Experimentation with the SensLab Testbed
Pietro Gonizzi
, Gianluigi Ferrari
, Vincent Gay
and J´er´emie Leguay
Department of Information Engineering, University of Parma, Viale G.P.Usberti 181/A, Parma, Italy
Thales Communications and Security, 160 Bd de Valmy, Colombes Cedex, France
Distributed Data Storage, Wireless Sensor Networks, Data Replication, SensLab.
Wireless sensor network (WSN)-based applications typically require to store data in the network. For instance,
in the surveillance of isolated areas, if no sink nodes are present, WSNs may archive observation data that are
periodically retrieved by an external agent. In contrast to conventional network data storage, storing data in
WSNs is challenging because of the limited power, memory, and communication bandwidth of WSNs. In our
study, we review the state-of-art techniques for data replication and storage in WSNs, and we propose a low-
complexity distributed data replication mechanism to increase the resilience of WSN storage capacity against
node failure and local memory shortage. We evaluate our approach through experimental results collected on
the SensLab large-scale real testbed. In particular, we show how the performance is affected by changing the
configuration of several key system parameters, such as (i) the transmission power of the nodes; (ii) the control
message overhead; (iii) the number of deployed nodes; and (iv) the redundancy. To the best of our knowledge,
this is one of the first works presenting experimental results at a really large scale on SensLab.
In contrast to conventional network data storage, stor-
ing data in wireless sensor networks (WSNs) repre-
sents a challenge because of the limited power, mem-
ory, and communication bandwidth of WSNs. Nowa-
days, sensors have reached higher capabilities, in
terms of speed processing and local storage than in
the past years (Mathur et al., 2006). This makes them
more attractive for in-network storage deployments.
WSNs usually consist of: on one hand, unattended
nodes that sense the surrounding environment; on the
other hand, a sink node which is in charge of collect-
ing data measurements and relaying them to a man-
agement entity. There are many reasons for which a
sensor node may not be able to transmit data to the
sink right after acquisition. First, the sink node may
not be always reachable from sensor nodes. For in-
stance, a mobile node can be used to periodically pull
out all the collected data. Second, when applications
do not require real-time collection, storing data units
and sending aggregate data bursts can contribute to
reduce the amount of radio transmissions, thereby in-
creasing the lifetime operation of the WSN. Illustra-
tive applications include habitat monitoring, such as
tracking animal migrations in remote-areas (Juang et
al., 2002), studying weather conditions in national
parks (Beaufour et al., 2002), etc. Such scenarios
require to collect and store as much data as possible
between two consecutive data retrievals performed by
an external agent. However, storing data on the sensor
node leads to local memory overflow if data retrieval
is not timely performed by the sink. To avoid data
dropping or overwriting, sensor nodes can cooperate
with each other by sharing acquired data.
Node failure is also a critical issue in WSNs. Pe-
riodic inactivity (e.g., for energy saving purposes),
physical destruction, and (software) bugs are likely
to appear in WSNs, leading to data loss. Thus, re-
dundancy by means of data replication (i.e., by stor-
ing copies of the same data onto various nodes) con-
tributes to increasing the resilience of the WSN. How-
ever, distributed data storage across nodes, with or
without redundancy, is a challenge as it requires to
properly select “donor nodes” (i.e., nodes available
to store data of other nodes) and entails communica-
tion overhead to transmit data to the selected nodes.
Therefore, network storage capacity and resilience
have an energy cost and this limits the network life-
Although distributed data storage and replication
have been studied in the literature (Neumann et al.,
Gonizzi P., Ferrari G., Gay V. and Leguay J..
REDUNDANT DISTRIBUTED DATA STORAGE - Experimentation with the SensLab Testbed.
DOI: 10.5220/0003803900150023
In Proceedings of the 1st International Conference on Sensor Networks (SENSORNETS-2012), pages 15-23
ISBN: 978-989-8565-01-3
2012 SCITEPRESS (Science and Technology Publications, Lda.)
2009; Piotrowski et al., 2009), there is still a number
of challenges to tackle. First, to the best of our knowl-
edge, most of previous studies do not encompass both
the distribution and the replication aspects. Second,
the proposed solutions are usually not tested experi-
mentally at large-scale, while we stress how real de-
ployment is of primary importance when developing
WSN applications.
