Industrial Applications of Collaborative Wireless Sensor Networks: A Survey

Mert Bal Department of Engineering Technology Miami University, Regional Campuses

Hamilton, Ohio, United States of America balm@miamioh.edu

Abstract— The objective of this paper is to summarize the recent literature and application examples of the use of Wireless Sensor Networks (WSN) in the industry. Main focus in this paper is primarily on the use of collaborative wireless sensor networks for condition and performance monitoring of the industrial machines and plants. Current challenges and potential future research issues are outlined for design of WSN-based industrial monitoring systems in order to provide guidelines to researchers and application developers.

Keywords— Wireless Sensor Networks, Collaborative Information Processing, Industrial Applications, Machine Monitoring, Process Control

I. INTRODUCTION

In the recent years, with the advancement of Micro Electro Mechanical Systems (MEMS) and Radio Frequency (RF) components and technologies, use of large networks of wireless micro-sensors has become very attractive for industrial monitoring and control applications that involve real-time data collection.

A wireless micro-sensor can be defined a small, battery-powered electronic device with computational and communication capabilities. A wireless sensor network (WSN) is a network containing such sensors which could be developed at a relatively low-cost and deployed in a variety of different settings to interact with the environment such as measuring a variety of physical properties, including temperature, acoustics, light, and pollution [1].

The capabilities for detailed physical monitoring and manipulation through WSN offer enormous opportunities for almost every scientific discipline, and it will alter the feasible granularity of engineering.

To date, various implementations of WSN for real-world data-collection applications have been reported in the literature. The main areas of applications include: industrial automation, condition monitoring, military or personal security, air traffic control, traffic and area surveillance, logistics and supply chain [2], environment monitoring, wildfire detection [3], structural condition monitoring [4], asset and people tracking, chemical plants monitoring; condition-based facility and infrastructure maintenance, medicine and healthcare [5].

A basic WSN application can be built on the fusion of three major phenomena: sensing, processing and communication. According to this model, spatially deployed sensor nodes coupled with the physical sensing units, gather raw data from the environment, process data to obtain meaningful information and share it with the network through radio communication.

Due to this distributed nature, the WSN has a great potential in industrial monitoring and control applications that require periodic real-time data collection or detecting exceptional events. For example, sensors for measuring various physical phenomena such as vibration, heat, noise, light etc. can be deployed in proximity of machines in industrial plants to monitor their performance and health. The analysis of the measured parameters can allow the detection of abnormal operating conditions and aids therefore in preventing potential machine failure [6]. In addition to machine monitoring, WSNs can be deployed to detect complex events such as process quality, machine utilization or production performance in factories and production plants [7].

In the past few years, there have been tremendous efforts toward the product research and development for implementation of WSN in the industry. Various researchers have explored the challenges and potential impacts of implementing WSN on industrial monitoring and control applications. This paper will review the recent research and development on the industrial applications of WSN, focusing on real-time data collection for monitoring and control.

In addition, this paper will elaborate the potential benefits of sensor collaboration, and discuss the challenging issues and future research opportunities in this area, focusing on distributed sensing and collaborative information processing through WSN.

The remainder of the paper is organized as follows: Section 2 briefly describes the background of the WSN in general; Section 3 outlines the challenges and developments in the wireless sensor collaboration; Section 4 presents the academic research and the progress in industry on the applications of WSN on industrial monitoring and control; Section 5 discusses the research challenges and opportunities and provides some brief concluding remarks.

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II. BACKGROUND: WIRELESS SENSOR NETWORKS

In the last few years, technological progress has been taking the spread of embedded control steps further. Through the integration of ubiquitous computing in daily life, the computation will eventually surround the living spaces. Different devices will gather and process information from many different sources to both control physical processes and to interact with human users [8]. In order to realize this vision, communication has been recognized as a crucial aspect in addition to computation and control. All these sources of information have to be able to transfer the information to the place where it is needed – an actuator or a user – and they should collaborate in providing as precise a picture of the real world as required.

