With the extreme unbalanced distribution of medical resources, there is a big gap between the developed areas and economically backward areas in terms of the equipment, technology service quality of medical, resulting in rapid demands for telemedicine [1] . The original intention of telemedicine is to improve the popularity of medical and health services via telecommunication for medics [2] . With the strong support of market policy and progress of wireless technology, telemedicine has been developed significantly [3] . Currently, relying on the advanced communication and computer technologies to transmit the data, voice, image, video and other information, telemedicine can realize the treatment, diagnosis, health care and consultation in real-time for the remote patients, as well as provide the education and training for remote medics, which breaks the space and time limitations [3] , [4] . Moreover, the telemedicine not only changes the medical experience for patients, but also improves the medic-patient relationship. When the patients seek medical treatment, the medic will take their emotions into account to strive for positive treatment evaluations. It is easy to see that, telemedicine will break the barriers among different industries, optimize the medical service process, improve the overall service efficiency, and constantly resolve the problems provided by complicated medical procedures.
As the core support of telemedicine, with decades of development and continuous consumption upgrading, the wireless communication technology has completed the evolutions from 1G to 5G
[5]- [7] . It realizes the high-quality transmission of three dimensional images to provide highquality video servicesdata acquisition, positioning, remote diagnosis and treatment and other fusion functions in real-time. Compared with other generations of wireless communications, 5G has advantages in terms of the low latency, high reliability and mobility, providing great opportunity for the development of telemedicine [8] . On the basis of traditional medicine, 5G technologies based telemedicine integrates mobile communication, Internet, Internet of things (IoT) [7] , cloud computing, big data, artificial intelligence (AI) [9] and other advanced information and communication technologies, applying to the remote surgery, remote consultation, remote health monitoring and emergency command. In particular, telemedicine will provide more choices and ways for rescue, especially in the fast moving state of vehicle and harsh environment.
It worth noting that, since that the 5G technology, business model and industrial ecology are still evolving and exploring, the architecture, system design and landing mode of telemedicine are not completed. These arise the following problems problems: The imperfect overall planning, and the problem of cross departmental coordination; lack of technical verification and feasibility study; inconsistent medical standards; privacy security [6] , [10] . On the other hand, with the spread of COVID-19 [11] , [12] , physical and mental health of people has been greatly impacted, leading to that the concern of people has gradually transferred from the disease treatment to disease prevention and health management. Moreover, in order to realize remote sharing of medical resources, the massive data storage and data redundancy will bring great load to the server. With these observations, the goal of this article is to provide a potential solution to realize 5G technologies-based remote e-health, spanning from the general architecture and framework of telemedicine, to satisfy the high-speed transmission of massive multimedia medical data and realize the sharing of medical resources. In order to track and control the spread of the COVID-19, the broad area epidemic prevention and control (BAEPC) design for COVID-19 is proposed, as well as the node value and content popularity (NVCP) based caching strategy is investigated to overcome the massive data storage and low-latency transmission issues.
The rest of this article is organized as follows. First, we provide a general architecture of the remote e-health. Then the 5G technologies based telemedicine framework is introduced for the remote hospital. Moreover, a broad area epidemic prevention and control scheme is investigated to prevent COVID-19, as well as the node value and content popularity based caching strategy is studied. Finally, we draw the main conclusions and interesting future research.
Relying on computer technology and remote sensing, telemetry, remote control technologies, telemedicine gives play to advantages of medical technologies and equipments in city hospital to conduct remote diagnosis, treatment and consultation for patients in remote areasi.e., remote imaging, remote nursing and other medical activities. The proposed remote e-health architecture based on cloud network is shown in Fig. 1 , which consists of the city hospital and many corresponding remote hospitals. The concepts of the proposed architecture is that, with the internet as link, grading diagnosis and treatment as the core and the substance hospital as the support, the remote hospitals and advanced city hospitals will be connected to this platform. By this way, the remote hospitals can also enjoy the remote outpatient service, expert appointment, electronic prescription, online payment and other fast services through the internet. As the brain, the city hospital provides the technical supports and services for these remote hospitals, in the meanwhile that the remote hospitals share information and data for each other according to the networks, to improve the utilization of medical resources. For the city hospital, the details of the processing strategies can be summarized as follows:
• When a request for medical help from a remote hospital is received, according to the received contents, i.e., the images, voices and videos for the patients, the city hospital rapidly makes decisions and corresponding measures to cooperatively help remote hospital curing the patients, through the existing advanced technologies and equipments.
