Pickle Research Center [20]. PSTC has a pilot chemical plant .... [20] âJ.J Picle Research Campus,â http://en.wikipedia.org/wiki/J. J. Pickle. Research Campus.
RoamingHART: A Collaborative Localization System on WirelessHART Xiuming Zhu, Pei-Chi Huang, Song Han, Aloysius K. Mok University of Texas at Austin
Deji Chen, Mark Nixon Emerson Process Management
{xmzhu, peggy, shan, mok}@cs.utexas.edu
{deji.chen, mark.nixon}@emerson.com
Abstract—Localization in wireless sensor networks is an important functionality that is required for tracking personnel and assets in industrial environments, especially for emergency response. Current commercial localization systems such as GPS suffer from the limitations of either high cost or low availability in many situations (e.g., in-door environments that exclude direct line-of-sight signal reception). The development of industrial wireless sensor networks such as WirelessHART provides an alternative. In this paper, we present the design and implementation of RoamingHART: a collaborative localization system on WirelessHART as an industrially viable solution. This solution is built upon several technological advances. First, RoamingHART adds the roaming functionality to WirelessHART and thus provides a means for keeping mobile WirelessHART devices connected to the network. Second, RoamingHART employs a collaborative framework to integrate different types of distance measurements into the location estimation algorithm by weighing them according to their precision levels. RoamingHART adopts several novel techniques to improve distance estimation accuracy and decreases the RSSI pre-survey cost. These techniques include introducing distance error range constraints to the measurements, judiciously selecting the initial point in location estimation and online updating the signal propagation models in the anchor nodes. Our implementation of RoamingHART can be applied to any WirelessHART-conforming network because no modification is needed on the WirelessHART field devices. We have implemented a complete RoamingHART system to validate both the design and the effectiveness of our localization algorithm. Our experiments show that the mobile device never drops out of the WirelessHART network while moving around; with the help of even one dependable anchor, using RSSI can yield at least 75% of distance errors below 5 meters, which is quite acceptable for many typical industrial automation applications. Keywords-WirelessHART, Real-time embedded system, Localization, Roaming, UWB localization, Collaborative TOA/RSSI localization
I. INTRODUCTION Location determination is an important functionality in many industrial environments. While GPS is used widely for this purpose, it is too costly and more importantly, does not work well in many situations because it requires line-of-sight signal coverage. In recent years, there is a rising need for personnel and asset tracking in industry due to the hazardous environment. In particular, it is critical for the control center to know how many people are being exposed to danger and where they are located in the plant during emergencies. A solution is now possible with the wide deployment of WirelessHART networks in recent years such that low-cost and accurate location tracking becomes possible. Figure 1 shows a pilot plant located in the Jame R Fair Process Science and Technology Center (PSTC) [1] at the University The work is partially supported by NSF award 1014146 and ONR grant number N000141010359
Fig. 1: A pilot plant in PSTC. The white symbols with indices are the WirelessHART field devices. The control center is in the building behind
of Texas at Austin. The main facility is a 5-floor building full of pipes and valves. The control center is inside the building behind the plant. In this environment, GPS only works in a limited area because of signal shielding problems. However, the facility can be fully covered by the WirelessHART signal. Thus, there is an opportunity to build a localization application upon a WirelessHART network infrastructure. There are several challenges in using wireless sensor networks for location determination in this application environment. First, there is not yet a roaming functionality specified in WirelessHART and a WirelessHART network is not required to support any mobile device. As a result, a moving device may lose connection soon after it joins the network. Second, while it is possible to use only RSSI(Received Signal Strength Indication) for location estimation, the result is not satisfactory because RSSI is in general not accurate enough. While it is possible to improve the accuracy by deploying more WirelessHART devices in the environment, it is not practical because the number of WirelessHART devices has been determined by process control needs, not for localization. Finally, trilateration based on TOA (Time Of Arrival) measurements is very expensive if high coverage is needed. As shown in Figure 1, in order to get at least three direct distance measurements at any location, many anchors must be deployed to enable lineof-sight communication. In order to provide both a cost-effective and accurate enough localization service in the above scenarios, we have designed and developed RoamingHART: a collaborative localization system on WirelessHART. The technical contributions are as follows: • RoamingHART adds the roaming functionality on WirelessHART to provide the necessary platform for localizing mobile nodes. While WirelessHART is based on wireless communication, the current standard supposes that all devices are kept at the same location for a long time; there is little
