Digital twins (DTs) are virtual replicas of physical assets, systems, and processes that enable real-time monitoring, simulation, and analysis to improve decision-making, optimize operations, increase efficiency, and achieve cost savings across various Internet of Things (IoT) applications. This paper introduces an IEEE 1451-based smart transducer digital twin framework (DTF) for IoT real-time monitoring and control applications. The framework consists of an IEEE 1451-based smart transducer physical twin (PT), its corresponding digital twin (DT), and a bidirectional information exchange between them for real-time monitoring and control. In this paper, the IEEE 1451.0 and P1451.1.6-based smart transducer DT is developed using the Node-RED platform and message queue telemetry transport (MQTT) protocol. The PT is developed using embedded devices, and a sensor and actuator development kit. The PT and DT communicate using 1451.0 and P1451.1.6 messages to exchange temperature sensor data for controlling the temperature of a smart home in real-time. The monitoring and control results of the smart home are provided in the paper to test and validate that the physical twin and digital twin work correctly.

The Internet of Things (IoT) is highly heterogeneous regarding smart sensors/devices, connectivity, communication protocols, and data formats. The major challenges of IoT ecosystems are fragmentation (or disintegration) and cross-domain interoperability. To overcome the challenges of interoperability, standardized interfaces and protocols, harmonized interactions, and interoperability testing are essential. This paper introduces a harmonization model of interactions among IoT devices with diverse interfaces/connectivity and communication protocols to achieve interoperability by harmonizing their interactions. A harmonization implementation of interactions of the IEEE 1451-based IoT devices and MODBUS devices has been provided in the paper to test and verify that the proposed harmonization model works well. This harmonization model and implementation of interactions among IoT devices will provide a solid foundation for the IEEE P1451.99 standard specification.

Modern edge artificial intelligence (AI) chipsets and edge-intelligence-enabled smart sensors frameworks support real-time data processing and event detection at the signal source. Beyond measuring local conditions and transmitting corresponding signals, AI-enabled smart sensors provide the capability to interpret and analyze signals through local analytical operations. Its well-known semantics provide an abstraction layer for better comprehension and self-descriptive applications. This paper identifies and explains the advantages of semantics and ontology engineering in integrating edge AI-enabled smart sensors and their applications into federated automotive systems. Building on demonstrated work using AudioSet for event detection, the application presented as an AI mechanic at the edge demonstrates how smart sensor integration necessitates the re-evaluation of how automotive diagnostic trouble codes (DTCs) are generated and processed. While DTCs and their logic are intended to facilitate correct assembly and repair, they do not always clearly indicate the underlying issue. This paper also underlines the need to review ontologies and semantic models for smart sensor standard specifications in light of AI-enabled smart sensors.

Internet of Things (IoT) technologies (e.g., power-efficient occupancy-based energy management systems) are increasingly deployed in commercial buildings to reduce building energy consumption. However, the sensors involved in such systems are rarely adopted in residential houses due to their relatively high costs and users’ privacy concerns. Low-cost and nonintrusive IoT sensors have been proposed for residential houses for use with machine-learning algorithms. Furthermore, such sensors may be triggered very infrequently due to their nonintrusive nature, and it can take several days/weeks to collect sufficient training data. There is a research gap in accurately detecting occupancy information in residential houses with limited training data. This article proposes a trust-based occupancy detection scheme, which achieves high detection accuracy based on limited training data collected by nonintrusive, low-cost sensors. First, rather than directly taking raw sensor data as inputs, the semantic meanings (i.e., human activity sequences) are extracted from the data based on the order of triggered sensors. Second, the extracted human activity sequences are fed into the proposed trust-based sequence matching scheme for further occupancy detection. Comprehensive experimental results show that, when compared to existing occupancy detection algorithms, the proposed scheme can reliably achieve higher accuracy, especially when only limited training data is available.

A co-simulation may comprise several heterogeneous federates with diverse spatial and temporal execution characteristics. In an iterative time-stepped simulation, a federation exhibits the Bulk Synchronous Parallel (BSP) computation paradigm in which all federates perform local operations and synchronize with their peers before proceeding to the next round of computation. In this context, the lowest performing (i.e., slowest) federate dictates the progression of the federation logical time. One challenge in co-simulation is performance profiling for individual federates and entire federations. The computational resource assignment to the federates can have a large impact on federation performance. Furthermore, a federation may comprise federates located on different physical machines as is the case for cloud and edge computing environments. As such, distributed profiling and resource assignment to the federation is a major challenge for operationalizing the co-simulation execution at scale. This paper presents the Execution Performance Profiling and Optimization (EXPPO) methodology, which addresses these challenges by using execution performance profiling at each simulation execution step and for every federate in a federation. EXPPO uses profiling to learn performance models for each federate, and uses these models in its federation resource recommendation tool to solve an optimization problem that improves the execution performance of the co-simulation. Using an experimental testbed, the efficacy of EXPPO is validated to show the benefits of performance profiling and resource assignment in improving the execution runtimes of co-simulations while also minimizing the execution cost.

A malicious process in a distributed system can fabricate its internal state in its communications with its peers. These state fabrications can cause other processes in the distributed system to make incorrect control decisions. Smart grid systems have a unique advantage in the detection of falsified state attacks because process control decisions have an observable effect on a shared physical infrastructure. The physical infrastructure acts as a high-integrity message channel that broadcasts changes in individual process states. This work proposes a new distributed security mechanism called physical attestation that combines physical feedback with methods from computer security to detect state fabrications in the smart grid. The theory of physical attestation is proven using an information flow security property called nondeducibility, and supported with experimental results from a simulation test bed.