Wireless Sensor Networks Application Centric Design 2011 Part 4 potx

Research on traffic monitoring network and its traffic flow forecast and congestion control model based on wireless sensor networks, Measuring Technology and Mechatronics Automation, 200

while downloading encoded packets from APs, until it collects enough for sensor data

recov-ery using the iterative BP algorithm The number of excessive encoded packets compared to

k sensor packets is measured by the reception overhead 6; i.e., for successful recovery MC

needs in total k  = (1+ )· k encoded packets, where   usually is a small positive

num-ber Since each encoded packet is an innovative representation of the original data, any subset

of k  = (1+ )· k taken from the set of all the encoded packets in the network allows for

restoration of the whole original data This property of rateless codes makes them a perfect

candidate to be used at the application level for content delivery in vehicular networks, since

packet losses caused by the varying link characteristics are compensated simply by reception

of the new packets and there is no need for standard acknowledgment-retransmission

mecha-nisms which can not be supported by a semi-duplex architecture as the one adopted In other

words, the usage of connection-oriented transport protocols like TCP can be avoided, as

UDP-like transport provides a satisfactory functionality Moreover, the loosing of packets caused by

channel error or by the receiver deafness during the selection of a different AP does not impact

on BC scheme, as MC continues downloading data without any need for (de/re)association,

session management or content reconciliation

4 Simulation Results

The simulation setup assumes that the urban area is covered by a regular hexagonal lattice,

where each non-overlapping hexagon represents the coverage area of a single AP and the

hexagon side length is equal to the AP transmission range MCs move throughout the lattice

using the rectangular grid that models urban road-infrastructure, associating with the nearest

AP The overlay hexagonal AP lattice is independent and arbitrarily aligned with the

under-lying rectangular road-grid The MCs move according to the Manhattan mobility model (Bai

et al., 2003), a model commonly used for metropolitan traffic In brief, Manhattan mobility

model assumes a regular grid consisting of horizontal and vertical (bidirectional) streets; at

each intersection, MC continues in the same direction with probability 0.5 or turns left/right

with probability 0.25 in each case The MC speed is uniformly chosen from a predefined

inter-val and changes on a time-slot basis (time-slot duration is a model parameter), with the speed

in the current time-slot being dependent on the value in the previous time-slot Besides

tem-poral dependencies, Manhattan mobility model also includes spatial dependencies, since the

velocity of a MC depends on the velocity of other MCs moving in the same road segment and

in the same direction; as we are interested only in I2V communications from the perspective

of a single user (i.e., a single MC), spatial dependencies are omitted in our implementation

The purpose of the simulations is to estimate the duration of the download phase, as the most

important and the lengthiest phase of the data refreshment period In each simulation run,

while moving on the road grid, the MC starts receiving the encoded data from the AP in

whose coverage zone it is currently located The reception of the encoded packets continues

until the MC collects enough to successfully decode all the original data If during this

pro-cess, MC happens to move to another AP zone, it simply associates to a new local AP (i.e.,

handover takes place) and starts to receive its encoded packets Also, if the AP has

transmit-ted all of its encoded packets to the MC, but it failed to decode the data (e.g., due to link-layer

packet losses), the MC suspends data reception until it enters the new AP coverage zone The

6 This takes into account both the decoding overhead as well as the redundancy needed in the presence

of erasure channel.

simulation run ends when the decoding is finished and all the original data packets are trieved All the presented results are obtained by performing 1000 simulation runs for eachset of parameters

AP transmission range 400 m

N AP(no of APs in the system) 40

N s(no of sensor nodes per AP) 50

k · L (total amount of original data) 4 Mbit≈0.48 Mbyte

c, δ (rateless code parameters) 0.03, 0.5

k AP(no of encoded packets per AP) 3600

T SF(superframe duration) 100 ms

τ HO(handover time) 0.5 s

P PL(packet-loss probability) 0.3road-segment length 150 m

mobility model time-slot duration 2 sTable 1 Simulation Parameters

Table 1 summarizes the values for the communication and mobility model parameters used

in simulations The number of APs is chosen such that it provides a coverage area which isapproximately equal to a medium-sized city area The data packet length is estimated in suchway that is sufficient to accommodate single sensor readings and additional headers (i.e., IEEE802.11 MAC and LLC, network and transport layer) The values for bit-rate and superframeduration are selected as suggested in (Bohm & Jonsson, 2008) and (Eriksson et al., 2008),pessimistic assumption on packet-loss rate and estimate of the mean MC handover time weretaken from (Bychkovsky et al., 2006), the average road segment length (i.e., average distancebetween two intersections) from (Peponis et al., 2007) The number of encoded packets per

AP, k AP is chosen such that a MC could decode all original data with probability of 0.99,when downloading from a single AP and considering employed rateless code properties and

assumed link-layer packet-loss rate In other words, k AP >

1+(max)

· k · L/(1− P PL)

