International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12
line decor
  
line decor

 Volume 8, Issue 1 (January 2021), Pages: 31-40

----------------------------------------------

 Original Research Paper

 Title: Internet of radar things for cognitive robotics

 Author(s): Muhannad Almutiry *

 Affiliation(s):

 Electrical Engineering Department, Northern Border University, Arar, Saudi Arabia

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-3862-3129

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2021.01.005

 Abstract:

Adapting Radar into industry become popular due to the enhancement of computational and communication systems. Industry 4.0 opens the door to use 5G connection to provide effective communication between things in the industry-the concept of combining industry 4.0 and radar sensors to exceed the conventional radar application. The idea of this paper is to propose a novel scheme to connect radar into one network to be an internet of radar Things (IoRT). In this scheme, we will allow radars to communicate as one sensor that processes the data sequentially to support the right decision to be made by robotics, especially when the radar sensor is mounted in robotics. Experimental data are presented to validate the process. 

 © 2020 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Radar, Sensors, RF tomography, Internet of things

 Article History: Received 3 June 2020, Received in revised form 23 August 2020, Accepted 23 August 2020

 Acknowledgment:

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

  Almutiry M (2021). Internet of radar things for cognitive robotics. International Journal of Advanced and Applied Sciences, 8(1): 31-40

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9 Fig. 10 Fig. 11 

 Tables

 Table 1 

----------------------------------------------

 References (45)