In this paper, we propose a low complexity dis-
tributed data replication mechanism to increase the
resilience and storage capacity of a WSN-based ob-
servation system against node failure and local mem-
ory shortage. We evaluate our approach through ex-
perimental tests conducted on the SensLab real plat-
form (SensLAB, 2008). We show how careful config-
uration of key system parameters has a positive effect
on the performance. The portfolio of parameters in-
cludes: (i) the node transmit power, which should be
reduced to save energy; (ii) the communication over-
head, i.e., the amount of control messages exchanged
by the nodes; (iii) the number of deployed nodes; and
(iv) the data redundancy, i.e., the number of copies to
be stored per sensed data unit.
This paper is organized as follows. Section 2 is
dedicated to related works. In Section 3, we moti-
vate and detail the design of our greedy mechanism
for distributed data replication. Section 4 is devoted to
the large scale experiments performed on the SensLab
testbed, by considering the aforementioned scenarios.
At last, Section 5 concludes the paper.
Various schemes to efficiently store and process
sensed data in WSNs have been proposed in the past
years (Hongping and Kangling, 2010).
In distributed WSN storage schemes, nodes coop-
erate to efficiently distribute data across them. Pre-
vious studies focused on data centric storage (Rat-
nasamy et al., 2003; Madden et al., 2005; Awad et al.,
2009). According to a centric storage approach, data
collected in some WSN regions are stored at super
nodes that are responsible for these regions and/or ac-
cording to the type of data. Hash values are used to
distinguish data types and the corresponding storage
locations. Even if this approach is based on node co-
operation, it is not fully distributed, since super nodes
store all the contents generated by the others.
In a fully distributed data storage approach, all
nodes participate in sensing and storing in the same
way. All nodes, first, store their sensor readings lo-
cally and, once their local memories have filled up,
delegate other nodes to store them. A first signifi-
cant contribution in this direction is given by Data
Farms (Omotayo et al., 2007). The authors propose
a fully data distributed storage mechanism with pe-
riodical data retrieval. They derive a cost model to
measure energy consumption and show how a care-
ful selection of nodes offering storage, called “donor
nodes”, optimizes the system capacity at the price of
slightly higher transmission costs. They assume the
network is built in a tree topology and each sensor
node knows the return path to a sink node, who pe-
riodically retrieves data. Another study is proposed
in EnviroStore (Luo et al., 2007). The authors focus
on data redistribution when the remaining storage of
a sensor node exceeds a given threshold, by means
of load balancing. They use a proactive mechanism,
where each node maintains locally a memory table
containing the status of the memory of its neighbors.
Furthermore, mobile nodes (called
) are used to
carry data from an overloadedarea to an offloaded one
and to take data to a sink node. The major lack of the
abovestudies pertains to the absence of large scale ex-
periments to evaluate the system performance, which
is a key contribution of our work.
Data replication consists in adding redundancy to
the system by copying data at several donor nodes
(within the WSN) to mitigate the risk of node fail-
ure. It has been widely studied for WSNs and several
works are available in the literature (Neumann et al.,
2009; Hamed Azimi et al., 2010). A scoring func-
tion for suitably choosing a replicator node is pro-
posed in (Neumann et al., 2009). The function is in-
fluenced by critical parameters such as the number of
desired replicas, the remaining energy of a replicator
node and the energy of the neighbors of the replicator
node. In TinyDSM (Piotrowski et al., 2009), a reac-
tive replication approach is discussed. Data burst are
broadcasted by a source node and then handled inde-
pendently by each neighbor, which decides to store a
copy of the burst or not. Unlike the above approaches,
we propose a replication-based distributed data stor-
age mechanism with lower complexity, since the re-
sponsibility of finding donor nodes is not centralized
at the source node but handed over to consecutive
donor nodes in a recursive manner.
The main contributions of this paper are the fol-
lowing. First, we encompass, with a fully distributed
mechanism, both data replication and distributed stor-
age. Second, we conduct large scale experiments on
the Senslab real testbed in order to evaluate our solu-
tion. Even if the Senslab platform is suitable for test-
ing WSN-based applications (Ducrocq et al., 2010),
to the best of our knowledge, no previous studies with
results collected in Senslab have been published.
