۱- Introduction

۲- Related work

۳- Proposed the cognitive agent paradigm and design issues

۴- Problem formulation and solution

۵- Performance analysis

۶- Conclusion

References

Abstract

The service of mobile network develops rapidly nowadays, which generates various computing and resource-intensive applications, such as Internet of vehicles and virtual reality. Mobile edge computing (MEC) is close to data source and users, so terminals can execute tasks at the edge of network. In this way, the heavy load on core network can be relieved and tasks can be executed effectively. However, the demands of users vary from each other and users move all the time. It is difficult for the existing way of service supply to meet demands of all users. Cognitive Agent (CA) is put forward in this paper to help users cache and execute tasks on MEC in advance. In detail, CA is used to build personalized model combined with users’ behavior data. At the same time, it uses Long short-term memory neural network to forecast the moving trajectory of terminal equipment and the service types to be requested, uses the prediction result to generate caching strategy, cache business and shorten the delay of task execution. Besides, to further reduce the stress on MEC, we propose the collaboration of computing, communicating and caching resource with neighboring users’ equipment. To verify the effectiveness of CA, we build a model that assesses the performance of the system. Finally, we design a simulation experiment to execute resource request and resource collaboration. The result of the experiments show that CA can improve the efficiency of communication network, relieve the stress on network and improve the quality of services to users.

Introduction

At present, emerging information services and applications increase rapidly with the development of wireless communication and Internet of Things (IoT) [1,2]. More and more intelligent devices, such as wearable devices, remote medical system and virtual/augmented reality (VR/AR), need advanced communication technology and computing ability to guarantee ultra-reliable low-latency communication (URLLC), which is a great challenges for network suppliers [3,4]. Besides, wireless access device in large quantity outputs massive quantity of data all the time [5]. The reasonable use of valuable information is greatly significant for improving the quality of services supplied to users [6]. Mobile edge computing (MEC) supplies infrastructures of communication, computing and caching at the network edge that is close to data source or users [7]. It offers cloud services and information technology environment for edge applications and supplies high-quality communication services to users [8–۱۳]. The advantages of the emerging service model MEC can be summarized below: (i). Due to be close to users or data source, some problems can be solved, such as long delay and heavy data traffic. It well supports the real-time and bandwidth-dense Internet of Things facilities, especially for the emotion-aware multimedia systems [14–۱۶]. Taking unmanned driving for example, automobile needs to calculate running speed and the distance from others and sense surroundings in real time.