Scalable Control System for Bluetooth MobileDevices

Giorgio Corbellini§, Lukas Kuster§†, Thomas R. Gross†

§Disney Research †Dep. of Computer Science8006 Zurich, Switzerland ETH Zurich, Switzerland

Abstract—Bluetooth (BT) devices are usually groupedin a star topology in small clusters called personal areanetworks (PANs). The population of BT PANs is limitedto 8 devices, but setups in entertainment theme parks orinteractive installations might want to employ a largernumber of mobile devices. In those scenarios, only off-the-shelf devices, without any hardware modifications, can beaccepted, so a PAN must be extended to become a low-costcellular network for consumer products. We explore thedesign of such a cellular network of BT nodes. The paperdiscusses the practical aspects of a wireless control systemand reports the experience obtained from implementing aproof-of-concept system. The prototype implementation isbased on BT dongles, which act as cellular base stations,and Sphero robotic balls, which are low-cost consumermobile robots. This wireless control system allows therobots to be controlled by a single device; it is modularand scalable and offers handover and localization servicestypical of common cellular networks.

I. INTRODUCTION

Personal area networks (PANs) of consumer devicesare used in numerous settings. Nodes (members) of aPAN can be smartphones, smart watches, smart wrist-bands, other wearable accessories, or consumer robots.PANs connect these devices to one another and mayalso provide connectivity to local-area or wide-areanetworks. Commercial PANs that use low-power radiofrequency (RF) such as Bluetooth to exchange data andcommunicate are limited to few devices. Nevertheless,sometimes it is necessary or desirable to connect moremembers than are directly supported by a PAN, e.g.,to control a large number of devices using their nativeRF technologies. The scenario of controlling a largenetwork of devices is particularly appealing in caseof mobility within large areas while maintaining theconstraints imposed by an industry-standard PAN: nomodifications of the device hardware, and operationwithin the power budget/level of off-the-shelf (con-sumer) devices.

Controlling a large number of mobile consumerdevices requires flexible and scalable protocols to enablereliable communication. Such protocols must abstractfrom the limitations imposed by the RF standards tocope with the variable density of the network. We con-sider a control system for a large network of BluetoothClassic (BT) devices as a representative of the class of

Fig. 1: Concept art ( c©Disney): Interactive and intu-itive installation to control large number of consumerproducts with a single device.

such scenarios. The control system implements conceptsfrom cellular network systems for low-cost consumerdevices that have limited power communication range,slow mobility, and limited power processing units onboard.

The control system builds multiple localized PANsand tracks the mobility of mobile devices. The instan-taneous PAN density is kept below a set thresholdof fairness, therefore, the control system can enforcehandover of one or more devices from a PAN to anotherone.

We implemented a proof-of-concept system to con-trol a network of Sphero nodes [8] and use a testbedof 20 nodes to experiment with a localization protocolbased on RSSI estimates of multiple BT master devices.

II. BACKGROUND

Bluetooth Classic is a wireless technology for PANsover short distances [2]. The members of a PAN form apiconet, and a BT PAN can connect up to 8 devices witha star topology with one master and up to 7 slaves thatcommunicate using time-multiplexing. Thus, all nodesin the same piconet share the available bandwidth, sothe average throughput of a piconet with a few nodes ishigher than the average throughput of a large piconet.To allow nodes to join a piconet, the master initiatesan inquiry phase (looking for available slaves) and then

enters a paging phase (connecting to a slave node bysending a page frame).

BT Classic was not designed to handle large net-works of devices [11]. Networks with more than 8 activedevices can be organized into a scatternet [2] that inter-connects nodes that belong to different piconets usingsome border nodes (connected to multiple piconets).But the protocols for scatternet formation, routing, andmaintenance [4], [9] are not ideal for mobile scenariosas the network topology continuously varies, so differentapproach is needed.

III. WIRELESS CONTROL SYSTEM USING BT

With the goal of providing fairness and low-latencyto each node, a multiple-piconet system must optimizethe number of BT masters and slaves. Given a networkwith a fixed number of mobile nodes, the nodes ofthe same BT piconet equally split the available piconetbandwidth. Therefore, to maximize the throughput pernode, the number of nodes per piconet should beminimized. Such a setup leads to higher number ofactive piconets. On the other hand, all piconets areuncoordinated and do independent frequency hopping,thus, they can interfere with one another. Therefore, theusage of fewer dongles reduces the risk of interference.

We empirically tested various scenarios of BT in-terfering piconets to find the best tradeoff between thenumber of dongles (piconet masters) and slaves. Thedongles are all connected to the same computer via anUSB hub, and in every test the BT slaves form a circlewith radius of 50 cm around them.

