Patent Description:
A typical way to accomplishing these more immersive interactive experiences is to use head-mounted digital reality devices. These devices may usually include a central processing unit (CPU), a graphics processing unit (GPU) for processing intensive graphics operations, a vector unit for performing geometry transformations, and other hardware, firmware, and software. However, highly dynamic and interactive applications such as those including AR, VR, and MR experiences are primarily downloaded and hosted on the client device side, resulting in high hardware demands to execute the applications. Additionally, to accommodate the GPUs and achieve their desired performance, high quality head-mounted digital reality devices are currently physically tethered to very powerful and expensive personal computers (PCs). These requirements create larger adoption barriers due to high price points and limited mobility, which detracts from the overall experience. Moreover, streaming complex, interactive AR, VR and MR 3D graphics require high data transfer rates.

Reducing hardware and network demands along with adoption barriers for AR, VR and MR motivates the desire to offload computationally intensive tasks to one or more powerful remote servers, or cloud servers. Typical applications dominant today (asynchronous or one-way-delivery applications like instant messaging, web page loading, etc.) which employ cloud computing can tolerate approximately <NUM> of latency, and are supported by existing network infrastructure, content delivery networks (CDN), and centralized cloud computing. Current architecture employed for remote rendering is optimized for the delivery of static, predefined content with minimal levels of dynamic interaction. However, dynamic AR, VR and MR applications require real-time interaction, and thus extremely low latency (around <NUM>), placing very high demands on the network and limiting the quality and variety of digital reality experiences that users may enjoy. In addition, these drawbacks prevent or limit the provision of continuous cloud computing, cloud rendering, tracking and communication services to client devices.

<CIT> discloses a method, a device, and a non-transitory storage medium in which a network slice deployment service. The network slice deployment service includes storing network resource and capability information that indicates available network resources in a network. Network level requirement information pertaining to a request for network service from a user is generated. The network level requirement information is used to select available network resources based on the network resource capability information. The network slice deployment service calculates network resource utilization associated with available network resources so that optimal usage of network resources can be selected and end-to-end network slice deployment layout information can be generated and used to provision a network slice deployment layout that supports the request for network service.

<CIT> discloses a method and apparatus for facilitating user related information management in mobile edge computing. Method comprises receiving, at the mobile management entity, at least a first eNB indication, indicating presence of a first eNB in the MME coverage, detecting a handover of a user equipment (UE) between the first eNB and a second eNB and determining whether the handover involves the second eNB being within the MME coverage area and within the first MEC server coverage; within the MME coverage area, outside of the first MEC server coverage area, and within a second MEC server coverage area-thus resulting in the UE being transferred to the second eNB or outside of the MME coverage area. In some embodiments, in an instance in which the second eNB is within the MME coverage area and within the first MEC server coverage area, facilitating the UE being transferred to the second eNB and same MEC server.

Advantageous embodiments of the invention are subject to the dependent claims and are outlined herein below.

One or more drawbacks described in the background are addressed by the current disclosure through a system and method for enabling optimized real-time responsive and continuous (or substantially continuous, allowing for occasional network latency problems or service interruptions) location-based services in three-dimensional space through a distributed computing center network. The location-based services include real-time cloud computing of digital reality data, real-time rendering of digital reality data, real-time tracking of client devices, or real-time communication, or combinations thereof. The system and method offloads computationally intensive tasks to cloud servers that cover specific physical areas, or server zones, located in the proximity of users' devices. Methods described herein comprise dynamic network slicing and quality of service (QOS) management, which may prove advantageous for optimizing computing power, system bandwidth, and antenna beamforming and steering. In some embodiments, dynamic network slicing and QOS management also may be considered as location-based services in the three-dimensional space, in the sense that the actual provisioning of the service, the QOS, which server performs which functions, and at which levels, and the like, are also affected by and responsive to location of, e.g., the client devices. Instructions to perform the above methods may be embedded and performed in a digital reality virtual radio access network (VRAN) in the cloud server.

