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Abstract

This paper proposes an advanced routing method in the purpose of increasing IoT routing device’s power-efficiency, which allows to centralize routing tables computing as well as to push loading, related to routing tables computation, towards the Cloud environment at all. We introduced a phased solution for the formulated task. Generally, next steps were performed: stated requirements for the system with Cloud routing, proposed possible solution, and developed the whole system’s structure. For a proper study of the efficiency, the experiment was conducted using the developed system’s prototype for real-life cases, each represents own cluster size (several topologies by each size), used sizes are: 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27 and 29. Expectable results for this research – decrease the time of cluster’s reaction on topology changes (delay, needed to renew routing tables), which improves system’s adaptivity.
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Abstract

In the calculations presented in the article, an artificial immune system (AIS) was used to plan the routes of the fleet of delivery vehicles supplying food products to customers waiting for the delivery within a specified, short time, in such a manner so as to avoid delays and minimize the number of delivery vehicles. This type of task is classified as an open vehicle routing problem with time windows (OVRPWT). It comes down to the task of a traveling salesman, which belongs to NP-hard problems. The use of the AIS to solve this problem proved effective. The paper compares the results of AIS with two other varieties of artificial intelligence: genetic algorithms (GA) and simulated annealing (SA). The presented methods are controlled by sets of parameters, which were adjusted using the Taguchi method. Finally, the results were compared, which allowed for the evaluation of all these methods. The results obtained using AIS proved to be the best.
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