1S1D 10S10D 10S5D 10S2DSat. 9.007 10.26 11.34 24.710 Hz 8.837 8.762 10.01 25.8820 Hz 8.676 9.464 16.95 2550 Hz 8.445 10.93 13.73 26.29

TABLE I: Average round trip time of ping messages inpiconets of different sizes (D=Dongles, S=Slaves) fordifferent ping rates: Saturation mode, 10 Hz, 20 Hz,and 50 Hz.

The investigation consisted of measuring the qualityof the network in terms of average latency of BT frames.The latency, expressed by the round trip time (RTT),measures the time that elapses from the transmissionof a ping frame to the reception of the relative ACKframe.

1S1D 7S1D 14S2D 20S3DSat. 9.007 27.73 28.37 28.1710 Hz 8.837 32.13 31.57 30.0120 Hz 8.767 29.43 28.04 27.26

TABLE II: Average round trip time of ping messagesfor different number of piconets for different ping rates:Saturation mode, 10 Hz, and 20 Hz. The presence ofadditional piconets does not affect the average RTT.

We use two ping modes: a periodic mode anda saturation mode, where the system waits until theresponse of the previous message is received before thenext ping message is sent to the slave (that is a spherorobot).

Table I shows the average RTT of 1000 ping mes-sages. The length of every ping request is 23 bytesand the ping response is 22 bytes long. If only oneSphero device is connected to the system we measurean average round trip time of about 9 ms, independentlyof the rate or mode we send frames. This corresponds toa maximum rate of about 110 Hz. Tables I and II showthat the number of nodes in a piconet affects the RTTbecause transmission opportunities are multiplexed overtime.

Table II shows that piconets do not influence eachother thanks to the frequency-hopping procedure. Theinterference of multiple piconets is negligible for ourpurpose even if all piconets are saturated.

Empirical tests show that piconets with seven slavescan send ping frames with a maximum rate of about35 Hz. Higher rates result in increasing frame buffers.Therefore, the RTT also increases over time.

In conclusion, we identify a threshold of about30 Hz as maximum data rate within a piconet with ar-bitrary size, nevertheless, for faster systems minimizingthe number of nodes per BT master is a design choicethat keeps the latency low.

IV. SPHERO CONTROL SYSTEM

a) The Sphero Robot: The testbed is based onSphero mobile robots as BT slaves [8]. A Sphero roboticball is a small spheric robot with BT connectivity,an accelerometer, a gyroscope and LEDs. A Spherodevice keeps track of the rotations of the motors, andthis feature allows the device to estimate its positionrelatively to its starting position.

A smartphone or a computer can connect to theSphero device and send commands or requests com-pliant with the Sphero API [7]. A Sphero robot may becontrolled by (1) direct commands sent and immediatelyexecuted by the robot and (2) macros, which are shortsequences of commands that can be loaded into thememory and triggered by direct commands. The Spherocontrol system presented in this paper uses spheronSDK, which is a framework for JavaScript applicationsthat runs on the Node.js platform.

b) Platform: The control system (on a Linux ma-chine with Ubuntu 14.04) uses the BlueZ BT protocolstack, which supports a up to 16 dongles (serving a maxof 112 simultaneous slaves).

c) Software Architecture: The SpheroControllerprovides the user with functions to discover new Spherodevices and to bind them to a specific dongle andcan automatically scan and connect all the Sphero

Fig. 2: Measurement of RSSI using the inquiry method.

devices found during the scan. Spheros are connectedto the closest dongle. For each physical BT dongle, thesoftware maintains a list of all Sphero objects that arebound to it [5].

d) Ranging Using RSSI: The Received SignalStrength Indicator (RSSI) indicates the signal strengthof a radio signal. BT defines two alternative ways toestimate the RSSI of a master-slave radio link.

The first way can be used to measure the RSSI of anestablished BT connection. In connected state, an HCI(host controller interface) command of the BlueZ BTstack reports RSSI values. The result of the commandis an integer value within the golden receive powerrange [10]. The second way is to use the inquiry scanmethod and is preferable to the HCI method becausemultiple masters can start an inquiry process at the sametime to estimate the distance of all BT devices in thevicinity.

Figure 2 shows the distribution of RSSI valuesmeasured during the tests (about 500 values per eachof the three distances) using the inquiry method. TheRSSI values measured with the inquiry method shownin Figure 2 are Gaussian distributed.