"Cloud computing" refers herein to storing and processing data, such as digital reality data, through cloud servers. "Cloud rendering" refers herein to the process of generating photorealistic images and experiences from scene files that may include geometries, viewpoints, textures, lighting, sounds, and other information through cloud servers. For example, a processor in the cloud server may render a stereoscopic representation of a three-dimensional representation of a scene, may provide binaural audio output, and may provide haptic output through the digital reality devices to provide haptic sensation to a user. Communication techniques disclosed herein include the transmission and retrieval of data such as digital reality data from cloud servers to digital reality devices and vice-versa through network signals emitted by network antennas. "Tracking" refers herein to determining position or orientation of objects or devices. Tracking may be used to refer to tracking of client devices (e.g.. , digital reality devices or other connected computing devices) which may be further used to adjust computing, rendering, and communication of the digital reality content. These services are provided based on location in three-dimensional space. Three-dimensional space may refer to space in the physical world, a virtual world, which may include virtual, mixed or augmented reality (VR, MR and AR), or both the physical world and a virtual world.

Client devices refer herein to digital reality devices such as head-mounted display devices, see-through devices, and smart contact lenses; or other connected computing devices, such as mobile devices, wearable devices, personal computers, laptops, gaming devices, or Internet of Things devices.

According to an embodiment of the invention, a wide area distributed computing center network (WADCCN) is configured to cover and service large geographical areas, where cloud computing, rendering, tracking, and communication services in three-dimensional space are provided to client devices. The WADCCN includes various distributed computing centers (DCCs), one or more of which are connected to data centers (DCs) and are synchronized by the DCs. Each DC comprises one or more master servers. Each of the DCCs comprises one or more cloud servers. Synchronization involves the one or more master synchronizing data received from the cloud servers to maintain consistency and uniformity of digital reality and user data across the cloud servers. Two or more communicatively connected and synchronized DCCs form a WADCCN system. In further embodiments, at least one DCC communicatively connected to at least one DC may form a synchronized DCC, which may be configured to cover and service smaller geographical areas. A synchronized DCC may include a plurality of computing centers (CCs) communicatively connected to each other and synchronized by a DC. The DCs' one or more master servers may include computing resources sufficient to perform application and user data storage and synchronization received from different cloud servers located in CCs.

Data synchronization may further include the process of maintaining the consistency and uniformity of digital reality applications data and user data across the different cloud servers from various server zones.

In an embodiment, a DCC may include between about two and about fifty CCs synchronized by one or more DCs. The DCs may include facilities to house computer systems and associated components, such as telecommunication and storage systems, power supplies, redundant data communication connections, environmental controls, and various security devices. DCs may be located in remote areas and may cover relatively large geographical extensions (e.g., cities, countries, or continents) in order to provide services to a plurality of DCCs. In further embodiments, CCs, which house the cloud servers, may be smaller versions of DCs, and thus may also include facilities to house computer systems and associated components. However, CCs may differ from DCs in that CCs may be located in more densely populated areas in order to be closer to users, and thus provide better service to the users with reduced latency and optimized QOS.

In an embodiment, network connection antennas may be configured in areas relatively close to CCs, DCs, or areas serviced by the CCs in order to provide them with network connection and tracking services. In these embodiments, the antennas are connected through wired means to the CCs and DCs. Similarly, the antennas may also be installed within the CCs, DCs, or areas serviced by the CCs.

In some embodiments, in order to reduce hardware and network demands, contribute to the reduction of network latency, and improve the general merged reality experience, the system may connect through a network including millimeter-wave (mmW) or combinations of mmW and sub <NUM> communication systems, such as through 5th generation wireless systems communication (<NUM>). In other embodiments, the system may connect through wireless local area networking (Wi-Fi) providing data at, for example, <NUM>. Provided communication systems may allow for low end-to-end (E2E) latency and high downlink speeds to end points in the field, complying with parameters necessary for executing the typically highly-interactive digital reality applications or other highly-demanding applications. This results in high-quality, low latency, real-time digital application content streaming. In other embodiments, the system may communicatively connect through 4th generation wireless systems communication (<NUM>), may be supported by <NUM> communication systems, or may include other wired or wireless communication systems.

