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Towards 5G Enabled Tactile Robotic TelesurgeryQi Zhang, Jianhui Liu, and Guodong Zhao

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Abstract—Robotic telesurgery has a potential to provide extreme andurgent health care services and bring unprecedented opportunities todeliver highly specialized skills globally. It has a significant societalimpact and is regarded as one of the appealing use cases of Tac-tile Internet and 5G applications. However, the performance of robotictelesurgery largely depends on the network performance in terms oflatency, jitter and packet loss, especially when telesurgical system isequipped with haptic feedback. This imposes significant challenges todesign a reliable and secure but cost-effective communication solution.This article aims to give a better understanding of the characteristicsof robotic telesurgical system, and the limiting factors, the possibletelesurgery services and the communication quality of service (QoS)requirements of the multi-modal sensory data. Based on this, a viablenetwork architecture enabled by the converged edge and core cloudis presented and the relevant research challenges, open issues andenabling technologies in the 5G communication system are discussed.

Index Terms—Remote surgery, Tactile Internet, haptic communication,multi-modal sensory data, mobile edge computing, network slicing, soft-ware defined networking

1 INTRODUCTION

Tactile Internet coined by Prof. Fettweis envisions com-munication network is moving from communicating data,voice, and video to real-time steering and control. Indeed,Tactile Internet will create a new dimension for human-machine interaction. Tactile Internet has been regarded asthe next wave of innovation after Internet of Things, withan ambition to enable ”Internet of skills” globally. As one ofthe appealing use cases, robotic telesurgery has attractedtremendous interest from industry and academia. NASAhas been working on to send surgical robots to space sta-tion. Many big network industrial players, e.g., Vodafone,Huawei, Nokia Bell labs, etc. are developing communicationsolutions for telesurgery services. Ericsson and King’s Col-lege London demonstrated telesurgery in 5G world event2016. With the advancement in next-generation wirelesscommunication and networking technologies, it will no longbe in fiction that surgeons perform operations for patientremotely in space, in battle field, or in a moving ambulance.

Motivations of this article are to give an introductionto the characteristics of telesurgical system and its QoSrequirements. Through a better understanding of telesur-gical system, we shed light on the communication and

• Qi Zhang and Jianhui Liu are with the Department of Engineering,Aarhus University, Aarhus N, 8200 Denmark. E-mail: qz@eng.au.dk andjianhui.liu@eng.au.dk

• Guodong Zhao is with University of Electronic Science and Technology ofChina, Chengdu, China. E-mail: gdzhao@uestc.edu.cn

networking research directions for telesurgery. This articleaims to inspire researchers to design reliable and secure butcost-effective communication solutions tailored for tactilerobotic telesurgery.

Robotic telesurgery, i.e., remote surgery, envisionsthe surgeon and patient are geographically separated.Telesurgery creates a new way to provide extreme andurgent health care. A telesurgical system will be developedbased on robotic surgical system which consists of masterconsole and slave robot, i.e., teleoperator. Master consoleis a human-system interface (HSI) which is composed of ahaptic device for position-orientation input, a video displayand headphones for video and voice feedback and in thefuture it will have haptic feedback output. The teleoperatoris equipped with 3D video camera, a microphone and willhave a number of force sensors and tactile sensors in thefuture. The da Vinci Surgical System is currently the mostwidely used robotic surgical system and provides visualsensory only. In telesurgergy, the master console and tele-operator will be connected by reliable high speed commu-nication network to transport the real-time manipulationcommands and multi-modal sensory data.

Besides the high cost, the major limitation factor hindersthe current robotic system to become the standard techniqueof minimally invasive surgery worldwide is the lack of effec-tive haptic feedback including force (kinesthetic) and tactile(cutaneous) feedback [1]. Force feedback can enhance thedexterity and controllability of surgical instrument therebyimproving the surgical precision particularly for those diffi-cult manipulations e.g. coronary artery bypass grafting andreducing potential unintentional injuries during a dissectiontask [2]. Tactile sensing can determine mechanical propertiesof tissue e.g. viscosity and surface texture, which helps todetect exact tumor and lump locations in organ, allowingsurgeon to accurately identify the margin of the abnormalityto better conduct dissection [2].

