Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days.

The emergence of wireless body area network (WBAN) technology has brought hope and dawn to solve the problems of population aging, various chronic diseases, and medical facility shortage. The increasing demand for real-time applications in such networks, stimulates many research activities. Designing such a scheme of critical events while preserving the energy efficiency is a challenging task, due to the dynamic of the network topology, severe constraints on the power supply, and the limited computation power. The design of routing protocols becomes an essential part of WBANs and plays an important role in the communication stacks and has a significant impact on the network performance. In this paper, we briefly introduce WBAN and focus on the analysis of the routing protocol, classify, and compare the advantages and disadvantages of various routing protocols. Lastly, we put forward some problems and suggestions, which provides ideas for the follow-up routing design.

With the rapid development of industrial digitalization and intelligence, there is an urgent need to accurately depict the physical world in digital space, and, in turn, regulate and optimize the behavior of physical entities based on massive data collection and analysis. As a technology that combines virtual space and physical space, digital twin can satisfy all of the above needs, and has attracted widespread attention. Due to the promising application prospects of digital twins, both academia and industry have launched research in this field, and related studies have been conducted from different perspectives. Accordingly, some articles summarizing the existing work have also been published, but they are all from a single perspective, lacking a systematic introduction and summary. Based on this, this paper conducts a comprehensive review of the existing work on digital twins from four perspectives: data, model, network and application, and strives to gain a better understanding of the development of the field from the physical to the virtual and back to the physical. Meanwhile, current research challenges and future directions for the development of digital twins are all discussed.

As an emerging complex social network, the Social Internet of Vehicles (SIoV) potentially exposes user location privacy. In this letter, we propose a location privacy protection method based on double ${k}$ -anonymity that hides user locations and request information. The cloud server is introduced as a trusted third party to isolate the direct communication between users and the service provider, while correlation between identities and requests is also reduced by means of a permutation and combination method. Extensive simulations are conducted to demonstrate that the method can protect user location privacy to the greatest extent possible while still ensuring service availability.

With the development of Unmanned Air Vehicle (UAV) communication, Flying Ad Hoc Network (FANET) has become a hot research area in recent years, which is widely used in civil and military fields due to its unique advantages. FANET is a special kind of networks which are composed of UAV nodes, and can be used to implement data transfer in certain unique scenarios. To achieve reliable and robust communication among UAVs, a routing algorithm is the key factor and should be designed elaborately. Because of its importance and usefulness, this topic has attracted many researchers, and various routing protocols have also been put forward to improve the quality of data transmission in FANETs. Thus, in this paper, we give a survey on the state-of-the-art of routing protocols proposed in recent years. First, an in-depth research of the routing in FANETs recently has been brought out by absolutely differentiating them based on their routing mechanism. Then, we give a comparative analysis of each protocol based on their characteristics and service quality indicators. Finally, we propose some unsolved problems and future research directions for FANET routing.

In recent years, with the emergence of UAVs(Unmanned Aerial Vehicles) in military and civil applications, the FANETs(Flying Ad-Hoc Networks) composed of multiple UAVs has attracted extensive attention from researchers. As a new type of airborne self-organizing network, the particularity in FANETs such as time-varying network topology and dynamic link makes it difficult to maintain continuous communication when performing tasks. Therefore, it is challenging to design a routing protocol for FANETs to guarantee the quality of data transmission and make communication more effective. In this article, we propose a new opportunistic routing protocol based on trajectory prediction, named EORB-TP. To be specific, we first predict the position of nodes in three-dimensional space and solve the problem of uncertainty of node contact in opportunistic communication. Secondly, we define the node's trajectory metric value to measure the node's trajectory characteristics and effectively avoid the excessive consumption of edge nodes. In addition, when choosing relay nodes, an energy-saving data forwarding strategy is designed to deal with the limited energy resources and storage space of UAVs. Simulation results show that compared with the state-of-the-art protocols, our protocol can increase the delivery rate by approximately 40% at best and can reduce the delay by approximately 80%.