Embedded Intelligent Objects in Food Logistics Technical Limits of Local Decision Making

  • Reiner Jedermann
  • Javier Palafox-Albarran
  • Amir Jabarri
  • Walter Lang


The efficiency of transport monitoring systems in the supply chain of food products can be improved by autonomous control, which means that decentralized intelligent objects have the ability to process information, to render, and to execute decisions. In our example the supervision and data evaluation tasks are distributed in a network of wireless sensors as local decision platforms. The supervision network can also include semi-passive RFID tags. The application of such battery powered embedded devices is limited by the reliability and range of communication as well as by the required energy resources. Autonomous control helps to overcome the first restriction. Communication is reduced and the system is less dependent from unreliable network links, but the power required for calculation increases the total energy consumption. In this paper the communication limitation of passive UHF RFID and active wireless sensors were analyzed by laboratory experiments and field tests in sea containers. Several algorithms for local data evaluation by autonomous control were evaluated on typical target systems for wireless units. Calculation times and the resulting energy consumption were measured and compared with the energy that is required for communication.


Sensor Node Wireless Sensor Network Wireless Sensor Node Data Transfer Rate Kriging Variance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Barrenetxea G, Ingelrest F, Schaefer G, Vetterli M (2008) Wireless sensor networks for environmental monitoring: the sensorscope experience. Paper presented at the 20th IEEE international Zurich seminar on communications (IZS 2008), Zurich, Switzerland, pp 98–101Google Scholar
  2. 2.
    Becker M, Wenning B-L, Görg C, Jedermann R, Timm-Giel A (2010) Logistic applications with wireless sensor networks. In: HotEmNets 2010, IrelandGoogle Scholar
  3. 3.
    Böse F, Windt K (2007) Catalogue of criteria for autonomous control in logistics. In: Hülsmann M, Windt K (eds) Understanding autonomous cooperation and control – the impact of autonomy on management, information, communication, and material flow. Springer, Berlin, pp 57–72. doi:10.1007/978–3–540–47450–0_5Google Scholar
  4. 4.
    Botts M, Percivall G, Reed C, Davidson J (2007) OGC sensor web enablement: overview and high level architecture. Open geospatial consortium white paper. Retrieved from: Scholar
  5. 5.
    Callaway EH (2004) Wireless sensor networks – architectures and protocols. Auerbach Publication, London, New YorkGoogle Scholar
  6. 6.
    Clarke RH, Twede D, Tazelaar JR, Boyer KK (2006) Radio frequency identification (RFID) performance: the effect of tag orientation and package contents. Packag Technol Sci 19(1):45–54. doi:10.1002/pts.714CrossRefGoogle Scholar
  7. 7.
    Cole KS, Cole RH (1941) Dispersion and adsorption in dielectrics. J Chem Phys 9:341–351CrossRefGoogle Scholar
  8. 8.
    Crossbow (2005) TelosB mote platform. Product datasheet. Retrieved from:
  9. 9.
    Crossbow (2007) IMote2 – high-performance wireless sensor network node. Product datasheet. Retrieved from:
  10. 10.
    Demirkol I, Ersoy C, Alagoz F (2006) MAC protocols for wireless sensor networks: a survey. IEEE Commun Mag 44(4):115–121CrossRefGoogle Scholar
  11. 11.
    Guo F (2004) A new identification method for Wiener and Hammerstein systems. FZKA 6955. Forschungszentrum Karlsruhe, GermanyGoogle Scholar
  12. 12.
    IEEE Computer Society (2006) Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (WPANs). vol Standard 802 Part 15.4Google Scholar
  13. 13.
    Jabbari A, Jedermann R, Muthuraman R, Lang W (2009) Application of neurocomputing for data approximation and classification in wireless sensor networks. Sensor J 9:3056–3077CrossRefGoogle Scholar
  14. 14.
    Jabbari A, Kreowski H-J, Lang W (2010) Application of bio-inspired data processing in intelligent transportation systems. Paper presented at the intelligent systems (IS), 2010 5th IEEE international conference, London, UK, 7–9 July 2010, pp 315–320. doi:10.1109/IS.2010.5548366Google Scholar
  15. 15.
    James SJ, James C, Evans JA (2006) Modelling of food transportation systems – a review. Int J Refrigeration 29(6):947–957. doi:10.1016/j.ijrefrig.2006.03.017CrossRefGoogle Scholar
  16. 16.
    Jedermann R, Becker M, Görg C, Lang W (2011) Testing network protocols and signal attenuation in packed food transports. Int J Sensor Netw (IJSNet)Google Scholar
  17. 17.
