Techno-economical viability of cognitive solutions for a factory scenario

Recent advances in wireless communication theory and semiconductor technology brought wireless to virtually every aspect of our life, and this trend is expected to continue to increase in the future. Unfortunately, as the number of wireless applications grows, the same scarce spectrum is reused over and over again, resulting in increased interference, which jeopardizes the prospect of wireless meeting its high expectations. Dynamic Spectrum Access proposes to mitigate this problem by adapting the operational parameters of wireless networks to varying interference conditions. However, the involved increase in cost threatens to reduce the benefit of wireless in different environments. In this paper we examine the economic balance between the added cost and the increased usability brought about by DSA. We focus on a particular real-life scenario — the production floor of an industrial installation — where there is typically extensive utilization of the ISM band. IEEE 802.15.4 wireless sensors monitor production machinery, and IEEE 802.11 WLAN is used as the data backbone. We model the benefit achieved by adding RF sensing technology in terms of reliability and battery lifetime, and qualitatively assess the cost of interference and the potential gain of introducing sensing technology. Based on this techno-economic analysis, we conclude that if implemented correctly, spectrum sensing can bring business gains in real-life applications.

[1]  Janne Riihijärvi,et al.  Performance study of IEEE 802.15.4 using measurements and simulations , 2006, IEEE Wireless Communications and Networking Conference, 2006. WCNC 2006..

[2]  I. J. Wickelgren Local-area networks go wireless , 1996 .

[3]  Kim-Fung Man,et al.  Wireless communication network design in IC factory , 2001, IEEE Trans. Ind. Electron..

[4]  Martin Nilsson,et al.  Investigating the energy consumption of a wireless network interface in an ad hoc networking environment , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[5]  Ryuji Kohno,et al.  Clear Channel Assessment in Integrated Medical Environments , 2008, EURASIP J. Wirel. Commun. Netw..

[6]  Krishna M. Sivalingam,et al.  Performance comparison of battery power consumption in wireless multiple access protocols , 1999, Wirel. Networks.

[7]  G. Gaderer,et al.  A novel approach for Flexible Wireless Automation in Real-Time Environments , 2008, 2008 IEEE International Workshop on Factory Communication Systems.

[8]  Francesco De Pellegrini,et al.  On the use of wireless networks at low level of factory automation systems , 2006, IEEE Transactions on Industrial Informatics.

[9]  Guoliang Xing,et al.  Beyond co-existence: Exploiting WiFi white space for Zigbee performance assurance , 2010, The 18th IEEE International Conference on Network Protocols.

[10]  Kristofer S. J. Pister,et al.  Industrial Routing Requirements in Low-Power and Lossy Networks , 2009, RFC.

[11]  Randy H. Katz,et al.  Measuring and Reducing Energy Consumption of Network Interfaces in Hand-Held Devices (Special Issue on Mobile Computing) , 1997 .

[12]  Sofie Pollin,et al.  Harmful Coexistence Between 802.15.4 and 802.11: A Measurement-based Study , 2008, 2008 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom 2008).

[13]  Gilles Thonet,et al.  WIRELESS AD-HOC NETWORKS FOR INDUSTRIAL AUTOMATION: CURRENT TRENDS AND FUTURE PROSPECTS , 2005 .

[14]  V. Lecuire,et al.  Performance study of IEEE 802.15.4 for industrial maintenance applications , 2008, 2008 IEEE International Conference on Industrial Technology.

[15]  Feng Xia,et al.  Wireless Sensor/Actuator Network Design for Mobile Control Applications , 2007, Sensors.

[16]  Wook Hyun Kwon,et al.  IEEE 802.11b Performance Analysis in the Presence of IEEE 802.15.4 Interference , 2007, IEICE Trans. Commun..