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Context-Aware Spectrum Decision and Prediction Using Crowd-Sensing

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Abstract

The ever-increasing demand for the wireless communications specially in sub-6 GHz frequency ranges has led to radio resource scarcity where opportunistic spectrum access is its main solution. An online spectrum decision and prediction system can assist cognitive radio users in seeking idle frequency bands for opportunistic use. However, previous studies have not considered the use of crowd-sensing technique to collect spectrum and contextual information to present a hybrid spectrum decision/prediction service. In this paper, we propose a novel cloud-based service for spectrum availability decision and prediction, which brings more contextual parameters into the decision with the aim of improving the quality of decision. Location, time, and velocity of sensing nodes, the density of buildings around sensing nodes, and weather status have been considered as context information. In the proposed method, spectrum availability data and some of the mentioned context parameters are collected through crowd-sensing. Artificial neural network (ANN) classifiers are suggested to decide about the status of spectrum bands in the proposed architecture. We also propose a spectrum prediction service in our architecture to predict the future of spectrum bands and recommend ANN and k-nearest neighbor algorithms for prediction. The proposed architecture has been implemented and evaluated. Experimental results show that using the addressed contextual information, the quality of spectrum availability decision is improved.

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References

  1. Internet of Things (IoT). (2015). Number of Connected Devices Worldwide from 2012 to 2020 (in billions), Statista. [Online]. Retrieved April 10, 2017, from https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/

  2. Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6(4), 13–18.

    Article  Google Scholar 

  3. Chakraborty, A., Rahman, M. S., Gupta, H., & Das, S. R. (2017). SpecSense crowdsensing for efficient querying of spectrum occupancy. In IEEE conference on computer communications (INFOCOM 2017), Atlanta.

  4. Shinde, S. C., & Jadhav, A. N. (2016). Centralized cooperative spectrum sensing with energy detecion in cognitive radio and optimization. In IEEE international conference on recent trends in electronics, information & communication technology (RTEICT), Bangalore.

  5. Noorshams, N., Malboubi, M., & Bahai, A. (2010). Centralized and decentralized cooperative spectrum sensing in cognitive radio networks: A novel approach. In IEEE eleventh international workshop on signal processing advances in wireless communications (SPAWC), Marrakech.

  6. Fu, Y., Li, Z., Liu, D., & Liu, Q. (2014). Implementation of centralized cooperative spectrum sensing based on USRP. In International conference on logistics, engineering, management and computer science (LEMCS), Shenyang.

  7. Bi, Y., Jing, X., Sun, S., & Huang, H. (2016). Hierarchical fusion-based cooperative spectrum sensing scheme in cognitive radio networks. In 16th International symposium on communications and information technologies (ISCIT), Qingdao.

  8. Rajkumari, R., & Marchang, N. (2018). Binary decision-based collaborative spectrum sensingin infrastructure-less cognitive radio network. Annals of Telecommunications, 73(11–12), 677–688.

    Article  Google Scholar 

  9. Yin, W., & Chen, H. (2020). Decision-driven time-adaptive spectrumsensing in cognitive radio networks. IEEE Transactions on Wireless Communications, 19(4), 2756–2769.

    Article  Google Scholar 

  10. Di, L., Ding, X., Li, M., & Wan, Q. (2019). Spectrum sensing in cognitive radio based on hidden semi-Markov model. In International conference on advanced hybrid information processing.

  11. Wang, X., Umehira, M., Akimoto, M., Han, B., & Zhou, H. (2023). Green spectrum sharing framework in B5G era by exploiting crowdsensing. IEEE Transactions On Green Communications And Networking, 7(2), 916–927.

    Article  Google Scholar 

  12. Li, L., Xie, W., & Zhou, X. (2023). Cooperative spectrum sensing based on LSTM-CNN combination network in cognitive radio system. IEEE Access, 11, 87615–87625.

    Article  Google Scholar 

  13. Wang, Y., Ma, S., Chen, Q., Zhuang, J., & Jiang, D. (2023). A geodesic projection-based data fusion scheme for cooperative spectrum sensing. Digital Signal Processing, 137, 104006.

    Article  Google Scholar 

  14. Zhao, F., Zeng, Y., Zhang, X., & Zhou, K. (2018). Research of radio frequency channel occupancy prediction based on decision tree. In International conference on transportation & logistics, information & communication, smart city (TLICSC 2018).