Fig 3 presents the probability P SD that the MC successfully decodes the sensor data as a

function of time, for the BC service and T SF(BC) =0.1· T SF The value for T SFis selected suchthat it leaves enough room for the GC service and other usual best-effort services As it can

be observed from the figure, for higher bit-rates (i.e., R > 6 Mbit/s), the MCN is able to

successfully decode w.h.p all the data in the time span of several seconds The positive effect

of rateless coding is inherent in the fact that, even in the worst case, the data refreshment

period is below 15s, a value that still allows for real-time information updates and which could

be decreased further by assigning a larger superframe fraction to the BC service As opposed

to rateless encoded data delivery, the uncoded data delivery would result in retransmission

feedback implosion for BC service, overwhelming the sender (i.e., AP) with unwanted traffic

The probability of successful decoding for GC service is presented in Fig 4, where the fraction

of the superframe assigned to a single user is assumed to be T SF(GC) /N MN=0.01· T SF; the

val-ues for T SF(UC) and N MNare taken from the realistic analysis given in (Bohm & Jonsson, 2008)

Fig 4 demonstrates that for the standard GC service, the data refreshment period is of the

order of minutes rather than seconds, which limits its usage for the applications that tolerate

larger update periods However, this period would be significantly longer if rateless coding

was not used, since the link layer retransmissions would make the data delivery process

con-siderably less efficient Finally, it can be observed that, for the GC service, the differences in

transmission bit-rate have a significant impact on the download delay, which makes higher

bit-rates desirable

Fig 5 presents the duration of the time interval T0.99for which a MN, using the GC service,

decodes all the original data with probability P SD = 0.99, as a function of the number of

users N MN and for the fixed T SF(GC) =0.8· T SF The figure shows a linear increase in T0.99as

the rate decreases or N MNincreases, verifying that the content reconciliation phase is indeed

unnecessary, since the change of the AP does not introduce additional delays apart from the

handover time In other words, after a handover, MC seamlessly advances both with the

receiving and decoding processes

Finally, Fig 6 shows the cumulative distribution function F T of the number of transmitted

packets using the GC service from an AP to any MC within a single AP domain The

T(GC)

SF /N MNratio of the UC service is set to 0.005· T SFor 0.01· T SF As it can be observed,

the number of transmitted packets to a MC reaches the threshold value k AP equal to

3600 for the selected parameter values (Table I), in all cases but for R = 6 Mbit/s and

T SF(GC) /N MN =0.005· T SF This means that number of encoded packets per AP (i.e., k AP) is

properly dimensioned to allow a single user to collect enough of encoded packets to decode

all the data w.h.p while moving through a single AP coverage zone

To summarize the benefits provided by the proposed I2V data dissemination based on the

rateless codes over traditional methods, it is worth noticing first of all that, by their design,

rateless codes are tuned to the changing wireless link conditions and have a

close-to-the-minimal reception overhead Furthermore, each rateless coded packet is an equally important

representation of the original data, which makes lengthy TCP-like reliability mechanisms

un-necessary These factors influence the time allocations within the superframe, allowing larger

number of mobile nodes to be serviced during designated service-time portion of the

super-frame, or alternatively, service-time portion shortening, providing larger time allocations for

best-effort traffic Finally, while roaming through the network, mobile users can simply

con-tinue with data download from the new local AP after a handover, avoiding the redundant

content reconciliation phase

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

successfully decode w.h.p all the data in the time span of several seconds The positive effect

of rateless coding is inherent in the fact that, even in the worst case, the data refreshment

period is below 15s, a value that still allows for real-time information updates and which could

be decreased further by assigning a larger superframe fraction to the BC service As opposed

to rateless encoded data delivery, the uncoded data delivery would result in retransmission

feedback implosion for BC service, overwhelming the sender (i.e., AP) with unwanted traffic

The probability of successful decoding for GC service is presented in Fig 4, where the fraction

of the superframe assigned to a single user is assumed to be T SF(GC) /N MN=0.01· T SF; the

val-ues for T SF(UC) and N MNare taken from the realistic analysis given in (Bohm & Jonsson, 2008)

Fig 4 demonstrates that for the standard GC service, the data refreshment period is of the

order of minutes rather than seconds, which limits its usage for the applications that tolerate

larger update periods However, this period would be significantly longer if rateless coding

was not used, since the link layer retransmissions would make the data delivery process

con-siderably less efficient Finally, it can be observed that, for the GC service, the differences in

transmission bit-rate have a significant impact on the download delay, which makes higher

bit-rates desirable

Fig 5 presents the duration of the time interval T0.99for which a MN, using the GC service,

decodes all the original data with probability P SD = 0.99, as a function of the number of

users N MN and for the fixed T SF(GC) =0.8· T SF The figure shows a linear increase in T0.99as

the rate decreases or N MNincreases, verifying that the content reconciliation phase is indeed

unnecessary, since the change of the AP does not introduce additional delays apart from the

handover time In other words, after a handover, MC seamlessly advances both with the

receiving and decoding processes

Finally, Fig 6 shows the cumulative distribution function F T of the number of transmitted

packets using the GC service from an AP to any MC within a single AP domain The

T(GC)

SF /N MNratio of the UC service is set to 0.005· T SFor 0.01· T SF As it can be observed,

the number of transmitted packets to a MC reaches the threshold value k AP equal to

3600 for the selected parameter values (Table I), in all cases but for R = 6 Mbit/s and

