effect of packet sampling time on a colony of mobile routing robots for

EFFECT OF PACKET SAMPLING TIME ON A COLONY OF MOBILE ROUTING ROBOTS FOR COMMUNICATION LINK MAINTENANCE USING IEEE 802.15.4 DEVICES Cristian Duran-Faundez, Jonathan Mat´ıas Palma Olate, Eric Orellana-Romero, Pedro E. Melin

To cite this version: Cristian Duran-Faundez, Jonathan Mat´ıas Palma Olate, Eric Orellana-Romero, Pedro E. Melin. EFFECT OF PACKET SAMPLING TIME ON A COLONY OF MOBILE ROUTING ROBOTS FOR COMMUNICATION LINK MAINTENANCE USING IEEE 802.15.4 DEVICES . MOSIM 2014, 10`eme Conf´erence Francophone de Mod´elisation, Optimisation et Simulation, Nov 2014, Nancy, France.

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10th International Conference on MOdeling, Optimization and SIMlation - MOSIM14 - November 5-7-2014Nancy - France “Toward circular Economy”

Effect of packet sampling time on a colony of mobile routing robots for communication link maintenance using IEEE 802.15.4 devices C. DURAN-FAUNDEZ

J.M. PALMA OLATE,

Departamento de Ingenier´ıa El´ectrica y Electr´onica / Universidad del B´ıo-B´ıo Avda. Collao 1202, Casilla 5-C CP: 4051381, Concepci´ on - Chile [email protected]

Escuela de Ingenier´ıa de Ejecuci´ on en Electr´onica / Universidad del B´ıo-B´ıo Avda. Collao 1202, Casilla 5-C CP: 4051381, Concepci´on - Chile [email protected]

E. ORELLANA-ROMERO

P.E. MELIN

Magister en Ciencias de la Computaci´ on / Universidad del B´ıo-B´ıo Avda. Collao 1202, Casilla 5-C CP: 4051381, Concepci´ on - Chile [email protected]

Departamento de Ingenier´ıa El´ectrica y Electr´onica / Universidad del B´ıo-B´ıo Avda. Collao 1202, Casilla 5-C CP: 4051381, Concepci´on - Chile [email protected]

ABSTRACT: In this paper, we study the effects of the packet broadcasting period on resource consumption and effectiveness of a simple navigation algorithm for routing robots guidance. We implement a simulation environment in Matlab software. We take resource consumption parameters by measuring IEEE 802.15.4 XBee wireless modules. Our simulation results show that there is a certain sampling rate that would allow to obtain energy savings while maintaining the effectiveness of the guiding algorithm. KEYWORDS: Sampling time, energy consumption, accuracy, robotic sensor networks, positioning, link quality.

1

INTRODUCTION

Whether to remotely control an exploring robot or to communicate findings or any kind of data, many robotic applications require to maintain a wireless communication link between a robotic unit and a gateway node. Depending on the application, this gateway can be of different nature (e.g., a portable device, a base station, etc.), enabling remote monitoring/control, data storage, and other functionalities. Noticeably, the exploring robot coverage is constrained by the signal range so, when the task involves larger regions, routing units installation is required. An alternative to the installation of routing units is the use of a network of mobile routing robots that autonomously seek the best position to forward data packets between explorer nodes and the gateway, maintaining a certain link quality (Reddy, 2011; Palma Olate and Duran-Faundez, 2014). This concept is illustrated in Figure 1.

Figure 1 – A network of explorer robots. Recently, various algorithms using signal properties such as the RSS (received signal strength) have been reported to allow finding and following of moving agents by autonomous robots, a task referred in the bibliography as tethering. Tethering is the robot task of following a mobile agent (human, robot, etc.), with all the different required capabilities to it, in order to provide network connectivity (Zickler and Veloso,

