Dynamic task offloading for Internet of Things in mobile edge computing via deep reinforcement learning

Guo

Wireless Communications and Mobile Computing

et al. 2022

3D object detection is a significant aspect of the perception module in autonomous driving; however, with current technology, data sharing between vehicles and cloud servers for cooperative 3D object detection under the strict latency requirement is limited by the communication bandwidth. The sixth-generation (6G) networks have accelerated the transmission rate of the sensor data significantly with extreme low-latency and high-speed data transmission. However, which sensor data format and when to transmit it are still challenging. To address these issues, this study proposes a cooperative perception framework combined with a pillar-based encoder and Octomap-based compression at edges for connected autonomous vehicles to reduce the amount of missing detection in blind spots and further distances. This approach satisfies the constraints on the accuracy of the task perception and provides drivers or autonomous vehicles with sufficient reaction time by applying fixed encoders to learn a representation of point clouds (LiDAR sensor data). Extensive experiment results show that the proposed approach outperforms the previous cooperative perception schemes running at 30 Hz, and the accuracy of the object bounding box results in further distances (greater than 12 m). Furthermore, this approach achieves a lower total delay for the procession of the fusion data and the transmission of the cooperative perception message. To the best of our knowledge, this study is the first to introduce a pillar-based encoder and Octomap-based compression framework for cooperative perception between vehicles and edges in connected autonomous driving.

Section: Current Cooperative Perception Schemesmentioning

confidence: 99%

Pillar-Based Cooperative Perception from Point Clouds for 6G-Enabled Cooperative Autonomous Vehicles

Guo

Wireless Communications and Mobile Computing

et al. 2022

“…The heterogeneous nature of different service providers provides customers with diversified services. Regional workload: due to geographical dispersion, the workload can be redirected to the cloud closer to the customer [21]. Convenience: through the unified visualization of various available services, it provides customers with the convenience of relevant services.…”

Section: Concept Definition and Influence Factors 21 Multi-cloud Sche...mentioning

confidence: 99%

Research on Influencing Factors of Artificial Intelligence Multi-cloud Scheduling Applied Talents Training based on DEMATEL-TAISM

Bian

Xie

2022

Preprint

With the rapid development of Internet of things (IoT) technology and the increasing popularity of IoT devices, more and more computing intensive IoT applications came into being. However, due to the limited resources of IoT devices, cloud computing systems are required to compute intensive IoT applications. Further, in order to be subject to a single cloud computing service provider, multi-cloud computing will become an IoT service cloud computing solution. In view of the complexity of multi-cloud scheduling, the application of artificial intelligence will be an important technology to solve the multi-cloud scheduling of IoT. The corresponding talent training plays an important role in the development and implementation of the artificial intelligence multi-cloud scheduling of IoT. Firstly, this paper studies the key influencing factors of IoT’s artificial intelligence multi-cloud scheduling applied talents training. Combined with the characteristics of the development of China’s artificial intelligence industry, this paper summarizes the influencing factors from the four dimensional training path of government departments, universities, enterprises and scientific research institutes. According to the training purpose of artificial intelligence multi-cloud scheduling applied talents training, build an artificial intelligence multi-cloud scheduling applied talents training influencing factor index system. Then, the DEMATEL method is used to establish multiple correlation matrices according to the direct influence correlation between the factors, and calculate the influence degree, affected degree, center degree and cause degree of the factors; Using the improved AISM method, based on the idea of game confrontation, from the two opposite extraction rules of result priority and cause priority respectively, a group of confrontation level topological maps with comprehensive influence values reflecting the interaction of factors are obtained, and relevant suggestions are put forward in order to provide reference for the training of artificial intelligence multi-cloud scheduling applied talents.

“…In order to verify the performance of the proposed algorithm in this paper for optimization, this section conducts a comprehensive comparison with the improved algorithms of FOA, CFOA and IFFO, in eight commonly used benchmark functions. Firstly, this section lists the experimental environment and parameter settings of this paper; secondly, the eight benchmark functions are analyzed and demonstrated in detail; nally, a graphical presentation and detailed analysis are made based on the experimental results [16][17][18].…”

Section: Experimental Analysismentioning

confidence: 99%

Locality Sensitive Hashing-aware Fruit Fly Optimization Algorithm and its application in Edge Server Placement

Cao

Liu

Jin

2022

Preprint

As is well known that global optimization ability of Fruit fly Optimization Algorithm (FOA)is weak because it is easy to fall into local optimum. In this paper a Fruit Fly Optimization Algorithm based on Locality Sensitive Hashing-aware (LSHFOA)was proposed. The locality sensitive hashing mechanism to optimize the generation mechanism for swarm population individuals was used, which can improve the individual diversity of the population. Meanwhile, when the fruit fly population falls into local optimum, the locality sensitive hashing mechanism was adopted to change the population location, which is used for jumping out of local optimal limits. To verify the performance of LSHFOA, it was compared with FOA and its improvement algorithms CFOA, and IFFO with 8 representative benchmark functions. A large number of experimental results showed that LSHFOA has a faster convergence speed and higher precision of optimization for function optimization, especially in high-dimensional multi-peak functions. In the end, we apply this new Algorithm to solve the edge server placement (ESP) problem in the edge computing environment, the experimental results show that the new algorithm has excellent application effect.