A Management Framework for Device-free Localization

Huseyin Yigitler, Ossi Kaltiokallio and Riku Jantti

Abstract—Received signal strength based device-free local-ization (RSS-based DFL) is recently gaining momentum asan indoor localization technology, since it enables locatingpeople that are not cooperating with the system by carryinga device. The technology is based on monitoring the signalstrength measurements of the many wireless transceivers thatare deployed in the monitored area. The measurement modalitycan be used to accurately localize people and recent works haveshown that it can be used e.g. in residential monitoring. Despitethe recent advances in enhancing the accuracy of RSS-basedDFL, real-world requirements such as energy efficiency andadaptation to the changing communication conditions are oftenneglected in the related literature. In this paper we presenta management framework for RSS-based DFL which enablesnot only monitoring the environment and network, but to alsointeract with the dynamic environment and varying wirelesschannel. With the proposed framework, it is possible to makea considerable step forward so that RSS-based DFL can beused in long-term and real-world deployments.

I. INTRODUCTION

DEVICE-FREE LOCALIZATION (DFL) based on re-ceived signal strength (RSS) has received considerable

attention over the past few years in the literature and severalworks have proposed the use of the technology in indoorlocalization [1], [2], [3]. These systems exploit the RSS ofwireless sensors to localize people in the monitored area.Most works have exploited IEEE 802.15.4 based devices[2], [4], [3], however, the technology could be implementedusing any device capable of wireless communication and thatcan measure the RSS, e.g.: WLAN [1] and RFID [5]. Sincethe technology is based on the temporal and spatial RSSvariations that are caused by people; the technology doesnot require people to cooperate with the system by carrying adevice. Therefore, the measurement modality is also referredto as passive localization [1], sensorless sensing [6], or RFtomography [4], [7].

Recent works have demonstrated the use of RSS-basedDFL in open environments [2], in obstructed environments[3], and also in through-wall scenarios [8]. Most of thereported DFL techniques have sub-meter localization ac-curacy [2], [3], [8]. Therefore, the potential applicationsof these systems are of many, including ambient assistedliving (AAL), residential monitoring, cyber-physical systems(CPSs), security, safety, and surveillance [3], [8], [9]. AnRSS-based DFL system brings several advantages over othertraditional indoor localization technologies: it works in ob-structed environments, it is independent of ambient light

Huseyin Yigitler, Ossi Kaltiokallio and Riku Jantti are with the De-partment of Communications and Networking, Aalto School of ElectricalEngineering, Espoo, Finland (email:{name.surname}@aalto.fi).

This work was supported by the Finnish Funding Agency for Technologyand Innovation under project WISM II

conditions, and it can be used in through-wall scenarios.Moreover, a DFL system can be implemented using anydevice capable of wireless communication including low costwireless sensors.

Thus far, in the context of RSS-based DFL, the researchhas mainly focused on developing models and algorithmsto be used for extracting location information from theRSS measurements of the many static links of the wirelessnetwork. These systems are typically deployed for a shorttime period [2], [8], [10]. However, requirements of real-world deployments are often neglected such as: varyingcommunication conditions [11], fault management [12] andenergy efficiency [13]. In the future, when DFL systemsare integrated as part of AAL or CPSs to provide positionbased content, the importance of these requirements increasesand in such deployments network management must beaddressed.

One of the primary functionalities of wireless sensornetworks (WSNs) is data collection and dissemination. Dataare collected to e.g. monitor vital signs of patients in clinicalenvironments [14], thus the data flow is from the sensorsup to the access point of the network and onward to theend user. DFL systems collect RSS measurements of thewireless links, typically with low latency and high data rate(>100 kbit/s [3]). Many proposed systems are capable ofprocessing the data online [3], [2], [8], however, the emphasisis on estimating the location of the person. As argued [15], asensor network should also be able to present current statusof the network to the end user, an issue neglected in DFLsystems. Thus, a long-term real-world DFL system shouldnot only estimate the locations of people online but also thefunctionality and status of the network.

