5G Technologies Based Remote E-Health: Architecture, Applications, and Solutions
Wei Duan, Yancheng Ji, Yan Zhang, Guoan Zhang, Valerio Frascolla, Xin Li
aa r X i v : . [ c s . N I] S e p
5G Technologies Based Remote E-Health:Architecture, Applications, and Solutions
Wei Duan † , Yancheng Ji † , Yan Zhang § , Guoan Zhang † , Valerio Frascolla ‡ and Xin Li ♭ † School of Information Science and Technology,Nantong University, Nantong 226000, China § Maternity and Child Care Hospital, Edong Medical Group, Huangshi 435000, China ‡ Division of Research and Innovation, Intel Corporation ♭ Department of Physical Education,Zhengzhou University, Zhengzhou 450000, ChinaEmail: [email protected], [email protected], yzhang [email protected],[email protected], [email protected], [email protected]
Abstract
Currently, many countries are facing the problems of aging population, serious imbalance of medicalresources supply and demand, as well as uneven geographical distribution, resulting in a huge demand forremote e-health. Particularly, with invasions of COVID-19, the health of people and even social stabilityhave been challenged unprecedentedly. To contribute to these urgent problems, this article proposes ageneral architecture of the remote e-health, where the city hospital provides the technical supportsand services for remote hospitals. Meanwhile, 5G technologies supported telemedicine is introducedto satisfy the high-speed transmission of massive multimedia medical data, and further realize thesharing of medical resources. Moreover, to turn passivity into initiative to prevent COVID-19, a broadarea epidemic prevention and control scheme is also investigated, especially for the remote areas. Wediscuss their principles and key features, and foresee the challenges, opportunities, and future researchtrends. Finally, a node value and content popularity based caching strategy is introduced to provide apreliminary solution of the massive data storage and low-latency transmission.
I. I
NTRODUCTION
With the extreme unbalanced distribution of medical resources, there is a big gap between thedeveloped areas and economically backward areas in terms of the equipment, technology servicequality of medical, resulting in rapid demands for telemedicine [1]. The original intention oftelemedicine is to improve the popularity of medical and health services via telecommunicationfor 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 communi-cation 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 forthe remote patients, as well as provide the education and training for remote medics, whichbreaks the space and time limitations [3], [4]. Moreover, the telemedicine not only changesthe medical experience for patients, but also improves the medic-patient relationship. When thepatients seek medical treatment, the medic will take their emotions into account to strive forpositive treatment evaluations. It is easy to see that, telemedicine will break the barriers amongdifferent 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 consumptionupgrading, 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 high-quality video servicesdata acquisition, positioning, remote diagnosis and treatment and otherfusion 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 greatopportunity for the development of telemedicine [8]. On the basis of traditional medicine, 5Gtechnologies based telemedicine integrates mobile communication, Internet, Internet of things(IoT) [7], cloud computing, big data, artificial intelligence (AI) [9] and other advanced infor-mation and communication technologies, applying to the remote surgery, remote consultation,remote health monitoring and emergency command. In particular, telemedicine will provide morechoices 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 arestill evolving and exploring, the architecture, system design and landing mode of telemedicineare not completed. These arise the following problems problems: The imperfect overall planning, and the problem of cross departmental coordination; lack of technical verification and feasibilitystudy; inconsistent medical standards; privacy security [6], [10]. On the other hand, with thespread 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 todisease prevention and health management. Moreover, in order to realize remote sharing ofmedical resources, the massive data storage and data redundancy will bring great load to theserver. With these observations, the goal of this article is to provide a potential solution to realize5G technologies-based remote e-health, spanning from the general architecture and frameworkof telemedicine, to satisfy the high-speed transmission of massive multimedia medical data andrealize 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 investigatedto 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 theremote e-health. Then the 5G technologies based telemedicine framework is introduced for theremote hospital. Moreover, a broad area epidemic prevention and control scheme is investigatedto prevent COVID-19, as well as the node value and content popularity based caching strategyis studied. Finally, we draw the main conclusions and interesting future research.II. T
HE PROPOSED R EMOTE
E-H
EALTH A RCHITECTURE
