A proposition to exploit the partially linear structure of the nonlinear multicommodity flow optimization problem, Journal of Telecommunications and Information Technology, 2002, nr 3

Optimization problems arising in telecommunications are often large-scale nonlinear problems. Usually their big size is generated mainly by their linear parts but the existence of small or medium nonlinear parts prevents us from directly tackling them with linear solvers, which are efficient. Instead, the author has proposed a method to decompose big nonlinear problems into nonlinear and linear parts. Its coordination procedure uses two auxiliary solvers: quadratic and pure nonlinear. The procedure falls in the class of projection methods. Special cuts proposed by the author allow to avoid an excessive zigzagging while not enormously increasing the complexity of both the parts. The validity of these cuts can be analyzed within the framework of obtuse cone model. Here the author summarizes the method and analyses its applicability to nonlinear multicommodity flow problems. The structure and particular sizes of this problem make the method useful. The considerations are illustrated by a numerical example with a multicommodity flow problem

An Online Stream Monitoring Algorithm for Fraud Detection in the Transport of Goods, Journal of Telecommunications and Information Technology, 2020, nr 4

The process of monitoring vehicles used in road transports plays an important role in detecting fraud committed by drivers. Algorithm designers face a number of challenges, including large number of vehicles monitored, demands related to online calculations, and ability to easily explain fraud alarms triggered to supervisors who make final decisions about actions to be taken. In this paper, we propose rather general, lightweight stream, online heuristics. The vehicle’s position is periodically controlled by a GNSS device. The algorithm detects potential illegal activities along the route between the origin and the destination. Anomalies in the vehicle’s trajectory are detected, based on a multi-resolution analysis of the economy of routes. The economy metric is easily understood and verifiable by controllers. The solution is also capable of identifying clearly suspicious trajectories that popular geofencing approaches would overlook. The scale on which the solution may be adopted is obtained thanks to the stream – like nature of the algorithm: essentially, the resources used do not increase along with the size of the input stream (the number of GNSS frames generated for the vehicle). An experiment illustrating the algorithm’s viability is presented as wel

An Online Stream Monitoring Algorithm for Fraud Detection in the Transport of Goods

The process of monitoring vehicles used in road transports plays an important role in detecting fraud committed by drivers. Algorithm designers face a number of challenges, including large number of vehicles monitored, demands related to online calculations, and ability to easily explain fraud alarms triggered to supervisors who make final decisions about actions to be taken. In this paper, we propose rather general, lightweight stream, online heuristics. The vehicle’s position is periodically controlled by a GNSS device. The algorithm detects potential illegal activities along the route between the origin and the destination. Anomalies in the vehicle’s trajectory are detected, based on a multi-resolution analysis of the economy of routes. The economy metric is easily understood and verifiable by controllers. The solution is also capable of identifying clearly suspicious trajectories that popular geofencing approaches would overlook. The scale on which the solution may be adopted is obtained thanks to the stream – like nature of the algorithm: essentially, the resources used do not increase along with the size of the input stream (the number of GNSS frames generated for the vehicle). An experiment illustrating the algorithm’s viability is presented as well