For some industrial monitoring and control applications, the existing wired networking technologies are sufficient for building such networks of sensors and actuators. For many other industrial application types, however, the need to wire together all these entities constitutes a considerable obstacle to success: wiring is expensive, in particular, given the large number of devices imaginable in our environment; wires constitute a maintenance problem; wires prevent entities from being mobile; and wires can prevent sensors or actuators from being close to the phenomenon that they are supposed to monitor and control. Hence, wireless communication between such devices is, in many application scenarios, an inevitable requirement.

WSN is a new class of network, evolved in the past few years as a consequence of the developments in the MEMS technology to satisfy the growing requirements of computing. WSN consists of individual nodes that are able to interact with their environment by sensing or controlling physical parameters. Apart from the need to build cheap, simple to program, simple to network and potentially long-lasting sensor nodes, a crucial and primary ingredient for developing actual applications is the sensing facility with which a sensor node can be endowed. For many physical parameters, appropriate sensor technology can be integrated in a node of a WSN. Some of the popular ones are temperature, humidity, visual and infrared light, acoustic, vibration, acceleration, pressure, chemical sensors, mechanical stress and magnetic field. The wireless nodes can communicate with each other and interact with their environment in order to gather, process and convey the information.

Unlike traditional wired networks, wireless sensors and other data monitoring hardware can be deployed with much less effort at inaccessible locations and environments, which are impractical with conventional wired systems such as: bearings of motors, oil pumps, into the heart of whirring engines, or many unpleasant or hazardous environments. Installation of wireless systems is much faster and more inexpensive compared to the wired systems. In addition, for wired networks, the isolation may be required for cables running near to high humidity, magnetic field or high

vibration environment. It is also necessary to have redundant wire for critical operations in wired networks that pose difficulty in fault locations and isolations. Applications that have frequent re-location of devices also make wireless solutions more attractive than wired networks [9].

Wireless communication in industrial applications is mostly based on standardized technologies based on the IEEE 802.11 and IEEE 802.15 standard families [10]. These standard specifications are also designated as Wireless Local Area Networks (WLAN) and Wireless Personal Area Networks (WPAN). The IEEE 802.11-based standards offer high data rates in the order of tens of Mbit/s and ranges up to hundreds of meters, while the IEEE 802.15-based standards only supports data rates of hundreds of kbit/s to several Mbits/s with ranges from a few meters up to hundreds of meters. However, to provide greater data rate and range, IEEE 802.11 technology consumes greater energy. On the other hand, the IEEE 802.15 technology allows low-power operations of wireless devices on a limited bandwidth. In most industrial applications where the main focus is to gather real-time data concerning the processes and operations the energy is often a greater concern than the bandwidth. Therefore in the industrial applications of wireless sensor networks, which are mostly reported in the literature, the main focus is mainly on the IEEE 802.15-based standards, and particularly on the IEEE 802.15.4 standard [11].

Several industrial organizations, such as ISA [12], HART [13], WINA [14] and ZigBee [15], have been actively pushing the application of wireless technologies in industrial automation. As a milestone of such efforts, WirelessHART is ratified by the HART Communication Foundation in 2007. WirelessHART is the first open wireless communication standard specifically designed for process measurement and control applications [13]. Before WirelessHART is released, there have been a few standards available on wireless manufacturing automation, such as ZigBee [15] and Bluetooth [16]. However, these technologies could not meet the rigorous requirements of industrial control applications. The WirelessHART is specifically targeted to address strict timing requirements, high security concerns of industrial automation and control applications. At the very bottom, the WirelessHART adopts IEEE 802.15.4 as the physical layer. On top of that, WirelessHART defines its own time-synchronized MAC layer. The network layer supports self-organizing and self-healing mesh networking techniques. In this way, messages can be routed around interferences and obstacles [17].

III. WIRELESS SENSOR COLLABORATION FOR CONDITION MONITORING

Although, the potential of wireless communication in machine condition monitoring has been recognized by industries, the recent literature has reported many challenges and limitations of wireless communication on industry. In many industrial environments, the wireless transmission conditions differ

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greatly because of mobile metal obstacles and other radio disturbances. In such situations, routing schemes that use fixed routing tables are not able to provide the flexibility over mobile equipments, physical design limitations, and reconfiguration typical of an industrial application.