Relying on computer technology and remote sensing, telemetry, remote control technologies,telemedicine gives play to advantages of medical technologies and equipments in city hospitalto conduct remote diagnosis, treatment and consultation for patients in remote areasi.e., remoteimaging, remote nursing and other medical activities. The proposed remote e-health architecturebased on cloud network is shown in Fig. 1, which consists of the city hospital and manycorresponding remote hospitals. The concepts of the proposed architecture is that, with theinternet as link, grading diagnosis and treatment as the core and the substance hospital as thesupport, the remote hospitals and advanced city hospitals will be connected to this platform. Bythis 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 themeanwhile that the remote hospitals share information and data for each other according to the
Remote Cooperative
Diagnostics
Consultation Remote Consultation MedicalRecord EmergencySituationCity Hospital Remote Hospital Remote HospitalRemote HospitalRemote Hospital Patient
Medic
Intelligent AmbulanceIntelligent DeviceSchool, Factory,
Private Clinic
Fig. 1. Illustration of the remote e-health architecture. networks, to improve the utilization of medical resources. For the city hospital, the details ofthe processing strategies can be summarized as follows: • When a request for medical help from a remote hospital is received, according to thereceived contents, i.e., the images, voices and videos for the patients, the city hospitalrapidly makes decisions and corresponding measures to cooperatively help remote hospitalcuring the patients, through the existing advanced technologies and equipments. • For the difficult miscellaneous diseases, the city hospital convenes experts and relevantmedics to hold the consultation. Moreover, for very special and difficult cases, the remoteconsultation with other advanced city hospitals will be adopted. When the specific treatmentplan is formulated, the city hospital will promptly contact and assist the remote hospital totake corresponding measures. In the meanwhile, the electronic medical record is established. • According to the progress of conditions of the patients, the electronic medical record willbe 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 theremote 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 diagnosisand treatment, but also can well manage and distribute emergency medical services in remoteareas. Specifically, it can make medics break through the limitation of geographical scope andshare the case and diagnosis photos of patients, which is conducive to the development of clinicalresearch. 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 architectureand corresponding strategies are different from the traditional medical system. The key issuesand challenges for telemedicine are generally summarized as follows: • Privacy security : Any breakthrough in science and technology has to face the problem ofsecurity, the telemedicine technology is no exception. If the medics or medical equipmentsdo not consider the security of electronic data of patients, once these data are transmittedand leaked through the Internet, it will cause irreparable security risks. Therefore, it isnecessary that, adopting 5G technology and network security methods to authenticate,encrypt and protect the intelligent medical equipment for privacy preservations. Only bytaking precautions in advance, remote medical can realize the transformation from thepassive defense to active response. • Medical data and resource sharing : Medical data and resource sharing can not only helpthe rapid development of the telemedicine technology, but also significantly alleviate theshortage 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 electronicsystems of different hospitals are built by different enterprises; and there exists barriersbetween these systems among enterprises, resulting in a difficult integration for the datafrom different hospitals. Therefore, how to reasonably and legally realize the sharing ofmassive medical data to the Internet is still an open problem and challenge. • Massive connectivity and data cache : With the commercial application of 5G, the real-time data transmission problem for telemedicine technology has been solved in some degree,eliminating the barriers and distance for medical communication. However, the massive
Remote HospitalRemote HospitalRemote HospitalRemote Hospital
5G NetworkCity Hospital
Remote Consultation
Remote Assistance (Operation)
Remote Operation
5G Based GPSWearable Medical
Devices
E-Health Care
E-Health Education
Mobile Doctor
Intelligent Ambulance
Fig. 2. 5G technologies based telemedicine framework. connectivity from the medical devices, intelligent devices and remote hospitals, as wellas the cache of the massive medical data challenges the existing spectrum resources andnetwork structure. Therefore, it is necessary to adopt the technologies with the excellentspectrum efficiency and effective cache capacity.III. 5G T
ECHNOLOGIES B ASED T ELEMEDICINE F RAMEWORK