SENSORNETS 2012 - International Conference on Sensor Networks
3.1 Design Principles
We assume that a WSN is deployed to measure en-
vironmental data and store it until a sink node per-
forms a periodic retrieval of the whole data stored in
the WSN. Between two consecutive data retrievals,
the objective is to distribute multiple copies of every
data unit amongst nodes in order to avoid data losses
caused by local memory shortages or by node failures.
Data distribution relies on the proactive announce-
ment, by every node, of its memory status. Each node
periodically broadcasts a memory advertisement mes-
sage containing its current available memory space.
It also maintains an updated memory table containing
the memory statuses of all its detected 1-hop neigh-
bors. Upon reception of a memory advertisement, a
node updates its local memory table with the new in-
formation received. The memory table contains an
entry for each neighbor. Each entry contains the ad-
dress of the neighbor, the last received value of its
available memory space, and the time at which this
value has been received. When sending a replica of
acquired data, a node looks up in its table the neighbor
node with the largest available memory and most re-
cent information. Such neighbor is called donor node.
If no donor node can be found and there is no avail-
able space locally, then the acquired data is dropped.
At the end of the sensing period, a sink node is meant
to gather all data present in the WSN by sending re-
quests to all nodes.
Upon sending the data to the
sink, a node clears its local buffer and resumes sens-
ing the environment.
3.2 The Greedy Algorithm
The main parameters are listed in Table 1. Without
loss of generality, we consider a WSN with N fixed
nodes randomly deployed over an area whose surface
is A (dimension: [m
]). Nodes only interact with 1-
hop neighbours, i.e., with nodes within radio trans-
mission range, denoted as d (dimension: [m]). The
number of 1-hop neighbors of node i (i {1, ... ,N})
is denoted as V
. The i-th node has a finite local
buffer of size B
(dimension: [data units]) and sens-
ing rate r
(dimension: [data units/s]). Each node
is scheduled to broadcast, without acknowledgement
and every T
(dimension: [s]), its memory status
We do not detail the data retrieval phase, as this is not
the focus of our work.
to all nodes within the transmission range (i.e., 1-
hop neighbours). Each memory advertisement con-
sists of 4 fields relative to the sending node: node ID;
up-to-date available memory space; value of sensing
rate; and a sequence number identifying the memory
advertisement. Each node maintains a table which
records the latest memory status received from neigh-
bor nodes. Upon reception of a memory advertise-
ment from a neighbor, a node updates its memory ta-
ble, using the sequence number field to discard mul-
tiple receptions or out-of-date advertisements. An il-
lustrative scenario, with advertisement of the memory
status by node 1, is shown in Figure 1. The remaining
parameters (R, T and P
) will be described later.
Figure 1: Memory tables at a group of neighbors and adver-
tisement from node 1.
The proposed replication-based data storage
mechanism is fully decentralized, in the sense that
all nodes play the same role. It consists in creating
at most R copies of each data unit generated by a
node and distribute them across the network, storing
at most one copy per node. Each copy is referred to as
replica. Let us focus on node i {1,..., N}. At time
t, the node generates (upon sensing) a data unit. The
memory table of node i contains one entry per direct
neighbor. Node i selects from its memory table the
neighbor node, called donor, with the largest avail-
able memory space and the most recent information.
Denoting the neighbors of node i as {1,.. .,V
}, the
donor D
(t) is selected according to the following
heuristic rule:
(t) = arg max
t t
where t
denotes the time at which the available mem-
ory space B
) of node j was received by node i,
with B
) B
. If there is no suitable neighbor in
the memory table (i.e., B
) = 0, j {1,... ,V
there is no possibility to distribute replicas of the data
unit across the network. In this case, only one copy
can be stored in the local memory of node i, if i has
REDUNDANT DISTRIBUTED DATA STORAGE - Experimentation with the SensLab Testbed
Table 1: Main system parameters.
Symbol Description Unit
N Number of nodes scalar
A Surface of deployment area m
d Node transmission range m
Number of 1-hop neighbours of node i scalar
Node is buffer size, i {1,...,N} scalar
Node is sensing rate, i {1, ..., N} s
Common node transmit power W
Period of memory advertisement (from each node) s
R Maximum number of replicas per sensing data unit scalar
T Period of data retrieval (from the sink) s
some space locally. If a donor node can be selected,
node i sends to it a copy of the data unit, specifying
how many other copies are still to be distributed in
the WSN. In particular, the number of required copies
is set to either R 1 (if node i can store the original
data locally) or R (if node is local memory is full).
