Revisiting Slotted ALOHA: Density Adaptation in FANETs | Wireless Personal Communications Skip to main content
Springer Nature Link
Log in

Revisiting Slotted ALOHA: Density Adaptation in FANETs

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Unmanned aerial vehicles have been widely used in many areas of life. They communicate with each other or infrastructure to provide ubiquitous coverage or assist cellular and sensor networks. They construct flying ad hoc networks. One of the most significant problems in such networks is communication among them over a shared medium. Using random channel access techniques is a useful solution. Another important problem is that the variations in the density of these networks impact the quality of service and introduce many challenges. This paper presents a novel density-aware technique for flying ad hoc networks. We propose Density-aware Slotted ALOHA Protocol that utilizes slotted ALOHA with a dynamic random access probability determined using network density in a distributed fashion. Compared to the literature, this paper concentrates on proposing a three-dimensional, easily traceable model and stabilize the channel utilization performance of slotted ALOHA with an optimized channel access probability to its maximum theoretical level, 1/e, where e is the Euler’s number. Monte-Carlo simulation results validate the proposed approach leveraging aggregate interference density estimator under the simple path-loss model. We compare our protocol with two existing protocols, which are Slotted ALOHA and Stabilized Slotted ALOHA. Comparison results show that the proposed protocol has 36.78% channel utilization performance; on the other hand, the other protocols have 24.74% and 30.32% channel utilization performances, respectively. Considering the stable results and accuracy, this model is practicable in highly dynamic networks even if the network is sparse or dense under higher mobility and reasonable non-uniform deployments.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Pakrooh, Rambod, & Bohlooli, Ali. (2021). A survey on unmanned aerial vehicles-assisted internet of things: A service-oriented classification. Wireless Personal Communications, 119(2), 1541–1575.

    Article  Google Scholar 

  2. Alzahrani, B., Oubbati, O. S., Barnawi, A., Atiquzzaman, M., & Alghazzawi, D. (2020). UAV assistance paradigm: State-of-the-art in applications and challenges. Journal of Network and Computer Applications, 166, 102706.

    Article  Google Scholar 

  3. Guillen-Perez, A., & Cano, M.-D. (2018). Flying ad hoc networks: A new domain for network communications. Sensors, 18(10), 3571.

    Article  Google Scholar 

  4. Chriki, Amira, Touati, Haifa, Snoussi, Hichem, & Kamoun, Farouk. (2019). FANET: Communication, mobility models and security issues. Computer Networks, 163, 106877.

    Article  Google Scholar 

  5. De Francesco, C., De Giovanni, L., & Palazzi, C. E. (2018). The interference-aware drone ad-hoc relay network configuration problem. Electronic Notes in Discrete Mathematics, 69, 317–324.

    Article  MathSciNet  Google Scholar 

  6. Khan, S. K., Farasat, M., Naseem, U., & Ali, F. (2020). Performance evaluation of next-generation wireless (5G) UAV relay. Wireless Personal Communications, 113(2), 945–960.

    Article  Google Scholar 

  7. Zafar, W., & Khan, B. M. (2017). A reliable, delay bounded and less complex communication protocol for multicluster FANETs. Digital Communications and Networks, 3(1), 30–38.

    Article  Google Scholar 

  8. Khan, M.A., Safi, A., Qureshi, I.M., & Khan, I.U. (2017). Flying ad-hoc networks (FANETs): A review of communication architectures, and routing protocols. In 2017 First International Conference on Latest trends in Electrical Engineering and Computing Technologies (INTELLECT) (pp. 1–9).

  9. Seddik, M., Toldov, V., Clavier, L., & Mitton, N. (2018). From outage probability to ALOHA MAC layer performance analysis in distributed WSNs. In 2018 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1–6).

  10. Bekmezci, I., Sen, I., & Erkalkan, E. (2015). Flying ad hoc networks (FANET) test bed implementation. In 2015 7th International Conference on Recent Advances in Space Technologies (RAST) (pp. 665–668).

  11. Hussain, A., Hussain, T., Faisal, F., Ali, I., Khalil, I., Nazir, S., & Khan, H.U. (2021). Dlsa: Delay and link stability aware routing protocol for flying ad-hoc networks (FANCETs). Wireless Personal Communications.

  12. Marconato, E.A., Rodrigues, M., de Melo Pires, R., Pigatto, D.F., Filho, L.C.Q., Pinto, A.R., & Branco, K.R.L.J.C. (2017). AVENS - a novel flying ad hoc network simulator with automatic code generation for unmanned aircraft system. In HICSS.

