Study of Timing Properties on Data Collection and Query Processing

Techniques in Wireless Sensor Networks

Abderrahmen Belfkih, Bruno Sadeg, Claude Duvallet and Laurent Amanton

University of Le Havre, BP 1123, 76063 Le Havre cedex, France

Keywords:

Wireless Sensor Network, Data Collection, Query Processing, Sensors Database, Time Constraints.

Abstract:

The real-time aspect is an important factor for Wireless Sensor Network (WSN) applications, which demand a

timely data delivery, to reﬂect the current state of the environment. However, most existing works in the ﬁeld

of sensor networks are only interested in data processing and energy consumption, to improve the lifetime of

WSN. Extensive researches have been done on data processing in WSN. Some works have been interested in

improving data collection methods, and others have focused on various query processing techniques. In recent

years, the query processing using abstract database technology like Cougar and TinyDB have shown efﬁcient

results in many researches. This paper presents a study of timing properties through two data processing

techniques for WSN. The ﬁrst is based on a warehousing approach, in which data are collected and stored on a

remote database. The second is based on query processing using an abstract database like TinyDB. This study

has allowed us to identify some factors which enhance the respect of temporal constraints.

1 INTRODUCTION

Wireless sensor networks can be considered as a type

of ad hoc wireless networks. They are composed

of small wireless nodes, which are manually or ran-

domly deployed in a region of interest, to sense differ-

ent physical characteristics of the environment, like

temperature, humidity, pressure, light, and so on. The

nodes transmit the sensed data to a sink node referred

to as Base Station (BS).This process, called data col-

lection, must be able to meet certain deadlines, i.e. to

update data before the expiration of their validity in-

tervals, in order for the WSN reﬂects the current state

of the environment.

WSN applications, such as industrial process

monitoring and control, can be considered as time-

critical. They require strict deadlines to send data to

the sink nodes. WSN are often deployed without giv-

ing much importance to the temporal constraints in-

cluding the data freshness constraint and the deadline

constraints.

Data freshness constraint imposes a limit on the

data validity period from the time it is read from a

sensor. The constraint of data deadline also requires

that data have to be collected within a deadline (Bhat-

tacharya, 2008). In real-time applications, data fresh-

ness is a key performance factor. It is important

to properly study the efﬁciency of data management

mechanisms in WSN.

Nowadays, researchers are interested in data pro-

cessing techniques to offer an efﬁcient solution to in-

crease the WSN lifetime. They have proposed new

techniques for data aggregation, data collection and

query processing, to simplify the data extraction and

management in WSN. The query processing systems

such as Cougar (Fung et al., 2002) and TinyDB (Mad-

den et al., 2003) use the abstract database approach

which considers the WSN as a relational database.

They aim to improve the data extraction mechanisms

in WSN to save energy and to reduce the communica-

tion losses. (Garc´ıa-Hernando et al., 2008).

In this paper, we are interested in investigatingtwo

approaches for data processing in WSN (i) a query

processing approach using an abstract database and

(ii) a periodic data collection. We simulate temporal

constraint in order to identify which method givesbet-

ter data arrival time to the user and that ensures data

freshness in WSN. We consider the following perfor-

mance parameters: (i) the network convergence time,

which is the time that a sensor node takes to connect

to the base station, to build and to update its routing

tables. It can affect the arrival time of data, (ii) the

data collection time. The fastest is this parameter, the

more fresh are data, (iii) the database response time,

which can improve the data arrival time to the user.

The remainder of this paper is structured as fol-

Belfkih A., Sadeg B., Duvallet C. and Amanton L..

Study of Timing Properties on Data Collection and Query Processing Techniques in Wireless Sensor Networks.

DOI: 10.5220/0005273200770084

In Proceedings of the 4th International Conference on Sensor Networks (SENSORNETS-2015), pages 77-84

ISBN: 978-989-758-086-4

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

lows. Section 2 reviews related work. In Section

3, we describe the two approaches cited previously.

In Section 4, we carry out simulations, thereafter we

present and comment the results obtained. Finally,

in Section 5, we conclude the paper by showing how

to effectively exploit the two techniques and we give

some perspectives to this work.

2 RELATED WORK

In this section, we give an overview of some research

works, which have studied the time criterion for WSN

data processing. Thereafter, we present the different

abstract databases for sensor networks.

