@Override public boolean receiveData(String data, int id) { if (id != 0 && round_number >= K && data.length() != requested_bits[id] + 1 && data.length() != 0) { return false; } if (!is_first_round[id] && id != 0 && !obtained_sensor_reading[0]) { return false; } // first_reading / first sensor everybody is un-coded, and is assumed to be correct if (round_number < M || is_first_round[id] || id == 0) { is_first_round[id] = false; current_sensor_readings[id] = tools.binaryToDouble(data); obtained_sensor_reading[id] = true; } else { current_sensor_readings[id] = code_book.getDecodedValue(current_sensor_readings[0], data); obtained_sensor_reading[id] = true; } // calculate Y, prediction values, only after M readings if (round_number >= (M - 1)) { double temp = current_sensor_readings[id]; updatePastReadings(temp, id); this.updateInputData(id); this.trainNeuralNets(id); this.calculatePrediction(id); this.updateVarianceOfPrediction(id); } else { double temp_value = current_sensor_readings[id]; past_sensor_readings[id][M - round_number - 1] = temp_value; } diff[id] = current_sensor_readings[id] - predicted_sensor_readings[id][0]; boolean all_received = true; for (int i = 0; i < number_of_sensors; i++) { if (!obtained_sensor_reading[i]) { all_received = false; break; } } if (all_received) { Arrays.fill(obtained_sensor_reading, false); round_number++; double average = 0.0; for (int i = 0; i < number_of_sensors; i++) { average += Math.abs(diff[i]); } // System.out.println("Total prediction error: "+average); } return true; // received the data }