  13. Oubbati, O. S., Atiquzzaman, M., Lorenz, P., Tareque, M. H., & Hossain, M. S. (2019). Routing in flying ad hoc networks: Survey, constraints, and future challenge perspectives. IEEE Access, 7, 81057–81105.

    Article  Google Scholar 

  14. Kashyap, K. K., Agrawal, A., et al. (2018). FANET: Survey on design challenges, application scenario and communication protocols. IJRAR-International Journal of Research and Analytical Reviews (IJRAR), 5(4), 1024–1032.

    Google Scholar 

  15. Shen, Z., Zhang, X., Zhang, M., Li, W., & Yang, D. (2017). Self-sorting-based MAC protocol for high-density vehicular ad hoc networks. IEEE Access, 5, 7350–7361.

    Article  Google Scholar 

  16. Rossi, G.V., Leung, K.K., & Gkelias, A. (2015). Density-based optimal transmission for throughput enhancement in vehicular ad-hoc networks. In 2015 IEEE International Conference on Communications (ICC) (pp. 6571–6576).

  17. Mollahasani, Shahram, Eroğlu, Alperen, Demirkol, Ilker, & Onur, Ertan. (2020). Density-aware mobile networks: Opportunities and challenges. Computer Networks, 175, 107271.

    Article  Google Scholar 

  18. Singh, K., & Verma, A. K. (2020). TBCS: A trust based clustering scheme for secure communication in flying ad-hoc networks. Wireless Personal Communications, 114, 3173–3196.

    Article  Google Scholar 

  19. Fotouhi, Azade, Ding, Ming, & Hassan, Mahbub. (2021). Dronecells: Improving spectral efficiency using drone-mounted flying base stations. Journal of Network and Computer Applications, 174, 102895.

    Article  Google Scholar 

  20. Handouf, S., Sabir, E., & Sadik, M. (2018). Energy-throughput tradeoffs in ubiquitous flying radio access network for IoT. In 2018 IEEE 4th World Forum on Internet of Things (WF-IoT) (pp. 320–325).

  21. Kang, SeokYoon, Aldwairi, Monther, & Kim, Ki.-Il. (2016). A survey on network simulators in three-dimensional wireless ad hoc and sensor networks. International Journal of Distributed Sensor Networks, 12(9), 1550147716664740.

    Article  Google Scholar 

  22. Mozaffari, M., Saad, W., Bennis, M., Nam, Y., & Debbah, M. (2019). A tutorial on UAVs for wireless networks: Applications, challenges, and open problems. IEEE Communications Surveys Tutorials, 21(3), 2334–2360.

    Article  Google Scholar 

  23. Eroğlu, Alperen, Yaman, Okan, & Onur, Ertan. (2019). Density-aware cellular coverage control: Interference-based density estimation. Computer Networks, 165, 106922.

    Article  Google Scholar 

  24. Khan, M. A., Qureshi, I. M., & Khanzada, F. (2019). A hybrid communication scheme for efficient and low-cost deployment of future flying ad-hoc network (FANET). Drones, 3(1), 16.

    Article  Google Scholar 

  25. Jawhar, Imad, Mohamed, Nader, Al-Jaroodi, Jameela, Agrawal, Dharma P., & Zhang, Sheng. (2017). Communication and networking of UAV-based systems: Classification and associated architectures. Journal of Network and Computer Applications, 84, 93–108.

    Article  Google Scholar 

  26. Zheng, Z., Sangaiah, A. K., & Wang, T. (2018). Adaptive communication protocols in flying ad hoc network. IEEE Communications Magazine, 56(1), 136–142.

    Article  Google Scholar 

  27. Verma, P. K., Verma, R., Prakash, A., Tripathi, R., & Naik, K. (2016). A novel hybrid medium access control protocol for inter-M2M communications. Journal of Network and Computer Applications, 75, 77–88.

    Article  Google Scholar 

  28. Hernandez, A., Vazquez-Gallego, F., Alonso, L., & Alonso-Zarate, J. (2015). Performance evaluation of frame slotted ALOHA with intra-frame and inter-frame successive interference cancellation. In 2015 IEEE Global Communications Conference (GLOBECOM) (pp. 1–6).

  29. Sun, J., Liu, R., & Paolini, E. (2019). A dynamic access probability adjustment strategy for coded random access schemes. Sensors, 19(19), 4206.

