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PLANNING AVAILABLE WLAN IN DYNAMIC PRODUCTION ENVIRONMENTS
PLANNING AVAILABLE WLAN IN DYNAMIC PRODUCTION ENVIRONMENTS Svilen Ivanov†
Edgar Nett†
Stefan Schemmer‡
Otto-von-Guericke University – Magdeburg, Institute for Distributed Systems, Universitätsplatz 2, 39106 Magdeburg, Germany† rt-solutions.de GmbH, Oberländer Ufer 190a, 50968 Cologne, Germany‡
Abstract: What is still missing in the convergence of IT and automation technologies is the integration of wireless communication. In this paper we consider the problem of planning available Wireless LAN (WLAN) in production environments with dynamic radio-propagation properties. Our approach is an autonomic control loop with feedback simulation and optimization. We present in detail the simulation and calibration module which automatically keeps the knowledge about the current radio coverage up-to-date. Evaluation showed that the constrained least squares calibration method results in a more accurate model compared to other methods. The results are general to planning WLAN availability in production environments. Copyright © 2007 IFAC Keywords: Dependability, availability, wireless networks, industrial networks, network planning, radio propagation, automatic model calibration.
cell / production control layer with TCP / IP communication and no or soft real-time requirements, or the field bus layer with real-time requirements in the range from 100ms down to 10ms.
1. INTRODUCTION What is still missing in the convergence of IT and automation technologies is the integration of wireless communication. Even though this rises some tough challenges there are new application fields that drive a strong trend to deploy WLANs in industrial applications. Among these new applications mobile transport systems of all kinds are the most important and widely recognized sector. This sector spans from rail-guided baggage carriers that improve throughput and flexibility in airport baggage logistics, over warehouse systems with integrated transport entities, to automated guided vehicles and overhead monorail carriers that transport work pieces in assembly systems. In all these applications, providing wireless connectivity to the mobile entities promises more detailed and up-to-date supervision and diagnosis, more flexible control and improved scalability.
Using wireless communications in such demanding applications and environments poses some tough challenges. Besides fulfilling typical hardware requirements of industrial equipment, like DC voltage supply, standard industrial plugs, rugged housing with a sufficient protection against dust, water and heat, this mostly applies to the non-functional properties of the communication: reliability of the communication, availability of the infrastructure, security, and realtime (Nett, 2005). In this paper we consider the problem of planning WLAN availability in production environments with dynamic radio-propagation properties. As a case study we use a warehouse system with integrated transport entities. The network is used for task assignment, reports on task fulfilment, and update of the warehouse database. The challenges to the WLAN planning come from the fact that the environment is dynamic (continuously changing personnel, goods, machinery). This affects network coverage, which is tightly connected to its availability. WLAN planning
The envisaged applications can be characterized with respect to the kind and tightness of control they exert via the wireless medium. The spectrum ranges from monitoring and diagnosis only (no control) over commissioning (task assignment), to centralized or even autonomous motion control. Depending on the kind of application, the WLAN will be used for the
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methods exist, but they have a static nature i.e. plan availability initially, but do not detect and react to changes while the network is in operation. We present a method to plan and continuously monitor/restore WLAN availability in operation mode. It is based on an autonomic control loop consisting of measurements, simulation, optimization, and reconfiguration.
ERP Order
In the next section we define the application scenario in dependability terms. Section 3 describes the problem that we address in more detail. In section 4 we present our autonomic-control-loop method for planning WLAN availability; section 5 discusses related work. In section 6 we present in detail one of the core components in the control loop (model calibration and simulation) with evaluation results. Finally we conclude the paper and present future work.
Confirmation
Figure 1: Integration of business and technical processes. Orders/Confirmations are processed manually in this example. IT is required to automate the process (Nett, 2005). Correct Service; the system provides delivery of packets through the wireless medium. One basic requirement for packet delivery is network coverage. This means that on every place in the network, there is minimum 1 accessible access point (AP) with Average Received Signal Strength ( RSS ): RSS > RSS NA ( RSS is bigger than the not
2. APPLICATION SCENARIO The integration of business and technical processes in a warehouse occurs by communication between the ERP (Enterprise Resource Planning) system and the operators/transport entities. This includes task commissioning, report delivery, database updates. The tasks are mainly transport tasks (operators with forklifts / integrated transport entities) and maintenance tasks (operators use hand-scanners to monitor the state of the goods). An IT infrastructure is needed to ease the communication and reduce processing times (especially in plants with long distances); figure 1 shows an example. Wired networks may not be the optimal solution because of high deployment costs and limited mobility of vehicles and people. Among the wireless alternatives the IEEE 802.11 WLAN is a relevant candidate (proven and widely deployed standard, decreasing equipment prices). But the warehouse system also puts requirements to the communication: • High Availability: requires network connectivity with long mean time to failure (MTTF) relative to short mean time to repair (MTTR) • Cost efficiency (both hardware and maintenance costs) In order to describe the availability/dependability context in our scenario we define the meaning of the terms application, system, correct service, correct system state, fault, error, failure and argue about the need for availability.
acceptable level RSS NA ). Inter-cell interference is an issue, but has lower weight in this scenario because medium utilization is relatively low. Correct system state (normal operation); in order to ensure that correct service is fulfilled in case of a fault, the correct system state is the following redundant design. On every place in the network minimum N RED APs should be accessible with
RSS > RSS RED > RSS NA ( RSS is bigger than some redundancy threshold RSS RED ). Fault model; it consists of the following faults: • Introduction of new objects in the environment (i.e. goods, machinery, transport entities, people) that affect radio propagation and decrease RSS at the users locations • Hardware failure of an AP Error; as a result of a fault, the radio coverage is worse than the redundancy setting in normal operation, but still correct (sufficient to ensure packet delivery: min 1 AP and RSS > RSS NA ).
Application (service consumer); it is an ERP system used to assign tasks to the operators/entities and deliver reports from the tasks fulfilment. It comprises all network layers above the medium access layer. The application provides transport protocols for endto-end delivery.
Failure; there exists a place in the network where service is not correct ( RSS < RSS NA ). The values of the parameters are application-specific, for instance:
System; it is an infrastructure WLAN, the system boundary (interface to the application) is between the medium access layer and the upper layers.
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optimization. We apply the general principal of a feedback control loop, which is widely used in control engineering, to the problem at hand. Figure 2 shows our implementation of this idea to WLAN planning. The simulation module uses monitoring (measurements) from the real network to create and calibrate a model of the radio coverage. In this way it estimates the current coverage in the environment. This knowledge (simulation results) is used by the optimizer & warning module to assert the current system state w.r.t. service: normal, error, or failure. When it detects an error/failure it generates optimal configuration that restores the service. During this operation it also accesses the simulation module to evaluate the proposed configurations w.r.t. service (what-if simulations). The new configuration is automatically sent to the WLAN components by the network controller. In cases where manual effort is required (e.g. install a new AP) a message is sent to the administrators. So, the autonomic control loop consists of measurements from the network, calibration, simulation, optimization (configuration generation), and finally configuration update back to the network. In terms of control engineering the forward path is: “Optimizer & Warning” -> NC/Administrators -> “WLAN Infrastructure” and the feedback path is “WLAN Infrastructure” -> “Network Controller” -> “Simulator & Calibrator”. The feedback information (current coverage) is not monitored because it is hardly possible with the required density; rather it is produced by the simulation (based on measurements for modelcalibration). The complete procedure to plan availability in WLAN is in Figure 3, we now describe the steps: • Initial input; from business processes, administrators define the requirements and provide the initial model input • Initial simulation; from the initial input, the model is generated and calibrated. The initial simulation results are produced. • Initial optimization; from the initial simulation results and the requirements it performs initial optimization w.r.t. requirements. Generates network configuration for initial deployment. • Initial deployment; install the recommended infrastructure: manual work to position APs, automatic parameter configuration through the Network Controller. • Monitoring; the WLAN automatically collects signal strength measurements among the access points. The NC aggregates them to be used for simulation. • Online calibration & simulation; calibrate the model from the actual measurements and produce simulation results about the current coverage. • State assertion (warning); determines the system state w.r.t service and eventually generates a warning. • Online solution generation; start an optimizationsimulation cycle with what-if simulations (scenarios generated from the optimization module). The result is a configuration to bring the system in correct state.
