WSNAP: a Flexible Platform for Wireless SensorNetworks Data Collection and Management

M. Cerra, L. ZuechTretec S.r.1.Trento, Italy

Abstract - Flexible, distributed monitoring systems are essentialto support decisions in a variety of contexts such as, for instance,vehicular traffic control, emergency evacuation plans, energyoptimization services, pollutant detection or intelligentagriculture applications. In this paper, we present WSNAP, aplatform for Wireless Sensor Network (WSN) deployment.WSNAP relies on standard technologies, which enable users tointeract easily with different types of sensors. Its main advantageis the ability to be easily adapted to the requirements of differentapplications, regardless of network topology and sensor boardarchitecture. WSNAP was tested on a small WSN deployed at the"Dipartimento di Ingegneria e Scienza dell'Informazione" (DISI)of the University of Trento, Trento, Italy. In the following, afterdescribing the structure and the main features of the platform,the results of some experiments are reported.

Keywords - Wireless sensor networks, environmental monitoring,distributed measurement systems, databases.

I. INTRODUCTION

In last years the adoption of wireless sensor networks(WSNs) for distributed monitoring systems has becomeprominent. As known, a WSN consists of a quite large numberof tiny, low-power, embedded devices with sensing,computing, processing and wireless communicationcapabilities [1]. Usually, these devices are referred to assensor nodes. The collaborative work of these nodes is used toperform distributed and coordinated measurement of differentquantities (e.g., temperature, humidity, light or windintensity). WSNs have many potential fields of application,such as target tracking, surveillance, biomedical healthmonitoring, habitat monitoring and elderly care. In principle,WSNs are supposed to work unattended in harsh, hostile orout-of-reach locations. In fact, the flexibility of a wirelessnetwork made up of a variety of autonomous sensor nodes canbe hardly achieved with conventional wired solutions. Forsuch reasons, WSNs are very suitable for environmental,structural and energy monitoring.Environmental monitoring - besides being concerned withprecision agriculture [2],[3], reserve observations [4],[5],[6],natural phenomena [7] and pollution [8] - is increasinglymotivated by the severe climatic changes, which the world isundergoing. For instance, the SensorScope project wasfocused on the development of a WSN-based measurementsystem for real-time and long-term monitoring of naturalevents, such as glacier ice-melting [9].In the field of structural monitoring, up to now WSNs havebeen mostly used to check the health of buildings, bridges and

978-1-4244-4848-7/09/$25.00 ©2009 IEEE

C. Torghele, P. Pivato, D. Macii, D. PetriDISI - Dipartimento di Ingegneria e Scienza dell'Informazione

University of TrentoTrento, Italy

other civil structures by measuring various quantities, such asvibrations, deflections and stress [10]. The main objective ofthese technologies is to improve people safety, especiallywhen aged structures are used [11],[12]. One of the largest andmost famous examples of WSNs for structural healthmonitoring (SHM) is the 64-node network located on the mainspan of the Golden Gate Bridge in San Francisco, USA [13].The idea of using the WSNs in the field of energy monitoringapplications is defmitely more recent. In fact, to the best of ourknowledge, just a few research results exist in this field at themoment [14],[15]. However, WSNs are expected to have amajor role in next-generation solutions for reducing energyconsumption (e.g., through building automation systems [16]).Two quite common problems in environmental, structural andenergy monitoring applications are data arrangement andWSN management. In this paper we will present WSNAP(Wireless Sensor Network Application Platform), a platformfor WSN that was explicitly designed to meet the requirementsof manifold monitoring services (Le., based on different kindsof sensors). After a short overview of other existing solutions(Section II), we will summarize the key features of theplatform in Section III. In Section IV the results of someexperimental activities will be reported. Finally, conclusionswill be drawn in Section V.