In 2001, the first transaltantic telesurgery was demon-strated on the patient in Strasbourg, France by the surgeonlocated in New York. It used a dedicated asynchronoustransfer mode (ATM) fiber optic communication link witha constant latency 155 ms and no packet loss [3]. Despite thesuccessful demonstration, telesurgery has not been widelyused. The main reason lies in high communication cost,long latency, no reliability guarantee by public Internet.Some research work has studied the impact of latency ontelesurgical performance using robotic simulator dV-Trainerbased on visual feedback only [4]. The observations are thatthe surgical performance deteriorates as latency increases.

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3D HD video stream

Audio stream

Vital physical signs

Haptic feedback data

Manipulation commands

Audio stream

Feedback Link

Forward Link

Master Console

Teleoperator

Fig. 1. A close control loop between master console and teleoperator.

The latency impact is related to the surgical procedurecomplexity and the skills of individual surgeon. The delayimpact is mild below 200 ms. Surgeons are able to adapt tothe delay through training under a constant delay. However,it is challenging to conduct telesurgery with variable latency.

With direct haptic feedback, the tolerable latency andjitter will become more stringent, as small communicationlatency, jitter or packet loss will cause instability in theclosed haptic control loop. It is necessary to limit the syn-chronization error to prevent cyber sickness [5], [6]. Hence,the challenge is not only about minimizing the latency andjitter, but also synchronization between the 3D visual signaland haptic feedback.

Main contribution of this article: (i) identify the limitingfactors for telesurgical system, (ii) categorize telesurgeryservices and give the roadmap of their development, (iii)present a viable network architecture, (iv) summarize rele-vant research challenges and open issues, (v) discuss howthe enabling technologies in 5G can address these chal-lenges.

2 COMMUNICATION QOS REQUIREMENT FORROBOTIC TELESURGERY

The concept of master-slave robotic surgical system the-oretically allows the realization of telesurgery. In the daVinci system a proprietary short-distance communicationprotocol over optic fiber connects the master console andteleoperator. The telesurgical system is a close-loop com-munication system consisting of forward link and feedbacklink, as shown in Fig. 1.

Forward link transports real-time manipulation com-mands to control the motions and rotation of robotic armsat the teleoprator, along with voice stream from the surgeonto communicate with the surgical team remotely.

Feedback link transports real-time multi-modal sensoryfeedback from the teleoperator, including 3D video stream,force feedback e.g., pressure, and tactile feedback e.g. tissuemechanical properties, and patient’s physiological data e.g.blood pressure and heart rate, along with voice streamfrom assistant nurses, anaesthetist and other collaboratingsurgeons at the patient’s side.

The communication QoS requirements of multi-modalsensory data in telesurgery are listed in Table 1 and thedetails are elaborated below.

For 3D video visualization, two HD video streams fromsurgical system are processed and displayed as a single datastream to the 3D display device at the required resolution,frame rate and encoding standard. For 3D MediVision prod-ucts, the resolution of 1920x1080 pixels with 32bits/pixeland frame rate of 30 frame per second (fps) or 60fps perchannel, i.e., 60fps or 120fps for 3D display device. Assum-ing the medical video compression rate in the range of 1:29to 1:5, the required data rate is between 137 Mbps to 1.6Gbps. The delay should be below 150 ms to ensure opera-tion performance [4]. No comprehensive experiments havetested the tolerable jitter and packet loss for telesurgery.Video conferencing requires jitter below 30 ms and packetloss rate below 1% [7].

For haptic data transmission, the packet payload is smallwhich depending on the number of degrees of freedom andthe sample resolution. Therefore, the overhead of commu-nication protocol header becomes not trivial and should beincluded in data rate estimation. The typical sampling rateof uncompressed haptic signal is 1kHz, i.e. 1ms transmis-sion service time per sample. Perception-based haptic datareduction according to Weber’s Law is feasible. It is possibleto achieve average sample rate reduction up to 90% withsatisfactory subjective ratings [8]. For general teleoperationsystem not specific for telesurgery [7], [8], the data ratefor haptic data varies from 128 to 400 kbps, the latencyand jitter should be below 50 ms and 2 ms, respectively,and the required data loss is about 0.01%. The study in[10] investigated the effect of vibration feedback latencyon material perception and found the latency threshold is5.5 ms. Longer latency gives the user a wrong sensationof the mechanical properties. Telesurgery is mission criticaland requires high precision in manipulation, therefore, morestringent latency, jitter and packet loss are needed.

