Mobile Edge Intelligence and Computing of Intelligent Vehicle Internet – Jahanagahi

Mobile Edge Intelligence and Computing of Intelligent Vehicle Internet

HONG KONG, May 10, 2022 (GLOBE NEWSWIRE) — WIMI Hologram Academy, working in partnership with the Holographic Science Innovation Center, has written a new technical article describing their exploration of the edge intelligence and edge computing in the field of vehicle internet. This article follows below:

In the contemporary world, the application of virtual reality in the field of vehicle internet has become increasingly important. Compared with traditional networks, the design of cache algorithms on the Internet of Vehicles is more challenging, mainly due to the high mobility of vehicles, the frequently changing content requirements, and the harsh communication environment.

Edge caching technology is used on the Internet of Vehicles to assist in content delivery by storing or exacting content on the edge server. Scientists who are from WIMI Hologram Academy of WIMI Hologram Cloud Inc.(NASDAQ: WIMI), discussed the application of edge intelligence and edge computing in the field of vehicle internet, which includes two different scenarios depending on the role of the vehicle, including research on vehicles with different roles (ie, VaaC, and VaaS).

In the VaaC scenario, the vehicle acts as a cached-connected vehicle content consumer to access the required content from the edge server. In the VaaS scenario, the vehicle also acts as a content provider, caching the content in its storage unit. We focused on the placement of cache content, knowingly deciding which content should be cached, and divided existing studies into three categories: 1) The perceived time location cache, considering the temporary change of content importance/popularity; 2) the spatial location perception cache, which considers the different importance/popularity of the same content in different regions; 3) the mobility perception cache, which reduces the impact of vehicle mobility on content cache and delivery.

1. VaaC

With the edge servers widely deployed, timely content delivery services are available for vehicles passing through their coverage areas. Representative studies from traditional Internet of vehicles to smart Internet of vehicles are discussed in detail below.

1.1 Cache of the perceived time position

In the edge caching system, temporal locality includes two aspects: the freshness of the cached content and the time change of user requests. Considering the characteristics of the communication between the RSU (RoadSide Units) and the vehicle, the temporary data transmission problem is described as an NP-hard problem. The authors then develop a heuristic scheduling algorithm based on the user request requirements (eg, time limit) to improve the request service opportunity.

1.2 Spatial position-aware cache

In the vehicle environment, some information, such as traffic information, is related to the location of the vehicle. Therefore, the content freshness of the Internet of Vehicles may vary across different roads. For example, there is a deep learning-based caching solution to optimize the caching decisions for the Smart Internet of Vehicles, designed to reduce the delivery delays of entertainment content. In this scheme, the age and sex of passengers are detected by CNN (Convolutional Neural Network), and the multi-layer perception (MLP) is used to predict the appropriate content, caching the content on a specific regional edge server. The vehicle then determines which content can be accessed from the edge server based on the k-means algorithm and binary classification.

1.3 Cache of mobility perception

Providing a large capacity of content (such as video, music, and HD maps) to moving vehicles is challenging due to limited network capacity and intermittent connectivity. To minimize the download time of the vehicle, the problem of how to place large content on edge servers was studied. This paper develops three algorithms to mitigate the effect of vehicle mobility on cache performance. Others have proposed a caching strategy to minimize caching service latency in multiple EIS (Edge Information systems). Specifically, the mobility of the vehicle is predicted by a long, short-term memory (LSTM, Long Short-Term Memory) complex on a time series. Based on this work, an active caching strategy is developed using a deep reinforcement learning algorithm. Integrating edge caching and computing into EIS is a new research direction to address the problem of vehicle mobility and service life. In this case, how to effectively allocate limited resources is an important issue. For example, for the resource allocation problem in the integrated architecture, two joint optimization models are established to determine the optimal cache and computational decisions, and then solved based on deep reinforcement learning.