• RSS NA = -90dBm. This is a minimum sensitivity level for receivers used in the warehouse to enable communication at the needed data rate 11Mbps1. • RSS RED = -75dBm. Changes in the RF environment (e.g. good placement in large areas) can affect the received signal by 10-15dB (based on experience), so this is an acceptable redundancy level. • N RED = 2 APs. 1AP is not enough for redundancy; 2APs give an acceptable redundancy level (probability that 2APs fail simultaneously is low) and cost efficiency. Availability;
A=
MTTF + MTTR MTTR
(1)
Availability is required in this scenario because relatively long service outage times (e.g. 10-20% from working day) lead to production downtime. On the other hand the application can tolerate short service outages (up to 1-2 min.), so reliability need not be high. This is because the upper layer transport protocol is reliable (tasks/reports are not lost and are eventually delivered). The means to achieve availability in this scenario is called wireless network planning. Network planning includes various aspects at almost every network layer (physical coverage planning, addressing, security, traffic engineering). In this paper we focus on coverage planning aspect, which is a prerequisite for the others. 3. PROBLEM EXPOSITION The standard model-based pre-planning includes manual specification of the infrastructure (APs, antennas), environment (obstacles) and model parameters (path loss exponents, attenuation factors). The problem is that the standard model-based preplanning is not feasible because: • The models, even if one is willing to spend quite an effort for modelling (which is typically not the case with automation staff and pre-sales consultants), will never accurately fit the reality. • The environment is dynamically changing. So, even if the model fits in the beginning, it will not be able to predict the future behaviour of the WLAN. So, errors or failures might occur without being detected and corrected (or additional maintenance work is needed for this). Planning large contingencies incurs significant cost in terms of staff and equipment. Therefore, an approach is needed, which achieves an acceptable accuracy with minimum manual effort, and additionally automatically detects and reacts to changes in the environment. 4. APPROACH We approach the problem by using an autonomic control loop with feedback simulation and 1
The value -90dBm comes from product datasheets (Cisco Aironet, Netgear WAG511, 3Com).
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Figure 2: Control loop for available WLAN planning combination of these as a general control loop approach for planning WLAN availability also brings profit and has not been proposed so far. This is our solution approach.
• Online changes; automatic changes are performed by the NC and manual changes are sent to the WLAN Administrators. The increased availability is derived from two factors: • Increased MTTF; when an error is detected and subsequently corrected, the system is again in redundant state which reduces the probability for failure. • Decreased MTTR; when a failure is detected, the optimization module automatically suggests a measure to restore service leading to shorter repair times. There is no 100% guarantee that all cases will be correctly detected / corrected because the simulation model is only an approximation. However, the cases that are detected lead to increased availability. We claim that the use of the above procedure brings practically significant profit (in terms of increased availability and decreased human effort) to WLAN planning.
Network Controller; switched WLAN, IETF CAPWAP, IEEE 802.11k, 802.11v provide the means to: • collect WLAN measurements at runtime and accumulate them at a central location, • set RF configuration parameters for the whole deployment at runtime from a central location. We base on these techniques to perform central monitoring/control. Yet, clearly they do not provide simulation, prediction, calibration or optimization. Simulator & Calibrator; there are quite a number of simulation algorithms for RF, see (Sarkar, et al., 2003) for a detailed survey. Most of them rely on the user to build a model manually. Some allow including feedback from a walk around. There are also models that allow using real measurements for parameter calibration (Lähteenmäki, 1992; Seidel and Rappaport, 1992). However, calibration is done up to now only in the initial phase and does not continuously represent the current situation. There are commercial WLAN management tools (Cisco WLSE) that already use continuous inter-AP signal strength measurements for this purpose. They automatically detect errors (AP failures and RF environment changes) and automatically correct AP failures (by dynamic reconfiguration of nearby APs). In contrast, our method is open (both model calibration and solution generation) and we automatically correct both error types whenever possible. Hills, et al, (2004) propose a method to ease WLAN planning by predicting the results of AP re-allocation (what-if simulations). However, it is based on a complete measurement campaign in the environment, which is
The key to achieve this is to use an automatically calibrated radio prediction model that continuously represents the current situation. This is to be achieved using continuous signal strength measurements among the APs and a central evaluation of the results to calibrate the model and automatically find a solution (via optimization) if the requirements are not fulfilled. In this paper we present in more detail the Simulator & Calibrator module (Section 6). But before going into details we position our work within the research of others in order to shape our contribution at this level. 5. RELATED WORK A lot of research has been published regarding the individual blocks in Figure 2. However, the
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not feasible for our purpose, because: 1) unacceptable initial effort which is 2) out-of-date after a change. Initial input
Optimization & Warning; there are various methods and tools to calculate close-to-optimal configuration w.r.t. contrary2 criteria: signal strength, inter-cell interference, deployment costs; for instance (Amaldi, et al., 2004; Jaffres-Runser, et al., 2006; Sherali, et al., 1996; Unbehaun and Kamenetsky, 2003; Wertz, et al., 2004). They apply a combinatorial preselection from a set of possible AP-locations, and after that run an optimization of an objective function weighted by the user. However, this has also been done only at the initial step (without continuous feedback from the environment, assessment of the current situation, and new solution generation).
Initial simulation
Initial optimization
Initial deployment
Monitoring Correct Online calibration & simulation
There are also various publications about using IEEE 802.11 WLAN in industrial environments. For instance (Krommenacker and Lecuire, 2005) test the feasibility of 802.11g WLAN for industrial purposes and propose methods to configure AP parameters so that real-time requirements of specific traffic characteristics are met. Miaoudakis, et al. (2005) present radio signal measurements in a typical industrial environment and derive specific propagation parameters. Mathiesen, et al. (2005) give an overview on the benefits and limitations of wireless ad-hoc networks for industrial automation. Note that the presented coverage planning methods in our paper are also applicable when the networkbackbone is ad-hoc (mesh network). Decotignie (2002) surveys benefits, issues, and solutions about wireless networks on the field bus layer. However, to the best of our knowledge, we are the first to describe a method to automatically monitor and correct WLAN coverage service with the purpose of integration of business and technical processes (production control layer).
State assertion (warning)
Error / failure
Online solution generation Online changes
Figure 3: Algorithm for planning available WLAN editing (area-plan, walls, space-fillings), radio propagation simulation, and reference measurements sampling. It can be used for general coverage planning, cell-planning, and redundancy planning of WLAN. With manual parameter calibration the average prediction accuracy for 802.11a and 802.11b/g is within 4…10dB with standard deviation up to 7dB (evaluated in an office and in an industrial scenario). The planning tool was presented at the “Hannover Messe 2007” (a fare for industry solutions). The Simulator & Calibrator module uses this tool as a base for radio simulation. It extends the tool by a module for automatic model calibration from reference measurements. In this section we present the functionality of the Simulator & Calibrator module with focus on the calibration method.