2.1 Data Freshness in WSN

Suriyachai et al. have discuss the time-critical data

delivery in WSN (Suriyachai et al., 2010). They

have presented a novel TDMA-based Medium Ac-

cess Control (MAC) protocol named GinMAC, which

incorporates routing, topology control and reliability

control mechanisms. It aims to ensure timely data

delivery and the reliability control mechanism, with

minimum energy consumption in WSN. The authors

have shown that GinMAC is able to balance delay, re-

liability and energy consumption requirements for the

system with time-critical data delivery in WSN.

Sharaf et al. have presented a new approach based

on SQL-Like query syntax, named TiNA (Temporal

Coherecy-Aware in-Network Aggregation) (Sharaf

et al., 2003). TiNA exploits temporal coherency tol-

erance, to reduce the number of transmitted messages

by each node. The authors have proposed a new

clause named ”tct” (temporal coherencytolerance) for

each query, in order to express temporal coherency

tolerance. For example, the following query involves

getting the total light for rooms and grouping by ﬂoor,

with tct = 10%. The new reading is not reported if the

difference between it and old one is smaller than the

associated tct value.

SELECT {FLOOR, SUM(LIGHT)}

FROM SENSORS

GROUP BY {FLOOR}

EPOCH DURATION 30s

VALUES WITHIN 10%

Choi et al. consider that the aggregation time in WSN

can be affected by the deadline constraints, i.e, data

validity period or data delivery deadline (Choi et al.,

). They have proposed a new aggregation time control

(ATC) algorithm, which adjusts the aggregation time

of the node to ensure data delivery without exceeding

ﬁxed deadlines.

Hiromori et al. have proposed a new protocol,

named DD (Drainage Divide), for periodic data col-

lection in WSN multi-sinks (Hiromori et al., 2012).

DD transforms the network to a set of trees, rooted at

the sinks. Then, it adjusts them, in order that the de-

lay from each node to the corresponding sink, meets

given deadlines. The authors have also applied the

CSMA/CA-based MAC delay analysis to estimate the

transmission delay at each node. DD protocol has

guaranteed data delivery within time, and it has sat-

isﬁed the given delay constraints in WSN.

There are many research efforts to study temporal

constraints such as delay constraints, deadline con-

straints and data delivery deadline on data processing

techniques like data aggregation, data collection and

query processing. However, there are few existing

researches works which focus on studying temporal

constraints in query processing systems, also called

abstract databases.

2.2 Abstract Database for WSN

Abstract databases for WSN have been cited for the

ﬁrst time by Bonnet et al. (Bonnet et al., 2001). They

consider the WSN as a relational database that can be

queried using SQL-like queries. Abstract databases

present a declarative approach to facilitate the de-

scription and processing of queries in the WSNs. Data

required by the base station form a virtual table, in

which columns rerepresent the data requested by the

user.

Madden et al. have presented a new query pro-

cessing system, named TinyDB, using TinyOS (Levis

et al., 2005) as an operating system, for extracting

information from a network. It is composed of two

subsystems: (i) TinyDB application, running on each

node of the network, (ii) Java-based client interface,

allowing the user to describe the data using a declar-

ative SQL-LIKE queries, i.e., without knowing how

the data are processed in network. The users writes

the query that will be compiled and split into tasks,

then injected into the network. Once a node receives

the query, it processes it, and returns the results to the

user via the base station. For example, to calculate the

average temperature for all sensors when the light is

greater than 400 for a period of 10 seconds, we write

the following query:

SELECT AVG (temperature)

FROM sensors

WHERE light > 400

EPOCH DURATION 10s

TikiriDB (Laxaman et al., 2010) is a database ab-

straction layer for ContikiOS. It provides function-

SENSORNETS2015-4thInternationalConferenceonSensorNetworks

ality to collect data from WSNs without having any

information about programming knowledge of sensor

nodes. TikiriDB provides a TikiriSQL library, to re-

ceive queries from client, to parse them and to gen-

erate query packets. The results are sent to the gate-

way computer via the Serial Forwarder (SF) applica-

tion. The node connected to the gateway, receives the

queries, then injects them in WSN. It receives there-

after the results, which will be forwarded to the end

user by the SF application. In the following, here is

an example of TikiriDB query which permits to get

the light and the temperature from all sensors whose

temperature is above50. Each new reading will be de-

livered to the end user every 2 seconds for a duration

of 10 seconds.