Sphero robots can be programmed to light up in acolor that depends on the RSSI level; Figure 3 illustratesthe strength of the signal of 10 moving Spheros in anindoor lab scenario in the presence of other interfer-ing devices operating in the of 2.4 GHz band. Everymeasurement averages 8 consecutive values obtainedwith the HCI method. While measuring the RSSI ofa given location, every Sphero rotates on the z-axis tosmooth the effect of the non-omnidirectional antennapattern. In fact, we empirically observed that the relativeorientation of dongles and Spheros has an impact on theRSSI value. In the figure, a blue color indicates a strongsignal, whereas a red color indicates a weak signal. Inthis figure, the effect of multipath communication is vis-ible which affects the measurements and subsequentlyleads to erroneous estimates.

Fig. 3: A long exposure picture of moving Spherodevices that change their color according to the signalstrength. The values in the legend follow the the goldenreceive power range scale. Blue corresponds to a strongsignal whereas red indicates a weak signal.

e) Mapping (Smoothed Out) RSSI to Distance:The measured RSSI level is affected by multipath andthe radiation pattern of the receiving antenna [6]. Torange the Sphero devices, we empirically derived a logdistance path loss model based on RSSI measurements.We carried on the measurements in a noisy environmentwith several RF systems operating at 2.4 GHz. Wemeasured the RSSI level at different distances from aBT dongle.

f) Handing over Connection of Mobile Objects:A cellular BT network larger than 8 nodes uses multipledongles as base stations. As in regular cellular networks,the dongles are static and located at specific positionsso that their radio coverage areas partially overlap, sothat mobile devices may connect to the nearest dongleand disconnect from it when the signal level degrades.

In contrast to other radio systems, BT does notnatively support any handover mechanism among pi-conets [1]. Thus, we designed our own.

Since a BT slave can only be connected to onemaster, seamless handover is not an option; the con-nection must be torn down and then re-established withthe new nearest dongle. As soon as the RSSI level ofa connection drops below a given threshold, a Spherodevice should disconnect from the BT dongle, scanthe environment for a new closest BT dongle, andthen connect to this dongle if there is enough capacityavailable on the new dongle. To detect the closestdongle, every disconnected Sphero device estimates itsdistance with respect to all available dongles.

Figure 4 shows a snapshot of mobile scenario withnine Sphero devices. The snapshot is taken in a labspace and shows the situation right after an inquiryscan. In the figure there are three dongles with differentcolor IDs. The Sphero devices are moving toward anew location but are still connected to the dongles that

Fig. 4: Sphero devices connect to the dongle that hasthe strongest signal. The color of the Sphero indicatesthe dongle it is connected to.

measured the strongest signal.

g) Localization: Localization uses an arbitrarynumber of static dongles (with known location) to esti-mate the position of the Sphero devices. Per each Spherodevice, the system trilaterates the estimated distances ofthe three closest dongles using the path loss model wederived.

V. DISCUSSION

This paper describes a localization system for mo-bile devices using BT. The system is scalable for severalreasons: (1) Dongles use inquiry scans to range theSphero devices. The duration of the inquiry process doesnot depend on the number of discovered devices. (2)The current system can handle up to 112 Sphero devicesbecause of a limitation of the Linux BlueZ stack.

The system is also modular because of the wayits architecture is designed. SpheroExt modules can beextended with other modules that use different versionsof BT or different physical layers to communicate.

This work focuses on observing the interferencewithin a single piconet and among different piconetsshowing how local BT dynamics affect the designchoices of a cellular network in terms of handoverstrategies.

The system presented in this paper focuses on BTclassic technology, which is characterized by a slowpairing process and limitations in terms of piconet size.The low energy version of BT (BLE) saves energyby duty cycling the radio and this version supportsmesh networking [3], which in practice results in largerpiconets. Although BLE is more efficient than classicBT and can support more devices, the pairing processis still slow, and the control system would also rely onthe slow inquiry process instead of the HCI method.

VI. CONCLUSIONS

The paper describes a wireless control system forBluetooth (BT) devices as a proof-of-concept for net-working a larger number of BT devices than can beconnected in a Personal Area Network (PAN piconet).The control agent is a server that connects to a meshnetwork of dongles; this system has been demonstratedfor up to 20 Sphero robots.

An important practical aspect is the interference ofnodes (Sphero devices) within the same piconet on oneanother and also the interference among different pi-conets. Our measurement results confirm the suggestionbased on theory that in large networks it is a gooddesign choice to minimize the number of slaves perpiconet to increase the piconet fairness. There are manyscenarios that can employ a large number of BT devices.The approach discussed here shows that such networksare possible without any modification of the hardwareof the off-the-shelf devices. Working with un-modifiedconsumer products is crucial if we want to support thedesigners of applications for mobile devices, and it isencouraging so see that even a low-cost system likeBluetooth Classic allows building larger mesh networks.

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