Our method bases on existing work and brings profit in combining them. Also, it is not known which of the existing techniques are most appropriate in our context and whether they can just be combined in order to operate in our method, or additional work is needed. We are going to provide this knowledge. 6. RADIO SIMULATION WITH AUTOMATIC CALIBRATION
6.1 Simulator & Calibrator Design In this paper we consider large-scale fading (Rappaport, 2002). The requirements of the application to the radio modelling are: low modelling effort (low fidelity environment details), acceptable accuracy, and low running time (few seconds). Therefore, the group of ray-tracing models is less appropriate. From the statistical models we chose the attenuation factor model (or multi-wall model) which has been shown to satisfy these criteria (Lähteenmäki, 1992; Rappaport, 2002). Additionally we use the concept of space-fillings (or simply fillings). Fillings are areas in the environment with relatively homogeneous attenuation factor, which can change over time. They represent the places for goods in the
Part of this work is done within a project with research and industry participation. The institutions are Otto-von-Guericke University – Magdeburg, rt-solutions.de GmbH Cologne, and Phoenix Contact GmbH & Co. KG Blomberg (orderer). Currently, a first basic version of a WLAN planning tool is available. It implements infrastructure editing (APs, antennas), environment
2
Contrary because increased AP density leads to increased received power, but also higher inter-cell interference and deployment costs.
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there are solutions that use a single channel (Extricom 2007). Therefore, even with this abstraction our results have a practical significance.
warehouse (empty or filled with different good types). We use the following model equation: P ( d ) = P ( d 0 ) − 10 * n * log10 (
d ) − ∑ WAFi * K i − ∑ FillAF j * D j d0
(2) Calibration Method; a core question is what method to use for parameter calibration. Typically, manual measurements are used to fit model parameters to the environment but in our method these measurements are automatically done by the APs. Path loss exponent is often estimated by fitting measurement data to the model with linear regression (Rappaport, 2002). Regarding the attenuation factors different techniques have been proposed: linear regression (Seidel and Rappaport, 1992), linear least squares, and high resolution measurements around the obstacle (Bahl and Padmanabhan, 2000; Cheung, et al., 1998). The last method is feasible in our scenario only in a limited scope (e.g. initial deployment) because of the requirements to automatically react to changes (possibly new obstacles). Linear regression and linear least squares comprise the same method, widely used to fit linear model to measurements. For a set of interAP measurements it calculates model parameters which minimize the sum of squared differences between the measurements and the respective model predictions. There exist other optimization / minimization methods as well (e.g. linear programming which minimizes a single function). But least squares fits to our problem better. Therefore, and in order to calibrate all parameters with a single operation, we use linear least squares for calibration.
This model equation is a combination of standard path loss model (terms 1, 2 from Rappaport, 2002 sec. 4.9.1), attenuation factor model (term 3 from Rappaport, 2002 sec. 4.11.5), and the extension of fillings (term 4). The symbols mean: • P (d ) : the calculated signal strength at distance
d • P (d 0 ) : signal strength (measured or calculated by free-space propagation) at reference distance d 0 • n : Path-Loss-Exponent (model parameter) • WAFi : wall attenuation factor of wall type i in [dB] (model parameter) • K i : number of penetrated walls of type i •
FillAF j : filling attenuation factor of filling type
j in [dB/m] (model parameter) • D j : sum of travel distances (direct line) through fillings of type j. This propagation model is implemented in the WLAN planning tool mentioned above. The concept of spacefillings allows the user to specify different types of areas with low effort (no details needed). The parameters n , WAFi , and FillAF j model large-
Calibration Method Usage; if we use a separate attenuation factor for each obstacle we have a more exact model but these factors would be underdetermined due to the relatively small number of inter-AP measurements. Therefore (and it is recommended by others (Bahl and Padmanabhan, 2000; Lähteenmäki, 1992; Seidel and Rappaport, 1992) we are using separate obstacle types which reduces the parameters and makes calibration feasible. Additionally unconstrained least squares would result in the best mathematical solution to the calibration, but possibly unrealistic parameters. Therefore we are using constrained least squares (lower and upper bounds of the parameters).
scale fading; they are determined continuously through automatic measurements and parameter calibration. Automatic Measurements; a current trend is that WLAN hardware (e.g. from Atheros) use softwareimplemented MAC layer. This allows using the same antenna as an AP and as a monitor (RSS – Received Signal Strength measurement) simultaneously. Software MAC is a followed technology trend because it allows flexible updates and enables other emerging technologies (software-defined radio, cognitive radio). We use this feature to perform continuous inter-AP RSS measurements during network operation. The advantage to manual measurements is that we can instantly detect changes (large-scale effects) and react by reconfiguration. A disadvantage is that the AP is at a fixed position and multi-path effects can falsify the measurement. However, this effect can be balanced by using antenna diversity. Another negative effect is the conflict between serving the own channel and monitoring other channels (this can lead to data loss on the MAC layer). This can be solved by intelligent scanning techniques (for non-time-critical data), or by dual-transceiver APs (for all data types). In the current stage we abstract from this conflict by using a single channel for all APs. Even though most WLAN deployments are cell-based with separate channels,
6.2 Evaluation Results The evaluation took place in our university building (an industrial setting will be available for future evaluations) in an area of approx. 600m2. Since our infrastructure for inter-AP measurements is under development, we emulated inter-AP measurements. We manually measured the received signal strength of a single fixed AP at possible locations of the other APs. This is acceptable because the data processing to build the model is the same. A difference is that in the real scenario we would have more reference points and possibly better accuracy. Using our WLAN planning tool we predicted totally 6 APs would be needed for this area: 3 to cover the area and another 3 to fulfil the redundancy criteria. We installed one of
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Table 1: Model parameters for different calibration methods / literature sources Calibration method / literature source
Path loss exponent [-]
Thick WAF [dB]
Thin WAF [dB]
Constrained least squares
5.0
8.9
8.7
Unconstrained least squares
15.5
-1.7
0.3
(Lähteenmäki, 1992)
2.0
7.4
1.7
(Seidel and Rappaport, 1992)
2.0
2.4
1.4
(Bahl and Padmanabhan, 2000)
1.5
3.1
3.1
Prediction error [dB]
Figure 4: Empirical cumulative distribution of prediction error with different calibration methods materials, frequency, measurement errors. So we recommend calibration for the actual environment. Additionally the unconstrained calibration results in a closer mathematical solution, but also unrealistic model parameters (too big exponent and negative attenuation factor). On the other hand, constrained calibration keeps the parameters in realistic ranges, and therefore results in better predictions. We used a hypotheses test (ttest) to assert the quantitative difference between the calibration methods in a statistical way. It showed that constrained calibration is more accurate than the other tested calibration methods (see table 1 and table 2 for details; when compared to unconstrained calibration it is with [1.9…4.4] dB more accurate at 95% confidence). In order to obtain a more detailed view on the prediction error we show (Figure 4) its cumulative distribution for least squares and literature based parameters resulting to lowest error in this case (Lähteenmäki, 1992).