The physiological data can tolerate 250 ms latency withmoderate data rates [9]. The packet loss in telesurgery isnot as critical as in body area network, as it is the localanesthetist responsible for monitoring the physical vitalsigns. The remote surgeon mainly relies on the 3D videostream and haptic feedback.

For voice conversation, the data rate ranges from 22 to220 kbps. The delay and jitter should be smaller than 150 msand 30 ms, respectively. It can tolerate 1% packet loss [7].

3 ENVISIONED TELESURGERY SERVICES ANDNETWORK ARCHITECTURE

In this section we briefly describe the possible telesurgeryservices and explain a network architecture with the in-volved communication and networking protocols.

3.1 Envisioned Telesurgery Services

Telesurgery services can basically be categorized into base-line services and value added assisting services as shown inFig. 2.

Baseline telesurgery services will be developed throughseveral stages. (i) No haptic feedback: surgeon currentlyoperates the master console to manipulate the teleoperatorsolely resorting to the 3D video stream [3], [4]. (ii) Withgraphical display of haptic feedback: It is an indirect haptic

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TABLE 1Communication QoS requirements for the multi-modality sensory data in robotic telesurgery

Data types Latency Jitter Packet Loss Rate Data Rate Ref

2D camera flow < 150 ms 3-30 ms < 10−3 < 10 Mbps [4], [7]

3D camera flow < 150 ms 3-30 ms < 10−3 137 Mbps - 1.6Gbps [4], [7]Real-time multimedia stream

Audio flow < 150 ms < 30 ms < 10−2 22 − 200 Kbps [7], [8]

Temperature < 250 ms - < 10−3 < 10 kbps [9]

Blood pressure < 250 ms - < 10−3 < 10 kbps [9]

Heart rate < 250 ms - < 10−3 < 10 kbps [9]

Respiration rate < 250 ms - < 10−3 < 10 Kbps [9]

ECG < 250 ms - < 10−3 72 kbps [9]

EEG < 250 ms - < 10−3 86.4 kbps [9]

Physical vital signs

EMG < 250 ms - < 10−3 1.536 Mbps [9]

Force 3 − 10 ms < 2 ms < 10−4 128 − 400 Kbps [7], [8]Haptic feedback

Vibration < 5.5 ms < 2 ms < 10−4 128 − 400 Kbps [7], [8], [10]

Baseline Service No HapticFeedback

Baseline Service with Indirect

Haptic Feedback

Baseline Service with Direct Haptic

Feedback

Value Added Assisting Services

Fig. 2. The roadmap of robotic telesurgery services development.

feedback as a sensory substitution system and not intuitive,therefore, requires a longer learning curve [11]. (iii) Withdirect haptic feedback: the robotic arms will be equippedhaptic sensors. Such systems are expected to improve theoperation performance, especially for novices, as it can helpsurgeon make correct motions and shorten the learningcurve [5]. The indirect or direct haptic feedback can alsofacilitate remote diagnosis.

Value added assisting services will be enabled by theadvanced artificial intelligence (AI). The real-time multi-modal sensory data, pre-cached electronic medical recordsand related historical surgical records can be processed bydata analytics and machine learning algorithms in the edgeand core cloud. It can provide diverse assisting services tohelp surgeon in abnormality diagnosis and decision making.For instance, Verb Surgical’s is going to leverage advancedimaging and artificial neural networks to process the 3Dvideo and images and annotate the feed with anatomicaldata. It will help surgeons to interpret what they see andcan even give information about the boundaries of a tumorand suggest dissection strategy.

3.2 Network ArchitectureTo enable the baseline and value added assisting servicesfor telesurgery and to meet the required QoS of multi-modal sensory data, the communication network shall atleast support both enhanced mobile broadband (eMBB) tosupport 3D video and ultra-reliable low latency communi-cation (uRLLC) for haptic feedback. The master console andteleoperator are connected through Internet. The access net-work can be 4G or 5G wireless networks or fixed broadbandnetworks. Since mobile communication offers flexibility andhas advantages in extreme healthcare scenarios e.g. in amoving ambulance, this article focuses on how the 5G com-munication system will enable tactile robotic telesurgery.

