Modular Wireless Real-Time Sensor/Actuator Network ... - IEEE Xplore

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 3, NO. 2, MAY 2007

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Modular Wireless Real-Time Sensor/Actuator Network for Factory Automation Applications Hans-Jörg Körber, Housam Wattar, and Gerd Scholl, Member, IEEE

Abstract—Modern factory facilities are characterized by highly flexible manufacturing cells and highly dynamic processes, where clusters of fixed or moving sensors and actuators have to be controlled in a limited space under stringent real-time and reliability constraints. In such demanding industrial environments, wireless systems can also be beneficial by improving flexibility, cutting cables, and enabling solutions, which are cumbersome or even not possible to realize with wireline systems, especially in controlling moving or rotating parts. In this paper, we present a conceptual study of a wireless real-time system dedicated for remote sensor/actuator control in production automation. System development is based on user requirements, which were extracted from customer interviews and a market research. Low level measurements of frequency- and space-selective wireless channels in a factory-like environment were carried out. System design aspects, i.e., network topology, multiple access schemes, and radio technologies, will be thoroughly reviewed. The performance of a first prototype implementation will be discussed with emphasis on timing behavior and power consumption, as sensors and actuators of the wireless system are intended to operate without power lines or batteries. Index Terms—Factory automation systems, multipath radio propagation, real-time communications, wireless sensor/actuator network.

however, we wanted to investigate if a similar performance can also be achieved by employing standard components, especially for the central control unit. Therefore, this paper deals with the design issues of a highly modular and scalable implementation of a wireless sensor/actuator network (WSAN) intended as a complementary system or even a substitution of wired sensor/actuator buses like AS-Interface (AS-i) [16] or Seriplex [17]. The user requirements are reviewed in Section II. As channel coherence bandwidth and coherence time mainly determine system design, indoor wave propagation in factory environments will be characterized in Section III. In Section IV, basic design issues such as network topologies and multiple access schemes will be investigated. As the radio part of the sensor/actuator nodes is the key element in wireless data communications and mainly determines energy consumption, also various radio technologies were analyzed. In Section V, system performance is characterized on the basis of an implemented prototype system with a special focus on real-time behavior and energy consumption. This paper concludes with Section VI giving a short review and an outlook about future works.

I. INTRODUCTION II. USER REQUIREMENTS

T

HE interaction of automation subsystems depends highly on the performance of the underlying control and communication infrastructure. Thus, new approaches, which are now possible with wireless technologies, to improve flexibility and mobility, to facilitate installation, or to accelerate operations are exciting. WLAN and Bluetooth technologies are already established as commercial products on the factory floor [1]–[4], whereas in research and development, priority was laid on the introduction of wireless fieldbus systems in recent years [5]–[13]. In [14], a detailed overview of radio systems for factory automation is given. Up to now, only the WISA system [15] is commercially available for wireless device level communications. By using rotating magnetic fields at a frequency of about 120 kHz, sensors, actuators, and communication modules are also powered wirelessly by the WISA system. Feasibility of this approach has already been proven with many installations in the field. In our concept study,

Manuscript received March 4, 2007; revised April 5, 2007. This work was supported by the German public funded project EnAS. Paper no. TII-07-030038.R1. The authors are with the Institute for Electrical Measurement Engineering, Helmut-Schmidt-University, Hamburg 22393, Germany (e-mail: [email protected]). Digital Object Identifier 10.1109/TII.2007.898451

In [18], guidelines were derived for radio-based communication in industrial automation. Together with customer interviews and a market research carried out in the German public funded project EnAS [19], which is focused on the development of wireless energy autonomous sensor/actuator networks in production environments, the following conclusions can be drawn. The number of devices, i.e., sensors and actuators, being interconnected at the device level is usually high. Typically, short messages are exchanged, mainly in a cyclic manner. Short message lengths normally result in low bandwidth requirements. Furthermore, strict real-time boundaries have to be met. Reliability should be as high as in wired systems. Since process-relevant data are transmitted, faults have to be detected within the shortest possible time. In the following list, these and other additional user requirements are presented in more detail: — energy autonomous operation, as the user only has full benefit of wireless systems if not only communication lines but also power lines are cut; — real-time capability. The goal is 5 ms between trigger signal at sensor node and actuator activation; — Multisensor/actuator handling, i.e., monitoring and control of at least 64 sensor/actuator nodes, partially moving with , in a production cell a maximum velocity of ; with typical dimensions of

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— high reliability, i.e., the number of non-detected communication errors should be as low as in conventional fieldbus and sensor/actuator systems; — coexistence with other wireless standards. Although surely very important for a broad penetration of the market, quite a few customers do not see a problem implementing a frequency management plan for wireless services; — scalability and modularity, i.e., support of both small and large numbers of sensors and actuators without affecting system performance; — due to the small market volumes of specialized automation applications, standard commercial off-the-shelf components and modules should be employed to keep development, system, and service costs low; — wireless system should also be applicable for the global market with no or only minor modifications.