Table 2: Prediction error for different calibration methods / literature sources Calibration method / literature source
Average [dB]
Stdev. [dB]
Constrained least squares
7.4
7.0
Unconstrained least squares
10.5
8.0
(Lähteenmäki, 1992)
18.1
10.9
(Seidel and Rappaport, 1992)
21.4
12.1
(Bahl and Padmanabhan, 2000)
19.7
11.4
The detailed view also confirms that “constrained least squares” produce results with lowest discrepancies from reality. Regarding accuracy it is moderate in this case. About 60% of the predictions are below 7dB, which allows identifying coverage errors with a rough granularity. This means, if we consider a 7dB inaccuracy in the redundancy interval − 90dBm... − 75dBm from our application scenario, the system state is correctly identified in the ranges: • Prediction > -68dBm: correct state • -83 < Prediction < -82: error • Prediction < -97dBm: failure For the intervals [-97…-83] and [-82…-68] the system state can be asserted with a probability that depends on the proximity of the prediction to the intervals with correct identification. The remaining 40% cases might lead to inaccurate decisions. However, in a further evaluation with inter-AP measurements every AP is both reference point and a transmitter at the same time. So, we expect more reference points ( N * ( N − 1) given that N access
the APs and dedicated the locations of the other 5 APs as reference areas. In each area we did 2 reference measurements. In this way we obtained 25=32 different reference (or training) possibilities which we used for model calibration. Additionally we measured the received signal strength at 20 uniformly distributed places in the environment and used this data for evaluation. As prediction error of the model we used the absolute value of the difference between simulated and measured signal strength. The parameter-constraints were: path loss exponent [1…5], thick wall [1…20]dB, thin wall [0.5…10]dB. Table 1 and table 2 summarize the results. As expected, the calibration of the parameters for the particular environment reduces significantly the prediction error compared to using standard values from the literature (2.4-2.9 times lower difference and 1.6-1.7 times lower standard deviation; see table 1 and table 2 for details). Furthermore, parameters from different literature sources and from our calibration differ significantly. This is because of different
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models and algorithms. In: 59th IEEE Vehicular Technology Conference VTC 2004-Spring. Bahl, P. and V.N. Padmanabhan, (2000) RADAR: An InBuilding RF-based User Location and Tracking System. In: IEEE INFOCOM. 2000. Cheung, K.W., J. H.-M. Sau and R. D. Murch (1998). A New Empirical Model for Indoor Propagation Prediction. IEEE Transactions on Vehicular Technology, vol. 47, no. 3, August 1998. Decotignie, J. (2002). Wireless Fieldbusses - A Survey of Issues and Solutions. In Proceedings of the 15th IFAC World Congress, Barcelona, Spain, 2002. Extricom (2007). Enterprise Wireless LAN Solutions. http://www.extricom.com/content/solutions (accessed 28.05.2007). Hills, A., J. Schlegel and B. Jenkins (2004). Estimating signal strengths in the design of an indoor wireless network. IEEE Transactions on Wireless Communications, vol. 3, no. 1, Jan 2004, pp. 17-19. Jaffres-Runser, K., J.-M. Gorce and S. Ubeda (2006). QoS Constrained Wireless LAN Optimization within a Multiobjective Framework. IEEE Wireless Communications, vol. 13, no. 6, December 2006. pp. 2633. Krommenacker, N., V. Lecuire (2005). Building industrial communication systems based on IEEE 802.11g wireless technology. In 10th IEEE Conference on Emerging Technologies and Factory Automation, ETFA 2005. vol. 1, pp. 71-78. Lähteenmäki, J. (1992). Radiowave propagation in office buildings and underground halls. In: Proc. of European Microwave Conference, Espoo 1992, pp. 377-382. Mathiesen, M.L., Thonet, G., Aakvaag, N. (2005). Wireless Ad-Hoc Networks for Industrial Automation: Current Trends and Future Prospects. In Proceedings of the 16th IFAC World Congress, Prague, Czech Republic, July, 2005. Miaoudakis, A., A. Lekkas, G. Kalivas, S. Koubias (2005). Radio channel characterization in industrial environments and spread spectrum modem performance. In 10th IEEE Conference on Emerging Technologies and Factory Automation, ETFA 2005. vol. 1, pp. 87-93. Nett, E. (2005). WLAN in Automation - More Than an Academic Exercise?. In: Dependable Computing - Second Latin-American Symposium, LADC 2005, Salvador, Brazil, October 2005. DOI http://dx.doi.org/10.1007/11572329_3 Rappaport, T.S. (2002). Wireless Communications: Principles and Practice, second edition section. Prentice Hall PTR. Sarkar, T.K., J. Zhong, K. Kyungjung, A. Medouri and M. Salazar-Palma (2003). A survey of various propagation models for mobile communication. Antennas and Propagation Magazine, IEEE. Volume 45, Issue 3, June 2003, pp. 51-82. Seidel, S.Y. and T.S. Rappaport (1992). 914 MHz path loss prediction models for indoor wireless communications in multifloored buildings. Antennas and Propagation, IEEE Transactions on, 1992 Volume: 40 , Issue: 2, pp. 207217. Sherali, H.D., C.M. Pendyala and T.S. Rappaport (1996). Optimal Location of Transmitters for Micro-Cellular Radio Communication System Design. IEEE Journal on Selected Areas in Communications, vol. 14, no. 4, May 1996, pp. 662-673. Unbehaun, M. and M. Kamenetsky (2003). On the deployment of picocellular wireless infrastructure. IEEE Wireless Communications, vol. 10, no. 6, Dec 2003, pp. 70-80. Wertz, P., M. Sauter, G. Wölfle, R. Hoppe, and F.M. Landstorfer (2004). Automatic Optimization Algorithms for the Planning of Wireless Local Area Networks. In: 60th IEEE Vehicular Technology Conference (VTC) 2004 - Fall, Los Angeles (California, USA), Oct. 2004. pp. 3010-3014.
points can detect each other) and eventually more accurate prediction. 6.3 Conclusion and Contribution Conclusion for the application; we have shown that parameter calibration is an important step because otherwise traditional library/literature-based models could lead to larger discrepancies between model and reality. Furthermore, unconstrained calibration can lead to exacter adjustment of the model to the training data, but also unrealistic parameters and a low accuracy as a whole. In contrast, least squares with constraints results in realistic parameters and a model with higher accuracy. “Least squares with constraints” is appropriate for our application. Contribution of this part; the least squares method is known to be used for parameter calibration in radio propagation modelling. In this paper we additionally give a quantitative estimation of the accuracy of the method. We compare its two variants (with and without constraints) and the usage of literature-based parameters. Additionally, the innovation of the modelling approach is in the combination of existing methods: path-loss exponent, wall/filling attenuation factors, all parameters calibrated with constrained least squares. 7. CONCLUSION AND FUTURE WORK The contribution of the paper is twofold. Firstly, we propose an autonomic control-loop for availability planning of WLAN in industrial scenarios. And secondly, we describe an innovative method (in terms of combining existing methods) to model signal strength and automatically calibrate to reality. Evaluation showed that the used calibration method results in more accurate model compared to other methods. We plan to perform an evaluation in a wider space with concurrent inter-AP measurements (we are currently preparing the experimental setup), integrate continuous calibration and model update, and perform an experiment in an industrial environment. After that we will decide on future work regarding the single channel abstraction. We will choose and integrate an appropriate optimization module for configuration generation, and a network control module for automatic configuration update. Further we will integrate upper network layers in the control loop. The model of the upper layers will base on the presented physical layer model and will predict application layer metrics (bandwidth, latency, drops). This will allow WLAN planning with metrics even more tightly connected to productivity. REFERENCES Amaldi, E., A. Capone, M. Cesana, F. Malucelli and F. Palazzo (2004). WLAN coverage planning: optimization
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Edgar Nett†
Stefan Schemmer‡
Otto-von-Guericke University – Magdeburg, Institute for Distributed Systems, Universitätsplatz 2, 39106 Magdeburg, Germany† rt-solutions.de GmbH, Oberländer Ufer 190a, 50968 Cologne, Germany‡
Abstract: What is still missing in the convergence of IT and automation technologies is the integration of wireless communication. In this paper we consider the problem of planning available Wireless LAN (WLAN) in production environments with dynamic radio-propagation properties. Our approach is an autonomic control loop with feedback simulation and optimization. We present in detail the simulation and calibration module which automatically keeps the knowledge about the current radio coverage up-to-date. Evaluation showed that the constrained least squares calibration method results in a more accurate model compared to other methods. The results are general to planning WLAN availability in production environments. Copyright © 2007 IFAC Keywords: Dependability, availability, wireless networks, industrial networks, network planning, radio propagation, automatic model calibration.
