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2024, 22(9):925-932.
DOI: 10.11805/TKYDA2024082
Abstract:
Artificial Intelligence of Things(AIoT), as a deep integration of artificial intelligence and IoT technologies, has rapidly emerged as an important development direction for the new generation of information technology. To fully explore the potential of AIoT technology, this paper focuses on the application and development of AIoT technology in the field of intelligent connected vehicles. It delves into the integrated system architecture of AIoT and intelligent connected vehicles, systematically reviews the latest technological applications of AIoT in this field, and provides strategic recommendations for leveraging AIoT technology to promote the advancement and development of intelligent connected vehicle technology.
Artificial Intelligence of Things(AIoT), as a deep integration of artificial intelligence and IoT technologies, has rapidly emerged as an important development direction for the new generation of information technology. To fully explore the potential of AIoT technology, this paper focuses on the application and development of AIoT technology in the field of intelligent connected vehicles. It delves into the integrated system architecture of AIoT and intelligent connected vehicles, systematically reviews the latest technological applications of AIoT in this field, and provides strategic recommendations for leveraging AIoT technology to promote the advancement and development of intelligent connected vehicle technology.
2024, 22(9):933-943.
DOI: 10.11805/TKYDA2024112
Abstract:
Narrow Band Internet of Things(NB-IoT), as a low-power wide area network technology, is specially designed to connect a large number of low-power devices. The low-orbit satellite IoT based on this technology has lower transmission loss and delay, and can achieve seamless coverage of the earth through constellations. However, low-orbit satellites are highly dynamic and face QoS requirements from different users. These factors greatly affect the throughput performance of existing resource scheduling algorithms. In response to these challenges, this paper proposes a high-throughput NB-IoT low-orbit satellite IoT resource scheduling algorithm by comprehensively considering satellite channel characteristics, reliability and delay requirements between different users, and differential Doppler caused by high satellite dynamics in a scenario where a large number of IoT users request wireless resources and time-frequency resources are limited. Simulation results show that compared with existing methods, the resource scheduling algorithm proposed in this paper shows significant performance improvement in system throughput.
Narrow Band Internet of Things(NB-IoT), as a low-power wide area network technology, is specially designed to connect a large number of low-power devices. The low-orbit satellite IoT based on this technology has lower transmission loss and delay, and can achieve seamless coverage of the earth through constellations. However, low-orbit satellites are highly dynamic and face QoS requirements from different users. These factors greatly affect the throughput performance of existing resource scheduling algorithms. In response to these challenges, this paper proposes a high-throughput NB-IoT low-orbit satellite IoT resource scheduling algorithm by comprehensively considering satellite channel characteristics, reliability and delay requirements between different users, and differential Doppler caused by high satellite dynamics in a scenario where a large number of IoT users request wireless resources and time-frequency resources are limited. Simulation results show that compared with existing methods, the resource scheduling algorithm proposed in this paper shows significant performance improvement in system throughput.
2024, 22(9):944-951.
DOI: 10.11805/TKYDA2024023
Abstract:
Aiming at the traffic surge problem caused by massive smart device access in Power Internet of Things(PIoT), a resource scheduling strategy of Mobile Edge Computing(MEC) with delay and energy consumption equalization is proposed. Considering the channel conditions, the safety temperature protection mechanism of electric equipment and the energy consumption of equipment, the energy consumption model and thermal power consumption constraints on the equipment side are constructed based on the Landaer principle. Under the premise of ensuring queue stability, the long-term average time energy consumption of the system is minimized by jointly optimizing the task offloading decision, transmission power and computational resource allocation. To solve this stochastic optimization problem, Lyapunov theory is introduced to transform the problem into a deterministic optimization problem for each time slot. Simulation results show that this strategy is able to reduce the system energy consumption relative to the baseline scheme and achieve an equilibrium between energy consumption and delay.
Aiming at the traffic surge problem caused by massive smart device access in Power Internet of Things(PIoT), a resource scheduling strategy of Mobile Edge Computing(MEC) with delay and energy consumption equalization is proposed. Considering the channel conditions, the safety temperature protection mechanism of electric equipment and the energy consumption of equipment, the energy consumption model and thermal power consumption constraints on the equipment side are constructed based on the Landaer principle. Under the premise of ensuring queue stability, the long-term average time energy consumption of the system is minimized by jointly optimizing the task offloading decision, transmission power and computational resource allocation. To solve this stochastic optimization problem, Lyapunov theory is introduced to transform the problem into a deterministic optimization problem for each time slot. Simulation results show that this strategy is able to reduce the system energy consumption relative to the baseline scheme and achieve an equilibrium between energy consumption and delay.