• For the difficult miscellaneous diseases, the city hospital convenes experts and relevant medics to hold the consultation. Moreover, for very special and difficult cases, the remote consultation with other advanced city hospitals will be adopted. When the specific treatment plan is formulated, the city hospital will promptly contact and assist the remote hospital to take corresponding measures. In the meanwhile, the electronic medical record is established.
• According to the progress of conditions of the patients, the electronic medical record will be updated in real-time, until the patient is fully recovered. The electronic medical records are also shared with the remote hospitals for follow-up actions and future study. Moreover, for emergencies, the city hospital will dispatch the intelligent ambulance and medics to the remote hospitals.
All the city and remote hospitals will share and update the information through the cloud network.
Clearly, the use of telemedicine not only significantly reduce the time and cost of the diagnosis and treatment, but also can well manage and distribute emergency medical services in remote areas. Specifically, it can make medics break through the limitation of geographical scope and share the case and diagnosis photos of patients, which is conducive to the development of clinical research. In addition, it can provide a better medical education for medics in remote areas.
Since that the telemedicine technology is in its development stage, the design of its architecture and corresponding strategies are different from the traditional medical system. The key issues and challenges for telemedicine are generally summarized as follows:
• Privacy security: Any breakthrough in science and technology has to face the problem of security, the telemedicine technology is no exception. If the medics or medical equipments do not consider the security of electronic data of patients, once these data are transmitted and leaked through the Internet, it will cause irreparable security risks. Therefore, it is necessary that, adopting 5G technology and network security methods to authenticate, encrypt and protect the intelligent medical equipment for privacy preservations. Only by taking precautions in advance, remote medical can realize the transformation from the passive defense to active response.
• Medical data and resource sharing: Medical data and resource sharing can not only help the rapid development of the telemedicine technology, but also significantly alleviate the shortage of medics. However, when telemedicine is performed, it has to connect to Internet, and in this docking process, the systems of hospitals are relatively closed; the electronic systems of different hospitals are built by different enterprises; and there exists barriers between these systems among enterprises, resulting in a difficult integration for the data from different hospitals. Therefore, how to reasonably and legally realize the sharing of massive medical data to the Internet is still an open problem and challenge.
• Massive connectivity and data cache: With the commercial application of 5G, the realtime data transmission problem for telemedicine technology has been solved in some degree, eliminating the barriers and distance for medical communication. However, the massive connectivity from the medical devices, intelligent devices and remote hospitals, as well as the cache of the massive medical data challenges the existing spectrum resources and network structure. Therefore, it is necessary to adopt the technologies with the excellent spectrum efficiency and effective cache capacity.
On the basis of traditional medicine, 5G technologies based telemedicine integrates wireless communication technology of smart equipment and high-speed mobile communication technology in various modes, which can realize the operation of remote surgery, remote consultation, patient monitoring, command and decision-making for emergency rescue events. Moreover, 5Gbased telemedicine can also support the high-speed transmission of massive multimedia medical data, and further realize the sharing of medical resources. With this prospect, as shown in Fig.   2 , the remote hospital is readily allowed the patients, local medics, schools, factories, personal devices and local intelligent ambulances access to its server to apply the medical resources and share the medical data.
Nowadays, medical service has changed from the disease treatment to health care, meanwhile, the disease prevention and health management are becoming increasingly important. With the wearable medical devices and mobile private doctor, people can know their personal physical signs, i.e., blood pressure, heart rate and temperature, at any time and any where to enjoy high quality health services and e-health education. In addition, through the monitoring of these devices, medical institutions and medics can take the initiative to find individuals and groups with abnormal health status, and give health risk tips, health improvement or medical measures suggestions in advance. In this manner, the hospitals can improve diagnosis efficiency, and residents can reduce the cost of health consultation. In addition, based on internet of medical things (IoMT) and AI, for any emergency, the patients can be timely and tentatively cured in the ambulance to realize the vision of "In ambulance, in hospital". According to the 5G HD video With invasions of COVID-19, due to the continuous person-to-person transmission, the coronavirus rapidly spreads leading to cross infection for many patients. Since that there is no effective cure method and vaccine, and it is hard to detect millions of people on a large scale, the strict segregation and control measures have to be adopted. Unavoidably, the economic development and quality of life of the people have been greatly impacted, even resulting in a social panic.
Without radical cure, effective and rapid detection to prevent the spread of the coronavirus has become the primary task. Currently, the common detection method is that, at the entrance and exit with large flow of people, the thermal cameras or temperature guns are used to locally detect the temperature of people in turn. Clearly, such detections have the following defects:
• Omissions in personnel inspection: The tested personnel are passively restricted, not all of them will be detected. For example, some people do not take the initiative or cooperate It is easy to see that this intuitive way will inevitably be used by eavesdroppers providing troubles to patients.