cell / production control layer with TCP / IP communication and no or soft real-time requirements, or the field bus layer with real-time requirements in the range from 100ms down to 10ms.
1. INTRODUCTION What is still missing in the convergence of IT and automation technologies is the integration of wireless communication. Even though this rises some tough challenges there are new application fields that drive a strong trend to deploy WLANs in industrial applications. Among these new applications mobile transport systems of all kinds are the most important and widely recognized sector. This sector spans from rail-guided baggage carriers that improve throughput and flexibility in airport baggage logistics, over warehouse systems with integrated transport entities, to automated guided vehicles and overhead monorail carriers that transport work pieces in assembly systems. In all these applications, providing wireless connectivity to the mobile entities promises more detailed and up-to-date supervision and diagnosis, more flexible control and improved scalability.
Using wireless communications in such demanding applications and environments poses some tough challenges. Besides fulfilling typical hardware requirements of industrial equipment, like DC voltage supply, standard industrial plugs, rugged housing with a sufficient protection against dust, water and heat, this mostly applies to the non-functional properties of the communication: reliability of the communication, availability of the infrastructure, security, and realtime (Nett, 2005). In this paper we consider the problem of planning WLAN availability in production environments with dynamic radio-propagation properties. As a case study we use a warehouse system with integrated transport entities. The network is used for task assignment, reports on task fulfilment, and update of the warehouse database. The challenges to the WLAN planning come from the fact that the environment is dynamic (continuously changing personnel, goods, machinery). This affects network coverage, which is tightly connected to its availability. WLAN planning
The envisaged applications can be characterized with respect to the kind and tightness of control they exert via the wireless medium. The spectrum ranges from monitoring and diagnosis only (no control) over commissioning (task assignment), to centralized or even autonomous motion control. Depending on the kind of application, the WLAN will be used for the
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methods exist, but they have a static nature i.e. plan availability initially, but do not detect and react to changes while the network is in operation. We present a method to plan and continuously monitor/restore WLAN availability in operation mode. It is based on an autonomic control loop consisting of measurements, simulation, optimization, and reconfiguration.
ERP Order
In the next section we define the application scenario in dependability terms. Section 3 describes the problem that we address in more detail. In section 4 we present our autonomic-control-loop method for planning WLAN availability; section 5 discusses related work. In section 6 we present in detail one of the core components in the control loop (model calibration and simulation) with evaluation results. Finally we conclude the paper and present future work.
Confirmation
Figure 1: Integration of business and technical processes. Orders/Confirmations are processed manually in this example. IT is required to automate the process (Nett, 2005). Correct Service; the system provides delivery of packets through the wireless medium. One basic requirement for packet delivery is network coverage. This means that on every place in the network, there is minimum 1 accessible access point (AP) with Average Received Signal Strength ( RSS ): RSS > RSS NA ( RSS is bigger than the not
2. APPLICATION SCENARIO The integration of business and technical processes in a warehouse occurs by communication between the ERP (Enterprise Resource Planning) system and the operators/transport entities. This includes task commissioning, report delivery, database updates. The tasks are mainly transport tasks (operators with forklifts / integrated transport entities) and maintenance tasks (operators use hand-scanners to monitor the state of the goods). An IT infrastructure is needed to ease the communication and reduce processing times (especially in plants with long distances); figure 1 shows an example. Wired networks may not be the optimal solution because of high deployment costs and limited mobility of vehicles and people. Among the wireless alternatives the IEEE 802.11 WLAN is a relevant candidate (proven and widely deployed standard, decreasing equipment prices). But the warehouse system also puts requirements to the communication: • High Availability: requires network connectivity with long mean time to failure (MTTF) relative to short mean time to repair (MTTR) • Cost efficiency (both hardware and maintenance costs) In order to describe the availability/dependability context in our scenario we define the meaning of the terms application, system, correct service, correct system state, fault, error, failure and argue about the need for availability.
acceptable level RSS NA ). Inter-cell interference is an issue, but has lower weight in this scenario because medium utilization is relatively low. Correct system state (normal operation); in order to ensure that correct service is fulfilled in case of a fault, the correct system state is the following redundant design. On every place in the network minimum N RED APs should be accessible with
RSS > RSS RED > RSS NA ( RSS is bigger than some redundancy threshold RSS RED ). Fault model; it consists of the following faults: • Introduction of new objects in the environment (i.e. goods, machinery, transport entities, people) that affect radio propagation and decrease RSS at the users locations • Hardware failure of an AP Error; as a result of a fault, the radio coverage is worse than the redundancy setting in normal operation, but still correct (sufficient to ensure packet delivery: min 1 AP and RSS > RSS NA ).
Application (service consumer); it is an ERP system used to assign tasks to the operators/entities and deliver reports from the tasks fulfilment. It comprises all network layers above the medium access layer. The application provides transport protocols for endto-end delivery.
Failure; there exists a place in the network where service is not correct ( RSS < RSS NA ). The values of the parameters are application-specific, for instance:
System; it is an infrastructure WLAN, the system boundary (interface to the application) is between the medium access layer and the upper layers.
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optimization. We apply the general principal of a feedback control loop, which is widely used in control engineering, to the problem at hand. Figure 2 shows our implementation of this idea to WLAN planning. The simulation module uses monitoring (measurements) from the real network to create and calibrate a model of the radio coverage. In this way it estimates the current coverage in the environment. This knowledge (simulation results) is used by the optimizer & warning module to assert the current system state w.r.t. service: normal, error, or failure. When it detects an error/failure it generates optimal configuration that restores the service. During this operation it also accesses the simulation module to evaluate the proposed configurations w.r.t. service (what-if simulations). The new configuration is automatically sent to the WLAN components by the network controller. In cases where manual effort is required (e.g. install a new AP) a message is sent to the administrators. So, the autonomic control loop consists of measurements from the network, calibration, simulation, optimization (configuration generation), and finally configuration update back to the network. In terms of control engineering the forward path is: “Optimizer & Warning” -> NC/Administrators -> “WLAN Infrastructure” and the feedback path is “WLAN Infrastructure” -> “Network Controller” -> “Simulator & Calibrator”. The feedback information (current coverage) is not monitored because it is hardly possible with the required density; rather it is produced by the simulation (based on measurements for modelcalibration). The complete procedure to plan availability in WLAN is in Figure 3, we now describe the steps: • Initial input; from business processes, administrators define the requirements and provide the initial model input • Initial simulation; from the initial input, the model is generated and calibrated. The initial simulation results are produced. • Initial optimization; from the initial simulation results and the requirements it performs initial optimization w.r.t. requirements. Generates network configuration for initial deployment. • Initial deployment; install the recommended infrastructure: manual work to position APs, automatic parameter configuration through the Network Controller. • Monitoring; the WLAN automatically collects signal strength measurements among the access points. The NC aggregates them to be used for simulation. • Online calibration & simulation; calibrate the model from the actual measurements and produce simulation results about the current coverage. • State assertion (warning); determines the system state w.r.t service and eventually generates a warning. • Online solution generation; start an optimizationsimulation cycle with what-if simulations (scenarios generated from the optimization module). The result is a configuration to bring the system in correct state.