On the basis of traditional medicine, 5G technologies based telemedicine integrates wirelesscommunication technology of smart equipment and high-speed mobile communication technol-ogy in various modes, which can realize the operation of remote surgery, remote consultation,patient monitoring, command and decision-making for emergency rescue events. Moreover, 5G-based telemedicine can also support the high-speed transmission of massive multimedia medicaldata, 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, personaldevices and local intelligent ambulances access to its server to apply the medical resources andshare 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 thewearable medical devices and mobile private doctor, people can know their personal physicalsigns, i.e., blood pressure, heart rate and temperature, at any time and any where to enjoyhigh quality health services and e-health education. In addition, through the monitoring of thesedevices, medical institutions and medics can take the initiative to find individuals and groupswith abnormal health status, and give health risk tips, health improvement or medical measuressuggestions in advance. In this manner, the hospitals can improve diagnosis efficiency, andresidents can reduce the cost of health consultation. In addition, based on internet of medicalthings (IoMT) and AI, for any emergency, the patients can be timely and tentatively cured in theambulance to realize the vision of “In ambulance, in hospital”. According to the 5G HD videofeedback from the ambulance, the hospital can conduct real-time follow up and analyze the signsand conditions of patients in advance, to effectively reduce the risk of death. On the other hand,with the development of 5G-based global positioning system (5G-GPS), it can provide moreaccurate positioning, more intelligent navigation and more information services in real time forthe patients and ambulance, especially for remote areas. Predictably, telemedicine can improvethe medical experience of the patients, and constantly resolve the problems of “complicatedtreatment process”. Moreover, it also provides more possibilities to make up for the insufficientand unbalanced distribution of medical resources and solve the problem of social aging.IV. B
ROAD A REA E PIDEMIC P REVENTION AND C ONTROL FOR
COVID-19With invasions of COVID-19, due to the continuous person-to-person transmission, the coron-avirus rapidly spreads leading to cross infection for many patients. Since that there is no effectivecure method and vaccine, and it is hard to detect millions of people on a large scale, the strictsegregation and control measures have to be adopted. Unavoidably, the economic developmentand 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 hasbecome the primary task. Currently, the common detection method is that, at the entrance andexit with large flow of people, the thermal cameras or temperature guns are used to locally detectthe temperature of people in turn. Clearly, such detections have the following defects: • Omissions in personnel inspection : The tested personnel are passively restricted, not allof them will be detected. For example, some people do not take the initiative or cooperate
Remote HospitalWearable
Medical DevicesTrajectory 1 Trajectory 2
Trajectory 3
Trajectory 1
Intelligent Ambulance
Trajectory 1 Trajectory 2 emote Hospital Intelligent AmbulanceReProhibition
Fig. 3. The broad area epidemic prevention and control scheme. with the measurement, especially that the people in remote areas have weak awareness ofprotection; • Real-time issue : This kind of epidemic prevention and control is not real-time due to therapidly spreading of coronavirus. It is inevitable to cross infection in the detection process,especially in remote areas; • Locality issue : Due to that COVID-19 is a global problem, it is difficult to make personnelinformation open and personnel information transparent among different regions, whichmakes it necessary to provide a lot of manpower and material resources when people flowbetween regions; • Security issue : On one hand, patient information is presented by the text registration; on theother hand, most of the body temperature and pathological features are shown in the formof pictures. It is easy to see that this intuitive way will inevitably be used by eavesdroppersproviding 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 Km. The basic idea of thisscheme 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-networkfor centralized processing. In addition, the people should carry wearable medical devices, by thisway, the trajectories of people will be collected by the remote hospital to determine coordinates ofpeople during their outdoor activities. In this manner, the people can receive personal informationand surrounding conditions from the remote hospital at any time, to avoid cross infection whenabnormal body temperature occurs. Accordingly, when people themselves or close contacts haveabnormal body temperature, they will receive warning messages in time and make self isolationuntil temperature normal or days. Due to huge amount of data, it is considered that thepeople staying at home or in their vehicles are isolated, the remote hospital will not collect theircoordinates until they go out for activities or take the initiative to contact remote hospital. Whenthe fever have stayed high, after receiving the request for help, the patient will be sent to theremote hospital for a further observation and treatment by the ambulance.V. N ODE V ALUE AND C ONTENT P OPULARITY B ASED C ACHING S TRATEGY
Even that, the proposed BAEPC scheme can effectively and promptly confine and eliminatethe coronavirus, however, the massive data storage and data redundancy will bring great loadto the server. Moreover, due to that the key of telemedicine technology lies in long-distanceand 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 beintroduced to provide a preliminary solution. In what following, after defining the cache content,the proposed NVCP caching strategy will be discussed within two algorithms.