  15. Rawat, D. B., Reddy, S., Sharma, N., & Bista, B. B. (2015). Cloud-assisted GPS-driven dynamic spectrum access in cognitive radio vehicular networks for transportation cyber physical systems. In IEEE wireless communications and networking conference (WCNC), New Orleans, LA.

  16. Rawat, D. B., Shetty, S., & Raza, K. (2014). Game theoretic dynamic spectrum access in cloud-based cognitive radio networks. In IEEE international conference on cloud engineering (IC2E), Boston, MA.

  17. Rawat, D. B., Bajracharya, C., & Grant, S. (2017). nROAR: Near real-time opportunistic spectrum access and management in cloud-based database-driven cognitive radio networks. IEEE Transactions on Network and Service Management, 14(3), 745–755.

    Article  Google Scholar 

  18. Du, K., Wan, P., Wang, Y., Ai, X., & Chen, H. (2020). Spectrum sensing method based on information geometry and deep neural network. Entropy, 22(1), 94–107.

    Article  MathSciNet  Google Scholar 

  19. Min, A. W., & Shin, K. G. (2009). Impact of mobility on spectrum sensing in cognitive radio networks. In ACM workshop on cognitive radio networks (CoRoNet'09), Beijing.

  20. Luomala, J., & Hakala, I. (2015). Effects of temperature and humidity on radio signal strength in outdoor wireless sensor networks. In Federated conference on computer science and information systems (FedCSIS), Lodz.

  21. Harwood, M. (2009). CompTIA Network+ N10-004 Exam CRAM. Que Publishing Company.

  22. Xiang, A., Yang, P., Tian, C., Zhang, L., Lin, H., Xiao, F., Zhang, M., & Liu, Y. (2016). CARM: Crowd-sensing accurate outdoor RSS maps with error-prone smartphone measurements. IEEE Transactions on Mobile Computing, 15(11), 2669–2681.

    Article  Google Scholar 

  23. Ding, G., Wang, J., Wu, Q., Zhang, L., Zou, Y., Yao, Y.-D., & Chen, Y. (2014). Robust spectrum sensing with crowd sensors. IEEE Transactions on Communications, 62(9), 3129–3143.

    Article  Google Scholar 

  24. Wu, Q., Ding, G., Du, Z., Sun, Y., Jo, M., & Vasilakos, A. V. (2016). A cloud-based architecture for the internet of spectrum devices over future wireless networks. IEEE Access, 4, 2854–2862.

    Article  Google Scholar 

  25. Syed, T. S., & Safdar, G. A. (2017). History-assisted energy efficient spectrum sensing for infrastructure-based cognitive radio networks. IEEE Transactions on Vehicular Technology, 66(3), 2462–2473.

    Article  Google Scholar 

  26. Shirvani, H., & Shahgholi Ghahfarokhi, B. (2019). Cloud-based context-aware spectrum availability monitoring and prediction using crowd-sensing. In Cognitive radio, mobile communications and wireless networks. Springer.

  27. Gupta, M., Verma, G., & Dubey, R. K. (2016). Cooperative spectrum sensing for cognitive radio based on adaptive threshold. In Second international conference on Computational Intelligence & Communication Technology (CICT), Ghaziabad.

  28. Edwin, K., & Walingo, T. (2016). Optimal fusion techniques for cooperative spectrum sensing in cognitive radio networks. In International conference on advances in computing and communication engineering (ICACCE), Durban.

  29. Nagendra, B. V. T., Hota, C., & Raghurama, G. (2016). Cooperative spectrum sensing in cognitive radio: An archetypal clustering approach. In International conference on wireless communications, signal processing and networking (WiSPNET), Chennai.

  30. Yau, K. L. A., Komisarczuk, P., & Teal, P. D. (2009). A context-aware and intelligent dynamic channel selection scheme for cognitive radio networks. In 4th International conference on cognitive radio oriented wireless networks and communications (CROWNCOM'09), Hannover.

  31. Perez, J., & Santamaria, I. (2018). Adaptive clustering algorithm for cooperative spectrum sensing in mobile environments. In IEEE international conference on acoustics, speech and signal processing (ICASSP), Calgary.

  32. Perez, J., Santamaria, I., & Via, J. (2018). Adaptive EM-based algorithm for cooperative spectrum sensing in mobile environments. In IEEE statistical signal processing workshop (SSP), Freiburg.

