Traditional factories are turning into smart factories with the advent of various ICT technologies, and various control decisions are derived by AI technologies. In this circumstance, runtime verification of a control command is important for zero-defect manufacturing processes but challengeable because factories of the future are highly complex and heterogeneous systems. In this paper, we propose DigTwinOps, a Digital Twin framework for Runtime Verification of Cyber-Physical Production Systems (CPPSs). DigTwinOps features a Digital Twin Execution Engine (DTEE) that manages a Digital Twin Model to synchronize states of a real CPPS object in a production environment. With a monitoring and simulation combination process, a human worker can observe the states of the CPPS object and verify the effectiveness of control commands before applying it to a real production environment. The proposed framework is applied to a CPPS prototype production system, and the results show that the framework can work effectively in the controllability verification of control commands.

Data races are one of the most difficult types of bugs in concurrent multithreaded systems. It requires significant time and cost to accurately detect bugs in complex large-scale programs. Although many race detection techniques have been proposed by various researchers, none of them are effective in all aspects. In this paper, we compare the performance of five recent dynamic race detection techniques: FastTrack, Acculock, Multilock-HB, SimpleLock+, and causally precedes (CP) detection. We experimentally demonstrate the strengths and weaknesses of these dynamic race detection techniques in terms of their detection capability, running time, and runtime overhead using 20 benchmark programs with different characteristics. The comparison results show that the detection capability of CP detection does not differ from that of FastTrack, and that SimpleLock+ generates the lowest overhead among the hybrid detection techniques (Acculock, SimpleLock+, and Multilock-HB) for all benchmark programs. SimpleLock+ is 1.2 times slower than FastTrack on average, but misses one true data race reported from Mutilock-HB on the large-scale benchmark programs.

As the data rate and area capacity are enormously increased with the advent of 5G wireless communication, the network latency becomes a severe issue in a 5G network. Since there are various types of terminals in a 5G network such as vehicles, medical devices, robots, drones, and various sensors which perform complex tasks interacting with other devices dynamically, there is a need to handle heavy computing resource intensive operations. Placing a multiaccess edge computing (MEC) server at the base station, which is located at the edge, can be one of the solutions to it. The application running on the MEC platform needs a specific simulation technique to analyze complex systems inside the MEC network. We proposed and implemented a simulation as a service (SIMaaS) for the MEC platform, which is to offload the simulation using a Cloud infrastructure based on the concept of computation offloading. In the case study, the Monte-Carlo simulations are conducted using the proposed SIMaaS to select the optimal highway tollgate where vehicles are allowed to enter. It shows how clients of the MEC platform use SIMaaS to obtain certain goals.

Industrial Internet of Things (IIoT) applications can greatly benefit from leveraging edge computing. For instance, applications relying on deep neural network (DNN) models can be sliced and distributed across IIoT devices and the network edge to reduce inference latency. However, low network performance between IIoT devices and the edge often becomes a bottleneck. In this study, we propose ScissionLite, a holistic framework designed to accelerate distributed DNN inference using lightweight data compression. Our compression method features a novel lightweight down/upsampling network tailored for performance-limited IIoT devices, which is inserted at the slicing point of a DNN model to reduce outbound network traffic without causing a significant drop in accuracy. In addition, we have developed a benchmarking tool to accurately identify the optimal slicing point of the DNN for the best inference latency. ScissionLite improves inference latency by up to 15.7× with minimal accuracy degradation.