Vehicle computing: Vision and challenges
Journal of Information and Intelligence2023
Vehicles have been majorly used for transportation in the last century. With the proliferation of onboard computing and communication capabilities, we envision that future connected vehicles (CVs) will be serving as a mobile computing platform in addition to their conventional transportation role for the next century. In this article, we present the vision of Vehicle Computing, i.e., CVs are the perfect computation platforms, and connected devices/things with limited computation capacities can rely on surrounding CVs to perform complex computational tasks. We also discuss Vehicle Computing from several aspects, including several case studies, key enabling technologies, a potential business model, a general computing framework, and open challenges.
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Connected Living—Part I
IEEE Internet Computing2023
With the recent, remarkable advancements in the Internet of Things, wireless communication, edge/cloud computing, and artificial intelligence, the connection between human beings and their surrounding environments has dramatically increased, entering a new era of connected living. New challenges and opportunities are also brought up in the complex interaction between human beings and technologies. The focus of this special issue of IEEE Internet Computing is on investigating new technologies and human-technology interactions that will enable connected living to enhance well-being management and quality of life. The articles in this special issue investigate and present new technologies, visions, and projects in human–technology interactions toward connected living.
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An Open Approach to Energy-Efficient Autonomous Mobile Robots
EEE International Conference on Robotics and Automation (ICRA)2023
Autonomous mobile robots (AMRs) have the capability to execute a wide range of tasks with minimal human intervention. However, one of the major limitations of AMRs is their limited battery life, which often results in interruptions to their task execution and the need to reach the nearest charging station. Optimizing energy consumption in AMRs has become a critical challenge in their deployment. Through empirical studies on real AMRs, we have identified a lack of coordination between computation and control as a major source of energy inefficiency. In this paper, we propose a comprehensive energy prediction model that provides real-time energy consumption for each component of the AMR. Additionally, we propose three path models to address the obstacle avoidance problem for AMRs.
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Fuel Rate Prediction for Heavy-Duty Trucks
IEEE Transactions on Intelligent Transportation Systems2023
Fuel cost contributes significantly to the high operation cost of heavy-duty trucks. Developing fuel rate prediction models is the cornerstone of fuel consumption optimization approaches for heavy-duty trucks. However, limited by accurate features directly related to the truck’s fuel consumption, state-of-the-art models show poor performance and are rarely deployed in practice. In this paper, we use the truck’s engine management system (EMS) and Instant Fuel Meter (IFM) to collect a three-month dataset during the period of December 2019 to June 2020. Seven prediction models, including linear regression, polynomial regression, MLP, CNN, LSTM, CNN-LSTM, and AutoML, are investigated and evaluated to predict real-time fuel rate.
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Towards Resilient Network Slicing for Satellite-Terrestrial Edge Computing IoT
IEEE Internet of Things Journal2023
Satellite-Terrestrial Edge Computing Networks (STECNs) emerged as a global solution to support multiple Internet of Things (IoT) applications in 6G networks. The enabling technologies to slice STECNs such as Software-Defined Networking (SDN), satellite edge computing, and Network Function Virtualization (NFV) are key to realizing this vision. In this paper, we survey and analyze network slicing solutions for STECNs. We discuss slice management and orchestration for different STECNs integration architectures, satellite edge computing, mmWave/THz, and AI solutions to make network slicing adaptive. In addition, we identify challenges and open issues to slice STECNs.
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