II. RELATED WORK

When multiple heterogeneous quantities are measured byWSN nodes, the way in which sensor data are collected,stored, processed and retrieved is essential to supportdecisions. Usually, most of hardware/software monitoringsolutions based on WSNs are tailored to the requirements ofsome specific application. For instance, some researchers atthe University of Madeira, Portugal, implemented a WSN­based monitoring system for art conservation purposes [17].The network prototype consists of just 3 sensor nodes, whichmeasure humidity, temperature, light and pollutants. Througha sink node, the data are stored wirelessly into a databaseresident in a PC connected to the Internet. A web interfaceenable remote users to visualize the data in real time.Unfortunately, the flexibility and portability features of theproposed solution are not clearly described in the paper.Therefore, the possibility of extending the proposed solutionto other scenarios is not straightforward.

In the PermaSense project [18], a 10-node WSN based onthe TinyNode platform [19], with a fixed, single-hop, multi-

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sink topology, was installed in the Bernese Alps ofSwitzerland to gather permafrost rock-related parameters,which in tum were used to assess mountain slope stability.The solution developed for PermaSense was conceived andoptimized to tackle a very specific scientific problem. Thus, itis not very suitable for generic environmental monitoringpurposes.

More recently, the researchers of the Shanghai OceanUniversity, China, designed and implemented a WSN platformfor greenhouse monitoring [20]. The WSN is composed of aset of sensor nodes that collect data from the environment andtransmit the gathered information to a sink device.' Thesoftware applications for WSN management were developedonly using commercial tools (e.g., .NET Framework 2.0) .Such applications comprise a database, a data processingengine, a back-stage module supporting data visualization, anda graphical user interface (QUI) to display the status of thenetworks and other information.

The basic structure of the WSNAP project described in thispaper is quite similar to the solution proposed in [20].However, WSNAP extends the basic idea of the Chineseresearchers because it is not related to any specific monitoringapplication. Also, it relies only on open source technologies.WSNAP was developed for two complementary purposes, i.e,• Enabling network administrators to manage different

WSNs, regardless of the number of nodes, networktopology and sensor types;

• Allowing remote users to check the status of the networkas well as to visualize and to analyze the collected datasimply and intuitively.

III. PLATFORM DESCRIPTION

WSNAP is the result of a cooperation between the researchgroup in Embedded Electronics and Computing Systems(EECS) of the University of Trento, Trento, Italy and TretecS.r.l. , a spin-off company of the same University. WSNAPconsists of both firmware and software modules. The firmwarewas developed in C/NesC, in order to ease code porting ifdifferent sensor node architectures supporting TinyOS areused in the future [21]. The software side of WSNAP insteadcomprises various applications based on standard webtechnologies. Such applications run on a Personal Computer(PC) configured as a server and connected to the internet.WSN and PC are bridged by means of a special node, in thefollowing referred to as base-station, which gathers all datacollected by the other devices.The structure of WSNAP is shown in Fig. 1 and consists offour main parts, i.e, DeltaMultiSens, TretecTrawler, Databaseand Web Utilities .DeltaMultiSens includes the set of C/NesC modulescontrolling the behavior of a generic WSN node .DeltaMultiSens checks if a given node is the base-station or anonnal sensor node. The base-station checks its batteryvoltage level, waits for possible incoming data and transfersthem to the PC through a Universal Serial Bus (USB)connection. All the other nodes performs the same sequence ofoperations regardless of the network topology and the type ofon-board sensors. In particular, during the start-up phase,DeltaMultiSens detects what sensors are actually installed on agiven device . Afterwards, each node (except the base-station)periodically turns the sensor board on, samples the quantitiesmeasured by every sensor and fmally sends the collected data

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to another device, which is geographically closer to the base­station. Additionally, if the node receives a packet from someother nearby device, it forwards the corresponding datatowards the base station, according to a given multi-hopscheme. Once both local data transmissions and other nodes'data transmission are over, each WSN node enters into sleepmode and stays in such a state for a specified time interval.The length of this interval (namely the monitoring period) isset by the user and it can be changed by reprogramming thenodes.TretecTrawler is a Java application running on the PC thatdecodes the messages received from the base-station, plot thesensor data locally and finally stores them into a Database bymeans of Structured Query Language (SQL) commands. Thedatabase can be easily created and modified through Appserv,which integrates Apache, PHP and MySQL in a unique

environment [22]. Of course, the structure of the Databasedepends on the requirements of the specific monitoringapplication considered. In general, the Databas e shouldinclude at least:

• a node table containing some general information abouteach node such as its identification number (ID), itsposition, parent and child nodes and number of localsensors;

• a sensor table including sensor types, manufacturers,operating ranges, supply voltages and metrologicalcharacteristics (if available);

• a message table in which all information associated withany received message are orderly stored (e.g., time­stamps, IDs of source and destination nodes, sensorvalues, etc. .. ).