Fig. 1. Measurement setup.

III. INDOOR RADIO CHANNEL CHARACTERIZATION Of course, bandwidth requirements for sensor data transmission are normally low. But if sensor data of multiple sensors have to be transmitted in a very small time interval, bandwidth requirements increase. Therefore, the ISM frequency band at 2.45 GHz has been chosen for system operation due to the worldwide availability and the broad frequency band allowing a high data throughput. Otherwise, with respect to coexistence, interference, or jamming, for example, the European frequency band allocated at 869 MHz would have been a better choice. For indoor radio channel characterization, i.e., path loss and static/dynamic multipath propagation behavior, extensive work has already been carried out, especially in the 2.45-GHz band [20]–[24]. The static frequency-selective wideband channel , is commonly characterized by the coherence bandwidth which can either be determined with a network analyzer meain the frequency domain surement of the transfer function [21], [22], [24] or, alternatively, a time-domain measurement with a channel of the time-invariant impulse response sounder [23]. Channel sounders are normally also the means of choice for the measurement of a time-variant impulse response , where stands for the time variation of the system. is the corresponding time-variant transfer function. However, in our special case, where we expect scenarios with fixed scatterers and moving sensor nodes communicating with a rigidly mounted controller, the time-variant channel transfer is measured statically with a network analyzer, function as it offers a higher dynamic range compared to a channel sounder. As shown in Fig. 1, the first port of the network analyzer is connected to a small vertically polarized dipole antenna with a gain of 2 dBi, representing the antenna of a small sensor node. The second network analyzer port is connected with a vertically polarized patch antenna, representing the antenna of the controller unit. This antenna has a gain of 8 dBi and a beamwidth of 60 . For channel measurement, the dipole antenna is moved by a 2-axis system step by step in a horizontal -plane with . The time-variant transfer-funcdimensions of of a sensor virtually moving in space can be calcution lated from the space-variant but time-invariant network analyzer . Our measurements with a clear line measurements

Fig. 2. Space-dependent radio channel transfer function jH(f ; x; y )j measured in a machine hall and evaluated at fixed frequency of f = 2405 MHz.

of sight (LOS) were taken in a machine hall with dimensions of , representing an industrial pico-cell as defined in [25]. was evaluated at In Fig. 2, the transfer function MHz. Mean distance between antennas was 5 m. Coordinate grid spacing has been chosen to be 2.5 mm, which equals approximately 2% of the electromagnetic wavelength at 2.45 GHz. A periodic pattern, caused by a standing electromagnetic wave, can clearly be recognized. Normally, statistics of the time-variant radio channel are described by the frequency-time correlation function [26], where a wide sense stationary uncorrelated scattering (WSSUS) channel is commonly presumed. is obtained by building the autocorrelation of , i.e., (1) where stands for the expectation and , and represent a time and frequency offset relative to the origin of the coordinate system, respectively. Then, with a predefined value for the

KÖRBER et al.: MODULAR WIRELESS REAL-TIME SENSOR/ACTUATOR NETWORK

correlation coefficient , the coherence bandwidth are calculated with herence time

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and co-

(2) and (3) where the absolute value of the autocorrelation function at poand is reduced by the factor sition compared with the absolute value of the autocorrelation function MHz and . In our case, the frequency-time at correlation function is transformed to a frequency-space correlation function, i.e.,

R (1 =

Fig. 3. Normalized magnitude of the correlation function f ; x; y according to the radio channel measurement as depicted in Fig. 2.

