iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.1007/978-3-319-93659-8_77
A Deep Q-Network Based Simulation System for Actor Node Mobility Control in WSANs Considering Three-Dimensional Environment: A Comparison Study for Normal and Uniform Distributions | SpringerLink
Skip to main content
Springer Nature Link
Log in

A Deep Q-Network Based Simulation System for Actor Node Mobility Control in WSANs Considering Three-Dimensional Environment: A Comparison Study for Normal and Uniform Distributions

  • Conference paper
  • First Online:
Complex, Intelligent, and Software Intensive Systems (CISIS 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 772))

Included in the following conference series:

  • 1426 Accesses

Abstract

A Wireless Sensor and Actor Network (WSAN) is a group of wireless devices with the ability to sense physical events (sensors) or/and to perform relatively complicated actions (actors), based on the sensed data shared by sensors. This paper presents design and implementation of a simulation system based on Deep Q-Network (DQN) for actor node mobility control in WSANs. DQN is a deep neural network structure used for estimation of Q-value of the Q-learning method. We implemented the proposed simulating system by Rust programming language. We evaluated the performance of proposed system for normal and uniform distributions of events considering three-dimensional environment. For this scenario, the simulation results show that for normal distribution of events and the best episode all actor nodes are connected.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Akyildiz, I.F., Kasimoglu, I.H.: Wireless sensor and actor networks: research challenges. Ad Hoc Netw. 2(4), 351–367 (2004)

    Article  Google Scholar 

  2. Krishnakumar, S.S., Abler, R.T.: Intelligent actor mobility in wireless sensor and actor networks. In: IFIP WG 6.8 1st International Conference on Wireless Sensor and Actor Networks (WSAN 2007), pp. 13–22 (2007)

    Google Scholar 

  3. Sir, M.Y., Senturk, I.F., Sisikoglu, E., Akkaya, K.: An optimization-based approach for connecting partitioned mobile sensor/actuator networks. In: IEEE Conference on Computer Communications Workshops, pp. 525–530 (2011)

    Google Scholar 

  4. Younis, M., Akkaya, K.: Strategies and techniques for node placement in wireless sensor networks: a survey. Ad-Hoc Netw. 6(4), 621–655 (2008)

    Article  Google Scholar 

  5. Liu, H., Chu, X., Leung, Y.-W., Du, R.: Simple movement control algorithm for bi-connectivity in robotic sensor networks. IEEE J. Sel. Areas Commun. 28(7), 994–1005 (2010)

    Article  Google Scholar 

  6. Abbasi, A., Younis, M., Akkaya, K.: Movement-assisted connectivity restoration in wireless sensor and actor networks. IEEE Trans. Parallel Distrib. Syst. 20(9), 1366–1379 (2009)

    Article  Google Scholar 

  7. Akkaya, K., Senel, F., Thimmapuram, A., Uludag, S.: Distributed recovery from network partitioning in movable sensor/actor networks via controlled mobility. IEEE Trans. Comput. 59(2), 258–271 (2010)

    Article  MathSciNet  Google Scholar 

  8. Costanzo, C., Loscri, V., Natalizio, E., Razafindralambo, T.: Nodes self-deployment for coverage maximization in mobile robot networks using an evolving neural network. Comput. Commun. 35(9), 1047–1055 (2012)

    Article  Google Scholar 

  9. Li, Y., Li, H., Wang, Y.: Neural-based control of a mobile robot: a test model for merging biological intelligence into mechanical system. In: IEEE 7th Joint International Information Technology and Artificial Intelligence Conference (ITAIC 2014), pp. 186–190 (2014)

    Google Scholar 

  10. Oda, T., Obukata, R., Ikeda, M., Barolli, L., Takizawa, M.: Design and implementation of a simulation system based on deep Q-network for mobile actor node control in wireless sensor and actor networks. In: The 31st IEEE International Conference on Advanced Information Networking and Applications Workshops (IEEE WAINA 2017) (2017)

    Google Scholar 

  11. Llaria, A., Terrasson, G., Curea, O., Jiménez, J.: Application of wireless sensor and actuator networks to achieve intelligent microgrids: a promising approach towards a global smart grid deployment. Appl. Sci. 6(3), 61–71 (2016)

    Article  Google Scholar 

  12. Kulla, E., Oda, T., Ikeda, M., Barolli, L.: SAMI: a sensor actor network Matlab implementation. In: The 18th International Conference on Network-Based Information Systems (NBiS 2015), pp. 554–560 (2015)

    Google Scholar 

  13. Kruger, J., Polajnar, D., Polajnar, J.: An open simulator architecture for heterogeneous self-organizing networks. In: Canadian Conference on Electrical and Computer Engineering (CCECE 2006), pp. 754–757 (2006)

    Google Scholar 

  14. Akbas, M., Turgut, D.: APAWSAN: actor positioning for aerial wireless sensor and actor networks. In: IEEE 36th Conference on Local Computer Networks (LCN 2011), pp. 563–570 (2011)

