Minimum Hop Count and Load Balancing Metrics Based on Ant Behavior over HAP Mesh

In this paper we propose a routing algorithm based on Swarm Intelligence studies. In particular, this algorithm is based on AntNet routing with the extensions of novel metrics for the multi-objective optimization, that are minimum hop count and traffic load balancing. In order to build an optimal solution, the proposed algorithm will make use of ANT agents that consist of probe packets sent on the HAPs network. We have chosen as reference network a HAPs mesh in order to get advantages of the dynamic characteristics of these platforms. In this work we perform a comparison of a classical shortest length path and our algorithm that will try to find the minimum hop path respecting a maximum end-to-end delay bound and an equally distribution of the traffic on the HAPs network.

[1]  T. Lindvall ON A ROUTING PROBLEM , 2004, Probability in the Engineering and Informational Sciences.

[2]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[3]  Salvatore Marano,et al.  Aggregated resource reservation protocol in integrated scalable-terrestrial and Int-Serv satellite network , 2004, 2004 IEEE Wireless Communications and Networking Conference (IEEE Cat. No.04TH8733).

[4]  Ion Stoica,et al.  Stateless Core: A Scalable Approach for Quality of Service in the Internet , 2004, Lecture Notes in Computer Science.

[5]  Tarek Saadawi,et al.  Ant routing algorithm for mobile ad-hoc networks (ARAMA) , 2003, Conference Proceedings of the 2003 IEEE International Performance, Computing, and Communications Conference, 2003..

[6]  David Grace,et al.  High-altitude platforms for wireless communications , 2001 .

[7]  Thomas Stützle,et al.  Ant Colony Optimization , 2009, EMO.

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

[9]  Guy Theraulaz,et al.  The biological principles of swarm intelligence , 2007, Swarm Intelligence.

[10]  Danny Raz,et al.  A simple efficient approximation scheme for the restricted shortest path problem , 2001, Oper. Res. Lett..

[11]  Salvatore Marano,et al.  A scalable framework for in IP-oriented terrestrial-GEO satellite networks , 2005, IEEE Communications Magazine.

[12]  Jun Sun,et al.  A new pheromone updating strategy in ant colony optimization , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[13]  Thomas Stützle,et al.  Ant colony optimization: artificial ants as a computational intelligence technique , 2006 .

[14]  Kwang Mong Sim,et al.  Ant colony optimization for routing and load-balancing: survey and new directions , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[15]  Salvatore Marano,et al.  Multi-Constraints Routing Algorithm Based on Swarm Intelligence over High Altitude Platforms , 2007, NICSO.

[16]  Manuel López-Ibáñez,et al.  Ant colony optimization , 2010, GECCO '10.

[17]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[18]  Salvatore Marano,et al.  Integrated Services on High Altitude Platform: Receiver Driven Smart Selection of HAP-Geo Satellite Wireless Access Segment and Performance Evaluation , 2006, Int. J. Wirel. Inf. Networks.

[19]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[20]  P. Dempsey,et al.  Swarm intelligence for network routing optimization , 2005 .