01 1)

(4) Thus, the coherence time is transformed to coherence disand , defined through tances

(5) is the coherence length. The correlation function of the channel transfer function, which is given in Fig. 2, is shown in Fig. 3. The measurement . was taken in a machine hall with dimensions of Here the periodic behavior already seen in Fig. 2 can also be recognized. Typical limits for the correlation coefficient are 0.5 and 0.9. A value better than 0.9 corresponds to a highly correlated channel over space or frequency, whereas values smaller than 0.5 characterize an uncorrelated radio channel. In Fig. 4, the contour curve (black) of the correlation function, which is shown in Fig. 3, is graphically presented for over the space coordinates and . For comparison, another example is given (gray curve), where measurements have been carried out in a laboratory environment with dimensions of . The distribution of the narrowband channel magnitude shown in Fig. 5 fits well to a Ricean distribution with a Rice parameter dB. An average pathloss of 48 dB over the distance of approximately 5 m was calculated from the whole set of meapoints. Coherence surement data, i.e., over all frequency and bandwidths and min./max. coherence lengths for the machine hall and the laboratory are summarized in Table I. Comparable values were also achieved in [21] and [23]. Intersymbol interference can be avoided if the symbol rate is chosen less than the minimum coherence bandwidth of approximately 7 MHz. Our measurements have shown that the influence of the antenna configuration at LOS conditions on the coherence bandwidth is much stronger than the size or shape of the surrounding building. Thus, with respect to data available from the literature and our own measurements, a symbol rate in the range of

Fig. 4. Contour curves of the normalized magnitude of the correlation function f ; x X ; y Y , evaluated at : .

R (1 = 0 1 =

1 = )

=09

1 MHz should always be subcritical for data transmission in an industrial environment, which has also been proven by many Bluetooth-based applications [1], [15]. On the other hand, a freMHz has to be chosen if quency separation greater than frequency diversity techniques should effectively be employed. In many application scenarios, time-variation of the channel characteristics due to moving sensors or actuators also have to be taken into account. Assuming a velocity of for the sensor/actuator nodes and a coherence length of in the laboratory, the coherence time equals 8 ms, i.e., during , channel characteristics change significantly. IV. SYSTEM DESIGN Based on user requirements and characteristic channel parameters, the main aspects of system design, i.e., network topology, medium access schemes, and radio module selection, will be discussed in this section.

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Fig. 6. System architecture of the WSAN. Fig. 5. PDF of measured data overlaid with Ricean distribution.

TABLE I MEASURED COHERENCE BANDWIDTH B AND COHERENCE LENGTH L

A. Network Topology Within the last few years, wireless ad-hoc sensor networks (WSN) [28] became very popular as sensor or actuator information can be forwarded in a multi-hop path to where it is wanted without any given infrastructure. At the same time, the ruggedness and reliability of data transfer increase due to their distributed nature. Thus, WSNs also seem to be very promising in the context of device level applications as they are also principally designed to consume very small amounts of energy. This is generally achieved by an energy-aware hardware and software co-design. By far, the most energy savings are typically achieved by low or very low duty cycles. Accordingly, a trade-off between quality of service (reliability, real-time behavior) and node lifetime (energy consumption) has to be made. However, with respect to quality of service, no trade-off can be tolerated in real-time wireless sensor/actuator networks, so that we propose a classical infrastructure network [15] in a star topology, which will be discussed in more detail in subsection D. As reference system, the AS-i fieldbus, which is the leading system for wired sensor/actuator control, has been chosen. AS-i has a polling-based serial master/slave protocol, where the star-like connected slaves are polled by the master, that also serves as fieldbus gateway. During each polling cycle, 14 bits are transmitted from master to slave and 7 bits from slave to master in a time interval of 150 . The telegram contains a protocol data unit (PDU) for each peripheral device with a length of 4 bits, which is used for control and confirmation data so that 17 bits payload can be carried. A fully loaded AS-i Version 1 network (one master, 31 slaves) has a maximum response time of 5 ms per I/O, where an AS-i telegram is transmitted with a total data rate of 167 kbit/s. The same data

