Abstract
Networking with aerial vehicles has evolved considerably over a period of time. Its applications range across a wide spectrum covering areas of military and civilian activities. Connectivity between aerial vehicles in ad hoc mode allows formation of multiple control units in the sky which have an ability to handle complex tasks. One of the major applications of these aerial vehicles is to coordinate simultaneously with another ad hoc network operating on the ground. This formation is termed as cooperative ad hoc networking. These networks operate on multiple data-sharing in form of cognitive maps. Thus, an efficient traffic management strategy is required to form a robust network. In this paper, an ambient network framework for coordination between ground and flying ad hoc network is presented. A fault-tolerant and robust connectivity strategy is proposed using neural, fuzzy and genetic modules. quaternion Kalman filter and its variant \(\alpha -\beta -\gamma \) filter is used to form the neural and decision system for guided aerial network. Effectiveness of the proposed traffic management framework for aerial vehicles is presented using mathematical simulations.
Similar content being viewed by others
References
Sharma, V., & Kumar, R. (2014). A cooperative network framework for multi-UAV guided ground ad hoc networks. Journal of Intelligent & Robotic Systems, pp. 1–24.
Buchmayr, M., & Kurschl, W. (2011). A survey on situation-aware ambient intelligence systems. Journal of Ambient Intelligence and Humanized Computing, 2(3), 175–183.
De Amicis, R., Conti, G., Piffer, S., & Prandi, F. (2011). Service oriented computing for ambient intelligence to support management of transport infrastructures. Journal of Ambient Intelligence and Humanized Computing, 2(3), 201–211.
Atmojo, U. D., Salcic, Z., Kevin, I., Wang, K., & Park, H. (2015). System-level approach to the design of ambient intelligence systems based on wireless sensor and actuator networks. Journal of Ambient Intelligence and Humanized Computing 6(2), 153–169
Stavropoulos, Thanos G., Kontopoulos, Efstratios, Bassiliades, Nick, Argyriou, J., Bikakis, A., Vrakas, D., et al. (2015). Rule-based approaches for energy savings in an ambient intelligence environment. Pervasive and Mobile Computing, 19, 1–23.
Hagras, H., Callaghan, V., Colley, M., Clarke, G., Pounds-Cornish, A., & Duman, H. (2004). Creating an ambient-intelligence environment using embedded agents. IEEE Intelligent Systems, 19(6), 12–20.
Irizarry, J., Gheisari, M., Williams, G., & Roper, K. (2014). Ambient intelligence environments for accessing building information: A healthcare facility management scenario. Facilities, 32(3/4), 120–138.
Bekmezci, I., Sahingoz, O. K., & Temel, S. (2013). Flying ad-hoc networks (FANETs): A survey. Ad Hoc Networks, 11(3), 1254–1270.
Temel, S., & Ilker, B. (2014). Scalability analysis of flying ad hoc networks (FANETs): A directional antenna approach. In IEEE international black sea conference on communications and networking (BlackSeaCom).
Sharma, V., & Kumar, R. (2014). Service-oriented middleware for multi-UAV guided ad hoc networks. IT Convergence Practice, 2(3).
Chiaramonte, R. B., & Branco, K. R. L. J. C. (2014) Collision detection using received signal strength in FANETs. In IEEE international conference on unmanned aircraft systems (ICUAS).
Luo, C., Sally, I. M., Gerard, P., Luke, T., & De Nardi, R. (2013) UAV position estimation and collision avoidance using the extended Kalman filter. IEEE Transactions on Vehicular Technology, 62(6), 2749–2762.
Mohamed, N., Al-Jaroodi, J., Jawhar, I., & Lazarova-Molnar, S. (2014). A service-oriented middleware for building collaborative UAVs. Journal of Intelligent & Robotic Systems, 74(1–2), 309–321.
Perez, D., Maza, I., Caballero, F., Scarlatti, D., Casado, E., & Ollero, A. (2013). A ground control station for a multi-uav surveillance system. Journal of Intelligent & Robotic Systems, 69(1–4), 119–130.
Cevik, P., Kocaman, I., Akgul, A. S., & Akca, B. (2013). The small and silent force multiplier: A swarm UAVelectronic attack. Journal of Intelligent & Robotic Systems, 70(1–4), 595–608.
Shanmugavel, M., Tsourdos, A., White, B., & bikowski, R. Z. (2010). Co-operative path planning of multiple UAVs using Dubins paths with clothoid arcs. Control Engineering Practice, 18(9), 1084–1092.
Sahingoz, & O. K. (2014). Networking models in flying ad-hoc networks (FANETs): Concepts and challenges. Journal of Intelligent & Robotic Systems, 74(1–2), 513–527.
Capitn, J., Merino, L., Caballero, F., & Ollero, A. (2011). Decentralized delayed-state information filter (DDSIF): A new approach for cooperative decentralized tracking. Robotics and Autonomous Systems 59(6), 376–388.
Lilien, L., Gupta, A., Kamal, Z.-E.-H., & Yang, Z. (2010). Opportunistic resource utilization networks: A new paradigm for specialized ad hoc networks. Computers & Electrical Engineering, 36(2), 328–340.