  1. Almutiry M, Lo Monte L, and Wicks MC (2017). Extraction of weak scatterer features based on multipath exploitation in radar imagery. International Journal of Antennas and Propagation, 2017: 5847872. https://doi.org/10.1155/2017/5847872   [Google Scholar]
  2. Bailey T and Durrant-Whyte H (2006). Simultaneous localization and mapping (SLAM): Part II. IEEE Robotics and Automation Magazine, 13(3): 108-117. https://doi.org/10.1109/MRA.2006.1678144   [Google Scholar]
  3. Baker CJ and Hume AL (2003). Netted radar sensing. IEEE Aerospace and Electronic Systems Magazine, 18(2): 3-6. https://doi.org/10.1109/MAES.2003.1183861   [Google Scholar]
  4. Batth RS, Nayyar A, and Nagpal A (2018). Internet of robotic things: Driving intelligent robotics of future-concept, architecture, applications and technologies. In the 4th International Conference on Computing Sciences (ICCS), IEEE, Jalandhar, India: 151-160. https://doi.org/10.1109/ICCS.2018.00033   [Google Scholar]
  5. Botvinick EL and Berns MW (2005). Internet‐based robotic laser scissors and tweezers microscopy. Microscopy Research and Technique, 68(2): 65-74. https://doi.org/10.1002/jemt.20216   [Google Scholar] PMid:16228982
  6. Ding G, Wu Q, Zhang L, Lin Y, Tsiftsis TA, and Yao YD (2018). An amateur drone surveillance system based on the cognitive internet of things. IEEE Communications Magazine, 56(1): 29-35. https://doi.org/10.1109/MCOM.2017.1700452   [Google Scholar]
  7. Durrant-Whyte H and Bailey T (2006). Simultaneous localization and mapping: Part I. IEEE Robotics and Automation Magazine, 13(2): 99-110. https://doi.org/10.1109/MRA.2006.1638022   [Google Scholar]
  8. Falcone P and La R (2012). WiFi-based passive ISAR for high resolution cross-range profiling of moving targets WiFi-based passive ISAR processing scheme. In the 9th European conference on synthetic aperture radar, Nuremberg, Germany: 279–282. https://doi.org/10.1109/RADAR.2012.6212229   [Google Scholar]
  9. Fiorini L, Limosani R, Coviello L, Vitanza A, D'onofrio G, Greco F, and Cavallo F (2018). Design and development of a robotic sensorized handle for monitoring older adult grasping force. In the 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), IEEE, Enschede, Netherlands: 1095-1100. https://doi.org/10.1109/BIOROB.2018.8487649   [Google Scholar]
  10. Fong DY (2017). Wireless sensor networks. In: Geng H (Ed.), Internet of things and data analytics handbook: 197-213. John Wiley and Sons, Hoboken, USA. https://doi.org/10.1002/9781119173601.ch12   [Google Scholar] PMCid:PMC5730082
  11. Fue KG, Porter WM, Barnes EM, and Rains GC (2020). An extensive review of mobile agricultural robotics for field operations: Focus on cotton harvesting. AgriEngineering, 2(1): 150-174. https://doi.org/10.3390/agriengineering2010010   [Google Scholar]
  12. Gao J, Liu F, Ning H, and Wang B (2007). RFID coding, name and information service for internet of things. In the IET Conference on Wireless, Mobile and Sensor Networks, Shanghai, China: 36–39.   [Google Scholar]
  13. Giusto D, Iera A, Morabito G, and Atzori L (2010). The internet of things. In the 20th Tyrrhenian Workshop on Digital Communications, Springer, Berlin, Germany. https://doi.org/10.1007/978-1-4419-1674-7   [Google Scholar]
  14. Griffiths HD and Baker CJ (2006). Fundamentals of tomography and radar. In: Byrnes J and Ostheimer G (Eds.), Advances in sensing with security applications: 171-187. Springer, Dordrecht, Netherlands. https://doi.org/10.1007/1-4020-4295-7_08   [Google Scholar]
  15. He S and Chan SHG (2016). Wi-Fi fingerprint-based indoor positioning: Recent advances and comparisons. IEEE Communications Surveys and Tutorials, 18(1): 466-490. https://doi.org/10.1109/COMST.2015.2464084   [Google Scholar]
  16. Hu H, Yu L, Tsui PW, and Zhou Q (2001). Internet‐based robotic systems for teleoperation. Assembly Automation, 21(2): 143-152. https://doi.org/10.1108/01445150110388513   [Google Scholar]
  17. Jia S and Takase K (2002). Internet-based robotic system using CORBA as communication architecture. Journal of Intelligent and Robotic Systems, 34(2): 121-134. https://doi.org/10.1023/A:1015603327545   [Google Scholar]
  18. Jorge VA, Rey VF, Maffei R, Fiorini SR, Carbonera JL, Branchi F, and Kolberg M (2015). Exploring the IEEE ontology for robotics and automation for heterogeneous agent interaction. Robotics and Computer-Integrated Manufacturing, 33: 12-20. https://doi.org/10.1016/j.rcim.2014.08.005   [Google Scholar]
  19. Kadir WMHW, Samin RE, and Ibrahim BSK (2012). Internet controlled robotic arm. Procedia Engineering, 41: 1065-1071. https://doi.org/10.1016/j.proeng.2012.07.284   [Google Scholar]
  20. Khan Z, Lehtomaki JJ, Iellamo SI, Vuohtoniemi R, Hossain E, and Han Z (2017). IoT connectivity in radar bands: A shared access model based on spectrum measurements. IEEE Communications Magazine, 55(2): 88-96. https://doi.org/10.1109/MCOM.2017.1600444CM   [Google Scholar]
  21. Larranaga J, Muguira L, Lopez-Garde JM, and Vazquez JI (2010). An environment adaptive ZigBee-based indoor positioning algorithm. In the International Conference on Indoor Positioning and Indoor Navigation, IEEE, Zurich, Switzerland: 1-8. https://doi.org/10.1109/IPIN.2010.5647828   [Google Scholar]
  22. Malanowski M and Kulpa K (2012). Two methods for target localization in multistatic passive radar. IEEE Transactions on Aerospace and Electronic Systems, 48(1): 572-580. https://doi.org/10.1109/TAES.2012.6129656   [Google Scholar]
  23. Manzi A, Moschetti A, Limosani R, Fiorini L, and Cavallo F (2018). Enhancing activity recognition of self-localized robot through depth camera and wearable sensors. IEEE Sensors Journal, 18(22): 9324-9331. https://doi.org/10.1109/JSEN.2018.2869807   [Google Scholar]
