Combined Vehicles Trace from SUMO and OMNET++ for 5G Mobility Prediction - Filiposka, Sonja; Cristina Bernad Canto; Katja Gilly (My University, 2024)
Combined logs from SUMO and OMNET++ that provide the mobility trace of vehicles in a chosen area defined using Open Street Maps. The dataset is used as an input to an LSTM model for next cell prediction.
Parameters: t - timestamp in s, veh_id - vehicle id ,x - real world x coordinate, y - real world y coordinate, angle - angle of mobility vector, speed - current velocity in m/s, pos - position id from SUMO, lane - number of lane on multilane roads, slope - slope of terrain, signals - vehicle signals from SUMO ,x-1,y-1,x-2,y-2,x-3,y-3,x-4,y-4,x-5,y-5,x-6,y-6,x-7,y-7 = the x and y real world coordinates in the past where -x is how many seconds in the past, Time - same as t - timestamp in s, Object - same as veh_id, averageCqiDl, distance, measuredSinrDl, measuredSinrUl, rcvdSinrDl, rlcDelayDl, rlcPduDelayDl, rlcThroughputDl = 5G network communication parameters from OMNET, servingCell - cell id to which the vehicle is connected, servingCell-1,servingCell-2,servingCell-3,servingCell-4,servingCell-5,servingCell-6,servingCell-7 = cell to which the vehicle was connected in the past where x is the number of seconds in the past, servingCell1,servingCell2,servingCell3,servingCell4,servingCell5,servingCell6,servingCell7 = cell to which the vehicle will be connected in the future where x is the number of seconds in the future (these are the predicted output of the trained model)
Keywords: mobility, data, cell prediction, trace, logs