    Article  Google Scholar 

  30. Woo, Tai-Kuo. (2019). FRAM: Framed ALOHA for 5G super real-time multimedia random access with packet slicing. Wireless Personal Communications, 106(3), 1253–1273.

    Article  Google Scholar 

  31. Laya, A., Kalalas, C., Vazquez-Gallego, F., Alonso, L., & Alonso-Zarate, J. (2016). Goodbye, ALOHA! IEEE Access, 4, 2029–2044.

    Article  Google Scholar 

  32. Paolini, E., Liva, G., & Chiani, M. (2015). Coded slotted ALOHA: A graph-based method for uncoordinated multiple access. IEEE Transactions on Information Theory, 61(12), 6815–6832.

    Article  MathSciNet  MATH  Google Scholar 

  33. Casini, E., De Gaudenzi, R., & Del Rio Herrero, O. (2007). Contention resolution diversity slotted ALOHA (CRDSA): An enhanced random access schemefor satellite access packet networks. IEEE Transactions on Wireless Communications, 6(4), 1408–1419.

    Article  Google Scholar 

  34. Yoo, S., & Kim, K. (2018). Analysis of fairness problem for IEEE 802.15. 6 slotted ALOHA algorithm. Wireless Personal Communications, 102(1), 559–581.

    Article  Google Scholar 

  35. Deng, Der-Jiunn., & Tsao, Hsuan-Wei. (2011). Optimal dynamic framed slotted ALOHA based anti-collision algorithm for RFID systems. Wireless Personal Communications, 59(1), 109–122.

    Article  Google Scholar 

  36. Nguyen, C. T., Hayashi, K., Kaneko, M., Popovski, P., & Sakai, H. (2013). Probabilistic dynamic framed slotted ALOHA for RFID tag identification. Wireless Personal Communications, 71(4), 2947–2963.

    Article  Google Scholar 

  37. Liva, G. (2011). Graph-based analysis and optimization of contention resolution diversity slotted ALOHA. IEEE Transactions on Communications, 59(2), 477–487.

    Article  Google Scholar 

  38. Yao, Z., Li, V. O. K., & Cao, Z. (2004). Maximum throughput analysis and enhancement of slotted ALOHA for multihop ad hoc networks. In 2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577), 7, 4162–4166.

  39. Li, Yitong, & Dai, Lin. (2018). Maximum sum rate of slotted ALOHA with successive interference cancellation. IEEE Transactions on Communications, 66(11), 5385–5400.

    Article  Google Scholar 

  40. Park, P., Ergen, S. C., Fischione, C., Lu, C., & Johansson, K. H. (2018). Wireless network design for control systems: A survey. IEEE Communications Surveys Tutorials, 20(2), 978–1013. Secondquarter.

    Article  Google Scholar 

  41. Alassery, F., Ahmed, W.K.M., & Lawrence, V. (2015). MDSA: Multi-dimensional slotted ALOHA MAC protocol for low-collision high-throughput wireless communication systems. In 2015 36th IEEE Sarnoff Symposium (pp. 179–184).

  42. Guo, W., Wang, S., Chu, X., Zhang, J., Chen, J., & Song, H. (2013). Automated small-cell deployment for heterogeneous cellular networks. IEEE Communications Magazine, 51(5), 46–53.

    Article  Google Scholar 

  43. Babich, F., & Comisso, M. (2019). Impact of segmentation and capture on slotted ALOHA systems exploiting interference cancellation. IEEE Transactions on Vehicular Technology, 68(3), 2878–2892.

    Article  Google Scholar 

  44. Clazzer, F., Kissling, C., & Marchese, M. (2018). Enhancing contention resolution ALOHA using combining techniques. IEEE Transactions on Communications, 66(6), 2576–2587.

    Article  Google Scholar 

  45. Gandino, F., Ferrero, R., Montrucchio, B., & Rebaudengo, M. (2011). Probabilistic DCS: An RFID reader-to-reader anti-collision protocol. Journal of Network and Computer Applications, 34(3), 821–832.

    Article  Google Scholar 

  46. Paolini, Enrico, Stefanovic, Cedomir, Liva, Gianluigi, & Popovski, Petar. (2015). Coded random access: applying codes on graphs to design random access protocols. IEEE Communications Magazine, 53, 144–150.

    Article  Google Scholar 

  47. Liao, Chien-Hsing., Woo, Tai-Kuo., Chen, Chi-Chung., & Jiunn, Su. (2017). A novel grouping slotted ALOHA scheme to enhance throughput performance for wireless networks. Wireless Personal Communications, 96(1), 1229–1243.