• RSS NA = -90dBm. This is a minimum sensitivity level for receivers used in the warehouse to enable communication at the needed data rate 11Mbps1. • RSS RED = -75dBm. Changes in the RF environment (e.g. good placement in large areas) can affect the received signal by 10-15dB (based on experience), so this is an acceptable redundancy level. • N RED = 2 APs. 1AP is not enough for redundancy; 2APs give an acceptable redundancy level (probability that 2APs fail simultaneously is low) and cost efficiency. Availability;
A=
MTTF + MTTR MTTR
(1)
Availability is required in this scenario because relatively long service outage times (e.g. 10-20% from working day) lead to production downtime. On the other hand the application can tolerate short service outages (up to 1-2 min.), so reliability need not be high. This is because the upper layer transport protocol is reliable (tasks/reports are not lost and are eventually delivered). The means to achieve availability in this scenario is called wireless network planning. Network planning includes various aspects at almost every network layer (physical coverage planning, addressing, security, traffic engineering). In this paper we focus on coverage planning aspect, which is a prerequisite for the others. 3. PROBLEM EXPOSITION The standard model-based pre-planning includes manual specification of the infrastructure (APs, antennas), environment (obstacles) and model parameters (path loss exponents, attenuation factors). The problem is that the standard model-based preplanning is not feasible because: • The models, even if one is willing to spend quite an effort for modelling (which is typically not the case with automation staff and pre-sales consultants), will never accurately fit the reality. • The environment is dynamically changing. So, even if the model fits in the beginning, it will not be able to predict the future behaviour of the WLAN. So, errors or failures might occur without being detected and corrected (or additional maintenance work is needed for this). Planning large contingencies incurs significant cost in terms of staff and equipment. Therefore, an approach is needed, which achieves an acceptable accuracy with minimum manual effort, and additionally automatically detects and reacts to changes in the environment. 4. APPROACH We approach the problem by using an autonomic control loop with feedback simulation and 1
The value -90dBm comes from product datasheets (Cisco Aironet, Netgear WAG511, 3Com).
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Figure 2: Control loop for available WLAN planning combination of these as a general control loop approach for planning WLAN availability also brings profit and has not been proposed so far. This is our solution approach.
• Online changes; automatic changes are performed by the NC and manual changes are sent to the WLAN Administrators. The increased availability is derived from two factors: • Increased MTTF; when an error is detected and subsequently corrected, the system is again in redundant state which reduces the probability for failure. • Decreased MTTR; when a failure is detected, the optimization module automatically suggests a measure to restore service leading to shorter repair times. There is no 100% guarantee that all cases will be correctly detected / corrected because the simulation model is only an approximation. However, the cases that are detected lead to increased availability. We claim that the use of the above procedure brings practically significant profit (in terms of increased availability and decreased human effort) to WLAN planning.
Network Controller; switched WLAN, IETF CAPWAP, IEEE 802.11k, 802.11v provide the means to: • collect WLAN measurements at runtime and accumulate them at a central location, • set RF configuration parameters for the whole deployment at runtime from a central location. We base on these techniques to perform central monitoring/control. Yet, clearly they do not provide simulation, prediction, calibration or optimization. Simulator & Calibrator; there are quite a number of simulation algorithms for RF, see (Sarkar, et al., 2003) for a detailed survey. Most of them rely on the user to build a model manually. Some allow including feedback from a walk around. There are also models that allow using real measurements for parameter calibration (Lähteenmäki, 1992; Seidel and Rappaport, 1992). However, calibration is done up to now only in the initial phase and does not continuously represent the current situation. There are commercial WLAN management tools (Cisco WLSE) that already use continuous inter-AP signal strength measurements for this purpose. They automatically detect errors (AP failures and RF environment changes) and automatically correct AP failures (by dynamic reconfiguration of nearby APs). In contrast, our method is open (both model calibration and solution generation) and we automatically correct both error types whenever possible. Hills, et al, (2004) propose a method to ease WLAN planning by predicting the results of AP re-allocation (what-if simulations). However, it is based on a complete measurement campaign in the environment, which is
The key to achieve this is to use an automatically calibrated radio prediction model that continuously represents the current situation. This is to be achieved using continuous signal strength measurements among the APs and a central evaluation of the results to calibrate the model and automatically find a solution (via optimization) if the requirements are not fulfilled. In this paper we present in more detail the Simulator & Calibrator module (Section 6). But before going into details we position our work within the research of others in order to shape our contribution at this level. 5. RELATED WORK A lot of research has been published regarding the individual blocks in Figure 2. However, the
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not feasible for our purpose, because: 1) unacceptable initial effort which is 2) out-of-date after a change. Initial input
Optimization & Warning; there are various methods and tools to calculate close-to-optimal configuration w.r.t. contrary2 criteria: signal strength, inter-cell interference, deployment costs; for instance (Amaldi, et al., 2004; Jaffres-Runser, et al., 2006; Sherali, et al., 1996; Unbehaun and Kamenetsky, 2003; Wertz, et al., 2004). They apply a combinatorial preselection from a set of possible AP-locations, and after that run an optimization of an objective function weighted by the user. However, this has also been done only at the initial step (without continuous feedback from the environment, assessment of the current situation, and new solution generation).
Initial simulation
Initial optimization
Initial deployment
Monitoring Correct Online calibration & simulation
There are also various publications about using IEEE 802.11 WLAN in industrial environments. For instance (Krommenacker and Lecuire, 2005) test the feasibility of 802.11g WLAN for industrial purposes and propose methods to configure AP parameters so that real-time requirements of specific traffic characteristics are met. Miaoudakis, et al. (2005) present radio signal measurements in a typical industrial environment and derive specific propagation parameters. Mathiesen, et al. (2005) give an overview on the benefits and limitations of wireless ad-hoc networks for industrial automation. Note that the presented coverage planning methods in our paper are also applicable when the networkbackbone is ad-hoc (mesh network). Decotignie (2002) surveys benefits, issues, and solutions about wireless networks on the field bus layer. However, to the best of our knowledge, we are the first to describe a method to automatically monitor and correct WLAN coverage service with the purpose of integration of business and technical processes (production control layer).
State assertion (warning)
Error / failure
Online solution generation Online changes
Figure 3: Algorithm for planning available WLAN editing (area-plan, walls, space-fillings), radio propagation simulation, and reference measurements sampling. It can be used for general coverage planning, cell-planning, and redundancy planning of WLAN. With manual parameter calibration the average prediction accuracy for 802.11a and 802.11b/g is within 4…10dB with standard deviation up to 7dB (evaluated in an office and in an industrial scenario). The planning tool was presented at the “Hannover Messe 2007” (a fare for industry solutions). The Simulator & Calibrator module uses this tool as a base for radio simulation. It extends the tool by a module for automatic model calibration from reference measurements. In this section we present the functionality of the Simulator & Calibrator module with focus on the calibration method.