T SF(GC) /N MN =0.005· T SF This means that number of encoded packets per AP (i.e., k AP) is

properly dimensioned to allow a single user to collect enough of encoded packets to decode

all the data w.h.p while moving through a single AP coverage zone

To summarize the benefits provided by the proposed I2V data dissemination based on the

rateless codes over traditional methods, it is worth noticing first of all that, by their design,

rateless codes are tuned to the changing wireless link conditions and have a

close-to-the-minimal reception overhead Furthermore, each rateless coded packet is an equally important

representation of the original data, which makes lengthy TCP-like reliability mechanisms

un-necessary These factors influence the time allocations within the superframe, allowing larger

number of mobile nodes to be serviced during designated service-time portion of the

super-frame, or alternatively, service-time portion shortening, providing larger time allocations for

best-effort traffic Finally, while roaming through the network, mobile users can simply

con-tinue with data download from the new local AP after a handover, avoiding the redundant

content reconciliation phase

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Fig 6 Cumulative distribution function F Tof number of transmitted packets to MN in single

AP cell for GC service

Acknowledgment

This work was supported in part by by the Italian National Project “Wireless multiplatfOrmmimo active access netwoRks for QoS-demanding muLtimedia Delivery” (WORLD), undergrant number 2007R989S, as well as by the Tuscany Region projects “Metropolitan MobilityAgency Supporting Tools” (SSAMM) and “Microparticulate Monitoring via Wireless SensorNetworks” (MAPPS) The authors would like also to thank the partners of EU FP7-REGPOT-2007-3 - “AgroSense” project for their fruitful discussion and comments

5 References

Bai, F., Sadagopan, N & Helmy, A (2003) IMPORTANT: A framework to systematically

an-alyze the Impact of Mobility on Performance of RouTing protocols for Adhoc

NeT-works, Proc of IEEE INFOCOM 2003, San Francisco, CA, USA.

Bohm, A & Jonsson, M (2008) Supporting real-time data traffic in safety-critical

vehicle-to-infrastructure communication, Proc of IEEE LCN 2008, Montreal, QC, Canada.

Bychkovsky, V., Hull, B., Miu, A., Balakrishnan, H & Madden, S (2006) A Measurement

Study of Vehicular Internet Access Using in Situ Wi-Fi Networks, Proc of ACM Com 2006, Los Angeles, CA, USA.

Mobi-Byers, J., Considine, J., Mitzenmacher, M & Rost, S (2002) Informed Content Delivery Across

Adaptive Overlay Networks, Proc of ACM SIGCOMM 2002, Pittsburg, PA, USA.

Byers, J., Luby, M & Mitzenmacher, M (2002) A Digital Fountain Approach to Asynchronous

Reliable Multicast, IEEE Journal on Selected Areas in Communications 20(8): 1528–1540.

Cordova-Lopez, L E., Mason, A., Cullen, J D., Shaw, A & Al-ShammaŠa, A (2007) Online

Vehicle and Atmospheric Pollution Monitoring using GIS and Wireless Sensor

Net-works, Proc of ACM IntŠl Conference on Embedded Networked Sensor Systems (SenSys),

pp 87 ˝U–101

Eriksson, J., Balakrishnan, H & Madden, S (2008) Cabernet: Vehicular Content Delivery

Using WiFi, Proc of ACM MobiCom 2008, San Francisco, CA, USA.

Gerla, M., Zhou, B., Lee, Y Z., Soldo, F., Lee, U & Marfia, G (2006) Vehicular Grid

Commu-nications: The Role of the Internet Infrastructure, Proc of WICON Š06, Boston, MA,

USA

IEEE (2007) Ieee 802.11-2007 wireless lan medium access control and physical layers

specifi-cations

Jiang, D & Delgrossi, L (2008) IEEE 802.11p: Towards an International Standard for Wireless

Access in Vehicular Environments, Proc of IEEE VTC2008-Spring, Singapore.

Laisheng, X., Xiaohong, P., Zhengxia, W., Bing, X & Pengzhi, H (2009) Research on traffic

monitoring network and its traffic flow forecast and congestion control model based

on wireless sensor networks, Measuring Technology and Mechatronics Automation, 2009 ICMTMA ’09 International Conference on, Vol 1, pp 142 –147.

Luby, M (2002) LT Codes, Proc of IEEE FOCS 2002, Vancouver, BC, Canada.

Martinez, K., Hart, J & Ong, R (2004) Environmental Sensor Networks, IEEE Computer

Jour-nal 37: 50–56.

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IEEE Infocom 2004, Hong Kong.

Peponis, J., Allen, D., Haynie, D., Scoppa, M & Zhang, Z (2007) MEASURING THE

CON-FIGURATION OF STREET NETWORKS: the Spatial profiles of 118 urban areas in

Fig 6 Cumulative distribution function F Tof number of transmitted packets to MN in single

AP cell for GC service

Acknowledgment

This work was supported in part by by the Italian National Project “Wireless multiplatfOrmmimo active access netwoRks for QoS-demanding muLtimedia Delivery” (WORLD), undergrant number 2007R989S, as well as by the Tuscany Region projects “Metropolitan MobilityAgency Supporting Tools” (SSAMM) and “Microparticulate Monitoring via Wireless SensorNetworks” (MAPPS) The authors would like also to thank the partners of EU FP7-REGPOT-2007-3 - “AgroSense” project for their fruitful discussion and comments

5 References

Bai, F., Sadagopan, N & Helmy, A (2003) IMPORTANT: A framework to systematically

an-alyze the Impact of Mobility on Performance of RouTing protocols for Adhoc

NeT-works, Proc of IEEE INFOCOM 2003, San Francisco, CA, USA.