support to keep a mobile node connected to the network. Because of roaming functionality in our RoamingHART, a mobile WirelessHART device can move freely within the same network. • RoamingHART provides a collaborative framework to integrate different types of distance measurements that have different accuracies. This is essential in providing a costeffective way to build localization systems: on the one hand, RSSI measurements are ’free’ but inaccurate; on the other hand, accurate TOA based measurements cost considerably more. In contrast, RoamingHART does not need three accurate measurements to compute the location; rather, it can combine both RSSIs and TOA measurements to get the result. • RoamingHART adopts several key techniques to improve the accuracy of localization. First, it adds distance range constraints on weighted Least Square Error as the optimization objective function. Second, it carefully computes the weighted centroid as the initial point. Third, it utilizes optimized radio decay parameters to improve the accuracy in RSSI distance estimation. The combination of techniques is crucial for its success. • The implementation of RoamingHART and the encouraging experimental results in real-world testbeds validate our design and show the effectiveness of the RoamingHART approach. With only one accurate distance measurement device, we can get 75% of localization errors below 5 meters, which is quite sufficient for use in many industrial plants. • While RoamingHART is a research vehicle, it has been designed for integration into real-world process control systems. RoamingHART takes into consideration real industrial deployment constraints. The rest of the paper is organized as follows. Section II introduces the WirelessHART protocol. Section III describes the system design and the localization algorithm used in RoamingHART, followed by implementation in Section IV. Experimental evaluation is presented in Section V and Section VI discusses the related work. Section VII is the conclusion. II. W IRELESS HART WirelessHART [2] [3] is the first open wireless standard for the process control industry that was released in 2007. Since then, the adoption of WirelessHART is becoming more and more pervasive in process control plants. Figure 2 shows a typical WirelessHART network. There are several types of devices in the network. Each field device is a sensor installed in the plant and its sampling data are sent to the plant automation network through the gateway. The access points have direct connections with the gateway which collects packets from the WirelessHART network and forwards them to different destinations, either the network manager or the plant automation system. Both the gateway and the network manager can be software applications running on a PC and they must have a secure communication channel between them. The network manager is the control center of the whole network and is responsible for managing all the resources including scheduling communications for all the devices in the network. Router devices are employed
Fig. 2: A typical WirelessHART network
to relay messages if two wireless devices are far way from each other. In addition, a WirelessHART handheld device is used for configuring and monitoring field devices and is carried by a worker. However, the handheld device is defined without roaming functionality. In order to support a real-time, low power wireless mesh network, WirelessHART has been defined in four layers. The physical layer utilizes 802.15.4 radio for power saving; the MAC layer adopts TDMA (Time Division Multiple Access) to provide real-time delivery guarantee, and the network layer employs graph routing and source routing for mesh topology support. Finally, the application layer is defined to provide a uniform interface for easy configuration. The interface is command-oriented and hundreds of commands have already been defined. It is also possible for customers to define their own commands for custom use. Because WirelessHART adopts TDMA MAC layer, time synchronization is a key engineering issue. Before a device joins the network, it first synchronizes itself by listening to advertisements sent either by an access point or by a joined device. Then, it sends out a join request to the advertisement sender, which can be the access point or the joined device. The advertisement sender becomes the direct proxy and time master of the new joining device. The proxy will forward the join request to the network manager. After that, the network manager will configure the newcomer. After the device has joined completely, it needs to communicate with its time master periodically to keep itself synchronized with the whole network. The time master can also be reconfigured by the network manager. In order to make sure that any arbitrary device in the network is able to talk with the gateway, WirelessHART first builds up a link table on each device’s MAC layer to enable one-hop delivery. Then, routing tables will be configured to provide multihop connections. On top of all, sessions will be constructed for secure communications. To enhance the reliability of the mesh topology, WirelessHART defines a discovery link for all devices. The discovery link is a shared link. Each device can send a keep-alive packet on the link if necessary and it can also listen on this link to find out how many neighbors are around.
