## 1 Introduction  
Nowadays, diesel engines are the prime movers in commercial transportation, power generation, and off-road applications. Their major advantages compared to other common alternatives are a better fuel economy, a lower carbon footprint, and an increased reliability. The demands on engine performance indicators--increased power output, lower fuel consumption, and emissions--require a better understanding of engine processes, further experimental investigations, and the implementation of control strategies based on numerical models. In four-stroke engines, the process of gas exchange requires a very rapid and total release of exhaust gas from cylinders into the atmosphere, which is evaluated by exhaust back pressure (EBP), a flow resistance indicator on the exhaust duct. Unsteady friction and inertial forces, which are dependent on gas velocity, shape, and dimensions of the passages, occur. EBP is defined as the gauge pressure at the turbine outlet for turbocharged engines or at the exhaust manifold outlet for naturally aspirated engines [1]. Diesel engine control based on numerical models tends to become more complicated as the number of independent variables increases, so multidimensional, flexible, and adaptive add-ons based on artificial intelligence (AI) are required; these systems are able to learn from experiments, solve non-linear problems, use scarce data, and predict phenomena. To the knowledge of the authors, among the high number of articles studying the artificial neural network (ANN) prediction of diesel engine performance, there have been a lack of studies with EBP as an input parameter. Therefore, the goal of this work was to provide a better understanding of EBP effects on diesel engine parameters, at different loads and speeds, based on engine experiments and AI simulations in order to assess the reasonable performance loss (expressed mainly in power loss) caused by the emission and noise abatement devices. The main contribution of the paper is the AI application in the interpretation of exhaust pressure loss consequences; the added value of such an approach is the possibility to assess performance decline by means of virtual sensors implemented on smart devices. The opportunity of the applied research was given by recent progress in cost-effective embedded systems with local processing, storage, and communication capabilities that enable the edge-computing/cloud-computing paradigm of the IoT (the Internet of Things). Centimeter-scale smart devices built around a small SoC (system on chip) IC (integrated circuit) with a WiFi and/or a BLE (Bluetooth Low Energy) radio interface for NFC (near field communications) and a PCB (printed circuit board) antenna are now able to acquire sensor signals via integrated ADCs (analog-to-digital converters). Local signal conditioning and lower/medium complexity processing are performed and results are \"published\" in the cloud in real-time. These CPS (cyber-physical systems)--the so called \"smart dust\"/\"radio dust\"--can also receive cloud directives and download data flows because they are fast-enough for \"cyber-critical communications\" with time constants much smaller than industrial ones, particularly than vehicular ones. Internet protocols drive IoT WSAN (wireless sensors and actuator networks) that build a pervasive communication eco-system for a test-bench and even for vehicular add-ons to traditional CANs (computer area networks)/device nets on-board. The software subsystems of these compact smart devices are also shrunken down to mini-programs that reside in flash, non-volatile memory chips. Such firmware is built in a low scale IDE (integrated development environment), e.g., Arduino(tm). The actual efforts to embed AI in smart devices are discussed from the cloud computing/edge computing perspective. In terms of methods and apparatus, the study of the EBP effect on diesel engine performance needed a test-bench with appropriate sensors and instrumentation.