In data dissemination, the information flow is in theopposite direction, i.e., from the end user to the sensorsof the network. Data dissemination is often needed e.g. instructural health monitoring to spread configuration informa-tion [13]. Moreover, numerous works have shown that thecommunication conditions vary significantly over time [16],[17], [11] making network management mandatory to ensurefunctionality in the long-run. Network management servesthree purposes in DFL: first, the network can be configuredeasily reducing deployment time; second, adapting to thechanging communication conditions, i.e., the network canchange the frequency channel of operation if needed; third,it enables energy efficient networking, i.e. the system can goto sleep while the area is not occupied or changes of interestare not encountered. In real-world DFL deployments networkmanagement is mandatory, a research topic not addressed inrelated research.

The operation principle of the DFL system is based on

Proceedings of International Joint Conference on Neural Networks, Dallas, Texas, USA, August 4-9, 2013

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the assumption that the RSS of each node in the network areaffected by a person who remains in the same location. Thisassumption of the DFL algorithms put stringent optimizationconstraints on the network development due to physical prop-erties of the wireless channel. However, the rich literature onnetwork management for constraint devices [18], [19], [20],[21], rely on network and transport layer specification so thatthe management functionality is considered in the applicationlayer, which has considerable processing overhead. Thus, theproposed network management solutions can not be utilizedin DFL systems without sacrificing the performance. Theproposed management framework is based on the uniqueand stringent constraints of DFL networks, which leveragesthe DFL performance while providing network managementfunctionality.

In this paper, we make a considerable step forward so thatRSS-based DFL can be used in long-term and real-worlddeployments. First, we identify the requirements of a DFLsystem to enable long-term, real-world deployments. Second,we introduce a system not only capable of monitoring thelocations of people online but also the status of the network,which allows the system to identify the varying commu-nication conditions while operational. Third, to the best ofour knowledge, we are the first to introduce a DFL systemthat has the capabilities for network management. Networkmanagement enables easy and fast configuration during de-ployment, adaptation to the varying nature of the wirelesschannel and management of the scarce energy resources; allrequirements that must be fulfilled when considering a long-term real-world DFL deployment.

The paper is structured as follows: in Section II we firstaddress the physical restrictions and networking requirementsof a DFL system. Then we discuss about solutions thatare mandatory to enable long-term real-world deploymentswhen considering devices that are battery powered, e.g.implementations based on IEEE 802.15.4 devices. In SectionIII, we describe our solution. We begin from the bottomlevel describing the functionality of the wireless node andproceed to the top level explaining how data are processedand how the network management is addressed. Conclusionsare drawn in Section IV.

II. DEVICE-FREE LOCALIZATION SYSTEM OVERVIEW

It is well known that propagating radio waves are alteredby the medium, which is observed through the amount ofexperienced losses. Despite the fact that there are manysources of propagation losses, link shadowing is of particularinterest since the human presence in the medium causesadditional attenuation in the signal. Further, nodes in closeproximity of one another experience correlated shadowing,which depends on the position and geometry of the shadow[22]. As in computerized tomographic imaging [23], thedistribution of shadowing losses in an area of interest canbe determined using the signal strength measurements ofa dense wireless network [2]. Therefore, DFL is frequentlyreferred to as RF tomography [4], [7] or radio tomographicimaging (RTI) [2], [3].

Fig. 1. DFL system overview

In general, a DFL system is composed of a dense wirelessnetwork and a gateway as shown in Fig. 1. The network isformed by nodes which are placed in predefined positionsand allowed to communicate with each other in a prescribedmanner. The gateway is simply a computer attached tothe sink node, which is capable of sniffing the ongoingcommunication in the network. The aim of the system is todetermine the location of the person in Fig. 1, using the RSSmeasurements of the nodes. For this purpose, the networktypically follows a simple transmission schedule such that ata given time instant only one of the nodes is transmittingwhile the others are listening. Although measuring the RSSdoes not require to transmit any specific type of packets,the scheduled node broadcasts the most recently acquiredmeasurements so that the sink node receives and relays thesemeasurements to the computer. The computer stores the datafor later use and/or constructs the images of the shadowingfield and/or estimates the locations of people online.