volume has to be handled by the wireless system. Assuming a protocol overhead of 8 bytes per telegram for carrier recovery and synchronization, which is recommended for the radio module chosen in subsection C, additional 16 bytes or 128 bits have to be transmitted. The radio system has to handle a total data rate of 81 bits within 150 , which is equivalent to a data rate of 966 kbit/s and is about six times the total data rate of the AS-i telegram. Thus, the total data rate required for each sensor/actuator node was defined to be 1 Mbit/s. As our measured results showed a coherence bandwidth much greater than 1 MHz, the radio channel can be assumed to be frequency flat, allowing to employ low-cost radio modules without the need for complex channel equalization algorithms. B. Multiple Access Schemes In the time domain, network members can generally be separated by classical TDMA, polling-based, and token-based schemes. In token-based schemes, the right for transmission is passed from one station to another so that all network members except the active talker have to be in the listening mode. This contradicts to both real-time and energy consumption requirements. Another criterion is that the token-based scheme implies connectivity of all logical (token-bus) or physical (token-ring) neighbors, which cannot be guaranteed for all wireless connections at the same time. Radio messages normally exhibit a relative large overhead, as already indicated, when compared with wired systems because of preambles needed for bit synchronization on the receiver side, polling-based schemes are not advantageous with respect to fast system response times. In TDMA-based systems, a fixed number of time slots is allocated for each user. One drawback is that the fixed bandwidth allocation leads to a reduced bandwidth efficiency due to unused time slots. However, in our target application, the number of sensors and actuators remains constant after production line setup. Because sensor resolution does not change over time, data volume remains constant, so that there is no necessity for a dynamic bandwidth allocation. Also, the comprehensive study of TDMA systems presented in [29] points out that the advantages (simplified radio frontends, lower complexity of the base station) outweigh the disadvantages (time synchronization, bandwidth loss due to guard times). Of course, a multiple access scheme based on code division can also be employed principally but generally at the price of higher system complexity. Therefore,


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Fig. 7. Timing diagram of the WSAN protocol.

a TDMA scheme has been chosen. With the AS-i system as reference, at least 31 sensors or actuators have to be served by the wireless system. However, with a data rate of only 1 Mbit/s, not all of them can be addressed within 5 ms, so that the TDMA scheme has to be combined with a frequency division multiple access scheme. The usage of multiple carrier frequencies also allows to proactively manage fading or coexistence problems in choosing the adequate carrier frequency. If fading occurs, the frequency separation between new and current carrier frequency has to be chosen greater than the coherence bandwidth of 30 MHz, as described in Section III. Fig. 8. Test setup in master-slave configuration.

C. Radio Technology A variety of standards (IEEE 802.11, IEEE 802.15.1, IEEE 802.15.4) [30]–[32] and proprietary solutions [33], [34] for wireless operation in the ISM frequency band at 2.45 GHz exist. Comprehensive reviews of the standards from the perspective of employment in industrial application have been carried out in [14] and [27]. However, based upon the requirements of the WSAN described above, it can be stated that none of the standards offers a viable radio solution. Though IEEE 802.11 and IEEE 802.15.1 have a sufficient data rate, they do not support large numbers of network nodes, especially with very low energy consumption. On the other hand, IEEE 802.15.4 shows real-time capability in the case of its beacon-enabled mode but offers guaranteed time slots for only seven network nodes. Also the minimum beacon cycle with 15.36 ms is not sufficient. Cypress’ WirelessUSB technology runs with a data rate of 62.5 kbit/s, which is by far too low for the system that is going to be developed. As a result, the CC2400 narrowband radio transceiver from Chipcon/TI [35] has been chosen, since it presents a good compromise between design flexibility, availability, energy consumption, data rate, and radio characteristics. It has a sufficient data rate (1 Mbit/s) at a low energy ). In addition, the wake-up per bit ratio (less than 0.04 time can be reduced to approximately 250 , which is a clear advantage with respect to power consumption since the low power sleep mode can be entered more often. Furthermore, the CC2400 module offers a serial peripheral interface (SPI) with a data rate of up to 20 Mbit/s, avoiding a potential bottleneck in data exchange between the RF frontend module and the baseband unit. Finally, compared with other commercially available radio transceivers, the CC2400 offers an excellent

adjacent channel rejection. Another interesting alternative is the wideband chirped spread-spectrum solution [33], offering an extremely robust radio transmission. Recently, the system used the entire ISM band, but now also a 20-MHz version is available, which makes it very attractive from the perspective of coexistence. D. System Architecture Generally, complex production processes are cut into smaller subprocesses often realized in independent factory cells. Thus, it is mandatory that our system design is also adaptable to the factory structure. To ensure a maximum scalability with a defined timing behavior, we have chosen a cellular architecture. The multiple access scheme is similar to the classical TDMA approach presented in [10] and enhanced by multi-frequency operation. The network is organized in a star topology (see Fig. 6), where the base station (BS) serves as network controller and gateway to the upper level bus system. Accordingly, the BS has both a wired (field)bus and a wireless radio interface. For each single sensor/actuator module (SAM), a time and frequency slot is allocated by the BS so that collisions between the sensor and actuator nodes can be avoided. To achieve multi-frequency operation, the BS is made up of a central control unit and multiple CC2400 transceiver units, also employed in the sensor/actuator modules, offering the possibility for an application-specific configuration, i.e., the system performance can be varied, e.g., between high data throughput and high robustness for each factory or communication cell. For the control unit, various solutions are possible, for example, a field-programmable gate array

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Fig. 9. System response time, illustration of best/worst-case scenario.

