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A Sensor Query and Tasking Language Approach to Event Driven Programming in Sensor Networks


Journal of Computer and Communication Networks

Received On : 16 April 2025

Revised On : 30 May 2025

Accepted On : 25 June 2025

Published On : 10 July 2025

Volume 01, 2025

Pages : 140-150


Abstract

Sensor Query and Tasking Language (SQTL) is a computational scripting language that is used to enhance the communication between middleware and sensor applications of a sensor network. It provides versatility and a compact script for receiving sensor data and utilizing hardware characteristics, event management, and coordination of the events between the connected sensor nodes. In this paper, the necessity of SQTL as a part of the system that is critical for the work of the sensor networks is discussed. In the sensor applications, SQTL is a programming interface to the SINA middleware so that sensor messages can be easily connected and scripted in simple and concise ways. The programming language has procedural and object-oriented components and offers basic building blocks for accessing the hardware of the sensors, identifying the location, communication, and handling events. It is important to note that SQTL has the capacity to handle events in parallel and thus the sensor nodes can respond to messages, timers and timeouts. The advanced SQTL wrapper using XML syntax enables message transmission and execution between the sensor nodes effectively with better resource utilization. This paper reviews the constructs of SQTL, SEE and the constructs available in SEE and describes their role and importance in executing program and managing resources. Lastly, it provides an illustration of two sample applications that show how SQTL can be used in real life scenarios such as distributing maximum temperature detection and harmonized car tracking.

Keywords

Information Driven Sensor Querying, Sensor Execution Environment, Constrained Anisotropic Diffusion Routing, Sensor Query and Tasking Language.