A. Physical Constraints and Basic Requirements

A propagating radio wave is altered by reflection, diffrac-tion, scattering and waveguiding in addition to free spacepropagation [24, Chapter 4]. In general, stochastic models areutilized to represent all of these mechanisms and a distinctionis drawn between the losses due to small scale and large scaleeffects. The large scale losses are widely represented by apower law, which can be extended to cover the shadowinglosses of a link by modeling this as a weighted line integral ofa loss-field [22]. In this model, each point on the line joiningthe transmitter and receiver (link line) has a weighted contri-bution on the shadowing losses. Thus, the model explicitlyexplains the correlation among two links with an implicitdependence on the position and geometry of the shadow.The correlation among different links allows estimating theloss field using a finite amount of RSS measurements. As thenumber of correlated measurements modulated by the sameshadowing source increases, the distribution of the loss-fieldin the traversed area can be estimated. For example, the RSSmeasurements of a dense wireless network are affected bythe same loss-field, which render a convenient measurementsystem enabling the localization of the shadowing source.

The acquired RSS measurements are not only effected byshadowing. On the contrary, they reflect the overall effectof small scale, large scale and shadowing losses. Thus, theaccuracy of shadowing loss-field estimation depends on the

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level of shadowing loss information that can be extractedfrom the measurements. The effect of other losses can beaveraged out by increasing the number of measurementsaffected by the same shadowing loss field. For example,a significant improvement in accuracy is achieved by col-lecting measurements on multiple frequency channels [10].However, the loss field varies both in time and frequency inaccordance with the physical characteristics of the wirelesschannel. More specifically, the coherence bandwidth andcoherence time of the channel define the limits of themaximum frequency separation among the channels, and themaximum time delay between samples [25]. Within theselimits, the wireless channel can be considered constant andthe loss-field can be estimated accurately. As a drawback,the intrinsic broadcast nature of the wireless communicationdoes not allow simultaneous transmissions on the samefrequency channel, which dictates a schedule for the networkdepending on the coherence bandwidth and coherence timeof the channel. Therefore, the accuracy of DFL has a strongdependence on unknown properties of the wireless channeland on the transmission schedule of the network.

Ideally, the location of the people can be determinedin arbitrarily high resolution by increasing the density ofthe network either by decreasing the distance between thenodes (decreasing the area of interest) or by increasing thenumber of nodes. However, the distance between receiversalso affects the correlation among the small scale fadingcomponents that neighboring nodes encounter. Hence, thepositions of the nodes in the network can not be selectedconsidering only the resolution concerns, but also the phys-ical limitations imposed by other loss sources.

In summary, the shadowing-loss field can be estimated bysignal strength measurements of a dense wireless network.However, the performance depends strongly on physicalplacement of the nodes and the properties of the wirelesschannel, which is not known prior to deployment. Theaccuracy of DFL can be improved by increasing number ofmeasurements acquired for the same shadowing field, eitherby increasing the number of nodes or the frequency channelsused for communication. However, in either case, latencyof successive measurements increases making it harder tosatisfy the requirements dictated by the coherence bandwidthand coherence time. Therefore, a highly accurate DFL systemcan only be achieved by using the signal strength measure-ments of a tightly managed wireless network, which providesmoderate level of configurable features in order to adapt themeasurement system to the varying channel conditions.

B. Networking Requirements

The wireless network of a DFL system has a mesh topol-ogy, where the system monitors an area within the transmis-sion range of the nodes. In general, a DFL network followsa transmission schedule and does not require a sophisticatednetworking paradigm. The physical (PHY) layer specificationsolves most of the communication problems arising from themobility in the medium, such as carrier and symbol syn-chronization [26]. The coverage and connectivity problems

of such a network are addressed by the mechanisms of themedium access control (MAC) specification. Moreover, theunderlying communication does not need to follow sophis-ticated network layer rules for routing and convenient dataexchange mechanisms because of the topology. However, aDFL system needs to provide mechanisms to acquire as manymeasurements as possible modulated by the same loss-field.

