Zhanshan (Sam) Ma, Axel W. Krings, Robert E. Hiromoto



This is the second article in a two-part series in which we briefly review state-of-the-art research in communications and computing inspired by insect sensory systems. While the previous article focuses on the biological systems, the present one briefly reviews the status of insect-inspired communications and computing from the engineering perspective. We discuss three major application areas: wireless sensor network, robot and micro aerial vehicle (MAV), and non-cooperative behaviours in social insects and their conflict resolution. Despite the enormous advances in insect vision and mechanosensory inspired robot and MAV, micro-flight emulation, motion detection and neuromorphic engineering, etc., the potential inspiration from insect sensory system is far from being fully explored. We suggest the following promising research topics: (1) A new grid computing architecture emulating the neuronal population such as the visual neurons that support the compound eyes, the PN (Projection Neurons) in AL (Antennal Lobe) or the ORN (Olfactory Receptor Neurons) from insect sensory organs (sensilla). This may be further integrated and enhanced with the dendritic neuronal computing. (2) New generation of multimodal wireless sensor and ad-hoc networks that emulates insect chemosensory communication. The inspiration of multimodalities in insect sensory systems also implies that there are multiple parallel networks operating concurrently. Furthermore, the insect chemosensory is significantly robust and dependable with built-in anti-interference mechanisms. (3) Non-cooperative behaviours in social insects may offer insights to complement swarm intelligence (inspired by cooperative behaviours) or to devise new optimization algorithms. It may also provide inspiration for proposing survival selection schemes in evolutionary computing. We suggest using evolutionary game theory to model conflict resolution in social insects, given its success in modelling conflict resolution of other animals.