  1. M. A. Mahmood, W. K. G. Seah, and I. Welch, “Reliability in wireless sensor networks: A survey and challenges ahead,” Computer Networks, vol. 79, pp. 166–187, Mar. 2015, doi: 10.1016/j.comnet.2014.12.016.
  2. S. Ivanov, S. Balasubramaniam, D. Botvich, and O. B. Akan, “Gravity gradient routing for information delivery in fog Wireless Sensor Networks,” Ad Hoc Networks, vol. 46, pp. 61–74, Aug. 2016, doi: 10.1016/j.adhoc.2016.03.011.
  3. G. Anastasi, M. Conti, M. Di Francesco, and A. Passarella, “Energy conservation in wireless sensor networks: A survey,” Ad Hoc Networks, vol. 7, no. 3, pp. 537–568, May 2009, doi: 10.1016/j.adhoc.2008.06.003.
  4. J. Dong, D. Zhuang, Y. Huang, and J. Fu, “Advances in Multi-Sensor Data Fusion: Algorithms and Applications,” Sensors, vol. 9, no. 10, pp. 7771–7784, Sep. 2009, doi: 10.3390/s91007771.
  5. H. Y. Teh, A. W. Kempa-Liehr, and K. I.-K. Wang, “Sensor data quality: a systematic review,” Journal of Big Data, vol. 7, no. 1, Feb. 2020, doi: 10.1186/s40537-020-0285-1.
  6. J. Granjal, E. Monteiro, and J. S. Silva, “Security in the integration of low-power Wireless Sensor Networks with the Internet: A survey,” Ad Hoc Networks, vol. 24, pp. 264–287, Jan. 2015, doi: 10.1016/j.adhoc.2014.08.001.
  7. G. Amato, S. Chessa, C. Gennaro, and C. Vairo, “Querying moving events in wireless sensor networks,” Pervasive and Mobile Computing, vol. 16, pp. 51–75, Jan. 2015, doi: 10.1016/j.pmcj.2014.01.008.
  8. D. Díaz Pardo de Vera, Á. Sigüenza Izquierdo, J. Bernat Vercher, and L. Hernández Gómez, “A Ubiquitous Sensor Network Platform for Integrating Smart Devices into the Semantic Sensor Web,” Sensors, vol. 14, no. 6, pp. 10725–10752, Jun. 2014, doi: 10.3390/s140610725.
  9. H.-J. So and T. A. Brush, “Student perceptions of collaborative learning, social presence and satisfaction in a blended learning environment: Relationships and critical factors,” Computers & Education, vol. 51, no. 1, pp. 318–336, Aug. 2008, doi: 10.1016/j.compedu.2007.05.009.
  10. N. Nasser and Y. Chen, “SEEM: Secure and energy-efficient multipath routing protocol for wireless sensor networks,” Computer Communications, vol. 30, no. 11–12, pp. 2401–2412, Sep. 2007, doi: 10.1016/j.comcom.2007.04.014.
  11. H. A. Simon, “Bounded Rationality and Organizational Learning,” Organization Science, vol. 2, no. 1, pp. 125–134, Feb. 1991, doi: 10.1287/orsc.2.1.125.
  12. M. Chu, H. Haussecker, and Feng Zhao, “Scalable Information-Driven Sensor Querying and Routing for Ad Hoc Heterogeneous Sensor Networks,” The International Journal of High Performance Computing Applications, vol. 16, no. 3, pp. 293–313, Aug. 2002, doi: 10.1177/10943420020160030901.
  13. N. Sadagopan, B. Krishnamachari, and A. Helmy, “Active query forwarding in sensor networks,” Ad Hoc Networks, vol. 3, no. 1, pp. 91–113, Jan. 2005, doi: 10.1016/j.adhoc.2003.08.001.
  14. D. Niculescu, “Communication paradigms for sensor networks,” IEEE Communications Magazine, vol. 43, no. 3, pp. 116–122, Mar. 2005, doi: 10.1109/mcom.2005.1404605.
  15. S. Shekarpour, E. Marx, A.-C. Ngonga Ngomo, and S. Auer, “SINA: Semantic interpretation of user queries for question answering on interlinked data,” Journal of Web Semantics, vol. 30, pp. 39–51, Jan. 2015, doi: 10.1016/j.websem.2014.06.002.
  16. V. A. Epanechnikov, “Non-Parametric Estimation of a Multivariate Probability Density,” Theory of Probability & Its Applications, vol. 14, no. 1, pp. 153–158, Jan. 1969, doi: 10.1137/1114019.
  17. G. Chatzimilioudis, A. Cuzzocrea, D. Gunopulos, and N. Mamoulis, “A novel distributed framework for optimizing query routing trees in wireless sensor networks via optimal operator placement,” Journal of Computer and System Sciences, vol. 79, no. 3, pp. 349–368, May 2013, doi: 10.1016/j.jcss.2012.09.013.
  18. Y. Murasawa, “Measuring the natural rates, gaps, and deviation cycles,” Empirical Economics, vol. 47, no. 2, pp. 495–522, Sep. 2013, doi: 10.1007/s00181-013-0747-9.
  19. A. Deshpande, C. Guestrin, S. R. Madden, J. M. Hellerstein, and W. Hong, “Model-based approximate querying in sensor networks,” The VLDB Journal, vol. 14, no. 4, pp. 417–443, Oct. 2005, doi: 10.1007/s00778-005-0159-3.
  20. Chien-Chung Shen, C. Srisathapornphat, and C. Jaikaeo, “Sensor information networking architecture and applications,” IEEE Personal Communications, vol. 8, no. 4, pp. 52–59, 2001, doi: 10.1109/98.944004.
  21. A. Buckley et al., “LHAPDF6: parton density access in the LHC precision era,” The European Physical Journal C, vol. 75, no. 3, Mar. 2015, doi: 10.1140/epjc/s10052-015-3318-8.
  22. B. Sebastian and P. Ben-Tzvi, “Physics Based Path Planning for Autonomous Tracked Vehicle in Challenging Terrain,” Journal of Intelligent & Robotic Systems, vol. 95, no. 2, pp. 511–526, Apr. 2018, doi: 10.1007/s10846-018-0851-3.
  23. H. Antonson, S. Mårdh, M. Wiklund, and G. Blomqvist, “Effect of surrounding landscape on driving behaviour: A driving simulator study,” Journal of Environmental Psychology, vol. 29, no. 4, pp. 493–502, Dec. 2009, doi: 10.1016/j.jenvp.2009.03.005.
  24. O. Iqbal, V. I. T. Muro, S. Katoch, A. Spanias, and S. Jayasuriya, “Adaptive Subsampling for ROI-Based Visual Tracking: Algorithms and FPGA Implementation,” IEEE Access, vol. 10, pp. 90507–90522, 2022, doi: 10.1109/access.2022.3200755.
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Cite this Article

Kanev Boris Lisitsa, “A Sensor Query and Tasking Language Approach to Event Driven Programming in Sensor Networks”, Journal of Computer and Communication Networks, pp. 140-150, 2025, doi: 10.64026/JCCN/2025014.

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© 2025 Kanev Boris Lisitsa. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.