According to an embodiment, operating systems embedded within the cloud servers include instructions that, when executed, enable a real-time responsive and continuous location-based cloud computing, rendering, tracking, and communication services of client devices in three-dimensional space. These operating systems may include network operating system (NOS) configured to connect antennas to cloud servers and to the client devices; a digital reality virtual radio access network (VRAN) configured to perform dynamic network slicing and quality of service management; and an experience operating system including data and instructions used by the digital reality VRAN in order to perform dynamic network slicing and quality of service management.

In an embodiment, the NOS embedded in the cloud server may include an Open Network Automation Platform (ONAP), which is an open source software platform that delivers capabilities for the design, creation, orchestration, monitoring, and life cycle management of virtual network functions (VNFs), carrier-scale Software Defined Networks (SDNs) included in the VNFs, and higher-level services that combine the above functions. ONAP provides for automatic, policy-driven interaction of these functions and services in a dynamic, real-time cloud environment, achieving both faster development and greater operational automation.

Implementation of the digital reality VRAN involves relocating processors originally located at the radio sites to CCs and DCs, and is implemented using virtual machines (VMs) on the cloud servers and master servers, decentralizing the functions at the radio sites and virtualizing network functions in the radio access network (RAN). Because several CCs and antennas are used in, but not limited to, densely populated areas, the digital reality VRAN of the current disclosure is allowed to perform an efficient scaling and pooling of network resources in the proximity of users, bringing network signals closer to users and avoiding typical drawbacks of mmW spectrum signals of antennas used in some embodiments of the current disclosure.

The experience OS includes data and instructions used by the digital reality VRAN to determine the processing required to control and provide network signals to client devices for dynamic network slicing and QOS management. In some embodiments, the experience OS receives, stores, processes, and provides data and instructions to the digital reality VRAN related to the number, position, and orientation of client devices that are to be serviced by an antenna as well as the context that affects each client device and each antenna and which may have an effect on the dynamic network slicing and QOS management.

According to an embodiment, the servers of the CCs include memory and a processor, the processor being configured to execute instructions and data stored in the memory. The memory stores in a database or data structure a persistent virtual world system modeled based on the real world. The persistent virtual world system includes virtual replicas of real world entities found in the real world. The persistent virtual world system may further comprise purely virtual objects not existing in the real world, and applications that users can view and interact with in the locations where they have been configured. The memory may further include a replica editor which may include software and hardware configured to enable users to model and edit the virtual replicas of the real world entities as well as purely virtual objects and graphical representations of applications. The replica editor may be, for example, a computer-aided drawing (CAD) software that may store data and instructions necessary to input and edit virtual replicas. The replica editor may enable the input of explicit data and instructions that relate to each virtual replica, which refers to data and instructions that describe the shape, location, position and orientation, physical properties, and the expected functioning and impact of each replica and the system as a whole. Generally, the explicit data may include data that may not be obtained by the sensing mechanisms but which instead may need to be input digitally through the replica editor, such as priority data, building materials, wall thicknesses, electric installations and circuitry, water pipes, fire extinguishers, emergency exits, window locations, machine performance parameters, machine sensor and valve locations, etc. In some embodiments the VRAN performs the dynamic network slicing and quality of service management based on data from the persistent virtual world system.

In the current disclosure, the term "persistent" is used to characterize a state of a system that can continue to exist without a continuously executing process or network connection. For example, the term "persistent" may be used to characterize the virtual world system where the virtual world system and all of the virtual replicas, or other objects such as purely virtual objects and applications therein comprised continue to exist after the processes used for creating them cease, and independent of users being connected to the virtual world system. Thus, the virtual world system is saved in non-volatile storage location in the cloud server. In this way, virtual replicas, purely virtual objects and applications may interact and collaborate with each other when being configured for accomplishing specific goals even if users are not connected to the server.