Our method bases on existing work and brings profit in combining them. Also, it is not known which of the existing techniques are most appropriate in our context and whether they can just be combined in order to operate in our method, or additional work is needed. We are going to provide this knowledge. 6. RADIO SIMULATION WITH AUTOMATIC CALIBRATION
6.1 Simulator & Calibrator Design In this paper we consider large-scale fading (Rappaport, 2002). The requirements of the application to the radio modelling are: low modelling effort (low fidelity environment details), acceptable accuracy, and low running time (few seconds). Therefore, the group of ray-tracing models is less appropriate. From the statistical models we chose the attenuation factor model (or multi-wall model) which has been shown to satisfy these criteria (Lähteenmäki, 1992; Rappaport, 2002). Additionally we use the concept of space-fillings (or simply fillings). Fillings are areas in the environment with relatively homogeneous attenuation factor, which can change over time. They represent the places for goods in the
Part of this work is done within a project with research and industry participation. The institutions are Otto-von-Guericke University – Magdeburg, rt-solutions.de GmbH Cologne, and Phoenix Contact GmbH & Co. KG Blomberg (orderer). Currently, a first basic version of a WLAN planning tool is available. It implements infrastructure editing (APs, antennas), environment
2
Contrary because increased AP density leads to increased received power, but also higher inter-cell interference and deployment costs.
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there are solutions that use a single channel (Extricom 2007). Therefore, even with this abstraction our results have a practical significance.
warehouse (empty or filled with different good types). We use the following model equation: P ( d ) = P ( d 0 ) − 10 * n * log10 (
d ) − ∑ WAFi * K i − ∑ FillAF j * D j d0
(2) Calibration Method; a core question is what method to use for parameter calibration. Typically, manual measurements are used to fit model parameters to the environment but in our method these measurements are automatically done by the APs. Path loss exponent is often estimated by fitting measurement data to the model with linear regression (Rappaport, 2002). Regarding the attenuation factors different techniques have been proposed: linear regression (Seidel and Rappaport, 1992), linear least squares, and high resolution measurements around the obstacle (Bahl and Padmanabhan, 2000; Cheung, et al., 1998). The last method is feasible in our scenario only in a limited scope (e.g. initial deployment) because of the requirements to automatically react to changes (possibly new obstacles). Linear regression and linear least squares comprise the same method, widely used to fit linear model to measurements. For a set of interAP measurements it calculates model parameters which minimize the sum of squared differences between the measurements and the respective model predictions. There exist other optimization / minimization methods as well (e.g. linear programming which minimizes a single function). But least squares fits to our problem better. Therefore, and in order to calibrate all parameters with a single operation, we use linear least squares for calibration.
This model equation is a combination of standard path loss model (terms 1, 2 from Rappaport, 2002 sec. 4.9.1), attenuation factor model (term 3 from Rappaport, 2002 sec. 4.11.5), and the extension of fillings (term 4). The symbols mean: • P (d ) : the calculated signal strength at distance
d • P (d 0 ) : signal strength (measured or calculated by free-space propagation) at reference distance d 0 • n : Path-Loss-Exponent (model parameter) • WAFi : wall attenuation factor of wall type i in [dB] (model parameter) • K i : number of penetrated walls of type i •
FillAF j : filling attenuation factor of filling type
j in [dB/m] (model parameter) • D j : sum of travel distances (direct line) through fillings of type j. This propagation model is implemented in the WLAN planning tool mentioned above. The concept of spacefillings allows the user to specify different types of areas with low effort (no details needed). The parameters n , WAFi , and FillAF j model large-
Calibration Method Usage; if we use a separate attenuation factor for each obstacle we have a more exact model but these factors would be underdetermined due to the relatively small number of inter-AP measurements. Therefore (and it is recommended by others (Bahl and Padmanabhan, 2000; Lähteenmäki, 1992; Seidel and Rappaport, 1992) we are using separate obstacle types which reduces the parameters and makes calibration feasible. Additionally unconstrained least squares would result in the best mathematical solution to the calibration, but possibly unrealistic parameters. Therefore we are using constrained least squares (lower and upper bounds of the parameters).
scale fading; they are determined continuously through automatic measurements and parameter calibration. Automatic Measurements; a current trend is that WLAN hardware (e.g. from Atheros) use softwareimplemented MAC layer. This allows using the same antenna as an AP and as a monitor (RSS – Received Signal Strength measurement) simultaneously. Software MAC is a followed technology trend because it allows flexible updates and enables other emerging technologies (software-defined radio, cognitive radio). We use this feature to perform continuous inter-AP RSS measurements during network operation. The advantage to manual measurements is that we can instantly detect changes (large-scale effects) and react by reconfiguration. A disadvantage is that the AP is at a fixed position and multi-path effects can falsify the measurement. However, this effect can be balanced by using antenna diversity. Another negative effect is the conflict between serving the own channel and monitoring other channels (this can lead to data loss on the MAC layer). This can be solved by intelligent scanning techniques (for non-time-critical data), or by dual-transceiver APs (for all data types). In the current stage we abstract from this conflict by using a single channel for all APs. Even though most WLAN deployments are cell-based with separate channels,
6.2 Evaluation Results The evaluation took place in our university building (an industrial setting will be available for future evaluations) in an area of approx. 600m2. Since our infrastructure for inter-AP measurements is under development, we emulated inter-AP measurements. We manually measured the received signal strength of a single fixed AP at possible locations of the other APs. This is acceptable because the data processing to build the model is the same. A difference is that in the real scenario we would have more reference points and possibly better accuracy. Using our WLAN planning tool we predicted totally 6 APs would be needed for this area: 3 to cover the area and another 3 to fulfil the redundancy criteria. We installed one of
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Table 1: Model parameters for different calibration methods / literature sources Calibration method / literature source
Path loss exponent [-]
Thick WAF [dB]
Thin WAF [dB]
Constrained least squares
5.0
8.9
8.7
Unconstrained least squares
15.5
-1.7
0.3
(Lähteenmäki, 1992)
2.0
7.4
1.7
(Seidel and Rappaport, 1992)
2.0
2.4
1.4
(Bahl and Padmanabhan, 2000)
1.5
3.1
3.1
Prediction error [dB]
Figure 4: Empirical cumulative distribution of prediction error with different calibration methods materials, frequency, measurement errors. So we recommend calibration for the actual environment. Additionally the unconstrained calibration results in a closer mathematical solution, but also unrealistic model parameters (too big exponent and negative attenuation factor). On the other hand, constrained calibration keeps the parameters in realistic ranges, and therefore results in better predictions. We used a hypotheses test (ttest) to assert the quantitative difference between the calibration methods in a statistical way. It showed that constrained calibration is more accurate than the other tested calibration methods (see table 1 and table 2 for details; when compared to unconstrained calibration it is with [1.9…4.4] dB more accurate at 95% confidence). In order to obtain a more detailed view on the prediction error we show (Figure 4) its cumulative distribution for least squares and literature based parameters resulting to lowest error in this case (Lähteenmäki, 1992).