Based on the 5G communication system, a viable net-work architecture converging the edge and core cloud isillustrated in Fig. 3. The master console and teleoperatoraccess the network through distributed radio access net-work (D-RAN) or Cloud-RAN (C-RAN). Two geographi-cally separated surgeons can collaborate in one surgery, i.e.,manipulating the teleoperator remotely using a dual consolewhich is available in the da Vinci SI Surgical System. It isalso feasible to display the 3D video feedback at a thirdlocation for telesurgery demonstration, training and clinicalguidance purposes.

The source-terminals are responsible for encoding sen-sory data. Different compression schemes for multi-modalsensory data are needed to reduce the data rate, e.g., H.264for video, and perception-based data reduction for hapticfeedback. To be robust against transmission rate variation,dynamic codec configuration and scalable video coding canbe implemented. Multiplexer can be applied to interleavedifferent sensory packets into one serialized bitstream. To

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Base Station

Core Neworks

Core Cloud Server

Teleoperator

BBUBBU

RRH

Master Console

Storage Unit

Electronic Medical Record

Manipulation Commands

Voice Stream

Physical Signs

Haptic feedback

3D HD Video

Training MonitorCollaborating Console

Fig. 3. A converged edge-cloud enabled network architecture.

improve user experienced QoS, various control methodse.g., delay compensation, jitter smoothing and stability con-trol, are needed at the end-terminal.

D-RAN or C-RAN contains protocol stack to performradio access functions, from radio frequency, physical layer,medium access control, radio link control, packet data con-vergence protocol to radio resource control. The functionslike multiple access, radio resource allocation, Hybrid auto-matic repeat request (HARQ), etc. are implemented here.To support higher data rate and diverse services for theuse cases from different verticals with reduced CAPEX andOPEX, C-RAN will mostly be used. In C-RAN, base bandprocessing unit (BBU) is decoupled from remote radio head(RRH). RRH is located closer to the users able to servehigher data rate and lower latency traffic. BBUs pool isrealized by a centralized data center which leverages cloudtechnology to dynamically and flexibly pool computationalresource to perform baseband processing. Multiple RRHsare connected to the BBUs pool via high bandwidth andlow latency fronthaul. Centralization in C-RAN offers mul-tiplexing gain, inter-cell interference coordination, and en-ergy savings. To relax the requirement on franthaul, flexiblefunctional split of baseband functionality between RRH andBBU has been considered, i.e., keeping a subset of basebandfunctionality in RRH. Functional split is a trade-off betweencell coordination gain and reducing fronthaul bandwidth.For telesurgery, the MAC-PHY split or low PHY split optioncould be suitable depending on the fronthaul quality.

The backhaul network based on MPLS (MultiprotocolLabel Switching) or optical network are currently used toconnect RAN and mobile core network. SDN (Software-defined networking) is expected to be applied in the back-haul network. The network elements in MPLS and opticalnetwork will be implemented by programmable hardware-based switches and routers or even virtualized switcheswhich form the data plane and are controlled by the SDNcontroller e.g., Floodlight controller or OpenDaylight con-troller. In the backhaul network, functions like routing,network monitoring, congestion control and traffic-aware

reconfiguration communicate with the SDN controller viaNorthbound API [12].

The SDN-based mobile core network consists of simplenetwork gateways through which it is connected to theInternet. The centralized SDN core controller can controlQoS in a fine grained manner. Considering the differentiatedQoS requirements, video packets and haptic data could takedifferent forwarding paths. To increase reliability, multi-pathrouting can be used.

The data centers for BBUs pool and core cloud willpre-cache historic medical data, e.g., the electronic medicalrecords and relevant surgical records. The AI engines lo-cated at edge and core cloud will actively participate in dataanalytics and machine learning for value added services,as well as perform radio and network analytics to predictnetwork performance change (see Section 5.2, 5.3 and 5.4).

4 COMMUNICATION RESEARCH CHALLENGESAND OPEN ISSUES OF ROBOTIC TELESURGERY

Telesurgery imposes many critical research challenges in-cluding precision of robotic arms, bio-compatibility ofrobotic arms, and others. We focus on some of the mostimportant communication research challenges and relevantopen issues in this section.