  24. Metcalf J, Blunt SD, and Himed B (2015). A machine learning approach to cognitive radar detection. In the IEEE Radar Conference, IEEE, Arlington, USA: 1405-1411. https://doi.org/10.1109/RADAR.2015.7131215   [Google Scholar]
  25. Michael MP and Darianian M (2008). Architectural solutions for mobile RFID services for the internet of things. In the IEEE Congress on Services-Part I, IEEE, Honolulu, USA: 71-74. https://doi.org/10.1109/SERVICES-1.2008.33   [Google Scholar]
  26. Monte LL, Erricolo D, Soldovieri F, and Wicks MC (2010b). Radio frequency tomography for tunnel detection. IEEE Transactions on Geoscience and Remote Sensing, 48(3): 1128-1137. https://doi.org/10.1109/TGRS.2009.2029341   [Google Scholar]
  27. Monte LL, Patton LK, and Wicks MC (2010a). Direct-path mitigation for underground imaging in RF tomography. In the International Conference on Electromagnetics in Advanced Applications, IEEE, Sydney, Australia: 27-30. https://doi.org/10.1109/ICEAA.2010.5651674   [Google Scholar]
  28. Olivadese D, Giusti E, Petri D, Martorella M, Capria A, and Berizzi F (2013). Passive ISAR with DVB-t signals. IEEE Transactions on Geoscience and Remote Sensing, 51(8): 4508-4517. https://doi.org/10.1109/TGRS.2012.2236339   [Google Scholar]
  29. Pastina D, Sedehi M, and Cristallini D (2010). Passive bistatic ISAR based on geostationary satellites for coastal surveillance. In the 2010 IEEE Radar Conference, IEEE, Washington, USA: 865-870. https://doi.org/10.1109/RADAR.2010.5494500   [Google Scholar]
  30. Prestes E, Carbonera JL, Fiorini SR, Jorge VA, Abel M, Madhavan R, and Chibani A (2013). Towards a core ontology for robotics and automation. Robotics and Autonomous Systems, 61(11): 1193-1204. https://doi.org/10.1016/j.robot.2013.04.005   [Google Scholar]
  31. Qiu W, Giusti E, Bacci A, Martorella M, Berizzi F, Zhao HZ, and Fu Q (2013). Compressive sensing for passive ISAR with DVB-T signal. In the 14th International Radar Symposium, IEEE, Dresden, Germany, 1: 113-118.   [Google Scholar]
  32. Rawat P, Singh KD, and Bonnin JM (2016). Cognitive radio for M2M and internet of things: A survey. Computer Communications, 94: 1-29. https://doi.org/10.1016/j.comcom.2016.07.012   [Google Scholar]
  33. Ray PP (2016). Internet of robotic things: Concept, technologies, and challenges. IEEE Access, 4: 9489-9500. https://doi.org/10.1109/ACCESS.2017.2647747   [Google Scholar]
  34. Razafimandimby C, Loscri V, and Vegni AM (2016). A neural network and IoT based scheme for performance assessment in internet of robotic things. In the 1st International Conference on Internet-of-Things Design and Implementation, IEEE, Berlin, Germany: 241-246. https://doi.org/10.1109/IoTDI.2015.10   [Google Scholar]
  35. Salahuddin MA (2015). Introduction to wireless sensor networks. In: Benhaddou D and Al-Fuqaha A (Eds.), Wireless sensor and mobile ad-hoc networks: 3-32. Springer, New York, USA. https://doi.org/10.1007/978-1-4939-2468-4_1   [Google Scholar]
  36. Schlenoff C, Prestes E, Madhavan R, Goncalves P, Li H, Balakirsky S, and Miguelanez E (2012). An IEEE standard ontology for robotics and automation. In the IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Vilamoura, Portugal: 1337-1342. https://doi.org/10.1109/IROS.2012.6385518   [Google Scholar]
  37. Skiani ED, Mitilineos SA, and Thomopoulos SCA (2012). A study of the performance of wireless sensor networks operating with smart antennas. IEEE Antennas and Propagation Magazine, 54(3): 50-67. https://doi.org/10.1109/MAP.2012.6293950   [Google Scholar]
  38. Tan DK, Sun H, Lu Y, Lesturgie M, and Chan HL (2005). Passive radar using global system for mobile communication signal: Theory, implementation and measurements. IEE Proceedings-Radar, Sonar and Navigation, 152(3): 116-123. https://doi.org/10.1049/ip-rsn:20055038   [Google Scholar]
  39. Uradzinski M, Guo H, Liu X, and Yu M (2017). Advanced indoor positioning using ZigBee wireless technology. Wireless Personal Communications, 97(4); 6509-6518. https://doi.org/10.1007/s11277-017-4852-5   [Google Scholar]
  40. Vermesan O and Bacquet J (2017). Cognitive hyperconnected digital transformation: Internet of things intelligence evolution. In: Vermesan O and Bacquet J (Eds.), Cognitive hyperconnected digital transformation: Internet of things intelligence evolution: 97–155. River Publishers, Copenhagen, Denmark. https://doi.org/10.13052/rp-9788793609105   [Google Scholar]
  41. Wicks MC (2007). RF tomography with application to ground penetrating radar. In the Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers, IEEE, Pacific Grove, USA: 2017-2022. https://doi.org/10.1109/ACSSC.2007.4487591   [Google Scholar]
  42. Wu Q, Ding G, Xu Y, Feng S, Du Z, Wang J, and Long K (2014). Cognitive internet of things: A new paradigm beyond connection. IEEE Internet of Things Journal, 1(2): 129-143. https://doi.org/10.1109/JIOT.2014.2311513   [Google Scholar]
  43. Wu TY, Liaw GH, Huang SW, Lee WT, and Wu CC (2012). A GA-based mobile RFID localization scheme for internet of things. Personal and Ubiquitous Computing, 16(3): 245-258. https://doi.org/10.1007/s00779-011-0398-9   [Google Scholar]
  44. Zafari F, Gkelias A, and Leung KK (2019). A survey of indoor localization systems and technologies. IEEE Communications Surveys and Tutorials, 21(3): 2568-2599. https://doi.org/10.1109/COMST.2019.2911558   [Google Scholar]
  45. Zhuang Y, Hua L, Qi L, Yang J, Cao P, Cao Y, and Haas H (2018). A survey of positioning systems using visible LED lights. IEEE Communications Surveys and Tutorials, 20(3): 1963-1988. https://doi.org/10.1109/COMST.2018.2806558   [Google Scholar]