Fig. 4: A sample case of a mobile device dropping out the WirelessHART network Fig. 3: RoamingHART system architecture
III. ROAMING HART D ESIGN A. System Architecture We define an anchor node as a device that can provide both its own location information and the distance indication between itself and another device. Under this definition, all field devices can potentially serve as anchors, as shown in Figure 3. The transponder is the only mobile node in the WirelessHART network. The communication between the transponder and field devices is based on the WirelessHART protocol. Distance estimations are based on RSSIs, which are not accurate. In order to improve the localization accuracy, several other special anchors are added. These anchors are not WirelessHART devices and can provide more accurate distance estimations (e.g., measurements by TOA) but are usually more expensive. The transponder can communicate with them and get the distance information. Periodically, the transponder wraps all the distance indications —both RSSIs from WirelessHART field devices and distance measurements from the anchors—in a packet, and sends it to the gateway through the WirelessHART network. The gateway can then forward the packet to the locator through the plant network. Finally, the locator can compute the location of the transponder. Note that, the locator can be installed either on the same PC where the network manager and the gateway are or on a remote machine. Communication between the locator and the gateway can be based on TCP/IP for flexibility. There is another option to let the transponder compute the location by itself and send back the coordinates to the locator. However, this requires much higher requirements on the hardware and results in a considerably higher cost. This option is not adopted in RoamingHART at this stage. B. Roaming Functionality Design In practice, workers or assets in plants are not always moving. They may stop in one place for sometime and then go to the next location. Under such case, keeping a mobile device connected(Roaming) in WirelessHART is important to get the updated location. The concept of roaming, originally from GSM (Global System for Mobile Communications), refers to extending the connectivity service to a location different from the original point where the service was registered [4]. An example from the mobile phone industry is to keep a call session connected while the mobile phone is traversing between different cells. In WirelessHART, however,
this is not supported in the specification. WirelessHART assumes that a field device will not move after joining and all configuration activities are based on this assumption. As such, a WirelessHART implementation must be augmented to work with a mobile device. Figure 4 shows a mobile device in the current WirelessHART network. When the mobile device is at location A, it joins the network through device A and device A becomes its time master. However, later on, it moves to location B, which is out of the communication range, and it can no longer talk to device A. As a result, it loses synchronization and drops out of the network. A straightforward remedy is to let the mobile device automatically rejoin after leaving the network. However, the rejoin process usually takes quite some time to succeed. What is more, it also takes time for device A to realize what has happened. Because WirelessHART requires retransmission if no ACK is received, a considerable amount of retransmissions will be generated in this case and energy will be wasted. A better solution is to keep the mobile device connected. In order to do so, a handover operation [5] needs to be performed. In WirelessHART, a new direct proxy must be configured for the mobile device before it loses connection with the current proxy. One key issue of the handover is to determine when it is the time to do a reconfiguration. It is possible to utilize the location information for this purpose. The locator first gets the current location of the transponder and then computes the distance between the transponder and its proxy. If the distance is close to the transmission range limit, the handover operation will be triggered. This builds upon two assumptions. First, the location estimation is accurate enough. Second, the accurate transmission ranges of all field devices are available. However, sometimes, neither of the above holds in reality. For the former, up to now, it is very difficult to maintain the location estimation sufficiently accurate all the time. For the latter, even for the same device, the communication range may vary significantly in different directions and at different times. Finally, even though the locator can have the location information, it does not have the network inforamtion (e.g., the network topology, the link scheduling information and so on) that is shared only between the network manager and the gateway. Thus, the locator has to notify the network manager to do the handover and this increases the complexity of implementation. In RoamingHART, an easier and more efficient solution is adopted. The handover will be performed only in the gateway. As stated above, the transponder should send out the RSSIs of its neighbors, including its proxy, to the gateway periodically. The
gateway can first check the signal strength of current proxy. If the value is below a certain threshold, it may be the time to consider a handover. Because the gateway has all the network inforamtion, it can do this in a more convenient way.
M0 − Ai ∈ [ri − Δi , ri + Δi ], i ∈ [1, n]
(2)
where Δi denote the measurement error for anchor i. Based on formula 2, we specify a penalty function by formula 3.
C. Localization Algorithm Like in most wireless sensor networks, the signal strength is easy to get anywhere in a WirelessHART network. With this data, some signal decay model can be applied to estimate the distance. However, it is difficult to make the estimation accurate enough. Our experience shows that the estimation errors can be easily several meters. Also, different devices have different signal decay patterns and thus, it is not suitable to use only one set of model parameters for all devices. While TOA-based measurements can provide better accuracy, they have several limitations. First, the TOA-based measurement assumes that the two nodes have line-of-sight connection. Otherwise, the measured distance may not be the actual (point to point) distance and the measurement error can be much higher. Second, the multi-path effect may also compromise the accuracy. Third, TOA requires extra hardware and is more expensive. Thus, trilateration based solely on TOA-based measurements has severe limits in practice. In our approach, RoamingHART exploits all types of distance indications and tries to balance both the cost and the estimation error. Whereas localization with only RSSI estimations may result in large estimation error and using solely TOA-based measurements is very costly, RoamingHART provides a collaborative framework to combine them together to build a both cost-effective and accurate localization system. 1) objective Function Formulation: [6] provides a convenient way to compute 3D location with three distance measurements. However, if the distance measurements are not accurate enough, the result will be seriously compromised. For many location determination algorithms in wireless sensor networks, the following Least Square Error objective function [7] is used: M0 = argminΣni ωi (M0 − Ai − ri )2
the desired location of M0 should also satisfy the following constraints.