Upon reception of the copy, the donor node D stores
the copy in its memory and selects the next donor
node among its neighbors, according to (1), and dis-
carding the sending node and the source node from
the candidate nodes. Then, it sends the copy to the
chosen donor node, decrementing the number of re-
quired copies by 1. The replication process continues
recursively until either the last (R-th) copy is stored or
stops when one donor node cannot find any suitable
next donor node. In the latter case, the final number
of copies actually stored in the WSN is smaller than
We present a large-scale validation of our dis-
tributed storage mechanism on the SensLab
testbed (SensLAB, 2008). The SensLab plat-
form offers more than 1000 sensors at four sites in
France (Grenoble, Strasbourg, Lille, Rennes) where
researchers can deploy their codes and run experi-
ments. At each site, nodes are installed on an almost
regular grid, as depicted in Figure 2. Each node’s
platform embeds a TI MSP430 micro-controller and
operates in various frequency bands depending on the
radio chip (either CC1100 or CC2420).
All the experiments have been run in the Greno-
ble site of SensLab. The deployed WSN platform is
the wsn430v13, which adopts the CC1100 radio chip.
The duration of each experiment is set to 15 min. The
sensing period of the nodes is chosen randomly and
independently in the interval [0.1,5.1] s. All nodes
have the same buffer size, which equals B = 250 data
Figure 2: The Lille site of SensLab.
units. Nodes at the Grenoble site of SensLab are
deployed in a square room of 14x14 m
. The cho-
sen operating system is TinyOS 2.1.0 with a Car-
rier Sense Multiple Access with Collision Avoidance
(CSMA/CA) protocol at the MAC layer, which is the
default one included in the TinyOS distribution.
Several scenarios are presented in the following.
In particular, we study how the system performance
is affected by (i) the (common) transmission power
; (ii) the memory advertisment period T
; (iii) the
number of deployed nodes N; and (iv) the number of
replicas R.
4.1 Impact of Transmit Power
We argue that our greedy approach is significantly in-
fluenced by the network topology. For instance, in a
dense network, where nodes have several neighbours,
our data distribution mechanism could work better
than in a sparse network with only a few direct neigh-
The network topoloogy depends on the transmis-
sion power of the nodes. We run 4 experiments setting
the transmit power P
to 8.9 dBm, 0 dBm, -15 dBm,
and -20 dBm, respectively, deploying N = 78 nodes.
SENSORNETS 2012 - International Conference on Sensor Networks
We compute the number of detected neighbours in
each case, by counting the number of memory adver-
tisements received by each node. We assume node
x is a neighbor of node y if node y detects at least
one memory advertisement from node x, within the
overall experiment duration. Note that the larger the
number of received memory advertisments, the higher
the radio link quality. In Figure 3, the number of de-
tected neighbours (per node) and the corresponding
average value are shown considering two values of the
transmit power: (a) 0 dBm and (b) -20 dBm, respec-
tively. As expected, the average number of detected
neighbours decreases for decreasing transmit power.
In Figure 4, the average number of detected neigh-
bours, evaluated experimentally and analytically, is
shown as a function of the transmit power. Analytical
results are obtained as follows. Under the assump-
tion of omnidirectional antennas (as is the case for
SensLab nodes), the average number of 1-hop neigh-
bours, denoted as
, can be expressed as
= ρ · π · d
where ρ = N/A = 78/14
= 0.4 node/m
is the node
spatial density and d is the transmit range (dimen-
sion: [m]). As the receiver sensitivity of the CC1100
receiver, denoted as P
, is -88 dBm (at f
868 Mhz with 500 Kbaud rate), taking into account
a strong attenuation (the path loss exponent of the
SensLab environment is around 3.75), from Friis for-
mula one obtains:
d =
4π · f
· G
· G
= 6.9·(P
where G
= G
= 1 (omnidirectional antenna’s gain)
and c = 3·10
m/s is the speed of light. Using (3) into
(2), one obtains the average number of neighbours as
a function of the transmit power. As can be seen from
Figure 4, the agreement between analysis and experi-
ment is very good.