Specifically, for a wireless technology to be reliable in industrial applications, it must: (1) function in harsh industrial environments with unpredictable Electromagnetic Interference (EMI), RF fading, and multipath interference; (2) coexist in the field with other wireless devices or noise emitters such as machine equipment, communications devices, instant connect phones, pagers, cell phones, remote controls, and other wireless frequency emitters common in the industrial environment [18] and [19].

Wireless interferences to the mission-critical data can result in costly disasters in terms of money, manpower, time and even lives of employees or public. For example, as a result of radio interference, the VHF biomedical telemetry devices in two Dallas hospitals stopped working when a nearby local TV station tested HDTV broadcast for a short period [20]. Such incident raises the concerns over the effects of interferences. The noise and interferences are significant especially in the harsh industrial environments due to the wide operating temperatures, strong vibrations, airborne contaminants, excessive electromagnetic noise caused by large motors etc. Therefore, it is important to study and understand the characteristics in order to predict the communications performance in such operating condition. The measure of success for an industrial grade WSN is not how any individual network device performs, but how the system as a whole ensures a reliable flow of critical data. Reliability is an absolute requirement for any monitoring technology, because if the data is not reliable, the economic benefits of its low installation costs are rendered irrelevant. In addition, WSN are also exposed to many technical limitations including energy and memory constraints, available processing power, transmission rate, synchronization rate, and robustness in operation [21].

Research and experience have shown that optimal collaboration among sensor nodes can significantly improve the efficiency of sensing and processing in sensor networks. The nodes in WSNs often have to cooperate to fulfill their tasks as, usually, a single node is incapable of doing so, and they use wireless communication to enable this collaboration.

In most of the applications requiring long operation time, energy efficiency is an important consideration, since the sensor nodes are dependent on limited power, usually provided by on-board batteries. Numerous sensors that are close to the phenomenon can make the architecture of a system both simpler and more energy efficient due to distributed computation.

Wireless sensor collaboration also improves decision making in application where multiple forms of data have to be processed in order to make decisions. In such cases, a single sensor node is typically not able to decide whether an event

has happened. Several sensors have to collaborate to detect an event and only the joint data of many sensors provide enough information. Information is processed in the network itself in various forms to achieve this collaboration, as opposed to having every node transmit all data to an external network and process it “at the edge” of the network.

Several researchers have studied various aspects of wireless sensor collaboration. For example: Agre and Clare [22] have investigated the architectural aspects of the cooperative signal processing. They have emphasized the autonomy of the sensor nodes at the low level and have proposed a layered architecture for the distributed sensor networks, which integrates the cooperation into autonomy. The collective behavior of the complex sensor networks increases by moving to higher layers and the autonomy of the individual sensor nodes increases when moving to the lower layers.

Pradhan et al. [23] have proposed a method that allows minimizing the amount of inter-node communication whereas preserving the resolution of the data gathered. The goal is to compress sensor data from individual nodes to obtain minimal inter-sensor communication.

Sohrabi et al. [24] have developed a number of algorithms for establishing and maintaining connectivity in WSN. Their algorithms aim at the self-organization of the wireless sensor networks, exploiting the low mobility and abundant bandwidth, while coping with the severe energy constraint and the requirement for network scalability.

In [25], Chu et al. have presented data-querying and routing approaches in WSN with the objective of energy efficiency. Their approach relies on two key ideas: information driven sensor querying to optimize sensor selection and constrained anisotropic diffusion routing to direct data routing and incrementally combine sensor measurements to minimize an overall cost function.

Park et al. [26] have proposed a protocol for distributed, collaborative multi-hop routing of wireless sensor packets in order to transmit plant information throughout the network. The Breath protocol aims to optimize the routing sequences within the WSN and help improving robustness and reliability of the network.

A group of algorithms for wireless collaborative sensing and actuation are given in [27]. The proposed algorithms are validated for optimal control of lighting or temperature in industrial workspaces.

Chen et al. [28] have proposed a distributed collaborative estimation and control approach for wireless sensor and actuator networks that accounts for packet loss at wireless communications in noisy industrial environments. This approach has been designed, based on a locally collaborative control algorithm, which exploits the collaborations between actuators and sensors.