Fig. 2. DFL network topology

The connectivity graph of a DFL network is shown inFig. 2. Since each broadcast must be received by all theneighbors, the transmissions must obey the time divisionmultiple access (TDMA) rules and/or must follow round-robin (R-R) like transmissions. In either case, the transmis-sion turn is assigned based on the unique node identifier asshown in Fig. 2. The sink node (identifier 1) is the first inschedule and it begins every round of communication. InTDMA implementations, each node in the network transmitsat its own time slot. In a pure R-R schedule there is nostrict time slot for transmissions, but they are triggered byreception from the previous node in schedule. Furthermore,since in most of the considered deployment scenarios thewireless channel tend to have a wide coherence bandwidth,the network can communicate in different frequency channelsto alleviate the accuracy of the system. Therefore, a typicalDFL network requires a schedule, which determines theparticipating nodes, the order of the medium access, and thefrequency channel(s) of the transmissions, while keeping thedelay between transmissions minimal.

C. Energy Efficiency

The wireless network of a DFL system is composedof nodes that are capable of wireless communication andare able to measure the RSS. In other words, the utilizedwireless technology does not effect the underlying prin-ciple. However, the resolution concerns brings forth costconstraints in deployments, which leverage using low-cost

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Fig. 3. Measurement coordination in a DFL network

transceivers. Furthermore, most use-case scenarios of DFLrequire autonomous operation over extended periods of timewith a limited energy supply, i.e. a battery. Thus, the wirelessnetwork in a DFL system typically is composed of low-cost,low-power and battery-powered nodes.

Battery powered operation of the nodes restricts the life-time of the DFL system. For example, without any furtherconsideration of energy consumption and keeping all of thehardware components on, a sensor node can operate only fora few days. Especially for long-term deployments, energyefficiency is as important as accuracy and resolution. There-fore, the utilized wireless technology is preferably the oneaddressing energy constraints by providing intrinsic supportfor energy management based on various mechanisms as it isfor WSNs [27]. For example, IEEE 802.15.4 MAC/PHY [28]based nodes usually provide interfaces to turn off the radio,change the power mode of the processor, switch betweendifferent processor clock frequencies, etc, cutting the powerconsumption to a fraction. Therefore, despite the fact thatthe accuracy concerns require minimum delay transmissions,the wireless network must support power saving optionsby utilizing the energy management features of the nodeseffectively.

In general, an energy efficient DFL system developmentrequires accurate estimation of the shadowing loss-field on-line. This follows from the fact that the nodes can be put to anenergy efficient mode without disturbing the performance ofthe system only if the origin of the changes in the shadowingloss-field are known. In this case, it is possible to scheduleand confine the transmissions to and around an area wherea change in the shadowing loss-field is expected. Anotheroption is to reduce the number of nodes in a deploymentwithout significantly sacrificing the accuracy [3]. The numberof activated nodes can be reduced after the deployment byusing the localization accuracy as a measure. Thus, long-term deployment scenarios are only possible for systemswhich estimate the shadowing loss-field online and adaptthe transmission schedule and power mode of the nodesaccording to the state of the medium. Consequently, thewireless network should not only act as measurement systemproviding input to the DFL system, but also accommodatenecessary interfaces to adapt for energy efficient operationaccording to the shadowing loss-field estimates.

The DFL imaging algorithms are typically executed afterall the nodes in a schedule broadcast their measurements,

which corresponds to a complete set of measurements or around of measurements. As the imaging algorithms requireminimal time delay between successive transmissions in around, either the TDMA MAC must have very narrow timeslots and/or the transmissions must be scheduled in R-Rfashion. In case static schedules are used, completion of around triggers the next round of communication. Therefore,the energy constraints are neglected since the nodes are notallowed to change their power mode. Furthermore, since aDFL network relying on a static schedule can not counteractto variations in the channel, the system is at most best-effort.In such a system, all the nodes must participate to everycommunication event, which increases the durations of themeasurement round but also the energy requirements linearlywith respect to the number of nodes and frequency channels.In contrast, a DFL system allowing dynamic scheduling canadaptively alter the number of frequency channels and thenodes participating in a round of communication by keepingtrack of the state of the system using the output of theimaging algorithms. In summary, an energy efficient DFLsystem suitable for long-term deployments requires dynamicscheduling which takes into account the state of the imagingsubsystem as well as the energy constraints of the nodes.

