Improvement of the Routing in Opportunistic Networks by the Application of Unsupervised and Supervised Machine Learning Techniques

The dissertation thesis deals with the issue of special routing algorithms designed for com-munication in opportunistic networks. We proposed three extended routing algorithms combining unsupervised or supervised learning and existing routing methods. The oppor-tunistic communication networks are the ad-hoc networks where no assumption is made on the existence of a complete physical path between two nodes wishing to communicate. In opportunistic networks, the messages are transmitted when the node opportunistically meets another node; the characteristics of node movement can improve message transmis-sion. In practice, the network nodes can be mobile robots, wireless equipments carried by people, vehicles, wild animals, unmanned aerial vehicles. We have chosen three mutually independent problems to be solved. The rst problem is related to the applicability of unsu-pervised learning to the analysis of the data of node movements including implementation of the routing algorithm using the results of unsupervised learning. The second problem is related to the applicability of supervised learning and implementation of supervised learn-ing into a routing algorithm. The third problem is related to the enhancement of routing algorithms using unsupervised learning (statistical node mobility models) and the active node behavior. The active node behavior means that the node itself actively changes the route in order to move to the location more suitable for forwarding the message. In order to solve the rst problem, we proposed the Routing with Clustering algorithm. This method extends the standard epidemic routing with the limited message buer using knowledge extracted from the training data using clustering. In order to solve the second problem, we proposed a method combining the collection of labeled training data on message deliv-ery, a two class classier and an implementation of this classier into routing algorithm. In order to solve the third problem, we proposed the Active node movement algorithm (ANMA). Two versions of ANMA were designed. The rst version of the algorithm uses node densities in sectors. The second version of ANMA models data using GMRFs. Both versions of the algorithms make decisions about message forwarding and active node devi-ation from its planned route. All the proposed algorithms were tested on simulated data in the simulation environment. The experimental results were compared to the results obtained by the existing standard routing methods: Epidemic routing with the limited iii message buer and PRoPHET routing. All methods show a slight improvement in routing in opportunistic network from the viewpoint of a number of delivered messages or of the number of delivered messages in limited time. The signicant improvement was observed only in experiments using active node movement algorithm (ANMA), where nodes can improve routing using deviation from their planned routes.The dissertation thesis deals with the issue of special routing algorithms designed for communication in opportunistic networks. We proposed three extended routing algorithms combining unsupervised or supervised learning and existing routing methods. The opportunistic communication networks are the ad-hoc networks where no assumption is made on the existence of a complete physical path between two nodes wishing to communicate. In opportunistic networks, the messages are transmitted when the node opportunistically meets another node; the characteristics of node movement can improve message transmission. In practice, the network nodes can be mobile robots, wireless equipments carried by people, vehicles, wild animals, unmanned aerial vehicles. We have chosen three mutually independent problems to be solved. The rst problem is related to the applicability of unsupervised learning to the analysis of the data of node movements including implementation of the routing algorithm using the results of unsupervised learning. The second problem is related to the applicability of supervised learning and implementation of supervised learning into a routing algorithm. The third problem is related to the enhancement of routing algorithms using unsupervised learning (statistical node mobility models) and the active node behavior. The active node behavior means that the node itself actively changes the route in order to move to the location more suitable for forwarding the message. In order to solve the rst problem, we proposed the Routing with Clustering algorithm. This method extends the standard epidemic routing with the limited message buer using knowledge extracted from the training data using clustering. In order to solve the second problem, we proposed a method combining the collection of labeled training data on message delivery, a two class classier and an implementation of this classier into routing algorithm. In order to solve the third problem, we proposed the Active node movement algorithm (ANMA). Two versions of ANMA were designed. The rst version of the algorithm uses node densities in sectors. The second version of ANMA models data using GMRFs. Both versions of the algorithms make decisions about message forwarding and active node deviation from its planned route. All the proposed algorithms were tested on simulated data in the simulation environment. The experimental results were compared to the results obtained by the existing standard routing methods: Epidemic routing with the limited iii message buer and PRoPHET routing. All methods show a slight improvement in routing in opportunistic network from the viewpoint of a number of delivered messages or of the number of delivered messages in limited time. The signicant improvement was observed only in experiments using active node movement algorithm (ANMA), where nodes can improve routing using deviation from their planned routes