Monitoring and Smart Decision Architecture for DRONE-FOG Integrated Environment

  • Wendel Serra Universidade Federal do Sul e Sudeste do Pará - UNIFESSPA
  • Warley Junior Universidade Federal do Sul e Sudeste do Pará - UNIFESSPA
  • Isaac Barros Universidade Federal do Sul e Sudeste do Pará - UNIFESSPA
  • Hugo Kuribayashi Universidade Federal do Sul e Sudeste do Pará - UNIFESSPA
  • João Carmona Universidade Federal do Sul e Sudeste do Pará - UNIFESSPA

Resumo


Due to the limited computing resources of drones, it is difficult to handle computation-intensive tasks locally, hence, fog-based computation offloading has been widely adopted. The effectiveness of an offloading operation, however, is determined by its ability to infer where the execution of code/data represents less computational effort for the drone, so that, by deciding where to offload correctly, the device benefits. Thus, this paper proposes MonDroneFog, a novel fog-based architecture that supports image offloading, as well as monitoring and storing the performance metrics related to the drone, wireless network, and cloudlet. It takes advantage of the main machine-learning algorithms to provide offloading decisions with high levels of accuracy, F1, and G-mean. We evaluate the main classification algorithms under our database and the results show that Multi-Layer Perceptron (MLP) and Logistic Regression classifiers achieve 99.64% and 99.20% accuracy, respectively. Under these conditions, MonDrone-Fog works well in dense forests when weather conditions are favorable and can be useful as a support system for SAR missions by providing a shorter runtime for image operations.
Palavras-chave: UAV, offloading, smart decision, machine-learning

Referências

Albon, C. (2018). Machine learning with python cookbook: Practical solutions from preprocessing to deep learning. " O’Reilly Media, Inc."

Bradski, G. and Kaehler, A. (2008). Learning OpenCV: Computer vision with the OpenCV library. " O’Reilly Media, Inc.".

Giang, N. K., Lea, R., and Leung, V. C. (2020). Developing applications in large scale, dynamic fog computing: A case study. Software: Practice and Experience, 50(5):519–532.

He, D., Qiao, Y., Chan, S., and Guizani, N. (2018). Flight security and safety of drones in airborne fog computing systems. IEEE Communications Magazine, 56(5):66–71

Hou, X., Ren, Z., Cheng, W., Chen, C., and Zhang, H. (2019). Fog based computation offloading for swarm of drones. In ICC 2019-2019 IEEE International Conference on Communications (ICC), pages 1–7. IEEE

Junior, W., Oliveira, E., Santos, A., and Dias, K. (2019). A contextsensitive offloading system using machine-learning classification algorithms for mobile cloud environment. Future Generation Computer Systems, 90:503–520.

Kalatzis, N., Avgeris, M., Dechouniotis, D., PapadakisVlachopapadopoulos, K., Roussaki, I., and Papavassiliou, S. (2018). Edge computing in iot ecosystems for uav-enabled early fire detection. In 2018 IEEE International Conference on Smart Computing (SMARTCOMP), pages 106–114. IEEE.

Luo, C., Nightingale, J., Asemota, E., and Grecos, C. (2015). A uav-cloud system for disaster sensing applications. In 2015 IEEE 81st Vehicular Technology Conference (VTC Spring), pages 1–5. IEEE

Messous, M.-A., Sedjelmaci, H., Houari, N., and Senouci, S.-M. (2017). Computation offloading game for an uav network in mobile edge computing. In 2017 IEEE International Conference on Communications (ICC), pages 1–6. IEEE.

Mohamed, N., Al-Jaroodi, J., Jawhar, I., Noura, H., and Mahmoud, S. (2017). Uavfog: A uav-based fog computing for internet of things. In 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pages 1–8. IEEE.

Motlagh, N. H., Bagaa, M., and Taleb, T. (2017). Uav-based iot platform: A crowd surveillance use case. IEEE Communications Magazine, 55(2):128– 134.

Mukherjee, A., Dey, N., and De, D. (2020). Edgedrone: Qos aware mqtt middleware for mobile edge computing in opportunistic internet of drone things. Computer Communications

Perera, C., Zaslavsky, A., Christen, P., and Georgakopoulos, D. (2013). Context aware computing for the internet of things: A survey. IEEE communications surveys & tutorials, 16(1):414–454.

Premsankar, G., Di Francesco, M., and Taleb, T. (2018). Edge computing for the internet of things: A case study. IEEE Internet of Things Journal, 5(2):1275–1284.

Shahidinejad, A. and Ghobaei-Arani, M. Joint computation offloading and resource provisioning for edge-cloud computing environment: A machine learning-based approach. Software: Practice and Experience.

Shakarami, A., Shahidinejad, A., and Ghobaei-Arani, M. (2020). A review on the computation offloading approaches in mobile edge computing: A gametheoretic perspective. Software: Practice and Experience.

Shakhatreh, H., Sawalmeh, A. H., Al-Fuqaha, A., Dou, Z., Almaita, E., Khalil, I., Othman, N. S., Khreishah, A., and Guizani, M. (2019). Unmanned aerial vehicles (uavs): A survey on civil applications and key research challenges. IEEE Access, 7:48572–48634.

Tang, C., Zhu, C., Wei, X., Peng, H., and Wang, Y. (2019). Integration of uav and fog-enabled vehicle: Application in post-disaster relief. In 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS), pages 548– 555. IEEE.

Torres Neto, J. R., Rocha Filho, G. P., Mano, L. Y., Villas, L. A., and Ueyama, J. (2019). Exploiting offloading in iot-based microfog: experiments with face recognition and fall detection. Wireless Communications and Mobile Computing, 2019.

Waharte, S. and Trigoni, N. (2010). Supporting search and rescue operations with uavs. In 2010 International Conference on Emerging Security Technologies, pages 142–147. IEEE.

Yi, S., Li, C., and Li, Q. (2015). A survey of fog computing: concepts, applications and issues. In Proceedings of the 2015 workshop on mobile big data, pages 37–42.

Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., and Jue, J. P. (2019). All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture, (February).

Zheng, A. (2015). Evaluating machine learning models: a beginner’s guide to key concepts and pitfalls.
Publicado
18/07/2021
SERRA, Wendel; JUNIOR, Warley; BARROS, Isaac; KURIBAYASHI, Hugo; CARMONA, João. Monitoring and Smart Decision Architecture for DRONE-FOG Integrated Environment . In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 13. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 102-111. ISSN 2595-6183. DOI: https://doi.org/10.5753/sbcup.2021.16008.