In order to turn passivity into initiative, a BAEPC for COVID-19 is proposed as shown in Fig. 3 .
With the development of the high-definition cameras and video surveillance, currently, ultra long distance thermal camera (ULDTC) can monitor a circumference of 15 Km. The basic idea of this scheme is that distribute these rotatable ULDTC in different areas for independent monitoring, and centralize the collected information to the control center (remote hospital) via the 5G-network for centralized processing. In addition, the people should carry wearable medical devices, by this way, the trajectories of people will be collected by the remote hospital to determine coordinates of people during their outdoor activities. In this manner, the people can receive personal information and surrounding conditions from the remote hospital at any time, to avoid cross infection when abnormal body temperature occurs. Accordingly, when people themselves or close contacts have abnormal body temperature, they will receive warning messages in time and make self isolation until temperature normal or 14 days. Due to huge amount of data, it is considered that the people staying at home or in their vehicles are isolated, the remote hospital will not collect their coordinates until they go out for activities or take the initiative to contact remote hospital. When the fever have stayed high, after receiving the request for help, the patient will be sent to the remote hospital for a further observation and treatment by the ambulance.
Even that, the proposed BAEPC scheme can effectively and promptly confine and eliminate the coronavirus, however, the massive data storage and data redundancy will bring great load to the server. Moreover, due to that the key of telemedicine technology lies in long-distance and low-latency connections, TCP/IP networking approach is hard to satisfy these requirements.
In this section, a NVCP based caching strategy for content-centric networking (CCN) will be introduced to provide a preliminary solution. In what following, after defining the cache content, the proposed NVCP caching strategy will be discussed within two algorithms.
In this subsection, three node attributes are defined to evaluate the value of node, which are based on the graph theory and described. Moreover, we further considered that the Named-data Link State Routing Protocol (NLSR) is adopted to query the shortest path information. Given an 
2) Betweenness centrality: If a content router is on the shortest paths between the corresponding content routers, the content router is considered to be in a significant position. It is reasonable, due to that the content router in this position can affect the overall network by controlling or misinterpreting the transmission of information. The ability to characterize content router control information transfer is betweenness centrality (also known as node median) [13] . Defending σ st as the number of shortest paths between v s and v t ,
as the number of shortest paths from v s to v t through v i , the betweenness centrality of v i can be presented as
where n represents the number of content routers.
3) Eigenvector centrality: In fact, the influence of a content router is not only related to its own locality, but also to the influence of its neighbors [14] . If the content router is chosen by a very popular actor, the corresponding influence will also be increased. On the other hand, there is an influence on an influential node, it is clear that the influence will be even greater, where the eigenvector centrality is used to characterize the influence. We define C E (v i ) as the eigenvector centrality of a node, indicating the influence of the neighbors of nodes. It is also defended that C E (v i ) not only reflects the relative centrality of the network, but also reflects the long-term influence of the node.
The connectivity and betweenness centrality consider the value of nodes from routing paths of the requested contents, meanwhile that the eigenvector centrality takes the influence of neighbors into account. When select the cache locality, the NVCP considers the above three attributes simultaneously. Defining M(v i ) as the comprehensive attribute, we have:
where α, β, γ represent the weight of connectivity, betweenness centrality and eigenvector centrality, and the sum of them is 1. It is worth noting that, in our proposed scheme, three mentioned attributes have difference influences on the chosen of the cache locality. Based on which, when different attributes are used to evaluate the importance of nodes in a same network, the corresponding different results will be obtained. Therefore, the coefficients in the comprehensive attribute M(v i ) are determined by the related requirements of CCN.
Since that whether caching every content which pass through the content router is another problem for the CCN, the popularity is a factor to draw the content. The popularity of content can be estimated by the content request count during a measurement, which means that the more content request counts, the greater the popularity and probability of the content will be requested. Assuming that the count requesting for the content k at v i is f v i ,k , and the max count
of v i is f max v i , finally, we have the popularity of content k can be presented as
For the proposed NVCP, the core idea is based on the node value and content popularity, a table is considered to be added at each content node including the content name, the number of routing path and count of content request to store the information of content and cache node. It is remarkable that, in CCN/NDN, PIT records the requests that have not been satisfied, including the content name and corresponding arrival interface, to ensure the returned response packet to the content requester along the reverse path. Therefore, the source of a request is identified through PIT. By this way, when a consumer requests a content, the betweenness centrality and eigenvector centrality of the nodes on the delivery path will be calculated and normalized.