SELECT light,temperature

FROM sensors

WHERE temperature > 50

SAMPLE PERIOD 2s FOR 10s

Amato et al. have proposed MaD-WiSe (MAnage-

ment of Data in WIreless SEnsor networks) (Amato

et al., 2010), which considers a WSN as a highly dis-

tributed and dynamic database. MaD-WiSe takes ac-

count of various aspects related to database system

design. Users can express queries using an SQL-like

language, named MW-SQL, to collect, to ﬁlter, and to

manage sensors data.

Corona (Khoury et al., 2010) is a distributed in-

network query processing system which provides a

declarative query language SQL-Like to formulate

queries. Corona introduces the notion of freshness

into WSN, allowing the user to obtain data from a sen-

sor network with data freshness guarantees. It uses

multiple aggregate queries to reduce processing de-

lays and costly communication in WSN. It is capable

of running multiple applications simultaneously.

An example of Corona query is given in the fol-

lowing:

SELECT temperature

FROM sensors

WHERE light > 100

EPOCH 60s

FRESHNESS 10s

In this example, the user queries all sensors to get the

temperature greater than 50 for epoch period of 60

seconds. It deﬁnes also the freshness constraint clause

of 10 seconds to ﬁx the time allowed between two

acquisitions.

We note that temporal constraints have not been

considered in the most of abstract databases. They

have been studied only in the recent Corona work

(Khoury et al., 2010) . In the next section, we il-

lustrate our network model through two scenarios: a

periodic data collection and a query processing using

TinyDB and we present the parameters we selected to

discuss them in section 4.

3 NETWORK MODEL

In the two scenarios, we have used a network model

composed of four components: (i) sensor nodes

which report informations about the environment, (ii)

a base station which collects periodically data from

sensor nodes, (iii) a remote database used to store

the received data, (iv) a user which can connect to

the database and get information about WSN. In the

second scenario, we have added an abstract database

for WSN, named TinyDB, which allows the user to

send queries to the base station and to get queries re-

sponses.

3.1 First Scenario: Data Collection with

Remote Database

In this scenario, sensor nodes are randomly deployed

over an area and the base station is placed outside the

network. Nodes communicate amongst themselves

through wireless communications. Sensor nodes re-

trieve data values like temperature and humidity, and

send them to the base station. The data received are

transmitted to the remote database to be stored. Fi-

nally, they can be displayed through an interface to

the users.

Sensor network

User 3

Database server

Queries

Base Station

Data coll

ection

User 1

Data logging

User 2

response

Figure 1: Data collection with remote database.

3.2 Second Scenario: Query Processing

with WSN Abstract Database

This scenario is based on a query processing tech-

nique, in which we use an abstract database system,

TinyDB. User speciﬁes the data it want to collect from

WSN, through SQL-like declarative queries. It sends

StudyofTimingPropertiesonDataCollectionandQueryProcessingTechniquesinWirelessSensorNetworks

Sensor network

User 3

Database server

Queries

Base Station

Data coll

ection

User 1

User 2

Queries

Abstract database

TinyDB

Data logging

response

Figure 2: Query processing with abstract database.

them to the base station via the the abstract database

interface. The base station will inject these queries

over the network. Each sensor receives a query, re-

sponds with the values requested for a duration ﬁxed

in the query by the user. The base station sends the

responses to the user via the abstract database system

and they can be stored in a remote database.

We have considered some performance parame-

ters for the two approaches and we discussed them in

next section. These parameters are: (i) the network

convergence time, an important factor to ensure the

temporal constraints in WSN. It precedes the process

of data collection. It can affect on the arrival time of

data when it takes a long of time. We consider also

(ii) the data collection time, which presents an im-

portant factor to guarantee the freshness of data and

the data delivery deadline. When the data collection

is fast, data freshness will be better. Finlay, (iii) the

database response time which can be considered as an

important factor in the ﬁrst scenario, because it repre-

sents the only data available to the user. We will show

in the next section (simulations) that sometimes, data

freshness cannot be met in this stage.

In the next section, we implement the two scenar-

ios described above and we discuss the performance

parameters for the two approaches. We identify and

we analyze also different impacts which can affect

these parameters.

4 SIMULATION & RESULTS

4.1 Simulation Environment

4.1.1 First Scenario

We have used COOJA (

Osterlind et al., 2006), the

network simulator for ContikiOS, to create the ﬁrst

scenario. We have created one sink node as base sta-

tion and a set of sender nodes of type Tmote Sky with

48 kb of ﬂash memory. The nodes are randomly dis-

tributed in a area within 100m*100m and the sink is

placed outside of the deployment area. The number

of sensors vary from 10 to 100 with step of 10. Each

node sends temperature and humidity values to the

sink node every 60 seconds. Table 1 summarizes the

simulation parameters.