  1. Alon, U. 2007. An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman.
  2. Amos, M. 2004 (ed.). Cellular Computing. Oxford University Press. 217pp.
  3. Babaoglu, O. and G. Canright et al. 2006. ACM Trans. on Auton. & Adap. Syst. 1(1):26-66.
  4. Blanchard, M. and F. C. Rind, P. F. M. Vershure. 2000. J. Robot. Auto. Syst. 30:16-38.
  5. Bonabeau, Eric. M. Dorigo, G. Theraulaz. 1999. Swarm Intelligence. Oxford University Press. 320pp.
  6. Christensen, T. A. (ed). 2005. Methods in Insect Sensory Neuroscience. CRC Press. 435pp.
  7. Davidson, E. H. 2006. The regulatory genome: gene regulatory networks in development and evolution. Academic Press. 304pp.
  8. Detrain, C., J. L. Deneubourg, and J. M. Pasteels.(eds). 1999. Information Processing in Social Insects. Birkhauser Verlag. 415pp.
  9. Dobson, S., F. Massacci, et al. 2006. ACM Transactions on Autonomous and Adaptive Systems, 1(2): 223-259
  10. Dogaru, R. 2003. Universality and Emergent Computation in Cellular Neural Networks. World Scientific Press.
  11. Dorigo, M., T. Stützle. 2004. Ants Colony Optimization. The MIT Press.
  12. Fife, W. S. and J. K. Archibald. 2007. EURASIP J. on Embedded Systems. (1) 33
  13. Dressler, F. I. Carreras. 2007. Advances in Biologically Inspired Information Systems: Models, Methods, and Tools. Springer, 302pp.
  14. Drosopoulos, S. and M. F. Claridge. (eds). 2006. Insect Sounds and Communication. Taylor & Francis. 532pp.
  15. Galitski, T. 2004. Molecular networks in model systems. Annu. Rev. Genomics Hum. Genet. 2004. 5:177-87
  16. Gullan, P. J. and P. S. Cranston. 2005. The insects: an outline of entomology. 3rd Ed. Blackwell Publishing.
  17. Harrison, R. R. 2000. An analog VLSI motion sensor based on the fly visual system. Ph.D. Dissertation, CalTech, CA.
  18. Ideker, T., T. Galitski1, and L. Hood. 2001. Annu. Rev. Genomics Hum. Genet. 2001. 2:343-72
  19. Karlsson, M., M. Davidson et al. 2004. Annu. Rev. Phys. Chem. 2004. 55:613-49
  20. Koickal, T. J., A. Hamilton, S. L. Tan, J. A. et al. 2007. IEEE Trans. Circuits and Systems.54(1): 60-73.
  21. Klowden, M. J. 2007. Physiological Systems in Insects. 2nd ed. Academic Press.
  22. Liu, S. C., J. Kramer, G. Indiveri, T. Delbrück, R. Douglas2002. Analog VLSI: Circuits and Principles. MIT Press. 472pp.
  23. Lobontui, N, Goldfarb, M., et al. 1999. Design and Analysis of an Elastodynamic Inch-Worm Robotic Insect. IEEE Conference on Robotics and Automation, pp. 2120-2125, May 1999.
  24. Lodding, K. N. 2004. Queue, June 2004:68-75.
  25. London, M. and M. Hausser 2005. Dendritic Computation. Annu. Rev. Neurosci. 2005. 28:503-32
  26. Michelson, R.2002. The Entomopter. "Neurotechnology for Biomimetic Robots", The MIT Press, pp.481-50
  27. Ma, Z., and A. W. Krings. 2007. Insect Sensory Systems Inspired Computing and Communications. Technical Reports, TR-CS-09-01-2007, Computer Science Dept. University of Idaho.
  28. Maynard Smith, J. 1982. Evolution and the Theory of Games. Cambridge University Press.
  29. Miesenbock, G. and I. G. Kevrekidis. 2005. Annu. Rev. Neurosci. 2005. 28:533-63
  30. Motamed, M., and J. Yan. 2005. A review of biological, biomimetic and miniature force sensing for microflight. International Conference on Intelligent Robots and Systems (IROS). 2-6 Page(s): 3939 - 3946.
  31. National Research Council of National Academies. 2005. Catalyzing Inquiry at the Interface of Computing and Biology. The National Academy Press.
  32. Pornsin-sirirak, T. N. Y. C. Taia, et al. 2001. Sensors and Actuators A 89. (2001): 95-103
  33. Ratnieks, F. L. W., K. R. Foster, and T. Wenseleers. 2006. Conflict resolution in insect societies. Annu. Rev. Entomol. 2006. 51:581-608
  34. Ruffer, F. and N. Franceschini, 2004. "Visually guided micro-aerial vehicle: automatic take off, terrain following, landing and wind reaction," in Proceedings of ICRA 2004), vol. 3, pp. 2339-2346.
  35. Rich, R. L. and D. G. Myszka. 2005. J. Mol. Recognit. 18: 431-478
  36. Rind, F. C. 2005. Bioinspired sensors. in. "Methods in Insect Sensory Neuroscience". ed. by T. A. Christensen. CRC Press. pp. 213-235.
  37. Srinivasan. 2003. Journal of Robotic Systems 20(1), 35- 42
  38. Srinivasan, M. V., S. W. Zhang. et al. 2001. Biol. Bull. 200:216-221.
  39. Steltz, E., S. Avadhanula, R.J. Wood, and R. S. Fearing. 2005. Characterization of the Micromechanical Flying Insect by Optical Position Sensing, IEEE Int. Conf. on Robotics and Automation, Barcelona, April 2005.
  40. Stocker, A. A. 2006. Analog VLSI Circuits for the Perception of Visual Motion. Wiley.
  41. Thakoor, S., and N. Cabrol, et al. 2003. J. of Rob. Syst. 20(12), 687-706.
  42. Vogels, T. P., K. Rajan, and L. F. Abbott. 2005. Neural Network Dynamics. Annu. Rev. Neurosci. 28:357-76
  43. Wood, R. J., S. Avadhanula, E. Steltz, M. Seeman, et. al. 2005. Design, Fabrication and Initial Results of a 2g Autonomous Glider. The 31st Annu. Conf., IEEE Indust. Electron. Soc., Raleigh North Carolina.

Paper Citation

in Harvard Style

(Sam) Ma Z., W. Krings A. and E. Hiromoto R. (2008). INSECT SENSORY SYSTEMS INSPIRED COMMUNICATIONS AND COMPUTING (II): AN ENGINEERING PERSPECTIVE . In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2008) ISBN 978-989-8111-18-0, pages 292-297. DOI: 10.5220/0001069602920297

in Bibtex Style

author={Zhanshan (Sam) Ma and Axel W. Krings and Robert E. Hiromoto},
booktitle={Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2008)},

in EndNote Style

JO - Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2008)
SN - 978-989-8111-18-0
AU - (Sam) Ma Z.
AU - W. Krings A.
AU - E. Hiromoto R.
PY - 2008
SP - 292
EP - 297
DO - 10.5220/0001069602920297