A group connectivity model has been proposed in [29] for deployment of wireless sensors in a collaborative operation

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scheme suitable for harsh industrial environments where running wires is less practical and also prohibitively expensive. In the group connectivity model, the neighboring sensors are connected actually by having common keys which can be used for security enhancement such as encryption purposes within the industrial networks.

In the study presented by Hou and Bergmann [30], the applicability of data fusion for fault diagnosis in collaborative WSNs has been explored. The proposed system framework uses a network of battery-operated wireless sensor nodes, sampling signals at a high-rate from group industrial machines. The sampled data have been analyzed collaboratively using Dempster–Shafer classifier fusion for industrial machine condition monitoring and fault diagnosis.

IV. REVIEW OF INDUSTRIAL APPLICATIONS OF WSN

The great flexibility of the WSNs in replacing costly wired infrastructures for remote monitoring and control of machines have been recognized by a large number of researchers and industrial organizations. Various companies have utilized wireless sensors for remote monitoring of the health of machinery and industrial plant components by wirelessly measuring temperature, pressure, power usage and so forth. The wireless infrastructure, powered with ease in re-configurability has resulted significant cost savings in large projects.

General Motor (GM) is one of the organizations that have successfully utilized wireless sensing technology. GM has applied wireless sensor network technology to monitor the manufacturing equipment such as the conveyer belts and other types of machinery. Measured data such as the vibration, heat and other factors are detected and transmitted to a computer via a wireless mesh. By periodically collecting information from the machinery, the technicians could predict machine’s failure, mean time to failure and perform pre-emptive maintenance. The collected data also facilitates future improvement and faster repair of equipment from the data collected [31].

Honeywell [32] has commissioned a large number of projects involving wireless monitoring and control. Recently they installed wireless temperature sensor nodes onto the mill furnaces of the Nucor Corporation, one of the largest steel producers in the U.S. The wireless transmitters were placed a few feet from the base of furnace flames. The transmitters were installed on the cooling circuits for the furnace and encased on specially built protective boxes to withstand the extreme heat. Overall project was aimed to overcome challenge of obtaining accurate and reliable temperature readings on furnace. As described in their case study report [32] the use wireless technology was a better way to get these readings given the extreme environment.

In [33], an experimental wireless sensor network has been set up for an ore processing facility to monitor the wear conditions of the pumps that are used in the factory. The wear

rubble parts of the pumps are monitored through embedded wireless sensors that transmit the pump identification and the wear data. The objective of this study is to perform preventive maintenance to replace the uneven and unpredictable wear so that the 200 pumps in the facility can be operated at the optimum level.

Intel research has deployed a network of vibration sensing wireless nodes in order to monitor the condition of semiconductor fabrication equipment [34]. The goal of this implementation is to make feasible the detection of faulty parts, which need repair or changing, by analyzing their vibration signatures.

A similar implementation has been done by Rockwell Automation. Vibrations that occur in a commercial ship of BP have been monitored by a network of self-powered wireless sensor nodes. This project has studied whether the deployment and connectivity of motes is feasible in such a harsh environment [35].

Wan et al. [36] have developed a wireless sensor network application for monitoring the temperature of rollers in a continuously annealing line and detecting equipment failures. The system uses total of 406 wireless sensor nodes on a cluster-based hierarchical network.

Sung and Hsu [37] have proposed a wireless industrial monitoring framework based on ZigBee protocol. Wireless nodes perform various measurement functions including length filtering, ground vibration sensing, weight grading, electricity sensing, energy monitoring, temperature monitoring, and carbon dioxide concentration. ZigBee is used for wireless transmission to integrate all independent sensor signals in interfaces that allowed for centralization and real-time control.

Tan et al. [38] have presented a wireless distributed fault detection method for monitoring CNC machines using cutting force, vibration and sound measurements. A similar study has been conducted by Aruvali et al. [39]. In the monitoring process, the cutting force ratio is used to predict the in-process surface roughness regardless of the cutting conditions. Wireless sensor nodes, located on a turning machine have measured acceleration for detection of the vibration and acoustic signals. Another similar study, presented in [40], uses a solar-powered wireless sensor with an accelerometer which was installed inside a turning machine’s spindle in order to monitor the machine tool processing state in real time.