(1)
M0
where ωi is a weight factor, n is the number of anchors, ri is the estimated distance and M0 , Ai denote the unknown position, the ith anchor’s location respectively. Then, an optimization method is adopted to calculate the M0 that minimizes the expression in the formula 1. Choosing a suitable value for weight factor ωi is important to balance distance measurement errors. In RoamingHART, we use the reciprocal of the measurement error of Ai . The smaller the measurement error is, the bigger the weight factor will be. Getting measurement errors for TOA is relatively easier.Generally, it does not vary much with the environment and it can be got through simple testing process(e.g. comparing the actual distance with the measured distance). However, getting measurement errors for RSSI needs more effort, and we shall discuss it in detail in Subsection III-C3. Given that we not only have the estimated distances from the anchors but we can also get the measurement errors, it is straightforward to get the distance range for each anchor. Thus,
pi = ωi (max(|M0 − Ai − ri | − Δi , 0))2 , i ∈ [1, n]
(3)
where ωi = 1/(Δi ). In formula 3, if the estimated distance is within the range, there is no penalty. If not, the penalty is proportional to the square of the distance from the boundary. Also, for more accurate distance measurements, the outside boundary penalty is higher that others. Finally, combining formula 1 and formula 3, we get the Least Combination Error in formula 4. M0 = argmin(Σni ωi ((disi − ri )2 + pi ))
(4)
M0
where disi = M0 − Ai , ωi = 1/(Δi ). 2) Initial Point Chosen: For all iterative optimization methods, the selection of the initial point heavily affects the final result. In general, the closer the initial point to the actual position, the better result we can get. One way is to use the weighted centroid of the anchors (WCA) proposed in [8]. However, it is possible that the actual position is far from the centroid. Another option is to use the location computed by trilateration as proposed by [6]. This works perfectly if there are three accurate estimations. However, if there is any inaccurate measurements, the result will be compromised. In RoamingHART, we use weighted centroid of the intersections (WCI) as the initial point if there are not enough accurate distance measurements. Figure 5(a) shows an example in 2D. A1 , A2 , A3 are the anchors. P1,2 , P1,3 , P2,3 are the intersections. Then, the centroid of P1,2 , P1,3 , P2,3 (the red dot) is chosen as the initial point. We now extend the idea to the 3D case. Figure 5(b) shows the case when two spheres intersect each other. The intersections form the circle1 with the center F1 (for simplicity, the sphere centered by A3 is not shown). Similarly, there is the circle2 between A1 , A3 . Notice that, because of the measurement errors, the two circles may not intersect with each other. Figure 5(c) and Figure 5(d) show two different cases. The two circles are in different panels that intersect with Line1. With the help of Line1, we can find the intersections P1,2 , P1,3 in 3D. Notice that, , P1,3 but in Figure 5(c), P1,2 , P1,3 have their mirror images P1,2 they can be discarded either from physical considerations (e.g., the mobile node cannot be underneath the ground), or by the distance estimation from another anchor. It is easy to see that there are two intersections like P1,2 , P1,3 for two circles. Hence, there will be a total of six intersections if there are three anchors and all these intersections are possibly very close to the actual location. Now, we can compute the centroid. To avoid the result from being affected by ’bad’ intersections, weighting factors are added according to the estimation errors. The weighted centroid can be computed as in 5. Initial P oint = Σ(Wi,j ∗ Pi,j )/ΣWi,j
(5)
(a) Centroid of intersections in 2D
(b) Intersection of 2 spheres in 3D
(c) Two circles intersect with Line1 (d) Two circles do not intersect with Line1
Fig. 5: Choosing initial point by intersections
where Wi,j denotes the reciprocal of the cumulative error in computing Pi,j . 3) Improving the accuracy of RSSI: In our approach, a simplified Floor Attenuation Factor propagation model [9] is used to estimate the distance range from RSSI. The model can be expressed by Formula 6: P (d) ∈ [P (d0 ) − 10 ∗ n ∗ log(d/d0 ) − δ, P (d0 ) − 10 ∗ n ∗ log(d/d0 ) + δ]
(6)
where n is the rate at which the path loss increases with distance, P (d0 ) is the signal power at certain reference distance d0 (normally, d0 = 1), d is the distance and δ is an offset. In general, the parameter tuple (n, P (d0 ), δ) is set by experience. However, in our experience, the value of the parameter tuple varies from device to device. In fact, even for the same device, the parameter values vary with time because of battery drain. Periodic human pre-survey is an option but is very costly and extremely inefficient. In our approach, we adopt a similar way suggested in [10] to get the optimized values for (n, P (d0 )) for each anchor and then δ is computed as the model error. The process can be described as follows. i Based on the neighbor health reports sent by the anchor’s neighbors and the deployment map, we collect enough pairs of (rssii,j , disi,j ), where rssii,j denotes anchor i’s signal strength at anchor j and disi,j is the distance between anchor i and anchor j. ii Compute the optimized parameter tuple (n, P (d0 )) according to the formula 7 {n, P (d0 )} = argminΣj |rssii,j − Pd |