Table 1: Simulation Parameters.

Parameters Values

Operating system ContikiOS 2.6

Simulator Cooja

Mote type Tmote Sky

Simulation area (m) 100x100

Number of nodes 10-100

Number of sink Nodes 1

Source data rate (events/s) Random

Radio range (m) 80

Data packet size (bytes) 15

We use RPL (Routing Protocol for Low-Power

and Lossy Networks) (Winter et al., 2012) to provide

communication between sensor nodes and the base

station. RPL is a routing protocol designed for low

power and lossy networks and for large networks. It is

more efﬁcient for data delivery time than other exist-

ing known protocols such as LOAD (LOADis derived

from AODV), DSR and DSDV (Vucinic et al., 2013).

It provides efﬁcient routing paths guaranteeing data

delivery before deadline in WSN. We have used Con-

tikiMAC radio duty cycling protocol (Dunkels, 2011)

inspired from existing duty cycling protocols. It has

shown efﬁcient time management because it uses a

wake-up mechanism to deﬁnes a precise timing be-

tween packet transmission. We have used the default

Contiki MAC layer CSMA (carrier sense multiple ac-

cess) which is the only one MAC driver in Contiki.

The table 2 below summarizes the different layers

protocol used in simulation.

Table 2: Simulation layer protocol parameters.

Transport layer UDP

Network layer IPV6/RPL

Radio duty cycling ContikiMAC

MAC layer CSMA

Physical layer IEEE 802.15.4

4.1.2 Second Scenario

We used TinyDB as abstract database in TinyOS.

TinyDB runs on Mica2 motes with 128 KB ﬂash

memory because its code execution requires a ﬂash

memory upper than 48 KB. We have chosen to use

SENSORNETS2015-4thInternationalConferenceonSensorNetworks

TinyDB because it is the most widely used system

by researchers and it has shown its effectiveness in

maintaining energy and for data managing. The net-

work is based on Mica2 sensors randomly scattered

in the ﬁeld. We have varied the number of sensors

from 10 to 100 by step of 10. The sensor are queried

on demand by the user via the base station. Table 3

summarizes the simulation parameters.

Table 3: Simulation Parameters.

Parameters Values

Operating system TinyOS 1.x

Simulator Tossim

Mote type Mica2

Simulation area(m) 100x100

Number of sender nodes 10-100

Number of sink Nodes 1

Radio range (m) 80

Data packet size (bytes) 20

TinyDB uses a tree-Based Routing Protocol

(Gehrke and Madden, 2004) to provide the query dis-

semination and the result collection. Each node for-

wards the query to other nodes in the network, to form

the routing tree. This process ends when all nodes

will receive the query. TinyDB uses CSMA proto-

col, which is the already existing default MAC pro-

tocol used by TOSSIM. It uses also the Low power

listening (LPL) for low power communication and

the Drip/Drain routing. Drip is a transport-layer

component for disseminating messages throughout a

network and Drain is a collection routing layer for

TinyOS 1.x. Table 4 below summarizes the different

layers protocol used in simulation.

Table 4: Simulation layer protocol parameters.

Transport layer Drip/Drain

Network layer Tree-based routing protocols

Radio duty cycling Low-Power Listening

MAC layer CSMA

Physical layer IEEE 802.15.4

4.2 Simulation Description

4.2.1 First Scenario

Each sender node uses the UDP sender algorithm to

send its data to the base station. We have deﬁned a

transmission period of 60 seconds. We have chosen

a random waiting time (SEND

TIME), before send-

ing packets to avoid that the senders sending pack-

ets at the same time. We have activated the digital

sht11 sensor, relative to sensor Humidity and tem-

perature. A sender node listens to its neighbors us-

ing tcpip

handler() function and continues to send

data currently detected to the base station using the

”send

packet” function.

Algorithm 1: UDP sender process.

1: deﬁne period 60

2: deﬁne send time random Value

3: sensor activate(sht11 sensor)

4: while 1 do

5: if ev = tcpip

event then

6: tcpip

handler()

7: end if

8: if etimer

expired(period) then

9: etimer reset(period)

10: ctimer

set(backoff time, send time,

11: send packet, null)

12: end if

13: end while

4.2.2 Second Scenario

When TinyDB receives the query from the user, it

parses and translates it to SQL query into an execu-

tion plan. Then, it injects it into the network via the

base station. Each node receives the query, processes

it using a query processor and returns the requested

values. When a result arrives, TinyDB calls the result

listener method and displays the query result for the

user using a Java-based client interface.