(FPGA) or a fast microcontroller, whereby the FPGA solution offers greater design flexibility. The modular design of the base station offers a high degree of flexibility, e.g., two radio modules of the base station can be operated to serve a maximum number of communication nodes or to achieve full-duplex operation, i.e., one transceiver handles downlink and the other transceiver uplink communications at the same time. In such a full-duplex operation mode, only half of the sensors/actuators can be served in the same time and the SAM must have a second transceiver, but real-time behavior can be increased significantly. By evaluation of the radio signal strength indicator, also frequency diversity operation is possible, decreasing the probability for deep fading dramatically. Additionally, space diversity can be achieved by arranging the BS radio modules, each connected to an antenna, around the factory cell. If only a very limited number of communication nodes is to be served, it is not necessary that the base station is equipped with all radio modules. On the other hand, if a higher throughput is required, the base station can be equipped with additional modules, which can be of the same type, as well as of different types. With suitable radio modules, the system can even be upgraded to a MIMO system. stands for In Fig. 7, the timing diagram is shown. Here the superframe interval. In the current system implementation, a superframe interval of 6 ms is used. The BS starts the superframe with an initial beacon message, which is also used for time synchronization. The beacon message itself is comprised of a header and a payload, which includes configuration data for all SAMs as provided by the upper level process control system. Upon reception of the beacon, the SAMs start to send their messages in consecutive time slots. The first timeslot starts , which stands for “time to slot.” This time is needed after by the SAM for processing the beacon data. Right after the response of the last SAM, the communication with the upper level process control unit is handled in the “beacon preparation time” . For energy saving reasons, it is not necessary that a message is sent in each timeslot. Though a time slot is exclusively

allocated for each single SAM, only an alive signal in a predefined time interval is transmitted if system dynamics are low. Between alive signals, SAMs go into the sleep mode. V. SYSTEM EVALUATION System evaluation has been carried out with the setup shown in Fig. 8. The control application was implemented in standard C on a personal computer (PC) acting as an upper level control unit (ULCU). The BS handles the communication with the SAMs. PC and BS were connected by a standard Ethernet interface [36], where the user datagram protocol (UDP) [37] was employed in accordance with industrial standards like PROFINET [38] or Ethernet/IP [39]. Basically, there exist two scenarios with respect to system response time as shown in Fig. 9. In the best-case scenario, a sensor element connected to a SAM triggers an interrupt just before the SAM is allowed to transmit data in the allocated time slot. In the worst case, the allocated time slot was just missed so that a complete superframe cycle has to be waited for. stands for the time interval, which is required to transmit data to the BS, to handle the communication with the upper level process control system, and to access the desired actuator for activation. equals 6.1 ms as shown In the best case, the measured in Fig. 10. Measured latency time for the worst case is . Thus, in the current implementation, our demonstrator allows the remote access of 18 SAMs between 6 ms and 11 ms under laboratory conditions, where 1 ms is required for data exchange between ULCU and UDP server. To demonstrate system performance, measurements were taken with a Tektronix spectrum analyzer. In the spectrogram shown in Fig. 11, the F/TDMA principle is visualized, where the horizontal axis represents the frequency scale and the vertical axis stands for the time scale. The wireless system runs in parallel on two frequencies. RF signals of three SAMs (two operating in the lower and one in the higher frequency band)


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TABLE II PERFORMANCE COMPARISON OF DIFFERENT ENERGY SCAVENGING TECHNIQUES

Fig. 10. Measurement of best-case scenario.

Fig. 11. Measurement of system operation.

can be seen directly after the beacon message with updated control and user data provided by the UDP server. Mean power consumption of a SAM equals 10.13 mW operating in a 6-ms superframe cycle with beacon message containing 47 bytes payload and 6 bytes SAM message payload. For synchronization and CRC, 10 additional bytes are necessary. This small beacon payload is sufficient for information exchange with 18 SAMs within one superframe cycle, where each SAM offers four digital inputs and outputs, four analog inputs, and two analog outputs. In Rx-mode, SAM power consumption is 38.5 mW and in Tx-mode 31.5 mW, respectively. With a data rate of 1 Mbit/s, the receiver sensitivity is 83 dBm. Tx-power was set to 0 dBm. When there are no activities like frequency adjustment or data processing, the SAM enters sleep mode whenever it is possible, requiring only 0.3 mW. With relaxed real-time requirements, sleep mode can be entered, more often resulting in a lower energy consumption. Many important applications cannot be supported by wireless technologies if power lines are required. Thus, it is often desired that distributed SAMs should operate in a complete selfsustained [40] mode. Great advances in power consumption of analog RF and digital circuitry have already been achieved and strategies for minimal power consumption also exist, but power