    Article  Google Scholar 

  48. Jingrui, Su., Ren, Guangliang, & Zhao, Bo. (2021). Noma-based coded slotted aloha for machine-type communications. IEEE Communications Letters, 25(7), 2435–2439.

    Article  Google Scholar 

  49. Munari, Andrea. (2021). Modern random access: An age of information perspective on irregular repetition slotted aloha. IEEE Transactions on Communications, 69(6), 3572–3585.

    Article  Google Scholar 

  50. Zhang, Wenbo, Wang, Xin, Han, Guangjie, Peng, Yan, Guizani, Mohsen, & Sun, Jingyi. (2021). A load-adaptive fair access protocol for MAC in underwater acoustic sensor networks. Journal of Network and Computer Applications, 173, 102867.

    Article  Google Scholar 

  51. Richter, Y., & Bergel, I. (2018). Optimal and suboptimal routing based on partial CSI in random ad-hoc networks. IEEE Transactions on Wireless Communications, 17(4), 2815–2826.

    Article  Google Scholar 

  52. Sun, J., Liu, R., & Paolini, E. (2018). Detecting the number of active users in IRSA access protocols. In 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) (pp. 1972–1976).

  53. Sun, J., Liu, R., & Paolini, E. (2018). Detecting the number of active users in coded random access systems. In 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) (pp. 1–7).

  54. Shafiq, Z., Abbas, R., Zafar, M. H., & Basheri, M. (2019). Analysis and evaluation of random access transmission for UAV-Assisted vehicular-to-infrastructure communications. IEEE Access, 7, 12427–12440.

    Article  Google Scholar 

  55. Arribas, E., Mancuso, V., & Cholvi, V. (2019). Coverage optimization with a dynamic network of drone relays. IEEE Transactions on Mobile Computing, 19(10), 2278–2298.

    Article  Google Scholar 

  56. Yan, C., Fu, L., Zhang, J., & Wang, J. (2019). A comprehensive survey on UAV communication channel modeling. IEEE Access, 7, 107769–107792.

    Article  Google Scholar 

  57. Moltchanov, D. (2012). Distance distributions in random networks. Ad Hoc Networks, 10(6), 1146–1166.

    Article  Google Scholar 

  58. Kwak, B.-J., Song, N.-O., & Miller, L. E. (2005). Performance analysis of exponential backoff. IEEE/ACM Transactions on Networking, 13(2), 343–355.

    Article  Google Scholar 

  59. Shen, D., & Li, V. O. K. (2003). Performance analysis for a stabilized multi-channel slotted ALOHA algorithm. In 14th IEEE Proceedings on Personal, Indoor and Mobile Radio Communications, 2003. PIMRC 2003., 1, 249–253.

  60. Tyagi, S., & Jain, P. (2019). Optimization of slotted ALOHA using Q-Learning. In 2019 International Conference on Optical Wireless Technologies (OWT 2019), 01.

  61. Chiaraviglio, L., Cuomo, F., Maisto, M., Gigli, A., Lorincz, J., Zhou, Y., et al. (2016). What is the best spatial distribution to model base station density? A deep dive into two European mobile networks. IEEE Access, 4, 1434–1443.

    Article  Google Scholar 

  62. Zafar, W., & Khan, B. M. (2016). Flying ad-hoc networks: Technological and social implications. IEEE Technology and Society Magazine, 35(2), 67–74.

    Article  Google Scholar 

  63. Sharma, P. K., & Kim, D. I. (2019). Random 3D Mobile UAV networks: Mobility modeling and coverage probability. IEEE Transactions on Wireless Communications, 18(5), 2527–2538.

    Article  Google Scholar 

Download references

Funding

This work was supported by TÜBİTAK, Project 215E127. This paper is a part of my PH.D. Thesis at METU.

Author information

Authors and Affiliations

  1. Department of Software Engineering, Gümüşhane University, 29100, Gümüşhane, Turkey

    Alperen Eroğlu

  2. Department of Computer Engineering, METU, 06800, Ankara, Turkey

    Alperen Eroğlu & Ertan Onur

Authors
  1. Alperen Eroğlu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ertan Onur
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Alperen Eroğlu.

Ethics declarations

Conflicts of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Eroğlu, A., Onur, E. Revisiting Slotted ALOHA: Density Adaptation in FANETs. Wireless Pers Commun 124, 1711–1740 (2022). https://doi.org/10.1007/s11277-021-09428-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-09428-6

Keywords