A measurement round fulfilling the requirements above isshown in Fig. 3, where the receptions and transmissions arerepresented by up and down arrows, in respective order. Foreach round, the start is marked by the sink node, and eachnode follows the transmission schedule. The coordinationcommands must be distributed to the nodes at beginningof each round along with start command transmission. Thenodes must be able to keep track the state of the round,and perform specific actions according to the state such asreconfiguring the operation mode, switching the frequencychannel, enabling receivers or transmitters, generating mea-surement packages, and transmitting a suitable packet. In thisapproach, a round data is composed of measurements fromdifferent frequency channels in order to minimize the timedelay between measurements. Furthermore, the configurationdistribution is aligned with the start of the round so that themeasurement coherency is maintained, while the nodes thatare not taking part in a round can change their power mode.Consequently, a medium access scenario depicted in Fig. 3is a candidate implementation for DFL network supportingdynamic scheduling.

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D. Network Coordination and Management

For short-term deployments, the networking requirementslisted in the preceding subsection and the need for networkconfiguration and management are irrelevant. However, forlong-term deployments the wireless network requirementsalluded in the preceding subsection imply a tightly coor-dinated network. In addition to the coordination needs forthe medium access, the energy concerns render centralizedmanagement requirements. For a DFL network, with topol-ogy shown in Fig. 2, the gateway is the only to fulfillthese coordination and management requirements since thecomplete measurement data is not available to other entities.Therefore, the gateway does not only act as an infinitememory attached to the sink node, which listens to theongoing communications, as in the work of others, but alsoas a network coordinator and manager.

Fig. 4. Coordination components of the gateway

The coordination tasks of the gateway are tightly coupledwith cooperation among different system components toallow online DFL imaging and energy efficient operationas shown in Fig. 4. The network coordination task is per-formed by an adaptive scheduler, which requires an inputfrom the imaging subsystem, and the frequency channelranking (channel selector) subsystem. Thus, the acquiredRSS measurements are used for both imaging and networkingpurposes. As the generated schedule must be known alsoby the imaging algorithm for proper shadowing loss-fieldestimation, the generated schedules are shared through thecomponent storing the abstract representation of the network(described in subsequent). The generated schedule is broad-casted to the network through the sink node.

The utilization of battery-powered, low-energy and low-cost wireless networks brings forth reliability concerns, asthe long-term operation of these systems depends uponmultitudes of low-cost subsystems. As argued by Tolle andCuller [15], long-term deployments require tight monitoringand expert-system like alerting functionality implementedas an integral component of the gateway. The number oflinks in a DFL network is quadratic with number of nodes,which also increases linearly with the number of channelsused for communication making it impossible for an operator

to track the status of the network without monitoring aids.On the other hand, these networks must also provide hardconfiguration modification options to allow the operator tointerfere with the system if necessary. The monitoring andconfiguration options of the gateway constitute the networkmanagement components, whose interrelations are depictedin Fig. 5.

Fig. 5. Management components of the gateway

As it is for the coordination components, the gateway hasan abstract network representation, which contains the logicalprimitives (nodes and links) actively forming the network.The nodes are defined in terms of their identifiers as inthe instantiation in Fig. 2, whereas the links are defined interms of destination node identifier, source node identifierand the frequency channel. In this abstraction, the networkis formed by the nodes, and the destination nodes contain alist of active links. Thus, the signal strength measurementsreceived from a node is uniquely assigned to a logical link,which can generate online statistics. The status of all linksof a node constitutes the node statistics. Similarly, the statusof all nodes in the network forms the network statistics. Thenetwork statistics generate alerts for the network operatorif a node has some links that can not pass the predefinedthreshold filters, e.g.: packet reception rate is low, RSS isbelow the sensitivity region, etc. Similarly, the alerted nodeshave detailed alerts of the identified failures, such as whichlinks have failed. Finally, the links contain a detailed timehistory of the measurements, and the associated statistics.The configuration component of network management pro-vides necessary means for an operator to adjust the operationof the DFL system according to the application needs, or toforcibly adapt the system to the changing conditions.