4.1 Multi-modal Sensory Information

As shown in Fig. 1, multi-modal sensory data need to betransported from the teleoperator to master console. Differ-ent sensory modalities, i.e., visual, auditory and haptic, havedifferent requirement in terms of sampling, transmissionrate, latency, reliability, etc. [6]. It is shown in Table 1. Howto synchronize between video stream and haptic feedback ischallenging. Many researchers have worked on multiplex-ing scheme [7], [8]. The state-of-art multiplexing transmis-sion schemes for teleoperation system can be designed at thetransport or application layer. The majority of the researchassumes a constant bit rate link with certain transmissioncapacity.

The transport-layer approaches are developed on topof TCP or UDP protocols to optimize network schedulingbased on weighted priorities [8]. The application-layer ap-proaches focus on efficient encoding and adaptive statisticalmultiplexing [7]. The difficulty is that large video packetscan block haptic packets resulting delay and jitter viola-tion. Although theoretically preemptive-assume schedulingstrategy can prioritize haptic packets; it is not efficient inpractice due to protocol header overhead to transmit smallhaptic packets at high packet rate [8]. Currently multiplexeris not resilient to packet losses. Details about multiplexingschemes refer to [8] and the references therein.

From radio resource management and information the-ory point of view, multiplexing might not be optimal toutilize the resource and achieve the capacity. It is an openquestion how to design resource management for uRLLCtraffic (e.g., haptic data) when coexisting eMBB services(e.g., 3D video stream), since they are different in packet sizeand QoS requirements particularly latency and reliability.Moreover, video stream and haptic data need different errorcontrol schemes and congestion control methods.

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4.2 Ultra-low Latency and High Reliability

The performance of telesurgery largely depends on thenetwork performance, as latency, jitter and packet loss willjeopardize stability in the control system and degrade theperformance of operation and system transparency [8]. Toensure a stable control loop to complete high precisionteleoperation tasks, e.g., suture, haptic data requires below10 ms latency and 10−4 packet loss. Reliability and latencyto some extent are interconnected. Degraded reliability canresult in increased latency if using HARQ based scheme.For wireless communication system, communication latencyand jitter can be caused by limited transmission bandwidth,congestion and uncertainty in transmission e.g. interferenceand transmission rate variation.

Furthermore, Shannon capacity with arbitrary small er-ror probability assumes infinite blocklength which is onlyrelevant for large packet e.g. video packets. To ensure thereliability for short haptic packet with stringent delay, themaximal achievable rate with finite blocklength cannot becharacterized by Shannon capacity. It is an open questionand new information-theoretic metrics for finite packetlength are needed.

4.3 Security and Privacy

When telesurgical system communicates over public net-work, it is exposed to various security and privacy issueswhich can have serious impact on the operation perfor-mance and even risks to threaten patient’s life.

For instance, malware programs can exploit the vul-nerability in the robot system software to perform remotehijacking. Under the influence of malware programs, anyunauthorized tasks, e.g. wrong dissection, can be performedby the remote malicious users (adversary). In Denial-of-Service (DoS) attacks, adversary can send a large numberof flooding packets or any other spurious packets to thedestination to consume excessive amounts of network band-width thereby resulting in packet delay, jitter and loss [13].DoS attack can shut off the telesurgical system from theInternet or dramatically slowing down network links hin-dering the communication between master console andteleoperator. Man-in-the middle attacks and impersonationattack could alter the manipulation commands and sensoryfeedback data. Nevertheless, it might not be easy for theadversary to change the video and haptic data consistentlyin real time. Additionally, there exists the typical privacyissue to exchange the patient’s health data over Internet.

Therefore, security schemes should be implemented foraccess control, authentication, confidentiality and data in-tegrity. Although security for communication network andcyber physical system has been advanced, it is challengingto simultaneously provide security and fulfill the delayand jitter requirement in telesurgery, as cryptography andsecurity protocols will consume computational resource andintroduce communication overhead. In fact, security sup-port generally causes delay violation or communication QoSdegradation [13]. It is an open issue how to design cryp-tography and security schemes for real-time teleoperationsystem to meet the QoS and security constraints [13].

4.4 High Cost of Communication Services

Telesurgery has not become a medical routine basis. Oneof the limiting factors is high cost of the communicationservice. For the transatlantic telesurgery case, the connectionbetween New York and European Institution of Telesurgeryat Strasbourg was established through an ATM privatenetwork and 10 Mbps was reserved [3]. The cost for a 1-year availability of ATM lines point-to-point was between$100, 000 and $200, 000 in 2001 [3]. To enable telesurgeryservices to be widely used, cost-effective communicationsolutions must be in place.