A. Cache locality
In this subsection, three node attributes are defined to evaluate the value of node, which arebased on the graph theory and described. Moreover, we further considered that the Named-dataLink State Routing Protocol (NLSR) is adopted to query the shortest path information. Given anundirected graph G = ( V, E ) with n vertexes and m edges, where V = { v , v , ..., v n } representsa set of content routers, and E = { e , e , ..., e m } denotes the links between the content routers.Moreover, A = ( a ij ) n × n is the adjacency matrix of G , for v i directly connect with v j and a ij = 1 ,otherwise a ij = 0 . Connectivity : Different forwarding strategies result in different routing paths for the re-quested content, cache nodes will play different roles in these strategies. And hence, weregard the number of paths that the requested content pass through the cache node as theconnectivity of the node. Therefore, with the increasing paths, the request content becomesmore important. Defining the number of routing paths, which is requested content k passesthrough v i , as c s ( v i ) , and the maximum number of routing paths passing through v i as c maxs ( v i ) , the connectivity can be obtained as the ratio of c s ( v i ) to c s ( v i ) max defended as C s ( v i ) .2) Betweenness centrality : If a content router is on the shortest paths between the correspond-ing content routers, the content router is considered to be in a significant position. It isreasonable, due to that the content router in this position can affect the overall network bycontrolling or misinterpreting the transmission of information. The ability to characterizecontent router control information transfer is betweenness centrality (also known as nodemedian) [13]. Defending σ st as the number of shortest paths between v s and v t , σ st ( v i ) 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 C B ( v i ) = (cid:18) ( n − n − (cid:19) − X s = t = i ∈ v σ st ( v i ) σ st , where n represents the number of content routers.3) Eigenvector centrality : In fact, the influence of a content router is not only related to itsown locality, but also to the influence of its neighbors [14]. If the content router is chosenby a very popular actor, the corresponding influence will also be increased. On the otherhand, there is an influence on an influential node, it is clear that the influence will be evengreater, 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 neighborsof nodes. It is also defended that C E ( v i ) not only reflects the relative centrality of thenetwork, but also reflects the long-term influence of the node.The connectivity and betweenness centrality consider the value of nodes from routing paths ofthe requested contents, meanwhile that the eigenvector centrality takes the influence of neighborsinto account. When select the cache locality, the NVCP considers the above three attributes simultaneously. Defining M ( v i ) as the comprehensive attribute, we have: M ( v i ) = αC S ( v i ) + βC B ( v i ) + γC E ( v i ) , where α, β, γ represent the weight of connectivity, betweenness centrality and eigenvector cen-trality, and the sum of them is . It is worth noting that, in our proposed scheme, three mentionedattributes 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, thecorresponding different results will be obtained. Therefore, the coefficients in the comprehensiveattribute M ( v i ) are determined by the related requirements of CCN. B. Cache content
Since that whether caching every content which pass through the content router is anotherproblem for the CCN, the popularity is a factor to draw the content. The popularity of contentcan be estimated by the content request count during a measurement, which means that themore content request counts, the greater the popularity and probability of the content will berequested. Assuming that the count requesting for the content k at v i is f v i ,k , and the max countof v i is f max v i , finally, we have the popularity of content k can be presented as P v i ( k ) = f vi,k f maxvi . C. The NVCP cache strategy
For the proposed NVCP, the core idea is based on the node value and content popularity, atable is considered to be added at each content node including the content name, the number ofrouting path and count of content request to store the information of content and cache node. It isremarkable that, in CCN/NDN, PIT records the requests that have not been satisfied, includingthe content name and corresponding arrival interface, to ensure the returned response packetto the content requester along the reverse path. Therefore, the source of a request is identifiedthrough PIT. By this way, when a consumer requests a content, the betweenness centralityand 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 thistime, the content popularity will be calculated according to the count of content request. In ourproposed scheme, we design a variable ϕ to match the content popularity and node value givenas ϕ = P vi,k M ( v i ) , where P v i ( k ) is the popularity of content k at v i , and the values of P v i ,k and M ( v i ) are fixed and less than . In general, there are two cases: (1) P v i ,k ≥ M ( v i ) , it means TABLE IO
