m ulti m edia c omputing laboratory Biologically Inspired Cooperative Routing for Wireless 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

m ulti m edia c omputing laboratory I NTRODUCTION 1

m ulti m edia c omputing laboratory Introduction 2

m ulti m edia c omputing laboratory Introduction Biological system – Adaptation, reliability, robustness – Controlled by the individuals – Ex) Ant colony 3 Biologically-inspired cooperative routing

m ulti m edia c omputing laboratory 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 4

m ulti m edia c omputing laboratory ACO Biologically Inspired Cooperative Routing for WMSN 5

m ulti m edia c omputing laboratory ACO – Ant colony optimization – The models of collective intelligence of ants – Example 6 Both ants have no knowledge about the location of food The ants leave the pheromone along their paths F 1, F 2 When ant A 2 arrive F 0, F 2 =1, F 1 =0 → A 2 choose R 2 F 2 becomes 2. When A 1 arrive F 0 F 2 =2, F 1 =1 → A 1 choose R 2

m ulti m edia c omputing laboratory Stagnation in Network Routing Some problems of ACO’s optimal path p 0 – The congestion of p 0 – The dramatic reduction of the probability of selecting other paths Why is it not good? – p 0 may become nonoptimal if it is congested – p 0 may be disconnected due to network overload – Other nonoptimal paths may become optimal due to the dynamical changes in the network topology 7 The shortest path p 0 is only statistical (maybe nonoptimal)

m ulti m edia c omputing laboratory Stagnation in Network Routing Alleviation of stagnation problem – Pheromone control – Pheromone-heuristic control – Privileged pheromone laying 8

m ulti m edia c omputing laboratory Stagnation in Network Routing Alleviation of stagnation problem Pheromone control – Reduce the influences from past experience 9 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

m ulti m edia c omputing laboratory Stagnation in Network Routing Alleviation of stagnation problem Pheromone-heuristic control – Configure the probability function P k,l for an ant to choose a link (k,l) using a combination of both pheromone concentration F k,l and heuristic function η k.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 10 Determined by the queue length

m ulti m edia c omputing laboratory Stagnation in Network Routing Alleviation 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 11

m ulti m edia c omputing laboratory ACO-BASED ROUTING ALGORITHMS Biologically Inspired Cooperative Routing for WMSN 12

m ulti m edia c omputing laboratory ACO in Wired Nets ABC( ant-based control ) – Circuit-switched telephony networks – Each node have routing table – Pheromone updating rule Age of the arriving ant – Probabilistic transition rule 13 node 1node 2... node 2/0.37node 3/ node 3/0.24node 4/ Destination node Neighbor/probability

m ulti m edia c omputing laboratory 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 14

m ulti m edia c omputing laboratory 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 15

m ulti m edia c omputing laboratory Adaptive Stigmergy-Based Routing for Wireless Networks 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… 16 Pheromone increase linearly Pheromone decrease exponentially

m ulti m edia c omputing laboratory 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 17

m ulti m edia c omputing laboratory CONCLUSION Biologically Inspired Cooperative Routing for WMSN 18

m ulti m edia c omputing laboratory 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 19

m ulti m edia c omputing laboratory T HANK Y OU Any question? 20