(7)
n,P (d0 )
where Pd = P (d0 ) − 10 ∗ n ∗ log(disi,j ). iii We now have optimized (n, P (d0 )) and δ can be computed by formula 8. δ = mean{|rssii,j − Pd |}
(8)
For the mobile device, suppose the signal strength from anchor i is P (d). We can get the distance range by formula 9. d ∈ [10
P (d0 )−P (d)−δ n∗10
, 10
P (d0 )−P (d)+δ n∗10
]
(9)
IV. I MPLEMENTATION In this section, we will describe our implementation of RoamingHART. The implementation consists of two parts. On the device side, we integrate the distance measurement module that is based on the accurate TOA method into the WirelessHART device. On the PC side, we integrate our localization algorithm with the DeltaV Explorer which is an application in the DeltaV automation system [11]. The details are described in the following subsections. A. Integrating UWB device with WirelessHART device In order to get accurate distance measurements, we utilize the PulsON 400 RCM toolkit from the Time Domain [12] Corporation. The toolkit consists of four P400 RCM UWB boards and it uses the roundtrip time delay of the UWB (Ultra Wide Band) signal to measure distance. In addition to distance, the P400 RCM boards also report the measurement accuracy. Compared to other types of radios (Wi-Fi, 802.15.4), UWB has much wider bandwidth and higher SNR. Thus, the distance measurement accuracy is better [13]. However, because the frequency of UWB (normally between 3G and 10G Hz) is higher than Wi-Fi and 802.15.4, the penetrability of UWB is weaker because of its shorter wave length. Our experience with P400 RCM boards shows that with line-of-sight connection, 0.5 meter accuracy can be expected. However, if there are obstructions in between, two P400 RCM boards can only communicate through reflected signals and the actual measurement error can be several meters. The signal strength measurement can be obtained directly from any WirelessHART device [10]. Currently, we use the MC1322x chip made by Freescale [14] and we have installed the WirelessHART stack on it as [15]. In order to get both TOA and RSSIs from the same WirelessHART handheld device, we need to integrate the MC1322x and P400 RCM board together to form a ‘black box’. Fortunately, the P400 RCM board provides both the hardware interface (UART pins) and the software interface (UART driver and APIs [16]) that we can use for this purpose. On the MC1322x side, we program a protocol parser based on the P400 RCM serial communication protocol. Periodically, MC1322x sends out range requests to the P400 RCM board to get both the UWB distance measurements and the measurement errors. The result is wrapped in burst data
TABLE I: WirelessHART commands used in RoamingHART Command Cmd787 Cmd170
Fig. 6: Handheld device components and the device
Fig. 7: Software architecture on PC
sent to the gateway. The handheld device and the components inside are shown in Figure 6. B. Integrating RoamingHART in DeltaV WirelessHART networks are deployed in industrial process plants. RoamingHART is built on WirelessHART and to be used as part of a process control system to track personnel and assets. We put the localization algorithm of the RoamingHART system inside the DeltaV system [11]. DeltaV system is a well established digital automation system used by the process industry. The current version adopts WirelessHART networks to collect process data for automation control. In order to integrate RoamingHART with DeltaV system, we first connect the DeltaV Explorer with our wireless network manager [17] and wireless gateway through a TCP/IP connection as shown in Figure 7. We then implement the localization algorithm in DeltaV Explorer. Periodically, the DeltaV Explorer gets RSSIs and TOA measurements from the WirelessHART network and computes the locations accordingly. C. Relevant WirelessHART Commands The key WirelessHART commands used in RoamingHART are listed in Table I. Except Cmd170, which is specially defined and implemented only in our handheld device, all others are defined in WirelessHART and are implemented in every WirelessHART device. Hence our RoamingHART system can be used in every WirelessHART-conforming network. D. Implementing Roaming Functionality in the Gateway The key function of roaming in WirelessHART is to automatically reconfigure a new proxy for the handheld device before it loses connection with its current proxy. The steps taken in the roaming function are as follows: i Periodically, the gateway gets received signal strengths of the handheld’s neighbors from the handheld itself. It then checks the signal strength of the handheld’s current proxy.
