Ant Colony Optimization and Ad-hoc On-demand Multipath Distance Vector (AOMDV) Based Routing Protocol

Author(s):  
Xun-bing Wang ◽  
Yong-zhao Zhan ◽  
Liang-min Wang ◽  
Li-ping Jiang
2013 ◽  
Vol 3 (1-2) ◽  
Author(s):  
Vaibhav Godbole

Availability of cheap positioning instruments like GPS receivers makes it possible for routing algorithms to use the position of nodes in an ad hoc mobile network. Regular position based routing algorithms fail to find a route from a source to a destination in some cases when the network contains nodes with irregular transmission ranges or they find a route that is much longer than the shortest path. On the other hand routing algorithms based on Ant Colony Optimization (ACO) find routing paths that are close to the shortest paths even if the nodes in the network have different transmission ranges. The drawback of these algorithms is the large number of messages that needs to be sent or the long delay before the routes are established. In this paper, we propose a novel protocol AntNet-LA which combines the idea of ACO with information about position of all nodes. In this technique the distance between the nodes is considered to transmit the packets, hence overcomes the drawbacks of AntNet algorithm which considers only cumulative probability for packet transmission. We compare performance of AntNet-LA with AntNet, Ad-hoc On Demand Distance Vector (AODV), Ad-hoc On Demand Multipath Distance Vector (AOMDV), Dynamic Source Routing (DSR) and Destination-Sequenced Distance-Vector Routing (DSDV) protocols. We also compare performance of AntNet-LA with distance-aware protocols such as Location Aided Routing (LAR), Geographical AODV GeoAODV and Position Based ANT colony optimization (PBANT).


Mobile ad hoc networks (MANETs) are collection of nodes connected through wireless medium and do not require infrastructure for operation. Network Topology keeps on changing because mobility of nodes are high. Therefore, it is important for MANETs to provide excellent routing and security features. Since MANETs do not require any pre-existing infrastructure, they are extensively used in emergency and rescue and military applications. MANETs thus will form essentially an important part in wireless networks. In this paper, Ad hoc On-Demand Distance Vector (AODV) and Greedy Perimeter Stateless Routing (GPSR) routing protocol performance is compared with respect to Throughput and E2ED and observed that there is an improvement in throughput by 11% in case of GPSR. Simulation is performed using NS3.


2004 ◽  
Vol 3 (2) ◽  
pp. 142-145 ◽  
Author(s):  
Muhammad Asfand-e-Y ◽  
Muhammad Sher .

Author(s):  
Talib Abbas ◽  
Faizan Qamar ◽  
MHD Nour Hindia ◽  
Rosilah Hassan ◽  
Irfan Ahmed ◽  
...  

Robotica ◽  
2014 ◽  
Vol 33 (1) ◽  
pp. 157-180 ◽  
Author(s):  
Micael S. Couceiro ◽  
Amadeu Fernandes ◽  
Rui P. Rocha ◽  
Nuno M. F. Ferreira

SUMMARYAn extension of the well-knownParticle Swarm Optimization(PSO) to multi-robot applications has been recently proposed and denoted asRobotic Darwinian PSO(RDPSO), benefited from the dynamical partitioning of the whole population of robots. Although such strategy allows decreasing the amount of required information exchange among robots, a further analysis on the communication complexity of the RDPSO needs to be carried out so as to evaluate the scalability of the algorithm. Moreover, a further study on the most adequate multi-hop routing protocol should be conducted. Therefore, this paper starts by analyzing the architecture and characteristics of the RDPSO communication system, thus describing the dynamics of the communication data packet structure shared between teammates. Such procedure will be the first step to achieving a more scalable implementation of RDPSO by optimizing the communication procedure between robots. Second, an ad hoc on-demand distance vector reactive routing protocol is extended based on the RDPSO concepts, so as to reduce the communication overhead within swarms of robots. Experimental results with teams of 15 real robots and 60 simulated robots show that the proposed methodology significantly reduces the communication overhead, thus improving the scalability and applicability of the RDPSO algorithm.


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