    Google Scholar 

  15. Akbas, M., Brust, M., Turgut, D.: Local positioning for environmental monitoring in wireless sensor and actor networks. In: IEEE 35th Conference on Local Computer Networks (LCN 2010), pp. 806–813 (2010)

    Google Scholar 

  16. Melodia, T., Pompili, D., Gungor, V., Akyildiz, I.: Communication and coordination in wireless sensor and actor networks. IEEE Trans. Mob. Comput. 6(10), 1126–1129 (2007)

    Article  Google Scholar 

  17. Gungor, V., Akan, O., Akyildiz, I.: A real-time and reliable transport (rt2) protocol for wireless sensor and actor networks. IEEE/ACM Trans. Netw. 16(2), 359–370 (2008)

    Article  Google Scholar 

  18. Mo, L., Xu, B.: Node coordination mechanism based on distributed estimation and control in wireless sensor and actuator networks. J. Control Theor. Appl. 11(4), 570–578 (2013)

    Article  MathSciNet  Google Scholar 

  19. Selvaradjou, K., Handigol, N., Franklin, A., Murthy, C.: Energy-efficient directional routing between partitioned actors in wireless sensor and actor networks. IET Commun. 4(1), 102–115 (2010)

    Article  Google Scholar 

  20. Kantaros, Y., Zavlanos, M.M.: Communication-aware coverage control for robotic sensor networks. In: IEEE Conference on Decision and Control, pp. 6863–6868 (2014)

    Google Scholar 

  21. Melodia, T., Pompili, D., Akyldiz, I.: Handling mobility in wireless sensor and actor networks. IEEE Trans. Mob. Comput. 9(2), 160–173 (2010)

    Article  Google Scholar 

  22. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015)

    Article  Google Scholar 

  23. Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., Riedmiller, M.: Playing Atari with deep reinforcement learning. arXiv:1312.5602v1, pp. 1–9 (2013)

  24. Lei, T., Ming, L.: A robot exploration strategy based on Q-learning network. In: IEEE International Conference on Real-time Computing and Robotics (RCAR 2016), pp. 57–62 (2016)

    Google Scholar 

  25. Riedmiller, L.: Neural fitted Q iteration - first experiences with a data efficient neural reinforcement learning method. In: The 16th European Conference on Machine Learning (ECML 2005). Lecture Notes in Computer Science, vol. 3720, pp. 317–328 (2005)

    Chapter  Google Scholar 

  26. Lange, S., Riedmiller, M.: Deep auto-encoder neural networks in reinforcement learning. In: The 2010 International Joint Conference on Neural Networks (IJCNN 2010), pp. 1–8 (2010)

    Google Scholar 

  27. Kaelbling, L.P., Littman, M.L., Cassandra, A.R.: Planning and acting in partially observable stochastic domains. Artif. Intell. 101(1–2), 99–134 (1998)

    Article  MathSciNet  Google Scholar 

  28. Lin, L.J.: Reinforcement learning for robots using neural networks. Technical Report, DTIC Document (1993)

    Google Scholar 

  29. The Rust Programming Language (2007). https://www.rust-lang.org/

  30. GitHub - rust-lang/rust: A safe, concurrent, practical language (2007). https://github.com/rust-lang/

  31. ‘rust’ tag wiki - Stack Overflow (2007). http://stackoverflow.com/tags/rust/info/

  32. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: The 13th International Conference on Artificial Intelligence and Statistics (AISTATS 2010), pp. 249–256 (2010)

    Google Scholar 

  33. Glorot, X., Borde, A., Bengio, Y.: Deep sparse rectifier neural networks. In: The 14th International Conference on Artificial Intelligence and Statistics (AISTATS 2011), pp. 315–323 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information and Computer Engineering, Okayama University of Science (OUS), 1-1 Ridaicho, Kita-ku, Okayama, 700-0005, Japan

    Tetsuya Oda, Elis Kulla & Kengo Katayama

  2. Department of Information and Communication Engineering, Fukuoka Institute of Technology (FIT), 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka, 811-0295, Japan

    Makoto Ikeda & Leonard Barolli

Authors
  1. Tetsuya Oda
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elis Kulla
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kengo Katayama
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Makoto Ikeda
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Leonard Barolli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tetsuya Oda .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology , Fukuoka, Japan

    Leonard Barolli

  2. Department of Computer Science, COMSATS Institute of Information Technology , Islamabad, Pakistan

    Nadeem Javaid

  3. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology , Fukuoka, Japan

    Makoto Ikeda

  4. Department of Advanced Sciences, Hosei University , Tokyo, Japan

    Makoto Takizawa

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Oda, T., Kulla, E., Katayama, K., Ikeda, M., Barolli, L. (2019). A Deep Q-Network Based Simulation System for Actor Node Mobility Control in WSANs Considering Three-Dimensional Environment: A Comparison Study for Normal and Uniform Distributions. In: Barolli, L., Javaid, N., Ikeda, M., Takizawa, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2018. Advances in Intelligent Systems and Computing, vol 772. Springer, Cham. https://doi.org/10.1007/978-3-319-93659-8_77

Download citation

Publish with us

Policies and ethics

Search

Navigation