Bohm, A & Jonsson, M (2008) Supporting real-time data traffic in safety-critical

vehicle-to-infrastructure communication, Proc of IEEE LCN 2008, Montreal, QC, Canada.

Bychkovsky, V., Hull, B., Miu, A., Balakrishnan, H & Madden, S (2006) A Measurement

Study of Vehicular Internet Access Using in Situ Wi-Fi Networks, Proc of ACM Com 2006, Los Angeles, CA, USA.

Mobi-Byers, J., Considine, J., Mitzenmacher, M & Rost, S (2002) Informed Content Delivery Across

Adaptive Overlay Networks, Proc of ACM SIGCOMM 2002, Pittsburg, PA, USA.

Byers, J., Luby, M & Mitzenmacher, M (2002) A Digital Fountain Approach to Asynchronous

Reliable Multicast, IEEE Journal on Selected Areas in Communications 20(8): 1528–1540.

Cordova-Lopez, L E., Mason, A., Cullen, J D., Shaw, A & Al-ShammaŠa, A (2007) Online

Vehicle and Atmospheric Pollution Monitoring using GIS and Wireless Sensor

Net-works, Proc of ACM IntŠl Conference on Embedded Networked Sensor Systems (SenSys),

pp 87 ˝U–101

Eriksson, J., Balakrishnan, H & Madden, S (2008) Cabernet: Vehicular Content Delivery

Using WiFi, Proc of ACM MobiCom 2008, San Francisco, CA, USA.

Gerla, M., Zhou, B., Lee, Y Z., Soldo, F., Lee, U & Marfia, G (2006) Vehicular Grid

Commu-nications: The Role of the Internet Infrastructure, Proc of WICON Š06, Boston, MA,

USA

IEEE (2007) Ieee 802.11-2007 wireless lan medium access control and physical layers

specifi-cations

Jiang, D & Delgrossi, L (2008) IEEE 802.11p: Towards an International Standard for Wireless

Access in Vehicular Environments, Proc of IEEE VTC2008-Spring, Singapore.

Laisheng, X., Xiaohong, P., Zhengxia, W., Bing, X & Pengzhi, H (2009) Research on traffic

monitoring network and its traffic flow forecast and congestion control model based

on wireless sensor networks, Measuring Technology and Mechatronics Automation, 2009 ICMTMA ’09 International Conference on, Vol 1, pp 142 –147.

Luby, M (2002) LT Codes, Proc of IEEE FOCS 2002, Vancouver, BC, Canada.

Martinez, K., Hart, J & Ong, R (2004) Environmental Sensor Networks, IEEE Computer

Jour-nal 37: 50–56.

Ott, J & Kutscher, D (2004) Drive-thru Internet: IEEE 802.11b for Automobile Users, Proc of

IEEE Infocom 2004, Hong Kong.

Peponis, J., Allen, D., Haynie, D., Scoppa, M & Zhang, Z (2007) MEASURING THE

CON-FIGURATION OF STREET NETWORKS: the Spatial profiles of 118 urban areas in

the 12 most populated metropilitan regions in the US, Proc of 6th International Space Syntax Symposium, Istanbul, Turkey.

Pinart, C., Calvo, J C., Nicholson, L & Villaverde, J A (2009) ECall-compliant early crash

notification service for portable and nomadic devices, Proc of IEEE VTC2009-Spring,

Barcelona, Spain

Santini, S., Ostermaier, B & Vitaletti, A (2008) First Experiences using Wireless Sensor

Net-work for Noise Pollution Monitoring, Proc of 3rd ACM Workshop on Real-World less Sensor Networks (REALWSN’08), Glasgow, United Kingdom.

Wire-Shu-Chiung, H., You-Chiun, W., Chiuan-Yu, H & Yu-Chee, T (2009) A Vehicular Wireless

Sensor Network for CO2 Monitoring, Proc of IEEE Sensors, pp 1498 – 1501.

Stefanovic, C., Crnojevic, V., Vukobratovic, D., Niccolai, L., Chiti, F & Fantacci, R (2011)

Urban Infrastructure-to-Vehicle Traffic Data Dissemination Using Rateless Codes, to appear in IEEE Journal on Selected Areas on Communications special issue on Vehicular Communications and Networks

Tanner, J C (1957) The Sampling of Road Traffic, Journal of the Royal Statistical Society Series

C (Applied Statistics) 6(3): 161–170.