Cmd967 Cmd968 Cmd969 Cmd970 Cmd971
Definition Report neighbor receive signal level Customized Commands for RoamingHART. Report both UWB distance measurements and receive signal levels of its neighbors. Write a link to a device Delete a link from a device Write a graph edge to a device Delete a graph edge from a device Set the neighbor property flag (e.g., time master)
If it is above a certain threshold (by default, -85 dbm), the handheld is still within the communication range of its proxy. ii If the signal strength is below the threshold, it is time to consider reconfiguring a new proxy. To do this, the gateway selects the neighbor with the highest signal strength from the handheld’s signal strengths report. To prevent the ’ping-pong effect’ in the handover, reconfiguration is not performed if the highest signal strength is not significantly bigger (at least 5dbm) than the current proxy’s signal strength. Otherwise, the reconfiguration operation will be triggered. iii The reconfiguration process first allocates links and assigns a graph edge between the new proxy and the handheld through the current proxy. Then, it removes the links and the graph edge between the handheld and the current proxy. From then on, the new proxy starts to take charge of the communication. The handheld will communicate with the gateway through the new proxy in the first hop. iv Finally, the gateway configures the new proxy as the new time master for the handheld. The handheld will no longer try to synchronize itself to its original time master. V. VALIDATION AND E XPERIMENTAL E VALUATION In this section, we first use the simulation to test out our localization algorithm and then, we show the real-world experimental data. A. Validation of Localization Algorithm In order to validate the effectiveness of introducing distance range constraints and the initial point choosing method, we use Matlab to simulate randomly three anchors and one unknown node in a 50 meters * 50 meters * 3.5 meters space. For each anchor, both the minimum and maximum distances are simulated. Estimation errors of accurate distance measurements are between 0.0 and 0.5 meter while the errors of inaccurate measurements are between 0.0 and 7 meters. The simulation focuses on performance comparisons with various numbers of accurate distance estimations. 1) Initial Point Choice: In this section, we compare three approaches for the initial point choice: trilateration as proposed in [6], weighted centroid of anchors (WCA) as proposed in [8] and weighted centroid of intersections (WCI) as described in section III-C2. The cumulative probability results are shown in Figure 8. The figure confirms the description in Section III-C2. The trilateration proposed in [6] only works better than WCI when
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all three distance measurements are accurate. If there is any inaccurate measurement, the performance will be compromised. WCA performances worse than WCI in all cases. In our experiments, we use WCI to compute the initial point when three accurate measurements are not available. Otherwise, we use trilateration. 2) Objective Function: Least Square Error vs Least Combination Error: Here, we compare the performances of the two objective functions mentioned in section III-C1. One is the Least Square Error as given in formula 1. The other is the Least Combination Error as given in formula 4. We use the simplex optimization [18] to compute the location result. The cumulative distributions are shown in Figure 9. From the figure, we can see that the Least Combination Error outperforms the Least Square Error in all cases. B. Experiments on Real Testbeds In this section, we show the localization results obtained in real-world environments. One set of results is from an office area and the other is from the pilot chemical plant shown in Figure 1. 1) Experiment in Normal Office Area: In this experiment, we set up a testbed on the fifth floor of the University of Texas’s ACES building [19]. The deployment map is shown in Figure 10. The black dots are WirelessHART anchors and the triangles are UWB anchors. The test area is the lower right quadrant (surrounded by the red rectangle). The size of the area is 33 meters by 27.8 meters. On the left side, several close-door offices are separated by walls. On the right side (surrounded by the green dash lines) is an open area as shown in Figure 11. The whole area is heavily covered by Wi-Fi signals. The roaming functionality is thoroughly tested in this experiment. In WirelessHART the communication range for the in-door
environment is 35 meters. While this guarantees that the entire test area is covered by WirelessHART signal, the star topology is very unstable in this setting. We put the access point inside the CS lab (denoted by the star symbol). Eight WirelessHART anchors (denoted by the black dots) are placed around the test area. The screen capture of the network topology on the network manager is shown as in Figure 12 after all the anchor devices have joined the network. In this figure, the red dot labelled index 1 is the gateway, the yellow dot labelled index 2 is the access point, and the blue dots on the left are the devices. The dashed lines with arrows denote point-to-point connections between nodes. The numbers denote the signal strengths in dbm. We can see from the figure that only five nodes have direct connections with the access point. The other three are two hops away. Some of the received signal strength indications are very small (below -85 dbm), indicating that the distances between two nodes are close to the maximum transmission range. Thus, it is very unstable for a mobile device to communicate with the same proxy if it moves around. The experiment lasts for four and half hours during which time the handheld is moving around the area freely without dropping out of the network, while experiencing at least three proxy switches. Due to the weak penetrability of the UWB signal, we can find only a small area where all three UWB anchors (denoted by the dashed red rectangle) can be heard. Even in the intersection of UWB coverage areas, UWB measurements suffer from heavy multi-path effects and result in higher measurement errors. All the locations tested in our experiment are outside the rooms, either in the open office area or in corridors. This is to guarantee that the handheld device can have line-of-sight connection with at least one UWB anchor. The distance cumulative distribution function is shown in Figure 13 and the statistical values are listed
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5
10 Distance Error(m)
15
20
Fig. 13: In-door experiment accuracy CDF TABLE II: Indoor Max, Min, Average and Median Distance Errors (m)
Fig. 10: In-door testbed deployment map
Fig. 11: Open office area and corridor