Once the request is satisfied, the data packet is returned on the inverse delivery path. At this time, the content popularity will be calculated according to the count of content request. In our proposed scheme, we design a variable ϕ to match the content popularity and node value given 
forward the interest packet to the next hop towards server end if end for that the popularity of content is more important than the value of node. Therefore, caching the content in the content router can obtain a higher cache hit rate. (2) P v i ,k < M(v i ), it means that the value of the node is high, but the corresponding popularity of the content is low. If caching content with a lower popularity will result in a waste of the cache space.
The main idea of the proposed NVCP is presented in Algorithms 1 and 2. In our proposed scheme, considering that the location of content router does not change, we have a fixed network topology. Therefore, the network can be seen as an undirected graph, the corresponding algorithms (such as Brande algorithm and Power Iteration) will be used to obtain C B (v i ) and C E (v i ) in advance, resulting in a computational complexity as O(V E) for these two algorithm.
Algorithm 1 is the process to obtain the betweenness centrality and eigenvector centrality. It is clear that, when the interest packet arrives at a content router, if the CS has the content, sends the content back to the consumer, otherwise calculates C B (v i ) and C E (v i ) according to the network topology. In the meanwhile, the values of C S (v i ) and f v i ,k increase by 1. On the other hand, algorithm 2 illustrates the process to select the appropriate cache locality and cache 
for node on the delivery path from server to consumer do if the content is provided by server then send the data packet back directly
forward the data packet to the next hop to the consumer end if end for content. According to the results given in Algorithm 1, calculate ϕ. If ϕ > 1, cache the content, otherwise forward the data packet to the next hop. In addition, considering the fixed locations of content routers, the values of C B (V i ) and C E (V i ) only need to be calculated once. By this way, when be requested, the popularity of content increases by 1, which is easy to realize. Clearly, compared with the existing works, our proposed algorithm significantly improve the efficiency for calculating the value of ϕ. Clearly, the computational complexities of Algorithms 1 and 2 are not extremely high, which are practical and acceptable.
The simulation uses a network topology generated randomly, which consists of 50 nodes and 150 links. There is a source server in the network, which is connected to a node randomly, and the edge nodes are connected to the consumers. Content requests are generated following the Zipf-Mandelbrot distribution with a = 0.7. The total number of different contents will be requested in the network as 10, 000. Further assume that the interests of each consumer are generated following the Poisson distribution with λ = 100/s. Comprehensive consideration of the various attributes of the node, for simplicity and fairness, in this article, the specific weight values of α (connectivity), β (betweenness centrality), and γ (eigenvector centrality) in the presented simulation results are equivalently given as 1/3. The Least Recently Used (LRU) [15] is employed as the cache replacement strategy and the total simulation time is 100s. More specially, the simulations results have been evaluated for various values of the cache size. The main simulation parameters are listed in Table III . Compared to the LCE, Prob(0.5) and MPC schemes, the proposed NVCP cache hit rate has a 11% to 15% improvement. The second and third subfigures show that as the cache capacity of the node increases, the average hop count and the average transmission delay decrease gradually.
Moreover, the performance of NVCP is better than the other schemes. This is due to that the LCE caches content indiscriminately, Prob(0.5) takes the probability caching, and the MPC only caches the most popular content without any requirements for the nodes. On the contrary, the NVCP comprehensively evaluates node value from the connectivity, betweenness centrality and eigenvector centrality, assigns different weights according to different requirements, which improves the response speed to the content request, as well as, reduce the network overhead.
Compared with the traditional cache strategies, the proposed NVCP has a great improvement of the average hop count and average transmission latency. Compared with LCE, prob(0.5) and MPC, the average hop count of NVCP is reduced by 0.08 ∼ 0.17 hops and the average transmission latency is reduced by 8 ∼ 15ms.
By seamlessly converging 5G technologies and telemedicine to realize the remote surgery, remote consultation and patient monitoring, people in remote areas can receive high quality services from developed areas, improving the utilization efficiency of medical resources and reducing the time and cost of the diagnosis. In this article, we first characterized the general architecture of the remote e-health, and then introduced 5G technologies supported telemedicine to satisfy the high-speed transmission of massive multimedia medical data, and further realize the sharing of medical resources. In addition, the BAEPC scheme was proposed to track and control the spread of the COVID-19. The challenges, opportunities, and future research trends, as
well as the open issues for the remote e-health are provided. Finally, the NVCP based caching strategy was investigated to overcome the massive data storage and low-latency transmission issues. The interesting future research avenues would be that introduce the "Big Data + AI" into telemedicine, to construct the application of AI assisted diagnosis and treatment; modeling and analyzing the imaging medical data to provide decision support for medics and improve the medical efficiency and quality; with the blockchain technology, encrypt the underlying data to realize the secure and reliable transmission of medical privacy data.