BTAIN THE BETWEENNESS CENTRALITY AND EIGENVECTOR CENTRALITY
Algorithm 1:
Set the forward path G: The network topology
Initialize c S ( v i ) , C B ( v i ) , C E ( v i ) , f v i ,k for node on the delivery path from consumer to sever do if content in cache then send content back to the consumerdiscard interest packet elseget the adjacency matrix of the nodes according G σ st : record the number of shortest paths between v s and v t σ st ( v i ) : record number of shortest paths from v s to v t through v i calculate C B ( v i ) , C E ( v i ) c S ( v i ) ← c S ( v i ) + 1 f v i ,k ← f v i ,k + 1 forward the interest packet to the next hop towardsserver end ifend for that the popularity of content is more important than the value of node. Therefore, caching thecontent in the content router can obtain a higher cache hit rate. (2) P v i ,k < M ( v i ) , it means thatthe value of the node is high, but the corresponding popularity of the content is low. If cachingcontent 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 proposedscheme, considering that the location of content router does not change, we have a fixednetwork topology. Therefore, the network can be seen as an undirected graph, the correspondingalgorithms (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. Itis 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 tothe network topology. In the meanwhile, the values of C S ( v i ) and f v i ,k increase by . On theother hand, algorithm 2 illustrates the process to select the appropriate cache locality and cache TABLE IIS
ELECT THE APPROPRIATE CACHE LOCALITY AND CACHE CONTENT
Algorithm 2:
Select cache locality and cache content G: The network topology
Input c S ( v i ) , C B ( v i ) , C E ( v i ) , f v i ,k for node on the delivery path from server to consumer doif the content is provided by server then send the data packet back directly elsecalculate C S ( v i ) , P v i ,k get C B ( v i ) , C E ( v i ) M v i ← αC S ( v i ) + βC B ( v i ) + γC E ( v i ) end ifif ϕ = P vi,k M ( v i ) ≥ then cache the contents else forward the data packet to the next hop to theconsumer end ifend for content. According to the results given in Algorithm 1, calculate ϕ . If ϕ > , cache the content,otherwise forward the data packet to the next hop. In addition, considering the fixed locations ofcontent 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 , which is easy to realize. Clearly,compared with the existing works, our proposed algorithm significantly improve the efficiencyfor calculating the value of ϕ . Clearly, the computational complexities of Algorithms and are not extremely high, which are practical and acceptable. D. Simulation Results
The simulation uses a network topology generated randomly, which consists of nodes and 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 followingthe Zipf-Mandelbrot distribution with a = 0 . . The total number of different contents will berequested in the network as , . Further assume that the interests of each consumer aregenerated following the Poisson distribution with λ = 100 /s . Comprehensive consideration ofthe various attributes of the node, for simplicity and fairness, in this article, the specific weight C ac h e h it r a ti o Prob(0.5)LCEMPCNVCP 0 500 1000 1500 20000.10.110.120.130.140.150.16 Cache size A v e r a g e t r a n s m i ss i on d e l a y / m s Prob(0.5)LCEMPCNVCP 0 500 1000 1500 20001.41.51.61.71.81.922.12.2 Cache size A v e r a g e hop c oun t Prob(0.5)LCEMPCNVCP
Fig. 4. The impact of cache size on the system performance for the proposed and existing caching schemes versus the cachesize. values of α (connectivity), β (betweenness centrality), and γ (eigenvector centrality) in thepresented simulation results are equivalently given as / . The Least Recently Used (LRU)[15] is employed as the cache replacement strategy and the total simulation time is s. Morespecially, the simulations results have been evaluated for various values of the cache size. Themain simulation parameters are listed in Table III. TABLE IIIS
IMULATION PARAMETERS
Parameter Default value Variation rangeNodes 50 -Links 150 -Delay/ms 10 -Bandwidth/Mbps 10 -Contents 10,000 -Consumers 18 -Cache size 1,000 ∼ , zipf(a) 0.7 . ∼ . Simulation time/s 100 -The proposed NVCP strategy is compared with the LCE, Prob(0.5) and MPC in terms ofthe cache hit ratio, average hop count and average transmission latency as show in Fig. 4. Itis easy to see that the cache hit ratios of the four cache strategies are gradually increased,and the cache hit ratio of the NVCP is significantly better than the others. It is resealable,because the LCE requires all nodes on the delivery path cache contents without difference,which results in a large amount of content redundancy and replace frequently. In addition, theProb(0.5) caches contents passing through the cache nodes with a fixed probability. Even taht the cache space is reduced, it still causes the