E. DFL As a Subsystem

The DFL system can act as a part of a larger system,for example, as a passive localization subsystem of a homeautomation system, or ambient assisted living system. Fur-thermore, since the information shared in the network is notrestricted, the DFL network can be utilized to collect per-vasive data or to distribute some specific action commands.On the other hand, the network monitoring feature of thegateway may generate alerts to the global system operator toalleviate quality of service. Thus, the gateway must be ableto share the information between different subsystems, andperform specific actions according to state of or commandsfrom the global system.

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III. EXAMPLE IMPLEMENTATION

In this section, a DFL system implementation, whichfulfills the requirements outlined in the preceding section,is described. The network implementation is based on TexasInstruments CC2431 IEEE 802.15.4 PHY/MAC complianttransceivers. The transceiver micro-controller units run acommunication software and a modified version of FreeR-TOS micro-kernel operating system both developed by re-searchers in Aalto University. The gateway is implementedin C++ programming language as a software running on MSWindows.

A. DFL Network

The DFL network must fulfill the requirements outlinedin Section II-B. In IEEE 802.15.4 PHY/MAC complianttransceivers based DFL network implementation, the config-uration distribution and command initiation are transmittedas beacon frames while the measurements are transmitted asdata frames. The configuration beacon frames can have apayload containing the activated nodes, the frequency chan-nels, the transmission power, and the measurement type. Thecommand in the beacon frame can be configure and/or start.The utilized transceiver hardware can measure correlatoroutput, and cyclic-redundancy-check (CRC) result of eachpacket reception in addition to RSS, which are useful mea-sures to identify losses during the transmission. Additionally,the nodes may contain node specific data that needs to betransmitted to the gateway. Thus, the data frames may containdifferent types of data, depending on the configuration andthe system. The frame types that can be transmitted in a DFLnetwork are summarized in Fig. 6.

Fig. 6. DFL network frame types and content

The DFL measurement coordination is performed as inFig. 3, so that the network is not allowed to initiate ameasurement round without receiving the start commandfrom the sink node. In addition to allowing tightly coor-dinated measurements, this approach enables configuration(schedule) distributions without causing measurement co-herency issues. The transmissions follow a pure R-R, so thatthe transmissions are initiated one after another once startcommand is received. Therefore, the transmission scheduledoes not require strict time slots for communication. Inthis way, the time synchronization need for communicationpurposes is avoided. However, this approach requires carefultimeout adjustments since a packet drop in one link affectssubsequent transmissions in the schedule.

B. Node Software

Fig. 7. DFL node software component relations

The DFL system require minimal implementation induceddelays, which are mainly dictated by the number of soft-ware layers and the complexity of the layers between thetransceiver and the application. Therefore, the node softwareimplementation is simplified by disabling the transport andnetwork layers of the communication stack, and tailoringthe MAC layer for the medium access depicted in Fig. 3.The relations between the different components for the DFLpurposes are depicted in Fig. 7.

Fig. 8. DFL node application state transition diagram

The state transition diagram of the DFL node applicationis shown in Fig. 8. The application can be either in idle,configure, active or sleep state. In idle state, the applicationwaits for commands from the gateway, thus, the radio ison and the application is waiting for valid beacon frames.Upon reception of a valid beacon frame, configure stateis activated, where the content of the received frame ischecked and the local parameters are updated accordingly.The application returns to the idle state if the received frameis invalid or it does not contain the start command or the nodeis not included in the schedule. Otherwise, the applicationswitches to the active state. In active state, the transmissionmetrics (RSS, correlator output and CRC) of each data framereception are stored in local database according to the activelink. After each reception, the application checks whetherit has the transmission turn. If so, it broadcasts the localmeasurement database on the configured frequency channels.The application returns to the idle state once each active nodein the network has transmitted. The application may switchto sleep state only from the idle state.