MOSIM14 - November 5-7-2014 - Nancy - France

2010). Examples of this concept are found in (Chen and Tan, 2009; Zickler and Veloso, 2010; Reddy, 2011). The task of maintaining links in a multi-robot scenario is a complex task, specially when the use of global positioning technologies (such as GPS) or other kind of external infrastructure is impossible. Of course, energy efficiency is a relevant issue in state-ofthe-art robotic applications. Moreover, this problem becomes critical when the robot mission involves autonomous work during extended periods of time and battery replacements are impossible (e.g., the use of robots for planetary exploration). In the case of mobile explorer robots, the problem of energy consumption can be faced in many ways, e.g. by selecting appropriate paths or by eliminating unnecessary resource expenses. A particular solution that can be used to support wireless data communication is the adoption of IEEE 802.15.4 based modules. This standard is defined to work on ISM bands and is designed o allow connection of multiple nodes with low-power requirements. Energy savings are obtained, among other factors, by switching nodes from active to sleeping modes. This is an advantage on energy-efficiency, however, the application of this kind of technology adds new problems important to consider, in order to take the best benefit from it. In this paper, we study the effects of the packet broadcasting period on resource consumption and effectiveness of a simple navigation algorithm for routing robots guidance proposed in (Palma Olate and Duran-Faundez, 2014). In a link maintenance scenario, it seeks to find near-optimal positions to forward packets in a single path considering one explorer router and multiple intermediate nodes. To do so, router robots uses as unique source of information RSSI (RSS Indicator) measures coming from the preceding and following nodes, so it can apply a simple heuristic to evaluate the benefit of a particular movement. In Section 2 we present the robot navigation algorithm and wireless modules adopted as case study. Adopted models, experiments and simulation results on the study of the effects of broadcasting sampling rate are presented in Section 3. Finally, Section 4 concludes and give some future directions of this work. 2 2.1

that a robot can move to a new position in a straight line with no errors. Also, the possible positions of the robot have been discretized, so robots move on a grid step by step, and next position is always in a 8-connected coordinate, thus possible actions are to move: up (A1), down (A2), right (A3), left (A4), up-right (A5), down-right (A6), down-left (A7), and up-left (A8). A ninth action (A9) considers a go-back movement so robot can undo last action. The algorithm operates as follows: Independent of the quantity of nodes on its radio range, each robotic router has two priority communication links: the preceding (V S: the one who is nearest the explorer, or is the explorer) and the following (V I: the one who is nearest the gateway, or is the gateway). Every unit starts in the proximities of the gateway. Robotic routers are activated when the RSSI value of its preceding node moves an established threshold. When the robot is activated, the algorithm performs a random movement as first action. Once this, the three goals, decrease nearest link M INvc , increase farthest link M AXvl , and decrease of RSSI differences, are evaluated in order to decide the future action. Let us define the RSSI difference Difn , the absolute RSSI diference Difa , and the digital diference of RSSI Difd , as: Difn

=

RSSIV S − RSSIV I

(1)

Difd

=

(2)

Difd

=

|RSSIV S − RSSIV I | Difn Difa

(3)

Goals are defined as:

M AXvl =



M AXV S M AXV I

M INvc =



M INV I M INV S

if Difd = −1 otherwise.

if Difd = −1 otherwise.

(4)

(5)

where goals M AXV I , M AXV S , M INV I and M INV S are defined as:

CASE STUDY Robotic router guiding algorithm

The positioning algorithm uses as unique source of information RSSI measures from preceding and following nodes. The adopted guiding algorithm is based on Q-Learning principles (Harmon and Harmon, 1996). For simplification purposes, this algorithm considers

M AXV I =



1 if RSSIV I (k) − RSSIV I (k − 1) > 0 0 otherwise. (6)

M AXV S =



1 0

if RSSIV S (k) − RSSIV S (k − 1) > 0 otherwise.

MOSIM14 - November 5-7-2014 - Nancy - France

(7)



M INV S =

0 1

if RSSIV S (k) − RSSIV S (k − 1) > 0 otherwise.

(8)

and 57mA for XBee Pro. Reception period consumes about 50mA for XBee and 52mA for XBee Pro. We should notice that data transmission drains less current than reception in the XBee (1mW) case. In the rest of this paper, we will refer to the XBee Pro. 3 3.1

M INV I =



0 1

if RSSIV I (k) − RSSIV I (k − 1) > 0 otherwise.