4.3 Simulation Results

4.3.1 Impact of Number of Nodes on Network

Convergence Time

The network convergence time is deﬁned as the time

needed for the sensors to connect to the base station

and to build routing tables. This step comes just be-

fore the process of data collection. It can affect the

arrival time of data. The shorter is the convergence

time, the quicker is the availability of the data. In Fig-

ure 3, the network convergence time increases when

the number of nodes increases. The network conver-

gence time depends on the DODAG building process,

which begins at the root (base station). The root starts

the dissemination of information about the structure

using DIO (DODAG Information Object) message.

The nodes not connected to the tree (without commu-

nication with the root) receive the message DIO and

process it. Then they decide to join or not the struc-

ture. Once the node has joined the structure, it has

a route to the root of the DODAG structure. Build-

ing the ﬁnal routing table depends on the number of

nodes and the path cost between nodes. We note also

StudyofTimingPropertiesonDataCollectionandQueryProcessingTechniquesinWirelessSensorNetworks

100

200

300

400

500

0 10 20 30 40 50 60 70 80 90 100

Time (second)

Number of nodes

Data collection

Figure 3: Network convergence time for data collection.

0 10 20 30 40 50 60 70 80 90 100

Time (second)

Number of nodes

Query Processing

Figure 4: Network convergence time with TinyDB.

in Figure 4 that the convergence time for TinyDB (ac-

quisition on demand) is less than that of data collec-

tion (periodically). TinyDB constitutes a routing tree

or Directed Acyclic Graph (DAG) with all sensors in

which the root is the sink (gateway). TinyDB uses a

selecting multi-hop tree based on efﬁciency of rout-

ing which depends on energy consumption. Sensors

make intelligent routing decisions, where every node

selects a parent based on link quality to the base sta-

tion. The nodes keep both a short list of neighbors

which have a recently heard transmit, and some rout-

ing information about the connectivity of those neigh-

bors to the rest of the network. They associate also a

link quality with each of their neighbors.

4.3.2 Impact of Number of Nodes on Data

Collection Time

Data collection time represents the time taken to get

the responses from all sensors connected to the base

station. It can affects the data validity time and then

not reﬂecting the current state of the environment.

The time required to send the data from sensor to the

base station depends on the sensors capacity, its po-

sition and the path quality depend on the number of

hops to the base station. Generally, the sender uses

multi-hop paths to send its data when the base station

is far located, which leads to transmission delay and

can cause a failure, i.e. to not respect the data validity

time.

100

200

300

400

500

600

0 10 20 30 40 50 60 70 80 90 100

Time (second)

Number of nodes

Data collection

Query processing

Figure 5: Completed Cycle Time.

Figure 5 shows the time spent by the nodes to

complete the sending of their data, using both data

collection technique and query processing technique.

We observe that the curve increases when the num-

ber of sensors increases for the two techniques. The

time required to send the data depends of the number

of hops and the availability of parent nodes to send

data received from children. Their availability is not

guaranteed all time because the choice of parent node

depends on the node decision to join or not the struc-

ture.

We also notice in Figure 5 that the time required

to obtain results from all sensors by using the tech-

nique of processing request is less than that of data

collection. With TinyDB, the data is regularly re-

ported and aggregated by a tree or a directed acyclic

graph from the nodes to an access point network. It

includes aggregation and ﬁltering operations inside

the sensor network to maintain all routing informa-

tion. The parent nodes near the root put agreements

with their children on a time interval for the data lis-

tened to them. The whole process is repeated for each

period and query.

4.3.3 Impact of the MAC Layer Protocols

The goal of this test is to observe the Complete Cy-

cle Time (CCT) vs Duplicate Packets Sending (DPS)

with either a periodic data collection, or a query pro-

cessing system (TinyDB). We have varied the number

of sensors in each time.

We note in Table 5 that the number of transmission

packets with TinyDB is greater than that with peri-

SENSORNETS2015-4thInternationalConferenceonSensorNetworks

Table 5: Duplicate sends with periodic data collection and

TinyDB query processing.