In active state, it is not possible to assume that eachtransmission of the neighbors will be received correctly dueto unreliability of the wireless communication. Thus, it is

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necessary to define timeout events within the active state tocheck whether a state transition is necessary. There are twopossible transitions from the active state: first, the applicationneeds to switch to the transmission state when it has thetransmission turn in the schedule; second, the applicationneeds to go to the idle state when the round ends. Therefore,there are two timeout events excluding each other. Uponreceiving a valid data frame or start command, the applicationcalculates the timeout durations by taking into account boththe implementation induced delays and the transmissiondelays as a function of packet length. Consequently, thetimeout events guarantee worst case operation in case ofpacket losses.

C. Sink Node Software

The sink node is a part of the gateway, and its primaryfunction is to relay the commands from the computer tothe network and packets from the network to the computer.Thus, it supports two types of communications; one with thecomputer over the serial port and the other with the networkover the wireless transceiver. The sink node has the samesoftware components as the node software. However, theDFL application is developed aiming at different operationas implied by the state transition diagram shown in Fig. 9.

Fig. 9. DFL sink node application state transition diagram

The sink node application can be either in idle, configureor active states. In idle state, the application waits for com-mands from the computer over the serial port. Upon receptionof a valid serial port frame, configure state is activated,where the content of the received frame is checked and thelocal parameters are updated accordingly. The applicationimmediately returns to idle state if the received frame isinvalid. If the received frame does not contain the startcommand, but only the configuration command, the receivedframe is relayed to the network, and the sink node returnsto the idle state. Otherwise, the application switches tothe active state. In active state, the application relays allthe received frames from the network to the gateway. Theapplication returns to the idle state once each scheduled nodein the network has transmitted. In active state, there is onetimeout event which triggers transition to the idle state, andthe timeout duration is calculated as it is done for the nodeapplication.

D. Gateway SoftwareThe gateway has four main functions as network coordina-

tor, network manager, global system client, and DFL imagingsubsystem. In addition to these functional components, theimplemented gateway software has rich set of user interfaceand data logging features, as depicted in Fig. 10.

Fig. 10. Gateway software components

The data flow in the gateway is depicted in Fig. 11.Once a measurement round is initiated by the scheduler,the gathered data is shared with the data logger and withthe abstract node, which relays the measurement to themeasurement container of its links while logging the nodespecific data. After completing a round of measurements,the user interfaces are refreshed with the new status, andthe imaging block is activated to update the estimates usingthe new measurements. The output of the imaging subsystemis redirected to the global system and to the image display.The configuration is checked and/or updated by the schedulerbefore starting a new round.

Fig. 11. Gateway software component relations

The transition diagram of the scheduler and measure-ment collection states are shown in Fig. 12. The configure,

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start and update states are transient, so that the applicationswitches to the next state after completing their operationswithout waiting for a specific event. In active state, thegateway waits for the measurements from each node in theschedule. Upon completion of a measurement round, theDFL imaging system updates the estimates and the userinterfaces refresh their respective views.

Fig. 12. DFL gateway software state transition diagram

IV. CONCLUSIONS

In this paper, we present a management framework forRSS-based DFL. With the presented system, we are able toaddress network management issues that are not consideredin related research such as: network configuration to decreasedeployment time, adaptation to the time varying channel tomaximize communication performance and energy efficientoperation to enable long-term deployments. We identify thatnot only a DFL system has to estimate the locations of peopleonline, but also use this as an input for network management.This follows from the fact that the nodes can be put to anenergy efficient mode without disturbing the performance ofthe system only if the origin of the changes in the shadowingloss-field are known. To the best of our knowledge, theseissues in the context of RSS-based DFL have not beenaddressed, and we argue that it is mandatory to develop DFLsystem by considering these so that the technology can beused in real-world long-term deployments.

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