Tubaishat, M., Zhuang, P., Qi, Q & Shang, Y (2009) Wireless sensor networks in intelligent

transportation systems, Wireless Communications and Mobile Computing 2009 9(3): 287–

302

Vukobratovic, D., Stefanovic, C., Crnojevic, V., Chiti, F & Fantacci, R (2010) Rateless Packet

Approach for Data Gathering in Wireless Sensor Networks, to appear in IEEE Journal

on Selected Areas in Communications special issue on Simple Wireless Sensor Networking

Solutions) 28(7).

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Proc of IFIP Networking 2007, Atlanta, GA, USA.

Yousefi, S., Mousavi, M S & Fathy, M (2006) Vehicular Ad Hoc Networks (VANETs):

Chal-lenges and Perspectives, Proc of ITST 2006, Chengdu, China.

Improving Greenhouse’s Automation and Data Acquisition with Mobile Robot Controlled system via Wireless Sensor Network

István Matijevics and Simon János

X

Improving Greenhouse’s Automation and

Data Acquisition with Mobile Robot Controlled

system via Wireless Sensor Network

István Matijevics* and Simon János**

*University of Szeged, Institute of Informatics

Hungary

**Subotica Tech, Department of Informatics

Serbia

1 Introduction

The function of a greenhouse is to create the optimal growing conditions for the full lifecycle

of the plants Using autonomous measuring stations helps to monitor all the necessary

parameters for creating the optimal environment in the greenhouse The robot equipped

with sensors is capable of driving to the end and back along crop rows inside the

greenhouse This chaper deals with the implementation of mobile measuring station in

greenhouse environment It introduces a wireless sensor network that was used for the

purpose of measuring and controlling the greenhouse application Continuous

advancements in wireless technology and miniaturization have made the deployment of

sensor networks to monitor various aspects of the environment increasingly flexible

Climate monitoring is vitally important to the operation in greenhouses and the quality of

the collected information has a great influence on the precision and accuracy of control

results Currently, the agro-alimentary market field incorporates diverse data acquisition

techniques Normally, the type of acquisition system is chosen to be optimal for the control

algorithm to be used For traditional climate monitoring and control systems, all sensors are

distributed through the greenhouse and connected to the device performing the control

tasks These equipments use time-based data sampling techniques as a consequence of using

time-based controllers Typical applications of WSNs include monitoring, tracking, and

controlling Some of the specific applications are habitat monitoring, object tracking, etc In a

typical application, a WSN is scattered in a region where it is meant to collect data through

its sensor node The WSN-based controller has allowed a considerable decrease in the

number of changes in the control action and made possible a study of the compromise

between quantity of transmission and control performance In modern greenhouses, several

measurement points are required to trace down the local climate parameters in different

parts of the big greenhouse to make the greenhouse automation system work properly

Cabling would make the measurement system expensive and vulnerable Moreover, the

cabled measurement points are difficult to relocate once they are installed Thus, a wireless

6

sensor network (WSN) consisting of small-size wireless sensor nodes equipped with radio

and one or several sensors, is an attractive and cost-efficient option to build the required

measurement system In this work, we developed a wireless sensor node for greenhouse

monitoring by integrating a sensor platform provided SunSPOT by Sun Microsystems with

few sensors capable to measure four climate variables Continuous advancements in

wireless technology and miniaturization have made the deployment of sensor networks to

monitor various aspects of the environment increasingly flexible

2 Mobile platform

Mobile robotics is a young field of research Its roots include many engineering and science

disciplines, from mechanical, electrical and electronics engineering to computer, cognitive

and social sciences The Board Of Education is a complete, low-cost development platform

equipped with the needed sensors for humidity, temperature, light, etc As shown in

Figure 1, the Boe-Bot is a great tool with which to get started with robotics

Fig 1 Assembled Boe-Bot

The SunSPOT WSN module makes it possible for the Boe-Bot robot’s BASIC Stamp 2

microcontroller brain to communicate wirelessly with a web based user interface running on

a nearby PC The BASIC Stamp microcontroller runs a small PBASIC program that controls

the Boe-Bot robot’s servos and optionally monitors sensors while it communicates wirelessly

with the web server

3 Control scheme for mobile robots

A mobile robot needs locomotion mechanisms that enable it to move throughout its known

or unknown environment But there are a large variety of possible ways to move, and so the

selection of a robot’s approach to locomotion is an important aspect of mobile robot design

Figure 2, presents the control scheme for mobile robot systems In the laboratory, there are research robots that can walk, jump, run, slide, skate, swim, fly, and, of course, roll Any of these activities has its own control algorithm (Gy Mester, 2009)

Fig 2 Reference control scheme for mobile robot systems Locomotion is the complement of manipulation In manipulation, the robot arm is fixed but moves objects in the workspace by imparting force to them In locomotion, the environment

is fixed and the robot moves by imparting force to the environment In both cases, the scientific basis is the study of actuators that generate interaction forces, and mechanisms that implement desired kinematical and dynamic properties The wheel has been by far the most popular mechanism in mobile robotics and in man-made vehicles in general It can achieve very good efficiencies, and does so with a relatively simple mechanical implementation On Figure 3, the kinematics of the mobile robot is depicted In addition, balance is not usually a research problem in wheeled robot designs, because wheeled robots are almost always designed so that all wheels are in ground contact at all times (Gy Mester, 2009)

sensor network (WSN) consisting of small-size wireless sensor nodes equipped with radio

and one or several sensors, is an attractive and cost-efficient option to build the required

measurement system In this work, we developed a wireless sensor node for greenhouse

monitoring by integrating a sensor platform provided SunSPOT by Sun Microsystems with

few sensors capable to measure four climate variables Continuous advancements in

wireless technology and miniaturization have made the deployment of sensor networks to

monitor various aspects of the environment increasingly flexible

2 Mobile platform

Mobile robotics is a young field of research Its roots include many engineering and science

disciplines, from mechanical, electrical and electronics engineering to computer, cognitive

and social sciences The Board Of Education is a complete, low-cost development platform

equipped with the needed sensors for humidity, temperature, light, etc As shown in

