Swarm intelligence for routing in mobile ad hoc networks

Mobile ad hoc networks are communication networks built up of a collection of mobile devices, which can communicate through wireless connections. Routing is the task of directing data packets from a source node to a given destination. This task is particularly hard in mobile ad hoc networks: due to the mobility of the network elements and the lack of central control, routing algorithms should be robust, adaptive, and work in a decentralized and self-organizing way. In this paper, we describe an algorithm, which draws inspiration from swarm intelligence to obtain these characteristics. More specifically, we borrow ideas from ant colonies and from the ant colony optimization framework. In an extensive set of simulation tests, we compare our routing algorithm with a state-of-the-art algorithm, and show that it gets better performance over a wide range of different scenarios and for a number of different evaluation measures. In particular, we show that it scales better with the number of nodes in the network.

[1]  Charles E. Perkins,et al.  Ad-hoc on-demand distance vector routing , 1999, Proceedings WMCSA'99. Second IEEE Workshop on Mobile Computing Systems and Applications.

[2]  David A. Maltz,et al.  Dynamic Source Routing in Ad Hoc Wireless Networks , 1994, Mobidata.

[3]  Nj Piscataway,et al.  Wireless LAN medium access control (MAC) and physical layer (PHY) specifications , 1996 .

[4]  Marco Dorigo,et al.  AntNet: Distributed Stigmergetic Control for Communications Networks , 1998, J. Artif. Intell. Res..

[5]  Marco Dorigo,et al.  Ant colony optimization and its application to adaptive routing in telecommunication networks , 2004 .

[6]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[7]  Charles E. Perkins,et al.  Highly dynamic Destination-Sequenced Distance-Vector routing (DSDV) for mobile computers , 1994, SIGCOMM.

[8]  J. Deneubourg,et al.  Self-organized shortcuts in the Argentine ant , 1989, Naturwissenschaften.

[9]  David A. Maltz,et al.  A performance comparison of multi-hop wireless ad hoc network routing protocols , 1998, MobiCom '98.

[10]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[11]  A. M. Abdullah,et al.  Wireless lan medium access control (mac) and physical layer (phy) specifications , 1997 .

[12]  Jeffrey O. Kephart,et al.  The Vision of Autonomic Computing , 2003, Computer.

[13]  Léon J. M. Rothkrantz,et al.  Ant-Based Load Balancing in Telecommunications Networks , 1996, Adapt. Behav..

[14]  Tomasz Imielinski,et al.  Mobile Computing , 1996 .

[15]  Zygmunt J. Haas,et al.  A new routing protocol for the reconfigurable wireless networks , 1997, Proceedings of ICUPC 97 - 6th International Conference on Universal Personal Communications.

[16]  Dimitri P. Bertsekas,et al.  Data Networks , 1986 .

[17]  Luca Maria Gambardella,et al.  Using Ant Agents to Combine Reactive and Proactive Strategies for Routing in Mobile Ad-hoc Networks , 2005, Int. J. Comput. Intell. Appl..

[18]  Guy Theraulaz,et al.  A Brief History of Stigmergy , 1999, Artificial Life.

[19]  Luca Maria Gambardella,et al.  AntHocNet: an adaptive nature-inspired algorithm for routing in mobile ad hoc networks , 2005, Eur. Trans. Telecommun..

[20]  Paolo Santi,et al.  The Node Distribution of the Random Waypoint Mobility Model for Wireless Ad Hoc Networks , 2003, IEEE Trans. Mob. Comput..

[21]  Elizabeth M. Belding-Royer,et al.  A review of current routing protocols for ad hoc mobile wireless networks , 1999, IEEE Wirel. Commun..

[22]  M. Satyanarayanan,et al.  Mobile computing , 1993, Computer.