  33. Chakraborty, A., Bhattacharya, A., Kamal, S., Das, S. R., Gupta, H., & Djuric, P. M. (2018). Spectrum patrolling with crowdsourced spectrum sensors. In IEEE INFOCOM 2018—IEEE conference on computer communications, Honolulu.

  34. Zhai, L., Wang, H., & Liu, C. (2017). Distributed schemes for crowdsourcing-based sensing task assignment in cognitive radio networks. Wireless Communications and Mobile Computing, 2017, 66.

    Google Scholar 

  35. Lv, X., & Zhu, Q. (2018). A crowd cooperative spectrum sensing algorithm using a non-ideal channel. Algorithms, 11(4), 51–60.

    Article  MathSciNet  Google Scholar 

  36. Van den Bergh, B., Giustiniano, D., Cordobes, H., Fuchs, M., Calvo-Palomino, R., Pollin, S., Rajendran, S., & Lenders, V. (2017). Electrosense: Crowdsourcing spectrum monitoring. In IEEE international symposium on dynamic spectrum access networks (DySPAN), Piscataway.

  37. Marz, N., & Warren, J. (2015). Big Data: Principles and best practices of scalable real-time data systems. Manning Publications Co.

  38. Rajendran, S., Calvo-Palomino, R., Fuchs, M., Van den Bergh, B., Cordobes, H., Giustiniano, D., Pollin, S., & Lenders, V. (2018). Electrosense: Open and big spectrum data. IEEE Communications Magazine, 56(1), 210–217.

    Article  Google Scholar 

  39. Kleber, N., Chisum, J., Striegel, A., Hochwald, B., Termos, A., Laneman, J. N., Fu, Z., & Merritt, J. (2016). RadioHound: A pervasive sensing network for Sub-6 GHz dynamic spectrum monitoring, arXiv preprint arXiv:1610.06212.

  40. Li, X., Zhu, Q., Xia, W., & Chen, Y. (2023). A resource-efficient green paradigm for crowdsensing based spectrum detection in internet of things networks. IEICE Transactions on Communications, E106-B(3), 275–286.

    Article  Google Scholar 

  41. Tumuluru, V. K., Wang, P., & Niyato, D. (2010). Channel status prediction for cognitive radio networks. Wireless Communications and Mobile Computing, 12(10), 862–874.

    Article  Google Scholar 

  42. Gohider, N. (2016). Context augmented spectrum sensing in cognitive radio networks. Master Thesis, University of Waterloo.

  43. Chen, D., Yin, S., Zhang, Q., Liu, M., & Li, S. (2012). Mining spectrum usage data a large-scale spectrum measurement study. IEEE Transactions on Mobile Computing, 11(6), 1033–1046.

    Article  Google Scholar 

  44. Madushan Thilina, K. G., Saquib, N., Choi, K. W., & Hossain, E. (2013). Machine learning techniques for cooperative spectrum sensing in cognitive radio networks. Journal on Selected Areas in Communications IEEE, 31(11), 2209–2221.

    Article  Google Scholar 

  45. Madushan Thilina, K. G., Choi, K. W., Saquib, N., & Hossain, E. (2012). Pattern classification techniques for cooperative spectrum sensing in cognitive radio networks: SVM and W-KNN approaches. In IEEE global communications conference (GLOBECOM), Anaheim.

  46. Uyanik, G. S., Canberk, B., & Oktug, S. (2012). Predictive spectrum decision mechanisms in cognitive radio networks. In IEEE globecom workshops (GC Wkshps), Anaheim.

  47. Zhao, Y., Hong, Z., Luo, Y., Wang, G., & Pu, L. (2017). Prediction-based spectrum management in cognitive radio networks. IEEE Systems, 99, 1–12.

    Google Scholar 

  48. Riahi Manesh, M., Subramaniam, S., Reyes-Moncayo, H., & Kaabouch, N. (2017). Real-time spectrum occupancy monitoring using a probabilistic model. Computer Networks, 124, 87–96.

    Article  Google Scholar 

  49. Huk, M., & Mizera-Pietraszko, J. (2015). Contextual neural-network based spectrum prediction for cognitive radio. In Future generation communication technology (FGCT), Luton.

  50. Sun, J., Wang, J., Ding, G., Shen, L., Yang, J., Wu, Q., & Yu, L. (2018). Long-term spectrum state prediction: An image inference perspective. IEEE Access, 6, 43489–43498.

    Article  Google Scholar 

  51. Chen, S., Shen, B., Wang, X., & Yoo, S.-J. (2020). Geo-location information aided spectrum sensingin cellular cognitive radio networks. Sensors, 20(1), 213.