At this point, it is of interest to evaluate the time
required by the system to reach the network storage
capacity and the amount of dropped data due to local
memory shortage. The network storage capacity de-
notes the maximum amount of distinct data that can
be stored in the WSN. Given N nodes, the buffers
} (dimension: [data units]), and the sensing rates
} (dimension: [data units/s]), i {1,.. . ,N}, the
network storage capacity C (dimension: [data units])
can be expressed as C =
= N · B = 78· 250 =
19500. In Figure 5, the amount of data stored is
shown, as a function of time, for the four considered
experimental cases. As one can see, the capacity C
is reached later when a lower transmission power is
used — for instance at -20 dBm — since fewer neigh-
bors are detected. Consequently, data cannot be dis-
tributed efficiently through the network. On the other
0 50 100 150 200 250
senslab node id
(a) 0 dBm.
0 50 100 150 200 250
senslab node id
(b) -20 dBm.
Figure 3: Number of detected neighbours versus SensLab
node id. Nodes at the Grenoble site of SensLab are num-
bered from 1 to 256. B = 250 data units, N = 78 nodes,
= 25 s. The experiment duration is 15 mins.
hand, with a transmit power equal to 8.9 dBm, nodes
have a “larger” view of the network and the capac-
ity is reached earlier. For instance, at t = 400 s, the
stored data at 8.9 dBm and 0 dBm are 17000 data
units, about 90% of the capacity. The stored data at
-15 dBm and -20 dBm, at the same time istant, are
approximately 74% and 68% of the total capacity, re-
spectively. Moreover, two theoretical cases are con-
sidered for comparison. In the case with local storage
( “Local storage (anal)” curve), nodes fill their own
local buffer autonomously, i.e., the fill up time for the
i-node is t
= B
, where B
and r
are the buffer size
and the sening rate of node i, respectively. In this case,
the time interval to reach the storage capacity corre-
sponds to the longest storage filling time across all
nodes. As expected, the analytical curve relative to
local storage lower bounds the experimental curves.
In the case with ideal distributed storage (“Distr. stor-
age (ideal)” curve), a performance benchmark can be
obtained considering an ideal WSN where nodes can
communicate with any other node, considering in-
REDUNDANT DISTRIBUTED DATA STORAGE - Experimentation with the SensLab Testbed
-20 -15 -10 -5 0 5 10
average neighbors
Tx power (dBm)
average neighbors (exp.)
average neighbors (Friis, anal)
Figure 4: Average number of detected neighbors versus
node transmission power. The deployed nodes are N = 78.
stantaneous transmission. In this case, the WSN is
equivalent to a single super-node with a storage ca-
pacity C as defined above and sensing rate equal to
In Figure 6, the amount of dropped data is shown,
as a function of time, in the four cases considered
in Figure 5. Nodes drop newly acquired data once
their local memory has filled up and no neighbours
are available for donating extra storage space. At
t = 400 s, dropped data at 8.9 dBm and 0 dBm equal
600 data units, about 3% of the stored data. In the
cases with lower transmit power, e.g., at -15 dBm and
-20 dBm, the amount of dropped data is significantly
higher. As expected, data dropping starts in advance
with lowertransmission power. In the same figure, the
amount of dropped data, in the case with local storage,
is also shown for comparison.
0 200 400 600 800 1000 1200 1400
stored data [data units]
time [s]
8.9 dBm
0 dBm
-15 dBm
-20 dBm
Local storage (anal)
Distr. storage (ideal)
Figure 5: Data stored in the system varying the transmission
power of the node. No replication(R = 1), B = 250 data
units, N = 78 nodes, T
= 25 s. The experiment duration
is 15 mins.