    Jedermann R, Edmond JP, Lang W (2008) Shelf life prediction by intelligent RFID. Paper presented at the dynamics in logistics. first international conference, LDIC 2007, Berlin/Heidelberg, pp 231–238. doi:10.1007/978–3–540–76862–3_22Google Scholar
  18. 18.
    Jedermann R, Lang W (2009) The minimum number of sensors - Interpolation of spatial temperature profiles. Paper presented at the wireless sensor networks, 6th European conference, EWSN 2009, lecture notes in computer science (LNCS), Berlin/Heidelberg, pp 232–246. doi:10.1007/978–3–642–00224–3_15Google Scholar
  19. 19.
    Jedermann R, Moehrke A, Lang W (2010) Supervision of banana transport by the intelligent container. Paper presented at the Coolchain-management, 4th international workshop, Bonn, Germany, pp 75–84Google Scholar
  20. 20.
    Jedermann R, Stein K, Becker M, Lang W (2008) UHF-RFID in the food chain – from identification to smart labels. In: Kreyenschmidt J (ed) Coldchain manangement. 3rd International Workshop, Bonn, pp 3–15Google Scholar
  21. 21.
    Jennic (2006) Calculating 802.15.4 data rates. Application Note JN-AN-1035 Retrieved from:−15−4+Data+Rates-1v0.pdf
  22. 22.
    Meshnetics (2008) ZigBit Amp OEM Modules – Ultra-Compact 2.4 GHz 802.15.4/ZigBee Modules with Power Amplifier for Wireless Networking Applications. Product datasheet, Revision 2.2. Available from:
  23. 23.
    Palafox-Albarrán J, Jederman R, Lang W (2010) Prediction of temperature inside a refrigerated container in the presence of perishable goods. In: 7th International conference on informatics in control, automation and robotics (ICINCO), PortugalGoogle Scholar
  24. 24.
    Regattieri A, Gamberi M, Manzini R (2007) Traceability of food products: general framework and experimental evidence. J Food Eng 81(2):347–356. doi:10.1016/j.jfoodeng.2006.10.032CrossRefGoogle Scholar
  25. 25.
    Ruiz-Garcia L, Barreiro P, Robla JI, Lunadei L (2010) Testing ZigBee motes for monitoring refrigerated vegetable transportation under real conditions. Sensors 10(5):4968–4982. doi:10.3390/s100504968CrossRefGoogle Scholar
  26. 26.
    Schneider M, Kroner A (2008) The smart pizza packing – an application of object memories. In: 4th International conference on intelligent environments, 2008 IET. Seattle, WA, USA,pp 1–8Google Scholar
  27. 27.
    Tijskens LMM (2004) Discovering the future: modelling quality matters. Ph.D. thesis, University of Wageningen, NetherlandsGoogle Scholar
  28. 28.
    Tsironi TE, Gogou P, Taoukis PS (2008) Chill chain management and shelf life optimization of MAP seabream fillets: a TTI based alternative to FIFO. Paper presented at the Coldchain management. 3rd International workshop, Bonn, Germany,pp 83–89Google Scholar
  29. 29.
    Umer ML, Tanin E (2010) Spatial interpolation in wireless sensor networks: localized algorithms for variogram modeling and Kriging. GeoInformatica 14(1):101–134. doi:10.1007/s10707–009–0078–3CrossRefGoogle Scholar
  30. 30.
    Wackernagel H (2003) Multivariate geostatistics - an introduction with applications. Springer, BerlinzbMATHCrossRefGoogle Scholar
  31. 31.
    Walkowski AC (2008) Model based optimization of mobile Geosensor networks. In: Bernard L, Friis-Christensen A, Pundt H (eds) The European Information Society – taking geoinformation science one step further. Lecture notes in geoinformation and cartography. Springer, Berlin Heidelberg, pp 51–66. doi:10.1007/978–3–540–78946–8_3Google Scholar
  32. 32.
    Wang X, Jabbari A, Laur R, Lang W (2010) Dynamic control of data measurement intervals in a networked sensing system using neurocomputing. Paper presented at the international conference on networked sensing systems (INSS 2010), Kassel, GermanyGoogle Scholar
  33. 33.
    Wessels A, Jederman R, Lang W (2010) Embedded context aware objects for the transport supervision of perishable goods. Paper presented at the recent advances in elektronics, hardware, wireless and optical communications, Cambridge, pp 119–125Google Scholar
  34. 34.
    Zaknich A (2005) Principles of adaptive filters and self-learning systems. Advanced textbooks in control and signal processing. Springer, BerlinGoogle Scholar
  35. 35.
    Zweig SE (2008) LifeTrack technology for smart active-label visual and RFID product lifetime monitoring. Paper presented at the coldchain management, 3rd international workshop, Bonn, Germany, 29–36Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Reiner Jedermann
    • 1
  • Javier Palafox-Albarran
    • 1
  • Amir Jabarri
    • 1
  • Walter Lang
    • 1
  1. 1.Microsystems Center BremenUniversity of BremenBremenGermany

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