An OS for Mobile Ad hoc Networks Using Ant Based Hueristic to Distribute Mobile Services

Abstract

This paper presents the architecture of an operating system (called NanoOS) for applications distributed over mobile ad hoc networks (MANETs). Furthermore, a service distribution method inspired on the foraging behavior of ants is proposed.The NanoOS offers an uniform environment of execution where the movement of nodes is transparent to the tasks. Moreover, the code of the O S is distributed among the nodes i n order to support complex services while keeping the footprint small on each node. We propose a swarm optimization based heuristic to control the migration of the O S (and application) services i n order to reduce the communication overhead. Each service request leaves pheromone i n the nodes within its path to the service provider. A n optimization step occurs when the service provider migrates to the neighbor node with the higher pheromone concentration.Realized simulations have shown that our heuristic performs well. The total communication cost i n average is just 40% higher than the global optimum obtained using Branch and Bound, while a random distribution of services result i n a cost about 350% higher than the optimum. Moreover, our algorithm just uses local information and has a very low computational requirement.

Self-organizing Resource-Aware Clustering for Ad Hoc Networks

“…Smaller θ CHv means that the node v is very well suited to carry out the role of a clusterhead. The definition of the threshold can be seen in (1).…”

“…The first point says that the heuristic should privilege nodes with a small distance to some of the nodes inside the actual cluster. In order to observe that, two aspects must be addressed: (1) Include the distance to the next cluster member in the fitness function with the aim of reducing communication costs. (2) An implicit behavior of the heuristic makes that nodes far away from the clusterhead, but with good connection to the cluster are penalized since starting the timer in a later stage.…”

“…This kind of clustering is useful in various scenarios. An example is the operating system (OS) we are currently developing in our research group, called NanoOS [1].…”

Model Checking the Ant Colony Optimisation

“…The idea is that reaching location 5 represents that a tour is completed. Depending on the path taken, the preferences of the routes involved are updated according to (2) (lines 51-92). Variables path and tot_dist are reset to signal that the preference update has been executed, which allows the evaporation update to happen.…”

“…These mechanisms define solving strategies that make applications more robust, flexible and scalable. They have been successfully applied to several problems, such as sensor networks [2] [3].…”