2. VaaS

Edge server-based caching is limited by coverage and unreliable connections to the vehicle. Plus, caching content on a moving vehicle is a good solution. By leveraging vehicle mobility, edge caching can provide more cost-effective and practically enhanced services. Existing studies related to this direction are as follows:

2.1 Cache of the perceived time position

For vehicle caching, due to the limited onboard storage resources, the temporary locality of the content will not only affect the cache services but also affect the implementation of other functions on the vehicle. Therefore, determining how long the content will be cached is an important question. Previous methods for edge caching based on the content size have been proposed. We propose a new caching method, Hamlet, to generate content diversity between adjacent nodes by determining the frequency of cache updates for large and small capacity content. Based on this scheme, users can receive different content from adjacent cache nodes in a short time, improving the cache efficiency.

2.2 Spatial position-aware cache

Due to the flexible mobility of vehicles and multi-hop data transfer, VaaS mode will improve location-based cache performance. Specifically, by dividing urban areas into multiple hotspots based on dynamic mobility and vehicle density, historical data are locally matched to predict the future vehicle trajectory. The best utility of cache services can be achieved by integrating vehicles frequently visiting these hotspots into a cooperative cache scheme. In order to mitigate the impact of mobile and communication vulnerabilities on cache services, there is also a dynamic relay strategy for the in-car cache. The hot spot area content can be maintained by the cache scheme and intervehicle communication.

2.3 Cache of mobility perception

The predictable mobility of the vehicle can be exploited to improve the efficiency of cache-assisted content delivery. Mobile-aware caching in traditional device-to-device networks has been well studied, and these methods have recently been extended to vehicle networks. In previous studies, scholars have explored a new cache service where content cached in a vehicle can be requested by mobile or static user requests within the communication range. In this scenario, the relationship between caching vehicles and mobile users is the key to designing caching strategies.

3. Cached-enabled applications

In addition to typical content sharing and delivery services, there is great interest in developing new applications supported by edge cache servers. The following begins with cache-assisted sensing and positioning, and then introduces other applications in the Internet of Vehicles and intelligent transportation systems.

3.1 Cache-assisted perception and localization

Cache assistance perception includes automatic overtaking, cooperative collision avoidance, perspective, bird’s eye view, and other functions, in which the edge cache provides the vehicle with driving assistance and improves traffic safety perception content. On the other hand, the cache-assisted location includes vulnerable road user (VRU) discovery, where the edge cache improves the collaboration of RSU, vehicles, and pedestrians by caching location information.

3.2 Other applications

Edge caching is constantly emerging on the Internet of Vehicles, and some of these applications are described below.

(1) InfoRank: For the sake of efficient city perception, an information-based InfoRank algorithm has been developed. The proposed algorithm selects and ranks some intelligent vehicles to undertake the urban sensing task. Therefore, the monitoring of the vicinity of these vehicles can be completed at very little cost. In this algorithm, the vehicle stores the sensing data as a data cache server, reducing the burden of the edge server.

(2) Over-The-Top (OTT): A new OTT content preview system can be designed by implementing the edge caching mechanism. The vehicle and RSU connections are predicted based on a real test platform. In addition, a content prevalence estimation scheme is proposed to estimate user content requests. After this, the user-requested content is proactively prefetched on the Edge server.

(3) Safety information sharing: Data sharing is an effective method, which can reduce the data loss caused by the unreliable sensor system, and overcome the problem of the limited perception range of autonomous vehicles. Therefore, data security becomes an important task and being able to design a safe information-sharing system for autonomous vehicles. The system is designed to improve data security in two scenarios: false data propagation and vehicle tracking.

(4) Traffic management: In order to analyze the impact of edge caching on traffic control, earlier research proposed a traffic control scheme based on edge cache. Traditionally, it is difficult to obtain the optimal state of the traffic system. Therefore, the optimal state of the traffic network is contradictory to the user equilibrium. To reveal the relationship between the user equilibrium and the optimal state of the system, a communication cost model for cache-able vehicles is proposed. With this scheme, the traffic networks can be optimized from the communication aspects with the help of the edge cache.

Founded in August 2020, WIMI Hologram Academy is dedicated to holographic AI vision exploration and researches basic science and innovative technologies, driven by human vision. The Holographic Science Innovation Center, in partnership with WIMI Hologram Academy, is committed to exploring the unknown technology of holographic AI vision, attracting, gathering, and integrating relevant global resources and superior forces, promoting comprehensive innovation with scientific and technological innovation as the core, and carrying out basic science and innovative technology research.


Holographic Science Innovation Center

Email: pr@holo-science. com

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