(9) If the three goals are accomplished by preceding action, it repeats the same action, hoping to have the same result. If the three goals are not accomplished, the reverse action is performed (A9), making the router to stay at the coordinate where the three goals where accomplished. Then, when the A9 action was performed, the future action is determined through a learning matrix (similar to Q-learning), choosing the action that has better cumulated rewards. If the new action does not achieve to meet the three goals, the robotic router performs the A9 action. This cycle is performed until the performed action meets the three goals which ensures that the movement is suitable to approach the router to the optimal point. 2.2

60

Transmission period

P Lrcvd(d)

=

P t − P L(d)

(11) 

 d −10.γ. log10 + d0
Pt − P L(d0 ) − Xσ

(12)

For energy consumption estimation, we will apply the following simplified equation, which refers to the energy spent during a step (translation of the robot to the next position) transmitting M xp times per step:

(13)

where Et is the energy consumption of a transmission stage (including mode changing), tt is the time of the transmission stage, Er is the energy consumed during 1 second in reception mode, and TP is the time in which the robot moves DXP meters.

Reception period

Reception period

=

ET (M xp ) = M xp .Et + Er .(TP − M xp .tt )

50 45 Current [mA]

were Xσ represents noise and interference as a zeromean Gaussian random variable with standard deviation σ. In order to find the value of power received by the receiver node (in dB), we must substract this attenuation to the transmission power of the signal P t, so we have:

Xbee PRO XBee

55

Simulation environment

In order to get results, we developed a simulation using Matlab software. The simulator implements both path loss and energy consumption models. For the first case, RSSI measures are simulated using traditional Log-distance path loss shadowing model whose equation is given by (Patwari et al, 2001):   d P L(d) = P L(d0 ) + 10.γ. log10 + Xσ (10) d0

Wireless modules

For our study, we adopted wireless IEEE 802.15.4 XBee and XBee Pro modules, from (Digi International Inc.,). In order to have power consumption values, we implemented a measurement testbed as in (Piyare and Lee, 2013), using a Rigol DS1052E oscilloscope. Current consumption of a transmission stage at maximum power output is depicted is Figure 2.

SIMULATION RESULTS

40 35 30

Adopted parameters for simulations are given in Table 1.

25 20

3.2

15 10 -2

-1

0

1

2

3

4

5

Time [mS]

Figure 2 – Simulation of one router moving to a nearoptimal position. From the obtained data, current consumption during transmission is about 47mA for XBee (1mW)

Minimum broadcasting rate

It would seem evident that energy savings can be achieved if unnecessary transmissions are eliminated. This is real in cases where energy spent by data transmission is higher that the energy spent during reception, as the case of Mica2 motes from Crossbow (Shnayder et al., 2004). But low-power PAN devices

MOSIM14 - November 5-7-2014 - Nancy - France Parameter Value Et 0.29mJ tt 2.73ms Er 0.172mJ (per second) TP 1s PL = Power Level.

10 RSSI Preceeding node RSSI Following node

0 −10

RSSI (dB)

−20

Table 1 – XBee Pro parameters for power consumption estimation.

−30 −40 −50 −60

such as XBee not necessarily respect this principle (note that regular XBee consumes less power during communication than transmission). In any case, to minimize broadcast of messages used by robots to get RSSI values we should test our control algorithm. Let us define M xp as the quantity of packets broadcasted by the nodes during the time in which a router robot moves the distance of one step.

−70 −80 −90

50

100

150

k (steps)

Figure 4 – Obtained RSSI values by mobile router equaling during movement. 4 3.5

Figure 3 shows simulation of an algorithm’s execution, considering a scenario with one robotic router departing from a position near the gateway and moving to a near-optimal point (the one where RSSI values are most similar and maximum; see Figure 4).

3

Y coordinate (m)

2.5 2 1.5 1

4

0.5

3.5

0

3

−0.5 −0.5

2.5 Y coordinate (m)

0

0

0.5

1

1.5 2 X coordinate (m)

2.5

3

3.5

4

2

Figure 5 – Simulation of one router not finding a good direction.

1.5 1

depend (among other factors) on the initial movement decisions of the robot. As such, we recommend to take bigger values.