Figure 1, the Boe-Bot is a great tool with which to get started with robotics

Fig 1 Assembled Boe-Bot

The SunSPOT WSN module makes it possible for the Boe-Bot robot’s BASIC Stamp 2

microcontroller brain to communicate wirelessly with a web based user interface running on

a nearby PC The BASIC Stamp microcontroller runs a small PBASIC program that controls

the Boe-Bot robot’s servos and optionally monitors sensors while it communicates wirelessly

with the web server

3 Control scheme for mobile robots

A mobile robot needs locomotion mechanisms that enable it to move throughout its known

or unknown environment But there are a large variety of possible ways to move, and so the

selection of a robot’s approach to locomotion is an important aspect of mobile robot design

Figure 2, presents the control scheme for mobile robot systems In the laboratory, there are research robots that can walk, jump, run, slide, skate, swim, fly, and, of course, roll Any of these activities has its own control algorithm (Gy Mester, 2009)

Fig 2 Reference control scheme for mobile robot systems Locomotion is the complement of manipulation In manipulation, the robot arm is fixed but moves objects in the workspace by imparting force to them In locomotion, the environment

is fixed and the robot moves by imparting force to the environment In both cases, the scientific basis is the study of actuators that generate interaction forces, and mechanisms that implement desired kinematical and dynamic properties The wheel has been by far the most popular mechanism in mobile robotics and in man-made vehicles in general It can achieve very good efficiencies, and does so with a relatively simple mechanical implementation On Figure 3, the kinematics of the mobile robot is depicted In addition, balance is not usually a research problem in wheeled robot designs, because wheeled robots are almost always designed so that all wheels are in ground contact at all times (Gy Mester, 2009)

Fig 3 Robot kinematics and its frames of interests

Thus, three wheels are sufficient to guarantee stable balance, although, as we shall see

below, two-wheeled robots can also be stable (R Siegwart, 2004) When more than three

wheels are used, a suspension system is required to allow all wheels to maintain ground

contact when the robot encounters uneven terrain Motion control might not be an easy task

for this kind of systems However, it has been studied by various research groups, and some

adequate solutions for motion control of a mobile robot system are available (Gy Mester,

2009)

4 Using Potential Fields method for navigation

A potential field consists of two imaginary fields (attractive potential and repulsive potential)

and used to avoid a collision with unexpected obstacle while moving in a predetermined

path The Attractive Potential forces the robot to move through a predetermined path and

the Repulsive Field, assumed to be generated by obstacles, forces the robot to move a

different way to avoid the collision (O Khatib, 1986) The Artificial Potential Field approach

is a local path planner method that was introduced by Khatib This method defines obstacles

as repelling force sources, and goals as attracting force sources The path is then influenced

by the composition of the two forces, which produces a robot motion that moves away from

obstacles while moving towards the target goal The approach is mathematically simple and

is able to produce real-time acceptable results for collision avoidance even in dynamic

environments The most known limitation of this approach is the local minima, which refers

to locations that trap the robot and prevent it from reaching the target goal location This

main problem has been addressed by many different techniques that try to solve or at least

minimize its impact (O Khatib, 1985)

4.1 Attractive Potential Field

The attractive potential field corresponds to the component responsible for the potentials

that attract the robot towards the target goal position At all locations in the environment the

action vector will point to the target goal

Fig 4 Attractive potential field action vectors pointing to the goal and goal representation (M Goodrich, 2002)

Usually, the action vector is found by applying a scalar potential field function to the robot's position and then calculating the gradient of that function

] , [ ] , [

y

U x

U y

After defining: (M Goodrich, 2002)

 [ xG, yG] as the position of the goal;

 r as the radius of the goal;

 [ xR, yR]as the position of the robot;

 sas the size of the goal's area of influence;

  as the strength of the attractive field    0 

We can compute  x and  y using the following steps:

1 Find the distance d between the goal and the robot:

R G

x x

y y

1

tan

Fig 3 Robot kinematics and its frames of interests

Thus, three wheels are sufficient to guarantee stable balance, although, as we shall see

below, two-wheeled robots can also be stable (R Siegwart, 2004) When more than three

wheels are used, a suspension system is required to allow all wheels to maintain ground

contact when the robot encounters uneven terrain Motion control might not be an easy task

for this kind of systems However, it has been studied by various research groups, and some

adequate solutions for motion control of a mobile robot system are available (Gy Mester,

2009)