According to an embodiment, a plurality of sensing mechanisms mounted on the client devices continuously capture data from the real world that serves to enrich and update explicit data and instructions input through the replica editor. Thus, the persistent virtual world system stored in the server and each of the virtual replicas are kept updated with real-time, multi-source sensory data that mirror the conditions of the real world.

According to an embodiment, client devices include a power source, a memory, sensors, and transceivers, all operatively connected to a processor. In some embodiments, the transceivers are mmW transceivers. The power source is configured to provide power to the client devices; the memory may be adapted to store application program instructions and to store telemetry metadata of the client device from the sensors; the sensors, which may include one or more of an Inertia Measuring Unit (IMU), accelerometers, and gyroscopes, is configured to measure and report the velocity, acceleration, angular momentum, speed of translation, speed of rotation, and other telemetry metadata of the client device; the mmW transceivers may allow the client device to receive mmW from the antennas and to send the data back when interacting with digital reality content, and may also enable positional tracking of the client device; and the processor may be configured to implement application programs stored in the memory of the client device. In certain embodiments the sensors and mmW transceivers may be decoupled (i.e., separate from each other). In other embodiments, the sensors and mmW transceivers may be coupled together, forming one operational component within the client device.

In an embodiment, combining the capabilities of the sensors (e.g., IMU, accelerometers, gyroscopes, and accelerometers) with the positional tracking as provided by the mmW transceivers, may enable sub-centimeter or sub-millimeter positional and orientational tracking, which may increase accuracy when tracking the real-time position and orientation of the client devices and may improve the general user experience. Tracking of the client devices may be performed employing any of several techniques (e.g., time of arrival (TOA), angle of arrival (AOA), visual imaging, radar technology, etc.), and using systems such as global navigation satellite systems (GNSS), assisted GNSS (AGNSS), differential GPS (DGPS), satellite-based augmentation systems (SBASs), real-time kinematic (RTK) systems, or combinations thereof. In some embodiments, tracking of devices is implemented by a combination of AGNSS and inertial sensors in the devices.

According to an embodiment, methods such as server hopping, antenna hopping, and super-peer device implementation may be implemented in a synchronized CC. In an embodiment of server hopping, one or more users may enter a first server zone and engage with digital reality content retrieved from one or more sources, such as one or more digital reality applications. The digital reality content is computed and rendered by a first cloud server in a computing center to be thereafter accessed by a user via a digital reality device. The one or more digital reality devices are continuously tracked by one or more antennas. As a user moves towards a second server zone, the one or more antennas track the movement from the one or more client devices and send the user position to a master server. When users' client devices are found in-between server zones, the master server, based on the location data sent by the antennas from the client devices, instructs the cloud server from the first and second zone to partially compute and render digital reality data. After the one or more user digital reality devices are located in the second server zone, the one or more antennas instruct the master server to start retrieving and synchronizing digital reality data from the digital reality application as sent by the cloud server of the first server zone. Afterwards, the master server starts retrieving digital reality data from the cloud server of the first server zone, synchronizes the digital reality data, and sends the data to the cloud server of the second server zone so that the one or more users seamlessly keep receiving the digital reality content through their client devices when located in the second server zone.

In an embodiment of antenna hopping, continuing with the explanation of server hopping above, as a user approaches a server zone not completely covered by a first antenna but by a second antenna, the antennas may first send the user location data to the master server, and may then alternate, share, or completely switch servicing functions (e.g., communication and tracking) of the client devices depending on the user location, as instructed by the master server.

In an embodiment of super-peer device implementation, using the description of server hopping as an example, as two or more users move farther from a cloud server, the antennas may first send the user location data to the master server, which may then assign the client device closest to the cloud server, and thus, with the highest available QOS, as a super-peer device. The super-peer device may act as a provisional server for the other digital reality devices, aggregating and distributing digital reality data to the other peer devices.

