A survey on semantic communications:Technologies,solutions,applications and challenges

Semantic Communication(SC)has emerged as a novel communication paradigm that provides a receiver with meaningful information extracted from the source to maximize information transmission throughput in wireless networks,beyond the theoretical capacity limit.Despite the extensive research on SC,there is a lack of comprehensive survey on technologies,solutions,applications,and challenges for SC.In this article,the development of SC is first reviewed and its characteristics,architecture,and advantages are summarized.Next,key technologies such as semantic extraction,semantic encoding,and semantic segmentation are discussed and their corresponding solutions in terms of efficiency,robustness,adaptability,and reliability are summarized.Applications of SC to UAV communication,remote image sensing and fusion,intelligent transportation,and healthcare are also presented and their strategies are summarized.Finally,some challenges and future research directions are presented to provide guidance for further research of SC.

NOMA-Based Energy-Efficient Task Scheduling in Vehicular Edge Computing Networks: A Self-Imitation Learning-Based Approach

Mobile Edge Computing(MEC)is promising to alleviate the computation and storage burdens for terminals in wireless networks.The huge energy consumption of MEC servers challenges the establishment of smart cities and their service time powered by rechargeable batteries.In addition,Orthogonal Multiple Access(OMA)technique cannot utilize limited spectrum resources fully and efficiently.Therefore,Non-Orthogonal Multiple Access(NOMA)-based energy-efficient task scheduling among MEC servers for delay-constraint mobile applications is important,especially in highly-dynamic vehicular edge computing networks.The various movement patterns of vehicles lead to unbalanced offloading requirements and different load pressure for MEC servers.Self-Imitation Learning(SIL)-based Deep Reinforcement Learning(DRL)has emerged as a promising machine learning technique to break through obstacles in various research fields,especially in time-varying networks.In this paper,we first introduce related MEC technologies in vehicular networks.Then,we propose an energy-efficient approach for task scheduling in vehicular edge computing networks based on DRL,with the purpose of both guaranteeing the task latency requirement for multiple users and minimizing total energy consumption of MEC servers.Numerical results demonstrate that the proposed algorithm outperforms other methods.

FOG COMPUTING ENABLED INTERNET OF EVERYTHING

With the rapid development of ubiquitous networks and smart cities,the connection and communication of Internet of Everything(IoE)have drawn great attention from both academia and industry.The main challenge for constructing IoE is to enable real-time communication and high-efficiency computing among mobile devices.Mobile fog computing is promising to lower communication delay and offload network traffic.However,how to realize fog-enabled communication and computing in IoE with high-dynamic and heterogeneous network characters has not been fully investigated.Furthermore,deployment and reliable communications among fog nodes are also challenging.

A secure routing scheme based on social network analysis in wireless mesh networks

As an extension of wireless ad hoc and sensor networks, wireless mesh networks(WMNs) are employed as an emerging key solution for wireless broadband connectivity improvement. Due to the lack of physical security guarantees, WMNs are susceptible to various kinds of attack. In this paper, we focus on node social selfish attack, which decreases network performance significantly. Since this type of attack is not obvious to detect, we propose a security routing scheme based on social network and reputation evaluation to solve this attack issue. First, we present a dynamic reputation model to evaluate a node's routing behavior, from which we can identify selfish attacks and selfish nodes. Furthermore, a social characteristic evaluation model is studied to evaluate the social relationship among nodes. Groups are built based on the similarity of node social status and we can get a secure routing based on these social groups of nodes. In addition, in our scheme, nodes are encouraged to enter into multiple groups and friend nodes are recommended to join into groups to reduce the possibility of isolated nodes. Simulation results demonstrate that our scheme is able to reflect node security status, and routings are chosen and adjusted according to security status timely and accurately so that the safety and reliability of routing are improved.