4 Using Potential Fields method for navigation

A potential field consists of two imaginary fields (attractive potential and repulsive potential)

and used to avoid a collision with unexpected obstacle while moving in a predetermined

path The Attractive Potential forces the robot to move through a predetermined path and

the Repulsive Field, assumed to be generated by obstacles, forces the robot to move a

different way to avoid the collision (O Khatib, 1986) The Artificial Potential Field approach

is a local path planner method that was introduced by Khatib This method defines obstacles

as repelling force sources, and goals as attracting force sources The path is then influenced

by the composition of the two forces, which produces a robot motion that moves away from

obstacles while moving towards the target goal The approach is mathematically simple and

is able to produce real-time acceptable results for collision avoidance even in dynamic

environments The most known limitation of this approach is the local minima, which refers

to locations that trap the robot and prevent it from reaching the target goal location This

main problem has been addressed by many different techniques that try to solve or at least

minimize its impact (O Khatib, 1985)

4.1 Attractive Potential Field

The attractive potential field corresponds to the component responsible for the potentials

that attract the robot towards the target goal position At all locations in the environment the

action vector will point to the target goal

Fig 4 Attractive potential field action vectors pointing to the goal and goal representation (M Goodrich, 2002)

Usually, the action vector is found by applying a scalar potential field function to the robot's position and then calculating the gradient of that function

] , [ ] , [

y

U x

U y

After defining: (M Goodrich, 2002)

 [ xG, yG] as the position of the goal;

 r as the radius of the goal;

 [ xR, yR]as the position of the robot;

 sas the size of the goal's area of influence;

  as the strength of the attractive field    0 

We can compute  x and  y using the following steps:

1 Find the distance d between the goal and the robot:

R G

x x

y y

1

tan

3 Set  x and  y according to the rules:

) (

) cos(

r d x

s x

The last step presents three simple rules that characterize three different behaviors for the

robot according to its relative position towards the goal:

 In the first rule of step 3, d  r means that the robot is in the goal area In this

case, no forces act and  x and  y are set to zero

 In the second rule, r  d  s  r means that the robot is inside the area of

inuence of the goal The action vector is set using  , d and s

 In the third and last rule, d  s  r means that the robot is outside the goal

area and also outside its area of influence The action vector is set to with s and

 thus reaching higher values

4.2 Repulsive Potential Field

The repulsive potential field is the component that is responsible for forcing the robot to

stay away from the obstacles it encounters on its path All repulsive action vectors point

away from the obstacle surface driving the robot away from the obstacle

Fig 5 Repulsive potential field action vectors pointing away from the obstacle and obstacle

representation (M Goodrich, 2002)

Similarly to the Attractive Potential, we calculate the repulsive action vector

After defining: (M Goodrich, 2002)

 [ xO, yO] as the position of the obstacle;

 r as the radius of the obstacle;

 [ xR, yR]as the position of the robot;

 sas the size of the obstacle's area of influence;

 as the strength of the repulsive field    0 

We can compute  x and  y using the following steps:

1 Find the distance d between the obstacle and the robot:

R O

x x

y y

sign x

) (

) cos(

) (

d r s

If d  s  r then  x   y  0

Similar to the attractive potential rules, these rules are also simple and characterize three different behaviors for the robot according to its position relative to the obstacle It is important to notice that all action vectors need to point away from the obstacle, hence the need to use negative values (M Goodrich, 2002)

 In the first rule of step 3, the robot is within the radius of the obstacle, so the action vector needs to be infinite, expressing the need to escape from the robot

3 Set  x and  y according to the rules:

) (

) cos(

y

r d

s x

The last step presents three simple rules that characterize three different behaviors for the

robot according to its relative position towards the goal:

 In the first rule of step 3, d  r means that the robot is in the goal area In this

case, no forces act and  x and  y are set to zero

 In the second rule, r  d  s  r means that the robot is inside the area of

inuence of the goal The action vector is set using  , d and s

 In the third and last rule, d  s  r means that the robot is outside the goal

area and also outside its area of influence The action vector is set to with s and

 thus reaching higher values

4.2 Repulsive Potential Field

The repulsive potential field is the component that is responsible for forcing the robot to

stay away from the obstacles it encounters on its path All repulsive action vectors point

away from the obstacle surface driving the robot away from the obstacle

Fig 5 Repulsive potential field action vectors pointing away from the obstacle and obstacle

representation (M Goodrich, 2002)

Similarly to the Attractive Potential, we calculate the repulsive action vector

After defining: (M Goodrich, 2002)

 [ xO, yO] as the position of the obstacle;

 r as the radius of the obstacle;

 [ xR, yR]as the position of the robot;

 sas the size of the obstacle's area of influence;

 as the strength of the repulsive field    0 

We can compute  x and  y using the following steps:

1 Find the distance d between the obstacle and the robot:

R O

x x

y y

sign x

) (

) cos(

) (

d r s

If d  s  r then  x   y  0

Similar to the attractive potential rules, these rules are also simple and characterize three different behaviors for the robot according to its position relative to the obstacle It is important to notice that all action vectors need to point away from the obstacle, hence the need to use negative values (M Goodrich, 2002)

