B iologically I nspired C ooperative R outing for W ireless M obile S ensor N etworks

1. Biologically Inspired Cooperative Routing forWireless Mobile Sensor Networks S. S. Iyengar, Hsiao-Chun Wu, N. Balakrishnan, and Shih Yu Chang  IEEE System Journal Sep.2007 Ji Won Lee 18 Nov, 2008

2. Introduction

3. Introduction

4. Introduction Biologically-inspired cooperative routing • Biological system • Adaptation, reliability, robustness • Controlled by the individuals • Ex) Ant colony

5. Introduction • Biologically-inspired cooperative routing • Individual simplicity & Collective complexity How? • Self-organization • Positive feedback : ex) disposing pheromone • Negative feedback : ex) evaporation of pheromone • Stigmergy • Indirect communication used by ants in nature to coordinate their joint problem solving activity • Ex) Laying a pheromone

6. Biologically Inspired Cooperative Routing for WMSN aco

7. ACO Both ants have no knowledge about the location of food The ants leave the pheromone along their paths F1, F2 When ant A2 arrive F0, F2=1, F1=0 → A2 choose R2 F2 becomes 2. When A1 arrive F0 F2=2, F1=1 → A1 choose R2 • ACO • Ant colony optimization • The models of collective intelligence of ants • Example

8. Stagnation in Network Routing The shortest path p0 is only statistical (maybe nonoptimal) • Some problems of ACO’s optimal path p0 • The congestion of p0 • The dramatic reduction of the probability of selecting other paths • Why is it not good? • p0 may become nonoptimalif it is congested • p0 may be disconnected due to network overload • Other nonoptimal paths may become optimal due to the dynamical changes in the network topology

9. Stagnation in Network Routing • Alleviation of stagnation problem • Pheromone control • Pheromone-heuristic control • Privileged pheromone laying

10. Stagnation in Network RoutingAlleviation of stagnation problem Evaporation Method The pheromone values at all vertices of the paths are discounted by a factor Aging Method An ant disposes less and less pheromone as it moves from node to node Limiting and Smoothing Pheromone Method Limiting the max. pheromone amount and smoothing pheromone laying • Pheromone control • Reduce the influences from past experience

11. Stagnation in Network RoutingAlleviation of stagnation problem Determined by the queue length • Pheromone-heuristic control • Configure the probability function Pk,lfor an ant to choose a link (k,l) using a combination of both pheromone concentration Fk,land heuristic function ηk.l • a > b : ants prefer the paths with higher pheromone concentrations • When network becomes stable • a < b : ants prefer the paths having higher heuristic concentrations • Initial stage after setting up a new network

12. Stagnation in Network RoutingAlleviation of stagnation problem • Privileged pheromone laying • Permit a selected subset of ants which have the privilege to dispose larger amounts of pheromone than others • Ants only dispose pheromones during their return trips

13. Biologically Inspired Cooperative Routing for WMSN Aco-based routing algorithms

14. ACO in Wired Nets Destination node Neighbor/probability • ABC( ant-based control ) • Circuit-switched telephony networks • Each node have routing table • Pheromone updating rule • Age of the arriving ant • Probabilistic transition rule

15. ACO in Wired Nets • AntNet • Wired networks • Each node have the same routing table as ABC scheme • Pheromone updating rule • Privileged pheromone laying • Probabilistic transition rule • Each node send forward ant to randomly selected destinations • Backward ant returns to the source node following the path in reverse • Each intermediate node updates its routing tables according to the information extracted from the backward ants

16. Swarm Intelligence Using Stigmergy in Ad Hoc Networks • ARA( on-demand ad hoc routing algorithm ) • Stigmergy • indirect communication of the concerned individuals through changing their environment • Route discovery • Flood the forward ants • Pheromone updating rule • Probabilistic transition rule

17. Adaptive Stigmergy-Based Routing for Wireless Networks Pheromone increase linearly Pheromone decrease exponentially • Termite( adaptive routing algorithm ) • If networks change dynamically, traffic control is not good • If there is no pheromone, each packet is routed randomly and independently • The disposed stigmergy will have an influence on the adaptive routing table • To minimize the effect of pheromone…

18. Distributed ACO Routing Algorithm( ADRA ) in Ad Hoc Networks • ADRA( Ant-based distributed route algorithm ) • Ants use the simulated pheromones • Traveling distance, quality of the link, congestion, current pheromone • QoS parameter • The node changes the pheromones by itself( evaporation ) • Quality and age of link • Two types of ants • Anti-ant :Congestion repression ants • When an intermediate node’s load exceeds its predefined congestion threshold, it will send anti-ant to its upstream neighbor nodes to modify their probability routing tables • Enforce-ant :Shortcut reinforce ant

19. Biologically Inspired Cooperative Routing for WMSN conclusion

20. Conclusion & discussion • Goal of this paper • Overview of biologically-inspired routing algorithms • ACO • Pros. • Introduction of lots of ACO based routing algorithms • Cons. • Lack of exact explanation of each routing algorithm

21. Any question? Thank You