sourcing solely from small and lightweight batteries is not possible at the moment, if a long-term operation over many months or even years is required and battery exchange is not allowed. Therefore, it is necessary to break new ground. In [41] and [42], energy scavenging techniques are reviewed and a host of various approaches have been presented, especially from the viewpoint of wireless ad-hoc sensor networks, where power consumption and node lifetime are of central concern. In Table II, key data for various energy forms are summarized. Primary and secondary cells, i.e., rechargeable batteries, are also listed. Principally, micro-fuel cells or micro-combustion engines also come into consideration but are not included as time-to-market is expected to be much longer compared with the technologies given in Table II. In the last column, the required physical dimensions of the energy transducer for a predefined power consumption, in our case 10.13 mW, are listed. Looking at the data shown, it can be concluded that it is not possible to source power solely from one single energy reservoir so that generally, a smart combination of power sources has to be chosen. Also, not all techniques are suitable for a certain application to the same degree, for example, the use of solar cells will be not appropriate for applications with dust exposure. Therefore, an energy scavenging strategy has to be developed specifically for each application. An example is given in EnAS [19], where the WSANs are to be powered from the air flow or exhaust of a pneumatic system, which is already implemented in the primary production process. A first prototype developed by our project partner [43], FESTO already shows an energy density of 3.3 which lies about three orders of magnitude above the power levels listed in Table II. During system development, optimal trade-off between quality of service (real-time, reliability) and power consumption/node lifetime was studied in depth. For example, the superframe length can be defined by the user, and it will be possible to select between specific power saving modes by software switches. VI. CONCLUSION AND FUTURE WORK Based on the analysis of user requirements, a prototype implementation of a wireless sensor/actuator network has been presented. Results of our preliminary channel measurements in a factory-like environment are in good agreement with those obtained from the scientific literature. The wireless system has a modular architecture so that a high degree of scalability could

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be achieved without affecting performance and costs. A star network topology in combination with an F/TDMA scheme allows system realization in a relatively simple way with standard RF and digital components and modules. In the next step, an experimental setup will be established, which will be representative for a typical application. The measurement campaign will be expanded to different industrial sites to gain more information about factors affecting system performance, such as interference and jamming. Packet error rate measurements will be also included. Clearly, the measured values for response times were achieved under ideal conditions and cannot be guaranteed under harsh environmental and electromagnetic interference conditions. However, the system has enough performance margin to flexibly account for the negative effects of the wireless channel in the field. Therefore, in the next step, strategies will be developed to enhance robustness, reliability, and ability for coexistence. ACKNOWLEDGMENT The authors would like to thank DLR, the project execution organization, and their cooperation partners, especially the lead contractor FESTO AG for continuous support. REFERENCES [1] J. Weczerek, “Wireless profinet über WLAN und bluetooth (wireless profinet over WLAN and bluetooth),” in Proc. Wireless Automation, Darmstadt, Germany, May 2006, vol. 1939, pp. 93–100, VDI-Berichte. [2] T. Schildknecht, “Wie kann man SPS steuerungen per funk koppeln (how to couple PLC wirelessly),” in Proc. Wireless Automation, Darmstadt, Germany, May 2006, vol. 1939, pp. 69–79, VDI-Berichte. [3] Datasheet High Speed PROFIBUS Wireless Communication Modules. [Online]. Available: http://www.prolinxgateways.com/. [4] Datasheet CANview Bluetooth/WLAN. [Online]. Available: http://www.rmcan.com/. [5] Verbundprojekt, Drahtlose Feldbusse im Produktionsumfeld (Funbus), Abschlussbericht, 2002. [6] L. Rauchhaupt, “System and device architecture of a radio-based fieldbus—The RFieldbus system,” in Proc. 4th IEEE Int. Workshop Factory Communication Systems (WFCS), Västerås, Sweden, Aug. 2002, pp. 185–192. [7] A. Willig, “An architecture for wireless extension of PROFIBUS,” in Proc. IEEE Ind. Electron. Soc. Conf. (IECON), Nov. 2003, vol. 3, pp. 2369–2375. [8] A. Willig, “Polling-based MAC protocols for improving real-time performance in a wireless PROFIBUS,” IEEE Trans. Ind. Electron., vol. 50, no. 4, pp. 806–817, Aug. 2003. [9] K. C. Lee and S. Lee, “Integrated Network of Profibus-DP and IEEE 802.11 Wireless LAN with Hard Real-time Requirements,” in Proc. IEEE Int. Symp. Industrial Electronics (ISIE), Pusan, Korea, Jun. 2001, pp. 1484–1489. [10] P. Morel and A. Croisier, “A wireless gateway for fieldbus,” in Proc. 6th Int. Symp. Personal, Indoor and Mobile Radio Communications (PIMRC), Sep. 1995, vol. 1, pp. 105–109. [11] P. Morel, A. Croisier, and J.-D. Decotignie, “Requirements for wireless extensions of a FIP fieldbus,” in Proc. 6th IEEE Conf. Emerging Technologies and Factory Automation (EFTA), Nov. 1996, vol. 1, pp. 116–122. [12] L. Ferreira, M. Alves, and E. Tovar, “Hybrid wired/wireless profibus networks supported by bridges/routers,” in Proc. 4th IEEE Int. Workshop Factory Communication Systems (WFCS), Vasteras, Sweden, Aug. 2002, pp. 193–2002. [13] M. Alves, E. Tovar, F. Vasques, G. Hammer, and K. Rother, “Real-time communications over hybrid wired/wireless PROFIBUS-based networks,” in Proc. 14th Euromicro Conf. Real-Time Systems (ECRTS), Vienna, Austria, Jun. 2002, pp. 142–151. [14] A. Willig, K. Matheus, and A. Wolisz, “Wireless Technology in Industrial Networks,” Proc. IEEE, vol. 93, no. 6, pp. 1130–1151, Jun. 2005. [15] White Paper: Introduction to Wireless Interface for Sensors and Actuators and Wireless Proximity Switches. Germany: ABB Stotz-Kontakt GmbH, Aug. 2004.