5 ENABLING TECHNOLOGIES IN 5G FOR ROBOTICTELESURGERY

In this section we highlight several key enabling technolo-gies in 5G, although many other technologies can addressthe challenges in telesurgery, e.g. faster processor for videocoding, time-delay compensation control architecture [8],biological inspiration of tactile sensing [2], physical unclon-able function for low-cost authentication.

5.1 Physical Layer Design for Ultra-Low Latency

The design of 5G new radio (NR) air interface aims at 30-50Xlatency reduction in the RAN and several PHY designs havebeen proposed or specified in the standard. LTE has definedfast uplink access in Rel-14 which allows user to have aperiodic uplink grant every TTI (time transmission interval).Fast uplink access avoids the delay waiting for grant. Sparsecode multiple access based grant-free transmission for 5G inuser centric no cell framework has demonstrated 94.9% la-tency reduction [14]. Instead of using 1 ms TTI, a shorter TTIallows transmission duration smaller than one subframe,which has been specified in Rel-15. In the downlink, the datapart of subframe can be split into several parts and each partcan be scheduled independently by a new in-band channel.The OFDM symbol length in LTE and 5G NR is 66.67 µs and16.67 µs, respectively. The shorter TTI or subframe enablesshort scheduling unit which can help to reach the goal of5G user plane latency of 0.5 ms for uRLLC. Flexible andself-contained TDD subframe enables data transmission andits acknowledgement post-decoding to be contained withinthe same subframe. With the shorter slots and fewer HARQinterlace, it accelerates the associate control and feedback,thereby achieves lower latency.

More elaboration about PHY design for ultra-low latencyincluding OFDM numerology and frame structure is pre-sented in [5] and the references therein.

5.2 Mobile Edge Computing

To provide value added assisting services for telesurgery, AIengines need to process large volume of real-time 3D videoand images and tremendous pre-cached historic medicaldata, which requires large processing and storage power.To access traditional cloud infrastructure by Internet causeshuge pressure to the backhaul and core network and longlatency, which makes it simply not feasible to fulfil thelatency requirement. Mobile edge computing (MEC) is anatural development to move the computing and storage

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unit to the edge of the network close to where data isgenerated. MEC is a new technology recognized as oneof the key emerging technologies for 5G networks and thestandards of MEC are being developed by ETSI [15]. With itsunique capability, MEC is expected to improve latency andreliability, optimize network operation and improve userexperience; particularly, multiple edge nodes are utilizedsimultaneously.

MEC server can be deployed at multiple locations, e.g.LTE macro base station (eNodeB) site, RRH and BBUs poolof C-RAN, as they are already equipped with processingand storage unit. Thus most of the processing can bedone within RAN and alleviate the bandwidth pressure tocore network thereby shortening the latency. For example,pre-cached electronic medical records and relevant historicsurgery data such as the recorded motion of robot armscan be stored in the data center at the BBUs pool. Lever-aging the AI engine based on deep learning and patternrecognition algorithms, the MEC can process the 3D videoand images to highlight the relevant features using scalablevideo coding and annotate the images of the diagnosisresults, consequently minimize the required communicationresource. One open question here is how to strike a balancebetween computation latency and communication latency.

Additionally, radio analytics application in MEC canbetter estimate the channel quality and delay variation inthe network. This information can be exploited to optimizethe application level coding, channel coding and reducevideo-stall occurrence [15] and facilitate flexible resourceallocation. It is worth mentioning at due to the infrastructureand operation cost, the amount of processing power andstorage at MEC is orders of magnitude smaller than the tra-ditional clouds. Therefore, computing task split, offloadingand content caching in MEC are the open research topics.

5.3 Network Slicing

The 5G communication system aims to use a commoncommunication infrastructure to support various use casesof different verticals with diverse and extreme requirementsfor latency, throughput, reliability and availability. Networkslicing is an effective way to optimize 5G network to of-fer solutions tailored to all use cases by leveraging cloudtechnology, SDN, network function virtualization (NFV),end-to-end orchestration and management, and networkanalytics. It can address the latency, multi-modal sensorydata, reliability, security and cost challenges in telesurgery.