Liu, M., Jie, L., & Yuyu, Y. (2013). Research of UAV cooperative reconnaissance with self-organization path planning. In International conference on computer, networks and communication engineering (ICCNCE 2013). Amsterdam: Atlantis Press
Li, Y., Chen, H., Joo Er, M., & Wang, X. (2011). Coverage path planning for UAVs based on enhanced exact cellular decomposition method. Mechatronics, 21(5), 876–885.
Moon, J., & Prasad, J. V. R. (2011). Minimum-time approach to obstacle avoidance constrained by envelope protection for autonomous UAVs. Mechatronics, 21(5), 861–875.
Paw, Y. C., & Gary J. B. (2011). Development and application of an integrated framework for small UAV flight control development. Mechatronics, 21(5), 789–802.
Samar, R., & Rehman, A. (2011). Autonomous terrain-following for unmanned air vehicles. Mechatronics, 21(5), 844–860.
Jawhar, I., Mohamed, N., Al-Jaroodi, J., & Zhang, S. (2014). A framework for using unmanned aerial vehicles for data collection in linear wireless sensor networks. Journal of Intelligent & Robotic Systems, 74(1–2), 437–453.
Sharma, V., Kumar, R., & Rathore, N. (2015). Topological broadcasting using parameter sensitivity-based logical proximity graphs in coordinated ground-flying ad hoc networks. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA), 6(3), 54–72.
Qingwen, W., Gang, L., Zhi, L. & Qian, Q. (2015). An adaptive forwarding protocol for three dimensional flying ad hoc networks. In 5th international conference on electronics information and emergency communication (ICEIEC), pp. 142–145.
Tareque, M. H., Hossain, M. S., & Atiquzzaman, M. (2015). On the routing in flying ad hoc networks.” In IEEE federated conference on computer science and information systems (FedCSIS), pp. 1–9.
Zaouche, L., Enrico, N., & Bouabdallah, A. (2015). ETTAF: Efficient target tracking and filming with a flying ad hoc network. In Proceedings of the 1st international workshop on experiences with the design and implementation of smart objects, ACM, pp. 49–54.
Sharma, V., & Kumar, R. (2015). An opportunistic cross layer design for efficient service dissemination over flying ad hoc networks (fanets). In 2nd international conference on electronics and communication systems (ICECS), pp. 1551–1557.
Boussad, I., Julien, L., & Patrick, S. (2013). A survey on optimization metaheuristics. Information Sciences, 237, 82–117.
Paliwal, M., & Kumar, U. A. (2009). Neural networks and statistical techniques: A review of applications. Expert Systems with Applications, 36(1), 2–17.
Elleuch, M., Heni, K., & Mohamed, A. (2015). Exploiting neuro-fuzzy system for mobility prediction in wireless ad-hoc networks. In Advances in computational intelligence (pp. 536–548). New York: Springer
Gaxiola, F., Melin, P., Valdez, F., Castro, J. R., & Castillo, O. (2016). Optimization of type-2 fuzzy weights in backpropagation learning for neural networks using GAs and PSO. Applied Soft Computing, 38, 860–871.
Kumar, L. P. D., Grace, S. S., Krishnan, A., Manikandan, V. M., Chinraj, R., & Sumalatha., M. R.(2012). Data filtering in wireless sensor networks using neural networks for storage in cloud. In International conference on recent trends in information technology (ICRTIT), pp. 202–205.
Ghouti, L., Sheltami, T. R., & Alutaibi, K. S. (2013). Mobility prediction in mobile ad hoc networks using extreme learning machines. Procedia Computer Science, 19, 305–312.
Islam, Al, Alim, A. B. M., & Raghunathan, V. (2015). iTCP: An intelligent TCP with neural network based end-to-end congestion control for ad-hoc multi-hop wireless mesh networks. Wireless Networks, 21(2), 581–610.
Trawny, N., & Roumeliotis, S. I. (2005). Indirect Kalman filter for 3D attitude estimation. University of Minnesota, Dept. of Comp. Sci. & Eng. Tech. Rep, 2.
Ludwig, A. & Schmidt, K. D. (2010). GaussMarkov loss prediction in a linear model. In CAS E-Forum Fall, pp. 1–48.
Meinhold, R. J., & Singpurwalla, N. D. (1983). Understanding the Kalman filter. The American Statistician, 37(2), 123–127.
Tenne, D., & Tarunraj, S., (2000). Optimal design of \(\alpha \) - \(\beta \)- \(\gamma \) filters. In IEEE proceedings of the American control conference, Vol. 6, pp. 4348–4352.
Acknowledgments
We are very grateful to the EiC, AE, and the anonymous reviewers for their constructive comments and encouragement which helped in improving the overall quality of the paper. We are also highly obliged to the computer science and engineering department of “Thapar University”, Patiala for rendering their incessant help in providing infrastructure and work environment.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sharma, V., Kumar, R. G-FANET: an ambient network formation between ground and flying ad hoc networks. Telecommun Syst 65, 31–54 (2017). https://doi.org/10.1007/s11235-016-0210-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11235-016-0210-2