content redundancy and low content diversity. Insteadof storing all the content at each node on the path, MPC caches only the popular contents. Onthe contrary, the NVCP considers node value and content popularity comprehensively, where thecontent with higher popularity is cached in nodes with higher value, in the meanwhile that thecontent with lower popularity is cached in nodes with lower value, which significantly reducesthe replacement frequency, improves the content diversity, and reduces the content redundancy.Compared to the LCE, Prob(0.5) and MPC schemes, the proposed NVCP cache hit rate has a to improvement. The second and third subfigures show that as the cache capacity ofthe 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 theLCE caches content indiscriminately, Prob(0.5) takes the probability caching, and the MPConly caches the most popular content without any requirements for the nodes. On the contrary,the NVCP comprehensively evaluates node value from the connectivity, betweenness centralityand eigenvector centrality, assigns different weights according to different requirements, whichimproves 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 improvementof 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 . ∼ . hops and the averagetransmission latency is reduced by ∼ ms .VI. C ONCLUDING R EMARKS
By seamlessly converging 5G technologies and telemedicine to realize the remote surgery,remote consultation and patient monitoring, people in remote areas can receive high qualityservices from developed areas, improving the utilization efficiency of medical resources andreducing the time and cost of the diagnosis. In this article, we first characterized the generalarchitecture of the remote e-health, and then introduced 5G technologies supported telemedicineto satisfy the high-speed transmission of massive multimedia medical data, and further realizethe sharing of medical resources. In addition, the BAEPC scheme was proposed to track andcontrol the spread of the COVID-19. The challenges, opportunities, and future research trends, aswell as the open issues for the remote e-health are provided. Finally, the NVCP based cachingstrategy was investigated to overcome the massive data storage and low-latency transmissionissues. 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; modelingand analyzing the imaging medical data to provide decision support for medics and improve themedical efficiency and quality; with the blockchain technology, encrypt the underlying data torealize the secure and reliable transmission of medical privacy data.R EFERENCES [1] D. A. Perednia, and A. Allen, “Telemedicine technology and clinical applications,”
JAMA , vol. 273, no. 6, pp. 483-488,Feb. 1995.[2] S. Akselsen, A.K. Eidsvik, and T. Folkow, “Telemedicine and ISDN,”
IEEE Commun. Magazine , vol. 31, no. 1, pp. 46-51,Jan. 1993.[3] J. Fortney et al. , “Telemedicine-based collaborative care for posttraumatic stress disorder: a randomized clinical trial,”
JAMA Psychiatry , vol. 72, no. 1, pp. 58-67, 2015.[4] C.-F. Lin, “Mobile telemedicine: A survey study,”
J. Medical Systems , vol. 36, no. 2, pp. 511-520, 2012.[5] L. Dai et al. , “Non-orthogonal multiple access for 5G: solutions, challenges, opportunities, and future research trends,”
IEEE Commun. Magazine , vol. 53, no. 9, pp. 74-81, Sept. 2015.[6] X. Li et al. , “Enabling 5G on the ocean: a hybrid satellite-UAV-terrestrial network solution,”
IEEE Wireless Commun. , toappear 2020.[7] W. Feng, J. Wang, Y. Chen, X. Wang, N. Ge, and J. Lu, “UAV-aided MIMO communications for 5G Internet of Things,”
IEEE Internet of Things J. , vol. 6, no. 2, pp. 1731-1740, Apr. 2019.[8] J. G. Andrews, S. Buzzi, W. Choi, S. V. Hanly, A. Lozano, A. C. K. Soong, and J. Zhang, “What Will 5G Be?,”
IEEE J.Sel. Areas Commun. , vol. 32, no. 6, pp. 1065-1082, Apr. 2014.[9] D. M. Gutierrez-Estevez et al. , “Artificial intelligence for elastic management and orchestration of 5G networks,”
IEEEWireless Commun. , vol. 26, no. 5, pp. 134-141, Oct. 2019.[10] J. Lloret et al. , “ An architecture and protocol for smart continuous eHealth monitoring using 5G,”
Computer Networks ,vol. 129, no. 2, pp. 340-351, Dec. 2017.[11] Md. A. Rahman, M.S. Hossain, N. A. Alrajeh, and N. Guizani, “B5G and explainable deep learning assisted healthcarevertical at the edge: COVID-I9 perspective,”
IEEE Network , vol. 34, no. 4, pp. 98-105, Jul. 2020.[12] J. Li and X. Guo, “COVID-19 contact-tracing apps: A survey on the global deployment and challenges,” 2020,arXiv:2005.03599. [Online]. Available: https://arxiv.org/abs/2005.03599[13] M. Riondato, and E. M. Kornaropoulos, “Fast approximation of betweenness centrality through sampling,”
Data Min.Knowl. Disc. , vol. 30, pp. 438-475, Mar. 2016.[14] V. Dixon et al. , “Leveraging social network analysis for characterizing cohesion of human-managed animals,”
IEEE Trans.Computational Social Systems , vol. 6, no. 2, pp. 323-337, Mar. 2019.[15] D. Lee, J. Choi, J. Kim, et al, “LRFU: A spectrum of policies that subsumes the least recently used and least frequentlyused policies,”