 In the first rule of step 3, the robot is within the radius of the obstacle, so the action vector needs to be infinite, expressing the need to escape from the robot

 In the second rule, where the robot is outside the obstacle's radius but inside its

area of influence, the action vector is set to a high value in order to express the

need to escape the current location

 In the third rule, where the robot is outside the area of influence of the obstacle,

the action vector is set to zero, meaning that no repulsive forces are acting on the

robot (M Goodrich, 2002)

Since the repulsive force only acts when the robot is inside the area of influence of the

obstacle, the value of s must be carefully chosen A small value for s can cause trajectory

problems by causing abrupt changes on the path and some constraints on the speed of the

robot A large value for s may cause also problems on the robot's movement since it can

constrain movement in small places where the robot could pass

Fig 6 Potential Fields simulation

The repulsive force has the objective of repelling the robot only if it is close to an obstacle

and its velocity points towards that obstacle (M Goodrich, 2002)

4 WSN and Event-Based System for Greenhouse Climate Control

A wireless sensor network (WSN) is a computer network consisting of spatially distributed

autonomous devices using sensors to cooperatively monitor physical or environmental

conditions, such as temperature, sound, vibration, pressure, motion or pollutants, at

different locations (Sun Microsystems, 2002) The development of wireless sensor networks

was originally motivated by military applications such as battlefield surveillance Figure 7,

presents the sensor node architecture However, wireless sensor networks are now used in

many civilian application areas, including environment and habitat monitoring, healthcare

applications, home automation, and traffic control

Fig 7 Sensor Node Architecture

In addition to one or more sensors, each node in a sensor network is typically equipped with

a radio transceiver or other wireless communications device, a small microcontroller, and an energy source, usually a battery Figure 8, shows the typical wireless sensor network

Fig 8 Typical wireless sensor network (WSN) The size a single sensor node can vary from shoebox-sized nodes down to devices the size of grain of dust The cost of sensor nodes is similarly variable, ranging from hundreds of dollars to a few cents, depending on the size of the sensor network and the complexity required of individual sensor nodes (Sun Microsystems, 2005) Size and cost constraints on sensor nodes result in corresponding constraints on resources such as energy, memory, computational speed and bandwidth In computer science, wireless sensor networks are an active research area with numerous workshops and conferences arranged each year (S Scaglia, 2008) As commented above, this paper is devoted to analyzing diurnal and nocturnal temperature control with natural ventilation and heating systems, and humidity control as a secondary control objective Under diurnal conditions, the controlled variable is the inside temperature and the control signal is the vent opening The use of natural ventilation produces an exchange between the inside and outside air, usually provoking a decrease in the inside temperature of the greenhouse The controller must calculate the

 In the second rule, where the robot is outside the obstacle's radius but inside its

area of influence, the action vector is set to a high value in order to express the

need to escape the current location

 In the third rule, where the robot is outside the area of influence of the obstacle,

the action vector is set to zero, meaning that no repulsive forces are acting on the

robot (M Goodrich, 2002)

Since the repulsive force only acts when the robot is inside the area of influence of the

obstacle, the value of s must be carefully chosen A small value for s can cause trajectory

problems by causing abrupt changes on the path and some constraints on the speed of the

robot A large value for s may cause also problems on the robot's movement since it can

constrain movement in small places where the robot could pass

Fig 6 Potential Fields simulation

The repulsive force has the objective of repelling the robot only if it is close to an obstacle

and its velocity points towards that obstacle (M Goodrich, 2002)

4 WSN and Event-Based System for Greenhouse Climate Control

A wireless sensor network (WSN) is a computer network consisting of spatially distributed

autonomous devices using sensors to cooperatively monitor physical or environmental

conditions, such as temperature, sound, vibration, pressure, motion or pollutants, at

different locations (Sun Microsystems, 2002) The development of wireless sensor networks

was originally motivated by military applications such as battlefield surveillance Figure 7,

presents the sensor node architecture However, wireless sensor networks are now used in

many civilian application areas, including environment and habitat monitoring, healthcare

applications, home automation, and traffic control

Fig 7 Sensor Node Architecture

In addition to one or more sensors, each node in a sensor network is typically equipped with

a radio transceiver or other wireless communications device, a small microcontroller, and an energy source, usually a battery Figure 8, shows the typical wireless sensor network

Fig 8 Typical wireless sensor network (WSN) The size a single sensor node can vary from shoebox-sized nodes down to devices the size of grain of dust The cost of sensor nodes is similarly variable, ranging from hundreds of dollars to a few cents, depending on the size of the sensor network and the complexity required of individual sensor nodes (Sun Microsystems, 2005) Size and cost constraints on sensor nodes result in corresponding constraints on resources such as energy, memory, computational speed and bandwidth In computer science, wireless sensor networks are an active research area with numerous workshops and conferences arranged each year (S Scaglia, 2008) As commented above, this paper is devoted to analyzing diurnal and nocturnal temperature control with natural ventilation and heating systems, and humidity control as a secondary control objective Under diurnal conditions, the controlled variable is the inside temperature and the control signal is the vent opening The use of natural ventilation produces an exchange between the inside and outside air, usually provoking a decrease in the inside temperature of the greenhouse The controller must calculate the