2024, 22(9):952-958.
DOI: 10.11805/TKYDA2024034
Abstract:
Wireless Sensor Network(WSN) in the power system can sense and collect the status of the working equipment and environmental data in real time, which is an important technology to promote the development of smart grid. Aiming at the special requirements of network survival time, transmission delay, and transmission packet loss rate of WSN in substation scenarios, a WSN routing scheme based on reinforcement learning is proposed. The sending process of packets in WSN is abstracted as a Markov Decision Process(MDP), the rewards are reasonably set according to the optimization objective, and the optimal routing solution method based on Q-learning is given. Simulation results and numerical analysis show that the proposed scheme outperforms the benchmark scheme in terms of network survival time, transmission delay, and packet loss rate.
Wireless Sensor Network(WSN) in the power system can sense and collect the status of the working equipment and environmental data in real time, which is an important technology to promote the development of smart grid. Aiming at the special requirements of network survival time, transmission delay, and transmission packet loss rate of WSN in substation scenarios, a WSN routing scheme based on reinforcement learning is proposed. The sending process of packets in WSN is abstracted as a Markov Decision Process(MDP), the rewards are reasonably set according to the optimization objective, and the optimal routing solution method based on Q-learning is given. Simulation results and numerical analysis show that the proposed scheme outperforms the benchmark scheme in terms of network survival time, transmission delay, and packet loss rate.
2024, 22(9):959-966.
DOI: 10.11805/TKYDA2023410
Abstract:
In pedestrian positioning methods, the strapdown inertial navigation system requires processing data from the Inertial Measurement Unit(IMU) sensors, and the position of the pedestrian is obtained after algorithmic processing, thus placing high demands on the real-time performance and low power consumption of the chip. Since most pedestrian positioning algorithms are developed based on floating-point sensor data, the terminal device is generally required to handle floating-point data. The fifth-generation Reduced Instruction Set Computer(RISC-V) architecture, as an open-source architecture, can save on architectural licensing fees and has a wide range of applications in the field of the Internet of Things. Moreover, its floating-point(F) and vector(V) high-performance extension instructions can well meet the real-time requirements of pedestrian positioning algorithms. In response to the specific performance requirements of the pedestrian positioning system, a System on Chip (SoC) for pedestrian positioning based on a floating-point core vector processor optimized RISC-V architecture is proposed and verified in actual systems. A performance comparison analysis with several quasi-32-bit architecture RISC-V processors and standard processor schemes of algorithm-specific IPs (locate_IP) generated by High-Level Synthesis(HLS) components shows that the design has achieved a 34-fold improvement in performance and a 5.6-fold improvement in energy efficiency, meeting the requirements of micro terminals.
In pedestrian positioning methods, the strapdown inertial navigation system requires processing data from the Inertial Measurement Unit(IMU) sensors, and the position of the pedestrian is obtained after algorithmic processing, thus placing high demands on the real-time performance and low power consumption of the chip. Since most pedestrian positioning algorithms are developed based on floating-point sensor data, the terminal device is generally required to handle floating-point data. The fifth-generation Reduced Instruction Set Computer(RISC-V) architecture, as an open-source architecture, can save on architectural licensing fees and has a wide range of applications in the field of the Internet of Things. Moreover, its floating-point(F) and vector(V) high-performance extension instructions can well meet the real-time requirements of pedestrian positioning algorithms. In response to the specific performance requirements of the pedestrian positioning system, a System on Chip (SoC) for pedestrian positioning based on a floating-point core vector processor optimized RISC-V architecture is proposed and verified in actual systems. A performance comparison analysis with several quasi-32-bit architecture RISC-V processors and standard processor schemes of algorithm-specific IPs (locate_IP) generated by High-Level Synthesis(HLS) components shows that the design has achieved a 34-fold improvement in performance and a 5.6-fold improvement in energy efficiency, meeting the requirements of micro terminals.
