Analysis of the Performance of a Network of Unmanned Vehicles Considering Quality Criteria

Resumen: Geographic routing protocols have received more attention in recent years, the GPSR algorithm is a geographic routing protocol and is the combination of forward greedy routing and face routing, which are the pillars of geographic routing. This article proposes a geographical routing protocol for communication between unmanned vehicles named nodes to improve the performance of the network. This proposal obtains the distance between nodes by means of a GPS device distance is used as a criterion to determine the quality of the link. In the same process a control signal is obtained to validate the stability of the node, using the signal of control and quality of the link to assign a cost of this, this information is compiled by means of a sump of nodes of the network in a way that kruskal algorithm generates the minimum spanning tree of the network. The simulation carried out on the Matlab platform is developed taking into account the behavior of the physical layer and the data link layer using CSMA / CA of the 802.15.4 standard and providing network performance when evaluating 2 transmissions with different origin nodes. The results show that the proposed algorithm eliminates loops, improves the performance of the network and guarantees the delivery of packets to nodes within the network.

¿Cómo citar?

Jose Luis Sanchez Chacon, Dra. J. Castañeda-Camacho, Dr. Jose Fermi Guerrero-Castellanos, Dra. L-Cortez & M.I. Jose Miguel Hurtado Madrid †. Analysis of the Performance of a Network of Unmanned Vehicles Considering Quality Criteria. Memorias del Congreso Nacional de Control Automático, pp. 683-687, 2019.

Palabras clave

Sistemas de Eventos Discretos, Sistemas Multi-Agente

• Anis Koubaa, Mário Alves, E.T. (2005). Ieee 802.15.4 for wireless sensor networks: A technical overview. technical report.
• Ashish Nanda, P.N. and He, X. (2016). Geo-location oriented routing protocol for smart dynamic mesh network.
• Brad Karp, H. T. Kung (2000). Gpsr: Greedy perimeter stateless routing for wireless networks.
• Dayin, S.T.L.X.W.Y.C.Y.C. (2011). A matlab simulation of the kruskal algorithm for erecting communication network. c 2011 ieee.
• Dhoedt, B.L.P.D.M.I.M.N.V.D.B. and Demeester, P. (2005). Maximum throughput and minimum delay in ieee 802.15.4.
• Evangelos Kranakis, H.S. and Urrutia, J. (1999). Compass routing on geometric networks. Fabian Kuhn, Roger Wattenhofer, Y.Z. and Zollinger, A. (2003). Geometric ad-hoc routing: Of theory and practice.
• Garcia-Santiago Alejandro, Castaneda-Camacho Josefina, G.C.J.F. and Mino-Aguilar, G. (2018). Evaluation of aodv and dsdv routing protocols for a fanet: Further results towards robotic vehicle networks.
• Geon-Hwan, N.J.C. and Imtiaz, M. (2016). Multidrone control and network self-recovery for flying ad hoc networks.
• Laxmi P Gewali and Umang Amatya (2013). Node filtering and face routing for sensor network.
• Md. Hasan Tareque, M.S.H. and Atiquzzaman, M. (2015). On the routing in flying ad hoc networks.
• Mohapatra, P. and Krishnamurthy, S.V. (2005). Ad hoc networks technologies and protocols.
• Prosenjit Bose, Pat Morin, I.S. and Urrutia, J. (2006). Routing with guaranteed delivery in ad hoc wireless networks.
• Taneja, S. and Kush, A. (2010). A survey of routing protocols in mobile ad hoc networks.
• Yujun Li, Yaling Yang, and Xianliang Lu (2010). Rules of designing routing metrics for greedy,face, and combined greedy-face routing.