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Fig. 3 - Qualitative layout of the WSN deployed at the "Dipartimento diIngegneria e Scienza dell'Informazione" (DISI) of the University ofTrento, Italy. The map refers to the building hosting DISI. The numbersover the links denote the quality of the wireless connections : the lower thevalue, the better the link. Different colors refer to different types of radioconnections.

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(b)Fig. 4 - 3MATE! node (a) and external sensor board (b) (by courtesy ofTretec S.r.I., Trento, Italy).

Additionally, others tables can be created to store the batterylevels of the various nodes or to build specific graphs. Twoscreenshots showing the structure of a node table and a sensortable are displayed in Fig. 2(a) and 2(b), respectively. Thanksto the Web Utilities shown in the top-right comer of Fig. 1,remote users can retrieve different sets of sensor data from thedatabase. To this purpose, an HTTP server (e.g., Apache) and

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a PHP server must be installed on the PC. The databasequeries are encapsulated inside PHP page scripts.Accordingly, the retrieved data can be displayed on client PCwith standard web viewers such as Open Flash Chart [23].

IV. EXPERIMENTAL ACTIVITIES

The WSNAP platform was used to manage a small sensornetwork deployed at the "Dipartimento di Ingegneria eScienza dell'Informazione" (DISI) of the University of Trento.In the following subsections, at first the network topology andthe experimental setup will be presented. Then, the mainhardware features of the sensor nodes will be described.Finally, some experimental results will be reported andcommented.

A. Wireless Sensor Network Topology

The WSN deployed at DISI consists of ten nodes equippedwith different kinds of sensors, which will be described in thenext subsection. The network nodes are located over twofloors. The position of the nodes is qualitatively shown inFig. 3. Notice that the network topology is fixed and includesseveral multi-hop connections. Multi-hopping is essential inthe considered scenario, because the end-to-end distance aswell as the obstacles (e.g., walls, floors) between nodesprevent full network visibility. The base-station, also referredto as "node 0" and graphically represented with a laptop iconin Fig. 3, gathers the data collected by all the other devicesand transfers them to the server PC through a USB link. ThePC finally stores the sensor data into the database, asdescribed in Section III. The other network nodes are labeledwith numbers between 202 and 210. Node 202 is placedoutdoor, whereas all the others are located in various officesand laboratories of DISI. In Fig. 3, red and green lines refer toconnections between nodes on the same floor (i.e., ground andfirst floor, respectively). Blue lines instead represent interfloorcommunications. The numbers reported over the links denotethe quality of the connections evaluated on the basis of thereceived signal strength indication (RSSI) and the link qualityindicator (LQI) of each hopping node. The smaller thenumber, the higher the quality of the link. RSSI and LQIvalues are updated in real-time and they are also stored intothe database. Observe that the node in the bottom right comerof Fig. 3 (i.e., node 206) is not connected to any other device.In fact, it was intentionally installed a bit far away from theothers in order to test the maximum range of the network. Inthe considered case study, the maximum range is in the orderof30 m.

B. Node architecture and sensors

As stated above, the WSN prototype consists of 103MATE! nodes made by Tretec S.r.l., Trento. One of suchnodes is shown in Fig. 4(a) and includes:

• a microcontroller TI MSP430F1611;• a radio module TI/Chipcon CC2420 compliant with the

standard IEEE 802.15.4 [24];• an (optional) external flash memory to save data

permanently;

• a printed F-type antenna;• a SMA connector for an optional external antenna;• some expansion connectors for node programming and

sensor board interfacing.The piggy-back sensor boards shown in Fig. 4(b) and

installed on each WSN node (except the base-station) to runexperiments in different conditions include one or more of thefollowing sensors:

• a visible light sensor with human-eye responsivity;• an analog temperature sensor;• a digital air humidity sensor;• a capacitive soil moisture sensor.