    Article  Google Scholar 

  52. Supraja, P., Gayathri, V. M., & Pitchai, R. (2019). Optimized neural network for spectrum prediction using genetic algorithm in cognitive radio networks. Cluster Computing, 22(1), 157–163.

    Article  Google Scholar 

  53. Xing, L., Li, M., Wan, Y., & Wan, Q. (2019). Spectrum prediction in cognitive radio based on sequence to sequence neural network. In International conference on advanced hybrid information processing.

  54. Jain, S., Goel, A., & Arora, P. (2019). Spectrum prediction using time delay neural network in cognitive radio network. In Smart innovations in communication and computational sciences, Singapore.

  55. Du, K., Wan, P., Wang, Y., Ai, X., & Chen, H. (2020). Spectrum sensing method based on information geometry and deep neural network. Entropy, 22(1), 94–107.

    Article  MathSciNet  Google Scholar 

  56. Zhang, S., Wang, Y., Li, J., Wan, P., Zhang, Y., & Li, N. (2019). A cooperative spectrum sensing method based on information geometry and fuzzy C-means clustering algorithm. EURASIP Journal on Wireless Communications and Networking, 1, 17.

    Article  Google Scholar 

  57. Zhang, Y., Wan, P., Zhang, S., Wang, Y., & Li, N. (2017). A spectrum sensing method based on signal feature and clustering algorithm in cognitive wireless multimedia sensor networks. Advances in Multimedia, 6, 66.

    Google Scholar 

  58. Guo, L., Lu, J., An, J., & Yang, K. (2024). DSIL: An effective spectrum prediction framework against spectrum concept drift. IEEE Transactions on Cognitive Communications and Networking. https://doi.org/10.1109/TCCN.2024.3355430

    Article  Google Scholar 

  59. Ghosh, A., Kasera, S., & Van Der Merwe, J. (2023). Spectrum usage analysis and prediction using long short-term memory networks. In Proceedings of the 24th international conference on distributed computing and networking (pp. 270–279).

  60. Tidjani, S., Hammoudi, Z., & Moad, M. E. (2022). Low complexity multichannel spectrum prediction algorithm based on optimized neural network for spectrum allocation in cognitive radio internet of things. Transactions on Emerging Telecommunications Technologies, 33(9), 66.

    Article  Google Scholar 

  61. Kumar, A., Thakur, P., Pandit, S., & Singh, G. (2022). HSA-SPC: Hybrid spectrum access with spectrum prediction and cooperation for performance enhancement of multiuser cognitive radio network. Computer Networks, 203, 108596.

    Article  Google Scholar 

  62. Wu, G., Zhou, F., Ding, G., Wu, Q., & Li, X. Y. (2023). An efficient heterogeneous edge-cloud learning framework for spectrum data compression. IEEE Transactions on Mobile Computing, 22(7), 3823–3839.

    Article  Google Scholar 

  63. Li, R., & Li, J. (2014). A novel clouds based spectrum monitoring approach for future monitoring network. In 2nd International conference on systems and informatics (ICSAI), Shanghai.

  64. Google APIs for Android. (2016). Google Inc. [Online]. Retrieved November 25, 2016, from https://developers.google.com/android/

  65. OpenWeatherMap. (2016). Current weather and forecast. OpenWeatherMap Inc., [Online]. Retrieved November 28, 2016, from https://openweathermap.org/

  66. Shirvani, H., & Shahgholi Ghahfarokhi, B. (2018). OpenSAMP Dataset. University of Isfahan.

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Authors and Affiliations

  1. Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran

    Hussein Shirvani & Behrouz Shahgholi Ghahfarokhi

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  1. Hussein Shirvani
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  2. Behrouz Shahgholi Ghahfarokhi
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Contributions

Hussein Shirvani: Methodology, Software, Validation, Investigation, Writing—original draft, Visualization. Behrouz Shahgholi Ghahfarokhi: Conceptualization, Methodology, Validation, Investigation, Writing—original draft, Visualization, Supervision, Project administration.

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Correspondence to Behrouz Shahgholi Ghahfarokhi.

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Shirvani, H., Ghahfarokhi, B.S. Context-Aware Spectrum Decision and Prediction Using Crowd-Sensing. Wireless Pers Commun 135, 593–617 (2024). https://doi.org/10.1007/s11277-024-11076-5

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