Khakpour and Shenassa [41] have deployed and tested a network of 160 ZigBee-based wireless sensor nodes for monitoring and control of weaving machinery. The wireless network is intended to display the real time state of weaving machines and give information about the operator attendance on the machines.

Lee [42] has developed a self-powered sensing node for monitoring motor conditions. In this system, the sensing node, which constantly measures the condition of a motor, is

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powered through the electromagnetic pulses generated by the motor’s shaft rotation.

Salvadori et al. [43] have focused on the power management issues in WSN deployment for monitoring applications. They presented a monitoring system that utilizes a high-level intelligent power management system to optimize the power usage of the sensor network for monitoring applications in industrial plants.

Lu and Gungor [44] have studied a scheme of applying WSNs in online and remote energy monitoring and fault diagnostics for industrial motor systems using a nonintrusive approach. The approach is based on the analysis of the motor signatures generated through collected electrical signals from industrial motors.

V. DISCUSSIONS AND CONCLUDING REMARKS

WSN interacts with an industrial environment in the form of spatially distributed measurements, diagnosis and possible actuations. The majority of current industrial applications provides only monitoring capabilities and hence contain only sensors. Machine control sensors, actuators and controllers are often on a separate network. Wireless communication makes factory setup and modification easier, cheaper and more flexible. It provides a natural approach towards communication with mobile equipment where wires are in constant danger of breaking. It enables new applications where wireless transmission is the only option. Despite its advantages, the WSN in industrial applications presents significant challenges affects the performance of industrial monitoring systems. Path-loss and unpredictable multipath propagation resulting from harsh industrial environments are the main issues that affect the accuracy and efficiency of signal processing in wireless sensor networks. Typically, a main requirement for a wireless sensor network to cope with this condition is the reconfigurability. The degree of re-configurability of wireless network determines its flexibility to respond harsh environments (obstacles, interference etc.) and thus be more reliable in industrial applications.

Several hardware and software approaches have been developed over the years in order to increase re-configurability of the WSN deployments [45]. Most of these approaches suggest wireless sensor collaboration as a key building block. Some of the collaborative approaches for WSN have been reviewed in this paper. Although, some of them achieved a great success, more experimental work is necessary to make WSN applications more reliable and robust in real industrial applications.

Another key requirement of WSN for industrial monitoring and control applications is the interoperability of the hardware systems used. The wireless sensors deployed in an industrial setting, must be of hardware and network platforms that support interoperability with the other components in the existing industrial system. There are a variety of WSN platforms available in the market. The interoperability is an

essential characteristic. Hence, significant amount of efforts have been put into standards development for assuring the interoperability. Similarly, backward/forward compatibility is a key feature that is desired by the industry for possible upgrading and performance improvement of the wireless network.

Research interest in WSNs is likely to grow further with the increase in wireless sensor network applications in the industry. This paper has provided a discussion and a state-of-the-art review of the research literature concerning industrial monitoring through collaborative WSN. Some existing commercial and research-based system implementations in this area are presented. Some most significant research challenges and opportunities are identified for extending collaborative WSNs. It is believed that collaborative WSN will soon play an important role in many real world industrial applications.

REFERENCES

[1] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensor networks: A survey,” Computer Networks J., 38(4), 393–422, 2002.

[2] H. K. Maheshwari, A. H. Kemp, Q. Zeng, "Range based real time localization in wireless sensor networks", Wireless Networks, Information Processing and Systems, pp.422-432, 2009.

[3] Y. Li, Z. Wang, Y.Q. Song, “Wireless sensor network design for wildfire monitoring” Proc. of The Sixth World Congress on Intelligent Control and Automation, WCICA, Vol.1, pp. 109-113, Dallan, 2006.

[4] M. K. Meyer, M. R. Brambley, “Pros & cons of wireless”, ASHRAE Journal, pp. 54-59, Nov 2002.

[5] J. Yick, B. Mukherjee and D. Ghosal, “Wireless sensor network survey,” Computer Networks, 52(12), 2292-2330, 2008.

[6] D. Christin, P. S. Mogre and M. Hollick, “Survey on Wireless Sensor Network Technologies for Industrial Automation: The Security and Quality of Service Perspectives”, Future Internet, 2010, 2, pp. 96-125.