We also measure the amount of data distributed
throughout the network. The amount of distributed
0 100 200 300 400 500 600 700 800 900
dropped data [data units]
time [s]
8.9 dBm
0 dBm
-15 dBm
-20 dBm
Local storage (anal)
Figure 6: Data dropped in the system varying the transmis-
sion power of the node. No replication(R = 1), B= 250 data
units, N = 78 nodes, T
= 25 s. The experiment duration
is 15 mins.
data, both transmitted to and received by donor nodes,
is shown in Figure 7 as a function of time. As ex-
pected, distributed data increase with higher transmit
power, since a node potentially has more donor nodes
candidates than in other cases. However, we experi-
ence lot of data losses at the receiver. For instance,
compare the y-axis of Figure 7(a) and Figure 7(b) at
t = 600 s. In the ideal case with no collisions, the y-
axis should show the same values, i.e., all transmitted
data should be received at the donor node side, with
a 100% packet delivery ratio (PDR). As one can see,
however, in the cases at higher power — for instance
at 8.9 dBm and 0 dBm about 75% of transmit-
ted data are received, while the remaining 25% are
lost. For lower power settings, e.g., -15 dBm and
-20 dBm, the percentage of lost data is still higher,
reaching approximately 55% of the transmitted data.
We conclude that several collisions occur at the MAC
and physical layers, where a pure CSMA/CA proto-
col is used. However, we argue that high data losses
are caused also by the limited deploymentarea, which
is a room of 14x14 square meters. Such size can be
too small for deploying 78 nodes and leads to lots of
packet collisions.
4.2 Impact of the Memory
Advertisement Period
In this subsection, we show the results obtained vary-
ing the memory advertisement period T
of the
nodes. Recall from section 3 that the memory adver-
tisement period is the time interval each node period-
ically broadcast its memory status to neighbours. We
run 3 experiments setting the memory advertisement
period T
to 25 s, 50 s and 80 s, respectively, out
of a total experiment duration of 15 min. We deploy
SENSORNETS 2012 - International Conference on Sensor Networks
0 100 200 300 400 500 600 700 800 900
transmitted data[data units]
time [s]
8.9 dBm
0 dBm
-15 dBm
-20 dBm
(a) Transmitted data.
0 100 200 300 400 500 600 700 800 900
received data[data units]
time [s]
8.9 dBm
0 dBm
-15 dBm
-20 dBm
(b) Received data.
Figure 7: Distributed data, both transmitted and received, varying the transmission power of the node. No replication(R = 1),
B = 250 data units, N = 78 nodes, T
= 25 s. The experiment duration is 15 mins.
58 nodes with 250 data units as buffer, and a transmit
power fixed at -15 dBm. Again, we do not perform
replication (R = 1).
In Figure 8, the data stored is shown, as a func-
tion of time, considering the three cases. As one
can observe, all curves coincide within the overall ex-
periment duration and the storage capacity C, equal
to 14500 data units, is reached simultaneously. We
conclude that buffers are filled at the same speed in
the three cases. The amount of memory advertise-
0 100 200 300 400 500 600 700 800 900
stored data [data units]
time [s]
25 s
50 s
80 s
Figure 8: Data stored in the system varying the memory
advertisement T
. No replication(R = 1), B = 250 data
units, N = 58 nodes, transmit power P
= 15 dBm. The
experiment duration is 15 mins.
ments exchanged across the network (also referred to
as Hello messages) can be computed analytically con-
sidering the advertisment period T
. In particular,
the number of transmitted hello can be expressed as
= N ·t/T
. (4)
Similarly, the amount of received hello, on average, is
given by
· Tx
= V
· N · t/T
. (5)
The amount of exchanged hello messages impacts the
data distribution across the network. The distributed
data, both transmitted and received, are shown in Fig-
ure 9. Data distribution increases with larger memory
advertisement periods, i.e., T
= 80 s, while there
are less distributed data for shorter T
. This is due
to the fact that, with frequent memory advertisements,
the memory table of a node is updated continuously
with fresh information, and data is efficiently sent to
the best donor node. On the other hand, with spo-
radic memory advertisements, a node keeps on se-
lecting the same donor until it receives new state in-
formation. However, it is likely that data units sent
to such a donor are not stored, since the donor has
already filled up its local buffer. In this case, the
donor node forwards the data unit to another claimed
donor node. Data keeps on circulating in the network
passing through nodes which have no local space for
storage, but leading to energy depletion. Note that,
given the node sensing interval in [0.1-5.1] s, between
16 and 400 data units are generated within an 80 s
hello interval; therefore, on average, data acquired by
a node between two consecutive memory advertise-
ments is much more than the node buffer itself. An
advertisement period of 25 s is thus more reasonable.