0.5 0 −0.5

0

0.5

1

1.5 2 2.5 X coordinate (m)

3

3.5

4

Figure 3 – Simulation of one router moving to a nearoptimal position. This experiment was perform with parameter M xp = 20 (which means that nodes broadcast 20 messages per step) which is actually excessive. If power consumption depends on the quantity of broadcasted messages, then to find lower sampling rates is necessary. By the other way, to select a too small M xp parameter could affect the algorithm’s effectiveness. Figure 5 shows simulation of the same scenario but applying M xp = 5. In Figure 6 it is observed that RSSI values not tend to approach. For the selected scenario, the effectiveness limit is near M xp = 8, which is the minimum M xp value, at which the adopted algorithm finds a solutions in almost all cases. However, error can occur and they

Concerning energy consumptions, we observe that using XBee or XBee Pro modules, when using short data messages, energy consumptions are not allowed by reducing the quantity of broadcasts per period of time. In fact, short broadcasting is low power consuming even when transmitting at maximum power level. In the case of XBee Pro the energy consumed during transmission (included mode changing) Et is about 0.29mJ, which is spent during 2.73ms. In the other way, energy consumed in reception stage during the same quantity of time is 0.45mJ. Anyway, other kind of wireless devices are more energy consuming when transmitting, thus the search for minimal broadcastings is an important task to improve energy savings. 4

CONCLUSION

In this paper, we studied the effects of the broadcasting periods on the effectiveness of a positioning al-

MOSIM14 - November 5-7-2014 - Nancy - France

10 RSSI Preceeding node RSSI Following node

0

Piyare, R. and S.-r. Lee, 2013. Performance analysis of XBee ZB moule based wireless sensor networks. International Journal of Scientific & Engineering Research, 4(4), p. 1615-1620.

−10 −20 RSSI (dB)

2001 Spring. , vol.2, no., pp.1149,1153 vol.2, doi: 10.1109/VETECS.2001.944560.

−30 −40

Reddy, P.P and M.M. Veloso, 2011. RSSI-based physical layout classification and target tethering in mobile ad-hoc networks. 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, USA, p. 2327-2332.

−50 −60 −70 −80 −90

0

50

100

150

k (steps)

Figure 6 – Obtained RSSI values by a lost mobile router (RSSI values not tend to approach). gorithm for multi-robot routing applications and energy consumption. For our study case and simulation parameters inspired in real-world measurements, we show that there is a certain sampling rate that would allow to obtain energy savings while maintaining the effectiveness of the guiding algorithm. Future works will include the exploitation of other IEEE 802.15.4 advantages such as sleep modes and packet loss problems. Also, in order to improve simulation results, development of best quality measurement testbeds are considered. ACKNOWLEDGMENTS This work was supported by the Research Department of the University of B´ıo-B´ıo (Project DIUBB 121910 2/R) and Conicyt (Fondecyt Project No. 11121657). REFERENCES Chen, X. and J. Tan, 2009. An adaptive mobile robots tethering algorithm in constrained environments. The 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, USA, p. 1377-1382. Digi International Inc. www.digi.com Harmon, M.E. and S.S. Harmon, 1996. Reinforcement Learning: A Tutorial. Palma Olate, J.M. and C. Duran-Faundez, 2014. On maintaining connectivity of a colony of autonomous explorer mobile robots. [Accepted to be presented in] IEEE LARS-SBR 2014, Sao Paulo, Brazil. Patwari, N., R.J. O’Dea and Y. Wang, Relative location in wireless networks, 2001. IEEE VTS 53rd Vehicular Technology Conference, 2001. VTC

Shnayder, V. M. Hempstead, B.-R. Chen, G.W. Allen and Matt Welsh, 2004. Simulating the Power Consumption of Large-Scale Sensor Network Applications. Proceedings of the Second ACM Conference on Embedded Networked Sensor Systems (SenSys’04), Baltimore, MD. Zickler, S. and M. Veloso, 2010. RSS-based relative localization and tethering for moving robots in unknown environments. 2010 IEEE International Conference on Robotics and Automation, Anchorage, Alaska, USA, p. 5466-5471.