Deploying AI services on battery-powered mobility platforms such as autonomous vehicles, mobile robots, and large scale IoT sensor networks requires determining the most suitable execution environment for each workload across the cloud, edge, and device computing options. Because every placement option imposes different trade-offs among Accuracy, Latency, and Energy efficiency (ALE), stakeholders face a difficult, mission-critical decision that existing studies seldom address in a holistic, mission-aware manner. To fill this gap, we introduce the Mission-driven ALE (MALE) evaluation method. MALE couples ALE metrics with explicit mission objectives by allowing analysts to apply customizable weights to each criterion. The evaluation results are aggregated and visualized as heatmaps, helping transform a previously heuristic and opaque placement decision (black-box) into a more transparent and interpretable process (white-box). We examine the applicability of MALE through three representative case studies: Autonomous Vehicles, Real-Time Robotics, and IoT Sensor Networks, each reflecting distinct ALE priorities. By supplying a structured, mission-aware decision-support method, MALE strengthens stakeholder confidence and accelerates the optimization of AI service placement across the cloud–edge–device continuum, offering a practical foundation for future validation in real-world deployments.

While model-mediated teleoperation (MMT) is an effective alternative for ensuring both transparency and stability, its potential in transmitting surface haptic texture is not yet explored. This paper introduces the first MMT framework capable of sharing surface haptic texture. The follower side collects physical signals contributing to haptic texture perception, e.g., high frequency acceleration, and streams them to the leader side. The leader side uses the signals to build and update a local measurement-based texture simulation model that reflects the remote surface. At the same time, the leader runs local simulation using the model, resulting in non-delayed, stable, and accurate feedback of texture. Considering that rendering haptic texture needs tougher real-time requirements, e.g., higher update rate and lower action-feedback latency, MMT can be a perfect platform for remote texture sharing. An initial proof-of-concept system supporting single and homogeneous surface is implemented and evaluated, demonstrating the potential of the approach.

The rapid development of the AI industry had led to the widespread utilization of numerous edge devices in the real world. However, AI services still heavily rely on high-performance computing systems, which means that these devices are not being effectively utilized. To address these issues, EdgeCPS technology has emerged. EdgeCPS is a technology that supports the seamless execution of various services, including AI applications, through the interconnection of edge devices and edge servers, as well as resource and function augmentation. In order to appropriately utilize edge devices in an EdgeCPS environment, the paper proposes an EdgeCPS-AI knowledge sharing model for supporting computing partition services. The proposed EdgeCPS-AI knowledge is represented as a graph, which systematically structures AI service-related information that arises on virtual and physical edge devices. This knowledge provides not only partitioned AI weighted models in order to support the characteristics of EdgeCPS, but also enables flexible provision of various information required for AI services. Thus, this approach can facilitate the reconstruction of AI services according to service requirements and maximize the utilization of edge devices. To achieve this, a microservice-based computing partition AI service is devised and experimentally proven by constructing a Kubernetes system using 8 heterogeneous edge devices and a graph DB system consisting of 2,762 nodes. Experimental results show that performing computing partition services on edge devices is significantly more efficient in terms of overall resource consumption, with up to 85.57% improvement, as compared to performing the same tasks on high-performance computing systems.

This paper presents a new way of data abstraction for visual and haptic representations in immersive analytics using a mid-air haptic display. Visual and haptic abstraction is proposed to transform raw data (wind tunnel data) into another form of data for effective visual and haptic data mapping. Three main features are extracted: (i) Magnitude of Velocity, (ii) Recirculation Region, and (iii) Vorticity. For each feature, visual and haptic abstractions are defined based on data characterization and data reduction. A preliminary study shows a promising direction toward multimodal data interaction in immersive analytics.

This paper presents MetaTwin, a collaborative Metaverse platform that supports one-to-one spatiotemporal synchrony between physical and virtual spaces. The users can interact with other users and surrounding IoT devices without being tied to physical spaces. Resource sharing is implemented to allow users to share media, including presentation slides and music. We deploy MetaTwin in two different network environments (i.e., within the US, Korea-US international) and summarize users’ feedback about the experience.

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.

Simulation is a method of predicting future events and system states through analysis of the state model of the system over time. It can support to make decisions of mobile terminals (ex. threat assessment in an autonomous vehicle) operating in various situations under uncertain reality. Unlike a single simulation, which is executed in one mobile terminal using data collected from the same terminal, a multiple simulation, which supports to make sophisticated decisions through data collected from multiple terminals, is not only complicated to implement but also requires a large number of computations and has network delay problem in collecting data from plenty of data sources [1]. Consequently, it is impossible to implement in a single mobile terminal.