The main characteristics of the employed sensors are listed inTab. I. Typical accuracy refers to calibrated devices . The soilmoisture sensor is a prototype developed by Tretec S.r.l.,which was tested, but not properly calibrated.

TABLE I - T YPEAND MAINCHARACTERISTICS OF THE SENSORSUSEDDURINGTHE EXPERIMENTS AT DISL

Sensor typeOperating Operating Typical

Voltage Range Accuracy

Ambient Light [2.2,5 .0] V [0,20,000] lux ±501ux

Temperature [2.5,5.5] V [-40, +125] °C ±0.5°C

Air humidity [2.4,5 .5] V [O,IOO] %RH ±3%RH

Soil moisture [2.1, 15] V [O,IOO]%RH n.a

C. Experimental results

WSNAP enables remote users to monitor the data collectedby the WSN through a common web browser such asWindows Internet Explorer or Mozilla Firefox. From thewebpage http://www.wsnlab.it/wsnap/ users can performdifferent types of search in the database and they can build thecharts related to the quantities monitored by each node duringa specified time interval. In this example, all devices areprogrammed to collect and to transfer sensor data towards thebase-station every 30 seconds. However, the temporalresolution of the dynamically loaded charts can be much largerthan 30 seconds, because all data sequences extracted from thedatabase are selected on the server PC before being transferredto the client. This approach introduces some visualizationuncertainty, but it is essential to speed up webpage loading. Inorder to keep the loading time within reasonable limits for aremote user, the maximum number of sensor data that can betransferred between server and client to build a single chart isKma<=200. If the amount of collected samples during the timeinterval specified by the user is larger than Kma<, the serverapplication at first partitions the extracted data set into subsets ,then it computes the average value over each subset andfinally it transmits the resulting K averaged data points to theclient PC, with K:5 Kma<'Some examples of web charts displayed to a remote user areshown in Fig. 5(a)-5(d). They refer to the data collected in theweek between June 1 and June 8, 2009. The time resolution inall charts is equal to 1 hour. Fig. 5(a) displays the voltage

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levels of all batteries. They have not been replaced sinceDecember 2008 . Notice that such levels are approximatelyconstant over 1 week, but they differ from node to node,because the amount of dissipated energy over six months hasnot been the same for all devices. The chart in Fig. 5(b) showsthe environmental temperature patterns measured by all activenodes . Observe that the temperature values collected by indoornodes are stably between 20°C and 25 °C. Conversely, thecurve corresponding to the outdoor device (i.e. node 202)exhibits large daytime-nighttime oscillations, as expected.The chart in Fig. 5(c) shows the illuminance values (in lux)collected by two nodes, i.e. the same outdoor device as above(node 202) and a node that was intentionally placed in a darkenvironment (node 209) . Again, the curve related to node 202is characterized by obvious large daytime-nighttimeoscillations. The amplitude of the maxima depends on theweather, whereas the minimum (night) values are compatiblewith those measured by node 209. Therefore, it is reasonableto assume that the illuminance data collected by node 202 arenot affected by significant systematic offsets.Node 202 is also connected to a capacitive soil moisturesensor that was implanted into the ground. The correspondingrelative humidity chart is shown in Fig. 5(d). In this case, nocomparisons are possible, because only node 202 is equippedwith this kind of sensor. As the soil moisture sensors is aprototype, it was tested, but not properly calibrated.Nonetheless, the relative humidity values shown in Fig. 5(d)are sound.

V. CONCLUSIONS

WSNAP is a platform that was conceived to collect, tostore, to arrange and to visualize heterogeneous data setscollected by different sensor nodes in an intuitive and user­friendly form. WSNAP was used to manage a small WSNdeployed at the University of Trento . Several experimentsproved that the platform works correctly and with goodperformances. We think that the flexibility of WSNAP couldbe particularly beneficial for environmental, energy andstructural monitoring. Indeed, we expect that WSNAP will beeasily adapted to the requirements of different applications inthe next future .

VI. ACKNOWLEDGMENTS

The results presented in this paper are part of the projectTrentino Research and Innovation for Tunnel Monitoring(TRITON), funded by the project members and theAutonomous Province ofTrento.

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