Finally, as already observed in Section 4.1, signifi-
cant data losses occur at the MAC layer, as it can be
observed comparing the y-axis in Figure 9(a)- 9(b).
4.3 Impact of the Number of Deployed
In order to evaluate the impact of the number of
deployed nodes, tests have been executed with 60,
REDUNDANT DISTRIBUTED DATA STORAGE - Experimentation with the SensLab Testbed
0 100 200 300 400 500 600 700 800 900
transmitted data[data units]
time [s]
25 s
50 s
80 s
(a) Transmitted data.
0 100 200 300 400 500 600 700 800 900
received data[data units]
time [s]
25 s
50 s
80 s
(b) Received data.
Figure 9: Distributed data, both transmitted and received, varying the memory advertisement T
. No replication(R = 1),
B = 250 data units, N = 58 nodes, transmit power P
= 15 dBm. The experiment duration is 15 mins.
80 and 100 nodes, respectively, with a buffer size
equal to 250 data units, a memory advertisment pe-
riod T
= 25 s, the sensing interval uniformed and
randomly distributed in [0.1-5.1] s, and the transmit
power P
= 15 dBm, for an experiment duration of
15 min. We have generated the same set of curves as
for Section 4.2. Here we show the most relevant re-
sults. In Figure 10 the amount of stored data is shown,
as a function of time, for the three considered values
of N. To evaluate the amount of stored data S with
an arbitrary number of deployed nodes N, we have
derived a mathematical expression by fitting the three
curves in Figure 10 with a 2nd grade polynomial func-
tion of t, directly proportional to N:
N ·
4· 10
+ 0.6· t + 9.56
t < 900 s
226· N t 900 s
where the second line corresponds to the saturation
4.4 Impact of the Number of Replicas
As discussed in Section 3, redundancy is introduced
by setting the number of copies (referred to as repli-
cas) to a value R > 1. Replicas of a sensed data unit
follow a hop-by-hop replication from the generator
node to subsequent donor nodes. We computed the
average hop distance reached by the replicas from the
generator node, for various values of R. Our results,
depicted in Figure 11, show that replicas do not prop-
agate, on average, beyond 2 hops from the generator
node. This is in agreement with the proposed mech-
anism, since donor nodes are selected on the basis
of their available memory but not their physical po-
0 100 200 300 400 500 600 700 800 900
stored data [data units]
time [s]
N=100 nodes
N=80 nodes
N=60 nodes
Figure 10: Data stored in the system varying the number
of nodes N. No replication(R = 1), B = 250 data units,
= 25 s, transmit power P
= 15 dBm. The experi-
ment duration is 15 mins.
This paper has addressed the problem of redundant
data distribution for WSN-based observation systems.
We have proposed a low-complexity greedy mecha-
nism to distribute and replicate measurements with a
minimum signaling overhead. Through analytical re-
sults and experimentationson the SensLab large-scale
testbed, we have shown how the performance is af-
fected when different configurations of the main sys-
tem parameters are used. To the best of our knowl-
edge, this is one of the first works presenting experi-
mental results at a really large-scale on SensLab. Fur-
ther steps along these lines will consider the design of
more sophisticated mechanisms to increase the spa-
tial distribution of data. For this purpose, one goal is
to add a routing scheme for an efficient placement of
SENSORNETS 2012 - International Conference on Sensor Networks
1 2 3 4 5 6 7
average hop distance
copy number
Figure 11: Average hop distance reached by the k-th replica
versus k. k varies between 1 and 3 (R = 3), 1and 5(R = 5), 1
and 7 (R = 7), respectively. The average is computed on all
the unique data present in the system with full redundancy,
i.e., with exactly R copies.
data. Finally, we plan to extend our work to an IP-
based scenario, using the 6LowPAN communication
stack with optimized RPL routing strategy.
This work was funded by the European Community’s
Seventh Framework Programme, area “Internetcon-
nected Objects”, under Grant no. 288879, CALIPSO
project - Connect All IP-based Smart Objects. The
work reflects only the authors views; the European
Community is not liable for any use that may be made
of the information contained herein.
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REDUNDANT DISTRIBUTED DATA STORAGE - Experimentation with the SensLab Testbed