[7] S.C. Mukhopadhyay, Y.M. Huang, “Sensors: Advancements in Modeling, Design Issues, Fabrication and Practical Applications”, Springer-Verlag: Heidelberg, Germany, 2008.

[8] T. He, C.D.H., B.M. Blum, J.A. Stankovic, T. Abdelzaher, “Range-Free localization schemes in large scale sensor networks,” Proceedings of the 9th Annual International Conference on Mobile Computing and Networking, San Diego, 2003.

[9] K. S. Low, W. N. N. Win, M. J. Er, “Wireless Sensor Networks for Industrial Environments”, Proceedings of the 2005 International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’05), Vienna, Austria, 2005.

[10] IEEE Computer Society. IEEE Standard for Information Technology, Telecommunications and Information Exchange between Systems, Local and Metropolitan Area Networks, Specific Requirements, Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, 2007.

[11] IEEE Computer Society. IEEE Standard for Information Technology, Telecommunications and Information Exchange between Systems, Local and Metropolitan Area Networks, Specific Requirements, Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low Rate Wireless Personal Area Networks (LR-WPANs), 2007.

[12] ISA100: Wireless Systems for Automation, Available at: http://www.isa.org/MSTemplate.cfm?MicrositeID=1134&CommitteeID=6891 (Last Viewed, December 27, 2013).

1467

[13] HART Communication, Available at: http://www.hartcomm2.org/index.html (Last Viewed, December 27, 2013).

[14] WINA, Available at: http://www.wina.org (Last Viewed, December 27, 2013).

[15] ZigBee Alliance, Available at: http://www.zigbee.org (Last Viewed, December 14, 2013).

[16] Bluetooth Technology, Available at: http://www.bluetooth.com/Pages/Bluetooth-Home.aspx (Last Viewed, December 27, 2013).

[17] J. Song, S. Han, A. K. Mok, D.Chen, M. Lucas, and M. Nixon. "WirelessHART: Applying wireless technology in real-time industrial process control." In Real-Time and Embedded Technology and Applications Symposium, 2008. RTAS'08. IEEE, pp. 377-386. IEEE, 2008.

[18] R. Conant, “Wireless sensor networks: Driving the New Industrial Revolution,” Executive Speakout: Adaptive Automation in Action, Industrial Embedded Systems, Spring 2006.

[19] J. Akerberg, M. Gidlund, and M. Bjorkman. "Future research challenges in wireless sensor and actuator networks targeting industrial automation." Industrial Informatics (INDIN), 2011 9th IEEE International Conference on, pp. 410-415. IEEE, 2011.

[20] R. Hampton, “Lessons Learned From Interference to Wireless Medical Telemetry Service Systems”, IT Horizons, AAMI Publications, www.aami.org (Last Viewed, December 27, 2013).

[21] G. Zhao, “Wireless Sensor Networks for Industrial ProcessMonitoring and Control: A Survey” Network Protocols and Algorithms, Vol. 3, No. 1, 2011.

[22] J. Agre, L. Clare, “An integrated architecture for cooperative sensing networks,” Computer, vol.33, no.5, pp.106-108, May 2000.

[23] S.S. Pradhan, J. Kusuma, K. Ramchandran, "Distributed compression in a dense microsensor network," Signal Processing Magazine, IEEE, vol.19, no.2, pp.51-60, Mar 2002.

[24] K. Sohrabi, J. Gao, V. Ailawadhi, G.J. Pottie, “Protocols for self-organization of a wireless sensor network,” Personal Communications, IEEE, vol.7, no.5, pp.16-27, Oct 2000.

[25] M. Chu, H. Haussecker, F. Zhao, "Scalable information-driven sensor querying and routing for ad hoc heterogeneous sensor networks." Int'l J. High Performance Computing Applications, vol. 16, no. 3, pp.293-313, 2002.

[26] P. Park, C. Fischione, A. Bonivento, K. H. Johansson, and A.Sangiovanni-Vincent. "Breath: an adaptive protocol for industrial control applications using wireless sensor networks." Mobile Computing, IEEE Transactions on 10, no. 6 , pp. 821-838, 2011.

[27] M. Nakamura, A. Sakurai, S. Furubo, and H. Ban “Collaborative processing in Mote-based sensor/actuator networks for environment control application”, Signal Processing, 88(7), pp.1827-1838, 2008.

