REDUNDANT DISTRIBUTED DATA STORAGE

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

Keywords:

Distributed Data Storage, Wireless Sensor Networks, Data Replication, SensLab.

Abstract:

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.

1 INTRODUCTION

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-

time.

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.

2 RELATED WORK

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

mules

) 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 GREEDY

REPLICATION-BASED DATA

STORAGE

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

(i)

. 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

adv

(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

(i)

}, the

donor D

(i)

(t) is selected according to the following

heuristic rule:

(i)

(t) = arg max

j∈{1,...,V

(i)

}



)

t − t



(1)

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

(i)

}),

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

(i)

Number of 1-hop neighbours of node i scalar

Node i’s buffer size, i ∈ {1,...,N} scalar

Node i’s sensing rate, i ∈ {1, ..., N} s

−1

Common node transmit power W

adv

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 i’s 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

4 EXPERIMENTAL VALIDATION

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

adv

; (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-

bours.

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

(2)

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

r−min

, 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

r−min



1/3.75

= 6.9·(P

)

1/3.75

(3)

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 =

∑

i=1

= 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

100

0 50 100 150 200 250

neighbours

senslab node id

neighbours

mean

(a) 0 dBm.

100

0 50 100 150 200 250

neighbours

senslab node id

neighbours

mean

(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,

adv

= 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

∑

i=1

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.

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

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

adv

= 25 s. The experiment duration

is 15 mins.

We also measure the amount of data distributed

throughout the network. The amount of distributed

5000

10000

15000

20000

25000

30000

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

adv

= 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

adv

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

adv

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

5000

10000

15000

20000

25000

30000

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.

5000

10000

15000

20000

25000

30000

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

adv

= 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-

2000

4000

6000

8000

10000

12000

14000

16000

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

adv

. 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

adv

. In particular,

the number of transmitted hello can be expressed as

hello

= N ·⌊t/T

adv

⌋. (4)

Similarly, the amount of received hello, on average, is

given by

hello

· Tx

hello

= V

· N · ⌊t/T

adv

⌋. (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

adv

= 80 s, while there

are less distributed data for shorter T

adv

. 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

Nodes

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

5000

10000

15000

20000

25000

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.

5000

10000

15000

20000

25000

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

adv

. 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

adv

= 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:

S(N) ≃

(

N ·



−4· 10

−4

·t

+ 0.6· t + 9.56



t < 900 s

226· N t ≥ 900 s

(6)

where the second line corresponds to the saturation

point.

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-

sition.

5000

10000

15000

20000

25000

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,

adv

= 25 s, transmit power P

= −15 dBm. The experi-

ment duration is 15 mins.

5 CONCLUSIONS

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

0.5

1.5

2.5

1 2 3 4 5 6 7

average hop distance

copy number

R=3

R=5

R=7

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.

ACKNOWLEDGEMENTS

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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