Abstract
Wireless sensor networks (WSNs) have recently extended application areas to numerous sectors such as industrial automation, military applications, transportation systems, building management, and environment surveillance. In particular, WSNs provide flexible, reliable, cost-effective solutions to monitor the real-time status of automated manufacturing facilities. A facility-specific WSN for reliable monitoring and efficient management of industrial facilities is called the facility sensor network (FSN). In general, industrial facilities run various electromagnetic devices causing electromagnetic interference (EMI), which disturbs wireless data communication. To obtain accurate and reliable data in such environments, the FSN needs to deal with the EMI by proper deployment of sensor nodes and their validation and fusion. This paper proposes a data processing protocol, called Interfered Sensor Data Processing Protocol (ISDPP) to handle the EMI affecting wireless communication. ISDPP is developed with a data fusion algorithm and an exponentially weighted moving average/fuzzy logic-based error detection method to obtain reliable information from the FSN. To evaluate the performance, experiments in various settings are performed in a test-bed manufacturing facility. The experimental results indicate the interfered data, and outliers can be filtered out even if unexpected interferences occur in the facility. The FSN with the ISDPP can provide efficient real-time monitoring solutions for various industrial applications.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Ko HS, Lim H, Jeong W, Nof SY (2010) A statistical analysis of interference and effective deployment strategies for facility-specific wireless sensor networks. Comput Ind 61(5):472–479
Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) A survey on sensor networks. IEEE Commun Mag 40(8):102–105
Chong CY, Kumar SP (2003) Sensor networks: evolution, opportunities, and challenges. Proc IEEE 91(8):1247–1256
Abrishambaf R, Hashemipour M, Bal M (2012) Structural modeling of industrial wireless sensor and actuator networks for reconfigurable mechatronic systems. Int J Adv Manuf Technol. doi:10.1007/s00170-012-4070-y
Cheng ST, Hsu CL (2005) Genetic optimal deployment in wireless sensor networks. J Internet Technol 6(1):9–18
Jeong W, Nof SY (2009) A collaborative sensor network middleware for automated production systems. Comput Ind Eng 57(1):106–113
Tang F, You I, Guo S, Guo M, Ma Y (2010) A chain-cluster based routing algorithm for wireless sensor networks. J Intell Manuf. doi:10.1007/s10845-010-0413-4
Xu K, Wang Q, Hassanein H, Takahara G (2005) Optimal wireless sensor networks (WSNs) deployment: minimum cost with lifetime constraint. In: Proceedings of the IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob’05), Montreal, QC, Canada
Chin TL, Ramanathan P, Saluja KK (2006) Optimal sensor distribution for maximum exposure in a region with obstacles. In: Proceedings of the IEEE Global Telecommunications Conference (GLOBECOM’06), San Francisco, CA, USA
Hou YT, Lee TC, Jeng BC, Chen CM (2006) Optimal coverage deployment for wireless sensor networks. In: Proceedings of the 8th International Conference of Advanced Communication Technology, Phoenix Park, South Korea
Toumpis S, Tassiulas L (2006) Optimal deployment of large wireless sensor networks. IEEE Trans Inf Theory 52(7):2935–2953
Kraz V (2007) EMI issues in the manufacturing environment. Conformity 12(1):38–42
Jeong W, Nof SY (2008) Performance evaluation of wireless sensor network protocols for industrial applications. J Intell Manuf 19(3):335–345
Morrison R (1991) Noise and other interfering signals. Wiley, New York
Krishnamurthy L, Adler R, Buonadonna P, Chhabra J, Flanigan M, Kushalnagar N, Nachman L, Yarvis M (2005) Design and deployment of industrial sensor networks: experiences from a semiconductor plant and the North Sea. In: Proceedings of the 3rd International Conference on Embedded Networked Sensor Systems (SenSys’05), San Diego, CA, USA
Prajitno P, Mort N (2001) A fuzzy model-based multi-sensor data fusion system. In: Proceedings of SPIE—sensor fusion: architectures, algorithms, and applications V 4385:301–312, Orlando, FL, USA
Alag S, Goebel K, Agogino A (1995) Methodology for intelligent sensor validation and fusion used in tracking and avoidance of objects for automated vehicles. In: Proceedings of the American Control Conference, Seattle, WA, USA
Ibarguengoytia PH, Vadera S, Sucar LE (2006) A probabilistic model for information and sensor validation. Comput J 49(1):113–126
Lee SC (1994) Sensor value validation based on systematic exploration of the sensor redundancy for fault diagnosis KBS. IEEE Trans Syst Man Cybern 24(4):594–605
Wen YJ, Agogino A, Goebel K (2004) Fuzzy validation and fusion for wireless sensor networks. In: Proceedings of the ASME International Mechanical Engineering Congress and Exposition, Anaheim, CA, USA
Goebel K, Agogino A (1999) Fuzzy sensor fusion for gas turbine power plants. In: Proceedings of SPIE—sensor fusion: architectures, algorithms, and applications III 3719:52–61, Orlando, FL, USA
Solberg AHS, Jain AK, Taxt T (1994) Multisource classification of remotely sensed data: fusion of Landsat TM and SAR images. IEEE Trans Geosci Remote Sens 32(4):768–778
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Jeong, W., Ko, H.S., Lim, H. et al. A protocol for processing interfered data in facility sensor networks. Int J Adv Manuf Technol 67, 2377–2385 (2013). https://doi.org/10.1007/s00170-012-4657-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-012-4657-3