evaluation shows that we achieve high reliability and energy efficiency
for data collection in mobile settings with these lightweight mechanisms.
3 Evaluation
We perform simulations in COOJA [10] and use BonnMotion [11] to generate
the mobility scenarios. We simulate 50 nodes out of which three are sink nodes.
Do Sensor Networks need Mobile MAC Protocols? 3
0 5 10 15
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Density (Average Node Degree)
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Mobile Collect (A−MAC)
Contiki Collect (ContikiMAC)
(a) Reliability
0 5 10 15
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Density (Average Node Degree)E
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(m
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 P
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Mobile Collect (A−MAC)
Contiki Collect (ContikiMAC)
(b) Energy per received packet
Fig. 1: Reliability and energy performance of Mobile and Contiki Collect under
the random waypoint mobility model. Mobile Collect performs very well.
0 5 10 15
0
10
20
30
40
50
60
Density (Average Node Degree)
A
ve
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 N
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Mobile Collect (A−MAC)
(a) Number of duplicate packets
0 5 10 15
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Density (Average Node Degree)
A
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 N
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Mobile Collect (A−MAC)
(b) Number of lost routes
Fig. 2: Microbenchmarks that explain the performance of Mobile Collect
We use a random waypoint mobility model with a node speed between 2 and 8
m/s.
Our results are shown in Figure 1. Mobile Collect achieves much higher packet
delivery rates at lower energy consumption than Contiki Collect. Figure 1a shows
that the packet delivery rate (PDR) first increases, then for node degrees between
4 and 8 decreases before it increases again. For very low densities, the network is
sparse, so nodes do not find forwarders to forward their packets to the sink. This
improves with higher density, but as shown in Figure 2a the number of duplicate
packets then increases which is caused by packets not being acknowledged and
larger than triangle loops. The negative trend does not continue for medium node
degrees as shown in Figure 2b: the number of lost routes decreases as nodes can
more easily find forwarders as the network becomes more dense. Figure 1b shows
much better energy consumption per received byte for Mobile Collect than for
Contiki Collect mainly because the PDR is much higher and there is less energy
4 Hassanzadeh, Landsiedel, Hermans, Rensfelt, Voigt
(a) Robot
 0
 20
 40
 60
 80
 100
Contiki Collect Mobile Collect
Reliability (%)
(b) PDR for Robot’s Packets
 0
 1
 2
 3
 4
 5
Contiki Collect Mobile Collect
Energy Consumption per Packet (mJ/Packet)
(c) Energy per received packet
Fig. 3: Mobile Collect delivers more packets from the mobile robot at the same
energy cost as Contiki Collect.
wasted on packets that do not make it to the sink. This is very apparent for
Contiki Collect when the network is sparse.
We depict results from a robot sensor network in Figure 3. In this scenario,
one robot acting as data source moves from one cluster of static nodes to a second
cluster of static nodes. Both clusters have one sink each. Mobile Collect shows
much better performance than Contiki Collect since it (i) is able to buffer data
when moving from the first cluster to the second and (ii) with Mobile Collect it
is able to find new routes quickly as it comes in range of the second cluster. The
packet loss is mainly due to the early determination of the measurements: with
Mobile Collect all packets have eventually arrived at the sink.
4 Conclusions
Given the promising results of Mobile Collect in demanding mobility scenarios
and on real hardware, we doubt that sensor networks really need MAC layers
specifically designed for mobile scenarios.
5 Acknowledgements
This work has been partly funded by SSF, VR, the SICS Center for Networked
Systems (CNS), the Uppsala VINN Excellence Center for Wireless Sensor Net-
works WISENET, and partly supported the FP7 NoE CONET. CNS is funded
by VINNOVA, SSF, KKS, ABB, Ericsson, Saab SDS, TeliaSonera, T2Data, Ven-
dolocus and Peerialism. WISENET is funded by VINNOVA, Uppsala University,
Banverket, CRL Sweden, FOI, Imego, JonDeTech, Pricer, SenseAir, SICS, TNT-
Elektronik, TermoSense, VTT, Upwis, A˚AC Microtec, and WiseNet Holding.
The authors thank Gunnar Karlsson (KTH) and Peter Stenlund (Vendolocus)
for their input.
Do Sensor Networks need Mobile MAC Protocols? 5
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4. T. Liu et al. Implementing software on resource-constrained mobile sensors: Ex-
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There exists a number of MAC protocols targeted for mobile scenarios. These include MMAC, MS-MAC and AM-MAC. These MAC protocols have in common that they seem to be evaluated only in simulation. This might indicate that these MAC are either too complex to use or they are not needed, at least for data collection, the major task of sensor networks. In this paper we show that extending a traditional data collection protocol with lightweight, carefully selected mechanisms is sufficient to provide reliable data collection at low energy cost for mobile sensor networks where both sinks and sources move

Navid Hassanzadeh

Olaf Landsiedel

Frederik Hermans

Olof Rensfelt

Thiemo Voigt

CiteSeerX

Digitala Vetenskapliga Arkivet - Academic Archive On-line

Swedish Institute of Computer Science Publications Database

An adaptive mobility-supporting mac protocol for mobile sensor networks.

Beyond Interoperability: Pushing the Performance of Sensornet IP Stacks.

Bonnmotion: a mobility scenario generation and analysis tool. In SIMUTools,

Collection tree protocol.

Cross-level sensor network simulation with cooja. In SenseApp,

De-ass: An adaptive mac algorithm based on mobility evaluation for wireless sensor networks.

Design and evaluation of a versatile and efficient receiver-initiated link layer for low-power wireless.

Implementing software on resource-constrained mobile sensors: Experiences with Impala and ZebraNet.

MMAC: A mobility-adaptive, collision-free mac protocol for wireless sensor networks.

Do Sensor Networks need Mobile MAC Protocols?

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