[16] IEC 62026-2: Low-Voltage Switchgear and Controlgear—ControllerDevice Interfaces (CDIs)—Part 2: Actuator Sensor Interface (AS-i), Jul. 2000. [17] Seriplex Control Bus Version 2 Standards Specification. [Online]. Available: http://ecatalog.squared.com/techlib/docdetail. cfm?oid=09008926800aaa6e. [18] “Guidelines of the VDI/VDE,GMA,” in Radio-based Communication in Industrial Automation. Berlin, Germany: Beuth Verlag, 2003. [19] Governmental Funded Project, EnAS—Energy Autarkic Wireless Actuator and Sensor Networks. [Online]. Available: http://www.nextgenerationmedia.de/. [20] E. Walker, H. Zepernick, and T. Wysocki, “Fading measurements at 2.4 GHz for the indoor radio propagation channel,” in Proc. Int. Zurich Seminar Broadband Communications, Zurich, Switzerland, Feb. 1998, pp. 171–176. [21] H. MacLeod, C. Loadman, and Z. Chen, “Experimental studies of the 2.4-GHz ISM wireless indoor channel,” in Proc. 3rd Communication Networks and Services Research Conf. (CNSR), Halifax, NS, Canada, May 2005, pp. 63–68. [22] H.-J. Zepernick and T. A. Wysocki, “Multipath channel parameters for the indoor radio at 2.4 GHz ISM band,” in Proc. Vehicular Technology Conf., Houston, TX, May 1999, vol. 1, pp. 190–193. [23] K. Seong-Cheol, H. L. Bertoni, and M. Stern, “Pulse propagation characteristics at 2.4 GHz inside buildings,” IEEE Trans. Veh. Technol., vol. 45, no. 3, pp. 579–592, Aug. 1996. [24] G. J. M. Janssen, P. A. Stigter, and R. Prasad, “Wideband indoor channel measurements and BER analysis of frequency selective multipath channels at 2.4, 4.75, and 11.5 GHz,” IEEE Trans. Commun., vol. 44, no. 10, pp. 1272–1288, Oct. 1996. [25] L. M. Correia, Wireless Flexible Personalised Communications: COST 259, European Co-operation in Mobile Radio Research. Chichester, U.K.: Wiley, 2001, pp. 178–193. [26] A. U. H. Sheikh, Wireless Communication Theory and Techniques. Norwell, MA: Kluwer, 2004. [27] F. Pellegrini, D. Miorandi, S. Vitturi, and A. Zanella, “On the use of wireless networks at low level of factory automation systems,” IEEE Trans. Ind. Informat., vol. 2, no. 2, pp. 129–143, May 2006. [28] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A Survey on Sensor Networks,” IEEE Commun. Mag., vol. 40, no. 8, pp. 102–114, Aug. 2002. [29] D. D. Falconer, F. Adachi, and B. Gudmundson, “Time division multiple access methods for wireless personal communications,” IEEE Commun. Mag., vol. 33, no. 1, pp. 50–57, Jan. 1995. [30] Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, IEEE Std. 802.11, 1997. [31] Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Wireless Personal Area Networks (WPANs), IEEE Std. 802.15.1, 2005. [32] Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (LR-WPANs), IEEE Std. 802.15.4, 2003. [33] F. Schlichting, “nanoNET—Chirp spread spectrum (CSS) system for reliable use in difficult industrial environments,” in Proc. RADCOM, Hamburg, Germany, Mar. 2005. [34] Datasheet WirelessUSBTM LR 2.4-GHz DSSS Radio SoC. [Online]. Available: http://www.cypress.com/. [35] Data Sheet CC2400. [Online]. Available: http://www.chipcon.com/. [36] Data sheet NM7010B. [Online]. Available: http://www.iinchip.com/. [37] Internet Engineering Task Force, RFC 768—User Datagram Protocol. [Online]. Available: http://www.ietf.org/rfc/rfc768.txt. [38] D. W. Humphrey, White Paper Profinet: An ALL-Encompassing Industrial Ethernet Solution. Dedham, MA: ARC Advisory Group, Nov. 2005. [39] Ethernet/IP—CIP on Ethernet Technology. [Online]. Available: http:// www.odva.org/. [40] R. Want, K. I. Farkas, and C. Narayanaswami, “Guest editors’ introduction: Energy harvesting and conservation,” Pervasive Comput., vol. 4, no. 1, pp. 14–17, Jan.–Mar. 2005. [41] S. Roundy, D. Steingart, L. Frechette, P. Wright, and J. Rabey, “Power sources for wireless sensor networks,” in Proc. 1st Eur. Workshop Wireless Sensor Networks (EWSN), Berlin, Germany, Jan. 2004, pp. 1–17. [42] J. A. Paradiso and D. Starner, “Energy scavenging for mobile and wireless electronics,” Pervasive Comput., vol. 4, no. 1, pp. 18–27, Jan.–Mar. 2005. [43] Festo Corporation, Germany: Internal Technical Report, Esslingen, Germany, , Aug. 2006.