in Table II. In order to get the results corresponding to zero UWB anchor, we ignore all UWB measurement inputs for that case. As can be seen above, both statistical values and the CDF(Cumulative Distribution Function) are very encouraging. First, with only one UWB anchor, the median distance error quickly drops down to around 3 meters and we can get 75% of distance errors below 5 meters. Second, with two UWB anchors, the median error quickly drops below 2 meters and 95% distance error below 5 meters. The results are very close to those with three UWB measurements. It is noteworthy that the results show a bit higher ratio for the case of small distance errors. This is mainly because it is easier to get line-of-sight connections with
Fig. 12: Topology for in-door testbed
UWB Anchor Number 0 1 2 3
Min 0.72 0.07 0.06 0.03
Max 28.45 19.13 18.96 9.57
Average 9.07 3.93 2.14 1.52
Median 8.84 3.07 1.76 1.30
two UWB anchors than with three UWB anchors. Without lineof-sight connection, the measurement error is higher; for the case of three UWB measurements, trilateration is employed to get the initial point and this can be easily compromised by inaccurate measurements. This explains why sometimes the distance error with three UWB measurements is larger than that with only two UWB anchors. Compared to the simulation results shown in Figure 9, most experimental results are better. This is because in the simulation, there are a total of 3 anchors, including both UWB anchors and RSSI anchors. However, there are more RSSI anchors in this experiment. The handheld can get as many as five RSSIs at the same time and they provide a better estimation. 2) Experiment in Real Plant Area: In this experiment, we carry out our testing at the PSTC (James R Fair Process Science and Technology Center) [1] located in University of Texas at Austin’s Pickle Research Center [20]. PSTC has a pilot chemical plant used for multidisciplinary research and is shown in Figure 1. Figure 14 shows its map. The process plant area enclosed by the red dashed rectangle is the testing area where we carried out this experiment. The size of the area is 29.2 meters by 13 meters and the height of the building is 14.6 meters. Unlike the office environment in the in-door experiment, the Wi-Fi signal is much weaker here. Also, there is no wall that can block wireless signal completely. As before, we place eight WirelessHART nodes inside the plant. The screen capture of the network topology on the network manager is shown in Figure 15. Compared to the office area test case previously, the signal strengths between nodes are generally stronger. However, the UWB signal is less stable and it is very difficult to get line-of-sight connections under such an environment. The testing results are shown in Figure 16 and Table III. The results are still very encouraging. With only one UWB anchor, we can get at least 80% distance errors below 5 meters. A comparison of the results of both cases reveals some interesting observations. First, RSSIs in the latter environment give more accurate distance estimation. Both the median and average
TABLE III: Max, Min, Average and Median Distance Errors (m) in PSTC UWB Anchor Number 0 1 2 3
Min 0.25 0.30 0.28 0.29
Max 12.34 9.14 10.11 9.27
Average 4.86 3.14 2.72 2.32
Median 4.36 2.67 2.02 2.13
VI. R ELATED W ORK
Fig. 14: James R Fair Process Science and Technology Center Map
Fig. 15: Toplogy for PSTC testbed
errors in PSTC are much smaller than those in office-area case. The reason is that the office environment is heavily covered by Wi-Fi signal, which shares the same 2.4G bandwidth with WirelessHART, with the result that the WirelessHART signal is somewhat degraded. For the PSTC case, however, there is no strong Wi-Fi signal around and this is a much ‘quieter’ environment for WirelessHART. Second, in PSTC, the differences of results between two UWB anchors and three UWB anchors are smaller and the median error of two UWB anchors is even a bit smaller. The reason is the same as in the above case. In the process plant, it is very difficult to get line-of-sight connections between two UWB nodes. Thus, even though two UWB nodes can talk to each other, their connection is more likely by signal reflection, not by direct-line reception. As a result, the distance measurement errors are larger. In generally, in the PSTC experiments, the results with UWB anchors are a little worse than those in the office area, and this confirms that UWB signal does not perform well in the latter case.
1
In this section, we give a brief summary of related work and discuss their relationship to our results. Basically, the methods of localizing a sensor node in wireless sensor network fall into two categories: range-based and rangefree approaches. The former is based on distance measurements. The latter only utilizes the communication range. Because our method belongs to the first category, we only discuss range-based methods. There is an extensive literature that discusses localization with RSSI, with either the Wi-Fi antenna or the 802.15.4 antenna. RADAR [9] utilizes RSSI from multiple Wi-Fi anchors to locate and track users. HartFi [21] presents a collaborative localization system and enhances existing Wi-Fi localization system with low-power WirelessHART interfaces to achieve significant energy saving. [22] points out that the accuracy of RSSI is expected to be 30 feet in most cases in the in-door environment and can only be used for rough location estimation. Our experimental results both in this paper and in [10] are close to [22]. It is possible to get higher accuracy by deploying more anchors but this is not practical when RSSI deployment is determined by process control needs and not by localization needs. There are other works like Sextant [23] and [24] utilizing statistical models and optimization algorithms to improve the accuracy of the final estimation. Compared to RSSI-based methods, TOA based algorithms are relatively less well reported. Early research focus on using ultrasound for distance measurements like Active Bat [25] and Cricket [26]. In recent years, using UWB in localization becomes more and more popular. [13] investigates the UWB localization performances based on AOA(Angal of Arrival), TOA and signal strengths. [27] points out in a multi-path environment, the measurement errors of UWB will be significantly increased even there exists a line-of-sight connection, which is consistant with our experimental experience. There are a few papers discussing about using more than one distance indications for localization. [28] [29] [30] combine both TOA and AOA to localize mobile nodes in celluar networks. [31] [32] [33] analysis localization performance of AOA and TOA with UWB radio. However, to the best of our knowledge, no paper has implemented a localization system using both RSSIs and TOA like ours.