Periodic data

collection

Query processing

(TinyDB)

Nb.nodes CCT(sec) DPS CCT(sec) DPS

5 47.430 0 2.203 0

10 53.955 0 2.434 0

20 54.157 0 4.766 21

30 62.082 0 14.060 102

40 73.302 0 25.441 121

50 106.609 0 435.43 174

60 147.357 2 705.02 294

70 179.478 1 112.261 308

80 266.357 15 142.114 405

90 457.570 78 233.737 420

100 570.100 72 313.832 633

odic data collection. The MAC-layer protocol can be

considered as one of the causes to explain these re-

sults. Tossim implements CSMA

based MAC proto-

cols by default. The basic principles of this protocol

are ”listen before talk and contention”. Each node lis-

tens on the channel and checks if it is clear before

each sending. If the channel is not clear, it waits for

a randomly chosen period of time called the backoff,

until the channel becomes clear. When the number

of sensor nodes increases, the nodes compete for the

channel and make the channel occupied. This com-

petition causes a transmission delays and a backoff

time accumulation, which increases the time needed

to collect data from all sensors. On the other hand,

each sender repeatedly sends its packet, during the

full wake-up, until it receives a link layer acknowl-

edgment from the receiver (sink node), meaning that

the receiver has correctly received the packet. In pe-

riodic data collection, the sky motes implement a ra-

dio duty cycling protocol, named ContikiMac using

a wake-up mechanism to listen for packet transmis-

sions from neighbors and to precise timing between

transmissions (Dunkels, 2011), which explains the

decrease in duplicate Packets Sending.

4.3.4 Impact of Choosing the Database on

Average Response Time

We use in this test three DBMS

: PostgreSQL,

MySQL, SQLite, to evaluate queries response time.

Table 6 shows that SQLite database gives the best

average response time for the SELECT queries, then

we ﬁnd MySQL and ﬁnally PostgreSQL. For the IN-

SERT queries, PostgreSQL database has the less aver-

age response time compared to MySQL and SQLite.

SQLite takes a lot of time to insert data, because it

Carrier sense multiple access

DataBase Management System

does not have a central server to coordinate accesses.

It must close and reopen the database ﬁle, and in-

validate its cache, for each transaction. It does not

allow multiple concurrent writes in the same time.

PostgreSQL allows multiple transactions to proceed

with inserts concurrently. MySQL is better than Post-

greSQL for reading tables because it has a ”Query

Cache” to make queries faster. SQLite is faster than

MySQL because it uses a disk access to read and write

directly from the database ﬁles on disk.

Table 6: Query response time (ms).

Query type PostgreSQL MySQL SQLite

Insert queries 9.397 48.626 72.788

Select queries 0.992 0.690 0.225

4.3.5 Impact of Network Topologies on Data

Collection Time

We have tested 15 sender nodes and one sink node

with four network topologies. The result in Table 7

shows the time spent to collect data from all sensors

for the two technologies.

Table 7: Complete cycle time (ms).

Topologies Data collection Query processing

Star 62312 1832

Mesh 56002 1362

Grid 54121 2163

Tree 53515 2143

We note that the tree network topology for data

collection technology is more faster than the other

topologies. In fact, RPL forms a tree-like topology

rooted at the sink, reﬂecting the above results. Star,

mesh and grid topologies have not shown good re-

sults. RPL takes a time to to deﬁne DODAG networks

and to build path to the root. It can not fully exploit,

which affects the quality of paths. For the query pro-

cessing technology, the mesh topology gives the best

complete cycle time versus the other topologies. In

mesh topology, all nodes cooperate in the distribution

of data in the network. The same technology is used

by TinyDB. The nodes make an intelligent routing de-

cisions, where every node selects a parent based on

link quality to the base station.

5 CONCLUSION

In this work, we have studied and compared the tim-

ing properties of the data collection from a sensor

network using a static database versus an abstract

StudyofTimingPropertiesonDataCollectionandQueryProcessingTechniquesinWirelessSensorNetworks

in-network database, named TinyDB. We have eval-

uated temporal constraints such as data collection

time, database response time, and network conver-

gence time in both approaches. We have found, ac-

cording to the tests performed, that many factors can

affect the temporal constraints in a WSN. We have

determined that the network topology and the rout-

ing protocol together may play an important role on

data collection time. The convergence time also has

an impact on the process of data collection. We have

shown clearly the timing-response advantage of using

a TinyDB approach compared to accessing the data

stored in an external database. So we can conclude

that the great choice of the network topology and the

routing protocol with the right approach can improve

the temporal constraints in WSN. We plan to work in

this direction in a future works by taking into account

data temporal consistency.

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