Table 2: Prediction error for different calibration methods / literature sources Calibration method / literature source
Average [dB]
Stdev. [dB]
Constrained least squares
7.4
7.0
Unconstrained least squares
10.5
8.0
(Lähteenmäki, 1992)
18.1
10.9
(Seidel and Rappaport, 1992)
21.4
12.1
(Bahl and Padmanabhan, 2000)
19.7
11.4
The detailed view also confirms that “constrained least squares” produce results with lowest discrepancies from reality. Regarding accuracy it is moderate in this case. About 60% of the predictions are below 7dB, which allows identifying coverage errors with a rough granularity. This means, if we consider a 7dB inaccuracy in the redundancy interval − 90dBm... − 75dBm from our application scenario, the system state is correctly identified in the ranges: • Prediction > -68dBm: correct state • -83 < Prediction < -82: error • Prediction < -97dBm: failure For the intervals [-97…-83] and [-82…-68] the system state can be asserted with a probability that depends on the proximity of the prediction to the intervals with correct identification. The remaining 40% cases might lead to inaccurate decisions. However, in a further evaluation with inter-AP measurements every AP is both reference point and a transmitter at the same time. So, we expect more reference points ( N * ( N − 1) given that N access
the APs and dedicated the locations of the other 5 APs as reference areas. In each area we did 2 reference measurements. In this way we obtained 25=32 different reference (or training) possibilities which we used for model calibration. Additionally we measured the received signal strength at 20 uniformly distributed places in the environment and used this data for evaluation. As prediction error of the model we used the absolute value of the difference between simulated and measured signal strength. The parameter-constraints were: path loss exponent [1…5], thick wall [1…20]dB, thin wall [0.5…10]dB. Table 1 and table 2 summarize the results. As expected, the calibration of the parameters for the particular environment reduces significantly the prediction error compared to using standard values from the literature (2.4-2.9 times lower difference and 1.6-1.7 times lower standard deviation; see table 1 and table 2 for details). Furthermore, parameters from different literature sources and from our calibration differ significantly. This is because of different
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models and algorithms. In: 59th IEEE Vehicular Technology Conference VTC 2004-Spring. Bahl, P. and V.N. Padmanabhan, (2000) RADAR: An InBuilding RF-based User Location and Tracking System. In: IEEE INFOCOM. 2000. Cheung, K.W., J. H.-M. Sau and R. D. Murch (1998). A New Empirical Model for Indoor Propagation Prediction. IEEE Transactions on Vehicular Technology, vol. 47, no. 3, August 1998. Decotignie, J. (2002). Wireless Fieldbusses - A Survey of Issues and Solutions. In Proceedings of the 15th IFAC World Congress, Barcelona, Spain, 2002. Extricom (2007). Enterprise Wireless LAN Solutions. http://www.extricom.com/content/solutions (accessed 28.05.2007). Hills, A., J. Schlegel and B. Jenkins (2004). Estimating signal strengths in the design of an indoor wireless network. IEEE Transactions on Wireless Communications, vol. 3, no. 1, Jan 2004, pp. 17-19. Jaffres-Runser, K., J.-M. Gorce and S. Ubeda (2006). QoS Constrained Wireless LAN Optimization within a Multiobjective Framework. IEEE Wireless Communications, vol. 13, no. 6, December 2006. pp. 2633. Krommenacker, N., V. Lecuire (2005). Building industrial communication systems based on IEEE 802.11g wireless technology. In 10th IEEE Conference on Emerging Technologies and Factory Automation, ETFA 2005. vol. 1, pp. 71-78. Lähteenmäki, J. (1992). Radiowave propagation in office buildings and underground halls. In: Proc. of European Microwave Conference, Espoo 1992, pp. 377-382. Mathiesen, M.L., Thonet, G., Aakvaag, N. (2005). Wireless Ad-Hoc Networks for Industrial Automation: Current Trends and Future Prospects. In Proceedings of the 16th IFAC World Congress, Prague, Czech Republic, July, 2005. Miaoudakis, A., A. Lekkas, G. Kalivas, S. Koubias (2005). Radio channel characterization in industrial environments and spread spectrum modem performance. In 10th IEEE Conference on Emerging Technologies and Factory Automation, ETFA 2005. vol. 1, pp. 87-93. Nett, E. (2005). WLAN in Automation - More Than an Academic Exercise?. In: Dependable Computing - Second Latin-American Symposium, LADC 2005, Salvador, Brazil, October 2005. DOI http://dx.doi.org/10.1007/11572329_3 Rappaport, T.S. (2002). Wireless Communications: Principles and Practice, second edition section. Prentice Hall PTR. Sarkar, T.K., J. Zhong, K. Kyungjung, A. Medouri and M. Salazar-Palma (2003). A survey of various propagation models for mobile communication. Antennas and Propagation Magazine, IEEE. Volume 45, Issue 3, June 2003, pp. 51-82. Seidel, S.Y. and T.S. Rappaport (1992). 914 MHz path loss prediction models for indoor wireless communications in multifloored buildings. Antennas and Propagation, IEEE Transactions on, 1992 Volume: 40 , Issue: 2, pp. 207217. Sherali, H.D., C.M. Pendyala and T.S. Rappaport (1996). Optimal Location of Transmitters for Micro-Cellular Radio Communication System Design. IEEE Journal on Selected Areas in Communications, vol. 14, no. 4, May 1996, pp. 662-673. Unbehaun, M. and M. Kamenetsky (2003). On the deployment of picocellular wireless infrastructure. IEEE Wireless Communications, vol. 10, no. 6, Dec 2003, pp. 70-80. Wertz, P., M. Sauter, G. Wölfle, R. Hoppe, and F.M. Landstorfer (2004). Automatic Optimization Algorithms for the Planning of Wireless Local Area Networks. In: 60th IEEE Vehicular Technology Conference (VTC) 2004 - Fall, Los Angeles (California, USA), Oct. 2004. pp. 3010-3014.
points can detect each other) and eventually more accurate prediction. 6.3 Conclusion and Contribution Conclusion for the application; we have shown that parameter calibration is an important step because otherwise traditional library/literature-based models could lead to larger discrepancies between model and reality. Furthermore, unconstrained calibration can lead to exacter adjustment of the model to the training data, but also unrealistic parameters and a low accuracy as a whole. In contrast, least squares with constraints results in realistic parameters and a model with higher accuracy. “Least squares with constraints” is appropriate for our application. Contribution of this part; the least squares method is known to be used for parameter calibration in radio propagation modelling. In this paper we additionally give a quantitative estimation of the accuracy of the method. We compare its two variants (with and without constraints) and the usage of literature-based parameters. Additionally, the innovation of the modelling approach is in the combination of existing methods: path-loss exponent, wall/filling attenuation factors, all parameters calibrated with constrained least squares. 7. CONCLUSION AND FUTURE WORK The contribution of the paper is twofold. Firstly, we propose an autonomic control-loop for availability planning of WLAN in industrial scenarios. And secondly, we describe an innovative method (in terms of combining existing methods) to model signal strength and automatically calibrate to reality. Evaluation showed that the used calibration method results in more accurate model compared to other methods. We plan to perform an evaluation in a wider space with concurrent inter-AP measurements (we are currently preparing the experimental setup), integrate continuous calibration and model update, and perform an experiment in an industrial environment. After that we will decide on future work regarding the single channel abstraction. We will choose and integrate an appropriate optimization module for configuration generation, and a network control module for automatic configuration update. Further we will integrate upper network layers in the control loop. The model of the upper layers will base on the presented physical layer model and will predict application layer metrics (bandwidth, latency, drops). This will allow WLAN planning with metrics even more tightly connected to productivity. REFERENCES Amaldi, E., A. Capone, M. Cesana, F. Malucelli and F. Palazzo (2004). WLAN coverage planning: optimization
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