Hans-Jörg Körber received the diploma degree in electrical engineering in 1999 from the Helmut-Schmidt-University, University of the Federal Armed Forces, Hamburg, Germany, where he is currently pursuing the Dr.-Ing degree in electrical engineering. He is with the Institute for Electrical Measurement Engineering as a Research Scientist. His research interests are directed to wireless standards, networks, and ad-hoc sensor networks in the context of industrial applications. Especially, he focuses on protocol design and software and hardware engineering with respect to real-time and power consumption aspects.

Housam Wattar received the diploma degree in electrical engineering from the Technical University Hamburg, Harburg, Germany, in 2004. He is currently a Research Assistant at the Institute for Electrical Measurement Engineering, Helmut-Schmidt-University, University of the Federal Armed Forces, Hamburg. His research interests are wireless sensor networks in the context of industrial applications, especially radio channel properties and protocol design for robust and reliable radio communication.

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Gerd Scholl (M’89) received the Dipl.-Ing. degree in 1989 and the Dr.-Ing. degree in 1996, both in electrical engineering, from the Technical University of Munich, Munich, Germany. From 1991 to 2000, he was with the surface acoustic wave technology and wireless sensors group of the Siemens Corporate Research and Technology Center, Munich, where he was engaged in the design of surface acoustic wave resonators and low-loss devices for mobile communications and wireless sensor systems. He also was responsible for the realization of RF identification and sensor systems. From 2001 to 2003, he was with the R&D department of the surface acoustic wave division of EPCOS AG, where he was responsible for basic research and development of new components, modules, and system concepts for radio-based services. Since 2004, he has held the chair for electrical measurement engineering at the Helmut-Schmidt-University, University of Federal Armed Forces Hamburg, Hamburg, Germany. His current research interests are wireless sensors and sensor systems and new measurement techniques for the development of wireless solutions.