Basically, network slice (NS) is composed of a set ofnetwork element functions with flexible granularity acrossfrom radio access, transport to core network. Each NS is anindependent virtualized end-to-end network instance to en-sure end-to-end performance for a supported service group.Individual NS for any specific use case can be designed,created and activated dynamically by orchestrating the net-work functions on the distributed cloud infrastructure. Fortelesurgery, since multi-modal sensory data are generated,it might need to create individual slices for 3D video, hapticdata and physiological data to better fulfil the QoS of eachtype of sensory data. Virtualization allows instantiation ofnetwork functions at optimal locations in the forwardinggraph to shorten the latency. The centralized SDN controller

facilitates the low-latency forwarding path discovery andreliable multipath routing. Additionally, network coding(NC) combined with SDN can significantly reduce the la-tency and improve reliability exploiting the recoding andsliding window features of NC. The isolation among NSscan improve security and privacy.

Furthermore, the real-time traffic monitoring in the cen-tralized controller in SDN can flexibly adjust bandwidthfor the traffic fluctuation. The dynamic programmabilityand control in SDN, data analytics and machine learning,make it feasible to dynamically programming of networkslices to optimize the communication performance and userexperience. For example, with network data analytics topredict network performance variation, network slices canbe dynamically programmed to smoothly ensure the end-to-end performance.

Comparing with the expensive private network, networkslicing can significantly improve the network utilization andoffer cost-effective communication services. When the com-munication cost becomes trivial in the overall surgery cost,it will become a good business case for both healthcare in-dustry and network operator, thereby propelling telesurgeryto be adopted as medical routine basis.

5.4 Artificial Intelligence

Artificial Intelligence, in particular, convolutional neuralnetwork and deep learning, will play an important role intelesurgery. Besides offering diverse value added services,e.g., abnormality diagnosis and positioning, dissection strat-egy, various techniques based on AI can be designed and de-veloped to improve safety, security, accuracy and contributeto the stability of haptic control and reduce the congestionin the core network, thereby improve the overall latency andreliability.

The software-generated force and position signals canbe realized and applied to human operators to improvethe safety and accuracy. For example, a virtual “wall” canbe placed around anatomical structure to keep the surgicalinstrument from contacting it [11]. The predictive and in-terpolative/extrapolative modules can be deployed at theedge cloud to reduce the latency and packet loss impacton haptic control instability [6]. Moreover, the AI enginescan simultaneously process the 3D video stream and theon-going manipulation commands, and monitor the latencyvariation in the network. If the AI engines foresee the risksof a manipulation error, e.g., the robotic arm will cut anartery by mistake due to the delayed display of videostream, some software can generate a force to postponeor hinder the execution of manipulation meanwhile givewarnings to the surgeon. This will allow the surgeon to holdon the motion, thereby avoid the operation errors caused bythe delayed arrival of the sensory data feedback.

Additionally, the AI engine can monitor manipulationcommands and haptic feedback to perform real-time veri-fication of the manipulation. The historically collected sur-gical motions can verify the manipulation commands. Inthis way, any robotic malfunction or unauthorized opera-tion task due to security attack will be timely and reliablydetected.

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6 CONCLUDING REMARKS

Robotic telesurgery will offer a paradigm shift in healthcareand expand telemedicine domain. It brings unprecedentedopportunities as well as crucial challenges. This paper re-views the state-of-art robotic telesurgical system, identifiesthe limiting factors, and summarizes the communicationQoS requirements of the multi-modal sensory data andpoint out the relevant research challenges, open issues andthe enabling technologies in the 5G communication system.It is clear that the 5G communication system will play acritical role in tackling these challenges and transforminghealthcare.

To realize telesurgery and even make it commercialized,it needs not only to overcome the technical challenges butalso to be supported by a sustainable business model andlegal regulations. Envisioning the roadmap of telesurgerydevelopment and its wide adoption, it is expected to befirstly applied in telementoring scenarios using the dualconsoles, namely the experienced surgeon remotely con-ducts the most complicated tasks in the surgery. Such ap-plication scenarios have a good business case as those skillsare not commonly available; furthermore, it takes shortertime consuming less communication resource and is costeffective.

ACKNOWLEDGMENTS

The authors would like to thank Jørgen Bjerggaard Jensen,Clinical Professor in Urology at Aarhus University withspecial focus on bladder cancer and robotic surgery, for hisexperience in working with robotic surgery.