[28] J. Chen, X. Cao, P. Cheng, Y. Xiao, and Y. Sun. "Distributed collaborative control for industrial automation with wireless sensor and actuator networks." Industrial Electronics, IEEE Transactions on 57, no. 12, pp.4219-4230, 2010.

[29] J. Lee, T. Kwon, and J. Song. "Group connectivity model for industrial wireless sensor networks." Industrial Electronics, IEEE Transactions on 57, no. 5,pp.1835-1844, 2010.

[30] L. Hou and N. W. Bergmann, "Novel Industrial Wireless Sensor Networks for Machine Condition Monitoring and Fault Diagnosis",

IEEE Transactions on Instrumentation and Measurement, vol. 61, no. 10, 2012.

[31] P. Hochmuth, “GM cuts the cords to cut costs,” Mobility and wireless, Article of Techworld, Jun 2005.

[32] Honeywell Case Study Report, Available At: https://www.honeywellprocess.com/library/marketing/case-studies/Case-Study-Wireless-Nucor.pdf (Last Viewed, December 27, 2013).

[33] N. Aakvaag, M. Mathiesen, and G. Thonet, “Timing and Power Issues in Wireless Sensor Networks - An Industrial Test Case,” IEEE International Conference on Parallel Processing Workshops, pp. 419-426, Jun 2005.

[34] Intel Research Berkeley, “Collaborating to Change the World”, Ver. 1, 2004. (http://www.intel.com/research/print/berkeley_collab.pdf)

[35] F.M. Discenzo, D. Chung, and K. A. Loparo. "Pump condition monitoring using self-powered wireless sensors." Sound and Vibration 40, no. 5, pp. 12-15, 2006.

[36] Y. Wan, L. Li, J. He, X. Zhang, and Q. Wang. "Anshan: Wireless sensor networks for equipment fault diagnosis in the process industry." Sensor, Mesh and Ad Hoc Communications and Networks, 2008. SECON'08. 5th Annual IEEE Communications Society Conference on, pp. 314-322. IEEE, 2008.

[37] W.T. Sung, and Y.C. Hsu. "Designing an industrial real-time measurement and monitoring system based on embedded system and ZigBee." Expert Systems with Applications 38, no. 4, pp. 4522-4529, 2011.

[38] K.K. Tan, S. N. Huang, Y. Zhang, and T. H. Lee. "Distributed fault detection in industrial system based on sensor wireless network." Computer Standards & Interfaces 31, no. 3, pp.573-578, 2009.

[39] T. Aruväli, R. Serg, J. Preden, and T. Otto. "In-process determining of the working mode in CNC turning." Estonian Journal of Engineering 17, no. 1, pp.4-16, 2011.

[40] C.C. Ho, T. H. Kuo and T. T. Tsai. "A real-time condition monitoring system for a machine tool spindle." Key Engineering Materials 450, pp.259-262, 2011.

[41] K. Khakpour, and M. H. Shenassa. "Industrial control using wireless sensor networks." Information and Communication Technologies: From Theory to Applications, 2008. ICTTA 2008. 3rd International Conference on, pp. 1-5. IEEE, 2008.

[42] D. Lee, "Wireless and powerless sensing node system developed for monitoring motors." Sensors 8, no. 8, pp.5005-5022, 2008.

[43] F. Salvadori, M. de Campos, P. S. Sausen, R. F de Camargo, C. Gehrke, C. Rech, M. A. Spohn, and A.C. Oliveira. "Monitoring in industrial systems using wireless sensor network with dynamic power management." Instrumentation and Measurement, IEEE Transactions on 58, no. 9, pp.3104-3111, 2009.

[44] B. Lu, and V. C. Gungor. "Online and remote motor energy monitoring and fault diagnostics using wireless sensor networks." Industrial Electronics, IEEE Transactions on 56, no. 11, 4651-4659, 2009.

[45] R. Abrishambaf, M. Hashemipour, and Mert Bal. "Structural modeling of industrial wireless sensor and actuator networks for reconfigurable mechatronic systems." The International Journal of Advanced Manufacturing Technology 64, no. 5-8 pp.793-811, 2013.

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