0.8
Probability
VII. C ONCLUSION 0.6
0.4 Zero UWB anchor One UWB anchor Two UWB anchors Three UWB Anchors
0.2
0
0
5 10 Distance Error(m)
Fig. 16: PSTC experiment accuracy CDF
15
In this paper, we present the design and implementation of RoamingHART: a collaborative localization system on WirelessHART. RoamingHART adds the roaming functionality to WirelessHART and enables a WirelessHART handheld device to move freely inside a WirelessHART network without losing connection with the gateway. This provides a handle for real-time location tracking in WirelessHART. We propose a collaborative
framework for integrating different types of distance measurements that offer results of varying accuracy. Our collaborative scheme provides a cost-effective way for building localization systems that are accurate and less expensive. On the one hand, RSSIs are nearly ‘free’ to get everywhere in WirelessHART networks but RSSIs are not accurate distance indications. On the other hand, accurate measurements such as TOA are expensive and have limited coverage in real-world situations. Trilateration based on accurate distance measurements is often limited to only a small area. RoamingHART provides a suitable hybrid framework for this situation and reports favorable results. In addition, in order to improve localization accuracy and reduce the cost of RSSI pre-survey, RoamingHART exploits several key techniques, including introducing distance range constraints, judiciously computing weighted centroid as the initial point, online updating RSSI propagation model parameters for the anchors. Experimental results for the office environment and in a chemical process plant provide validation for both our design and implementation of RoamingHART. The practicality of our approach is demonstrated by intergrating our system into a well established process control system that is widely used in industry. In testing the roaming functionality, we showed that the handheld device did not lose connection at any location as long as it can sense a WirelessHART signal. The distributions of distance errors look very encouraging. For both cases, even with only one accurate distance measurement, we can get more than 75% of distance errors no more than 5 meters, which is quite sufficient for many industrial applications. For future work, we may consider better balancing the penalty and the least square error in formula 4 and extending our work to the real-time tracking of a mobile node. ACKNOWLEDGMENT We are grateful to Cho-Jui Hsieh and Si Si for their advice on the formulation of the Formula 3. R EFERENCES [1] “James R Fair Process Science and Technology Center,” http://www.utexas. edu/research/ceer/pstc/index.html. [2] “HART communication,” http://www.hartcomm2.org. [3] J. Song, S. Han, A. Mok, D. Chen, M. Lucas, and M. Nixon, “Wirelesshart: Applying wireless technology in real-time industrial process control,” in Real-Time and Embedded Technology and Applications Symposium, 2008. RTAS ’08. IEEE, april 2008, pp. 377 –386. [4] “Roaming Defintion,” http://en.wikipedia.org/wiki/Roaming. [5] “Handover Defintion,” http://en.wikipedia.org/wiki/Handover. [6] D. Manolakis, “Efficient solution and performance analysis of 3-d position estimation by trilateration,” Aerospace and Electronic Systems, IEEE Transactions on, vol. 32, no. 4, pp. 1239 –1248, oct 1996. [7] W. Foy, “Position-location solutions by taylor-series estimation,” Aerospace and Electronic Systems, IEEE Transactions on, vol. AES-12, no. 2, pp. 187 –194, march 1976. [8] J. Blumenthal, R. Grossmann, F. Golatowski, and D. Timmermann, “Weighted centroid localization in zigbee-based sensor networks,” in Intelligent Signal Processing, 2007. WISP 2007. IEEE International Symposium on, oct. 2007, pp. 1 –6. [9] P. Bahl and V. N. Padmanabhan, “Radar: an in-building rf-based user location and tracking system,” 2000, pp. 775–784. [10] X. Zhu, W. Dong, A. K. Mok, S. Han, J. Song, D. Chen, and M. Nixon, “A location-determination application in wirelesshart,” in Proceedings of the 2009 15th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, ser. RTCSA ’09. Beijing, China: IEEE Computer Society, 2009, pp. 263–270.
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