REFERENCES

[1] T. L. Ghezzi and O. C. Corleta, ”30 years of robotic surgery”, Worldjournal of surgery, vol. 40, no. 10, pp. 25502557, 2016.

[2] J. Konstantinova, A. Jiang, K. Althoefer, P. Dasgupta, andT. Nanayakkara, ”Implementation of tactile sensing for palpation inrobot-assisted minimally invasive surgery: A review”, IEEE SensorsJournal, vol. 14, no. 8, pp. 24902501, 2014.

[3] J. Marescaux, J. Leroy, F. Rubino, M. Smith, M. Vix, M. Simone,and D. Mutter, ”Transcontinental robot-assisted remote telesurgery:feasibility and potential applications”, Annals of surgery, vol. 235,no. 4, pp. 487492, 2002.

[4] M. Perez, S. Xu, S. Chauhan, A. Tanaka, K. Simpson, H. Abdul-Muhsin, and R. Smith, ”Impact of delay on telesurgical perfor-mance: study on the robotic simulator dv-trainer”, InternationalJournal of Computer Assisted Radiology and Surgery, vol. 11, pp. 581587, 2015.

[5] M. Simsek, A. Aijaz, M. Dohler, J. Sachs, and G. Fettweis, ”5G-enabled tactile internet”, IEEE Journal on Selected Areas in Communi-cations, vol. 34, no. 3, pp. 460473, March 2016.

[6] A. Aijaz, M. Dohler, A. H. Aghvami, V. Friderikos, and M. Frodigh,”Realizing the tactile internet: Haptic communications over nextgeneration 5g cellular networks”, IEEE Wireless Communications,vol. 24, no. 2, pp. 8289, April 2017.

[7] M. Eid, J. Cha, and A. E. Saddik, ”Admux: An adaptive multiplexerfor haptic - audio -visual data communication”, IEEE Transactionson Instrumentation and Measurement, vol. 60, no. 1, pp. 2131, Jan 2011.

[8] B. Cizmeci, X. Xu, R. Chaudhari, C. Bachhuber, N. Alt, andE. Steinbach, ”A multiplexing scheme for multimodal tele-operation”, ACM Trans. Multimedia Comput. Commun. Appl.,vol. 13, no. 2, pp. 128, Apr. 2017. [Online]. Available:http://doi.acm.org/10.1145/3063594

[9] M. Patel and J. Wang, ”Applications, challenges, and prospectivein emerging body area networking technologies”, IEEE WirelessCommunications, vol. 17, no. 1, pp. 8088, February 2010.

[10] T. Hachisu and H. Kajimoto, ”Vibration feedback latency affectsmaterial perception during rod tapping interactions”, IEEE Transac-tions on Haptics, vol. 10, no. 2, pp. 288295, April 2017.

[11] A. M. Okamura, ”Haptic feedback in robot-assisted minimallyinvasive surgery”, Current opinion in urology, vol. 19, no. 1, pp.460473, January 2009.

[12] V.-G. Nguyen, T.-X. Do, and Y. Kim, ”SDN and virtualizationbasedlte mobile network architectures: A comprehensive survey”, Wire-less Personal Communications, vol. 86, no. 3, pp. 14011438, Feb 2016.[Online]. Available: https://doi.org/10.1007/s11277- 015-2997-7

[13] A. A. E. Kalam, A. Ferreira, and F. Kratz, ”Bilateral teleoper-ation system using qos and secure communication networks fortelemedicine applications”, IEEE Systems Journal, vol. 10, no. 2, pp.709720, June 2016.

[14] J. Zhang, L. Lu, Y. Sun, Y. Chen, J. Liang, J. Liu, H. Yang, S. Xing, Y.Wu, J. Ma, I. B. F. Murias, and F. J. L. Hernando, ”PoC of scmabaseduplink grant-free transmission in ucnc for 5G”, IEEE Journal onSelected Areas in Communications, vol. 35, no. 6, pp. 13531362, June2017.

[15] Y. C. Hu, M. Patel, D. Sabella, N. Sprecher, and V. Young, ”Mobileedge computing - a key technology towards 5G”, ETSI White PaperNo.11, September 2015, accessed on 06/11/2017. [Online]. Avail-able: http://www.etsi.org/images/files/ETSIWhitePapers/etsiwp11 mec a key technology towards 5g.pdf