According to an embodiment, dynamic network slicing and QOS management is performed at the digital reality VRAN portion of the cloud server, and may use data from the experience OS that may be required to perform these functions. Dynamic network slicing and QOS management is referred herein as the ability to tailor a set of functions for use of the network for a client device. For example, dynamic network slicing and QOS management may determine the optimum beamforming, steering of antennas, server hopping, antenna hopping, super peer assignment, network functionality needed by client devices, and optimum number of subcarriers and total bandwidth per client devices required to optimize QOS.

The dynamic network slicing and QOS management may be based on parameters including point of service, context, priority, and security, for which relevant data are stored and updated at the different cloud servers and master servers, and which are managed at the experience OS portion of the servers. Accordingly, these parameters are available through the virtual replicas of the real world comprised within the persistent virtual world system stored in the cloud servers, including data and instructions that simulate the real appearance and behavior of each of the real elements.

Point of service refers herein to the location of a client device as related to the distance between a client device and an antenna. For example, the farther a client device goes from the antenna, because of signal attenuation, the more subcarriers that the client device may need to be assigned in order to compensate for this attenuation.

The direct or indirect environment of a client device and an antenna may be classified as "micro-context" and "macro-context". Context information may be input through the replica editor in the cloud server, may be captured through sensing mechanisms of the client devices, or may be inferred by the cloud servers, or combinations thereof. The term "micro-context" refers to the context immediately surrounding a client device and an antenna, such as any people, objects, or conditions that may directly affect sending and receiving of network signals. Micro-context may include data such as 3D image data, 3D geometries, 3D entities, 3D sensory data, 3D dynamic objects, video data, audio data, textual data, time data, material data, dimensional data, metadata, positional data, lighting data, temperature data, and service context data, amongst others, of the environment immediately surrounding and affecting a target real world entity.

For example, if a client device is receiving network signals inside of a building, relevant micro-context having an effect over the network slicing and QOS management may include building materials, wall thicknesses, and window locations, as well as buildings or other structures around the antennas or around the client device that may potentially attenuate the network signals, all or most of which may be relevant during processing by the digital reality VRAN. Further in this example, the digital reality VRAN may determine how to direct network signals such that the QOS may be optimized and may reach client devices with the least possible attenuation, including performing an optimum beamforming and steering of antennas in order to avoid buildings and other structures around the antenna, and directing the signals through windows or thinner walls where a client device may be located, instead of doing so through thicker walls or walls made of materials where network signals may have difficulties penetrating.

The term "macro-context" refers to the indirect context surrounding an antenna and client device. The macro context may be derived by the cloud server from a plurality of micro-contexts, giving rise to more holistic information of a system, such as the current efficiency of a manufacturing plant, air quality, climate change levels, company efficiency, city efficiency, country efficiency, etc. The macro context may be considered and computed at different levels based on goals, including local level (e.g., office or manufacturing plant), neighborhood level, city level, country level, or even planet level. Thus, depending on these goals, the same real world entity data and micro context data may derive different types of macro-contexts.

The term "service context" refers to the actual applications being used by a user or users in the vicinity. As each application consumes bandwidth, service context may provide the cloud server with valuable context information required to assess provisioning of network signals to each client device.

According to an embodiment, the dynamic network slicing and QOS management is performed using machine learning algorithms. Generally, during machine learning, a programmer provides a computer with a set of sample data and a desired outcome, and the computer generates its own algorithm on the basis of those data that it can apply to any future data. Thus, in the current disclosure, a set of dynamic network slicing and QOS management parameters along with data sets corresponding to each parameter and desired outcomes may be provided for training the machine learning algorithms. These algorithms may go through numerous iterations during training in order to generate trained machine learning models that may be used when performing dynamic network slicing and QOS management. The training and inference may be performed by at least one processor in the cloud server.

"Priority" or "priority data" refers to the relative importance that certain users may have with respect to the service providers, which may be determined by the type of contract agreed by the different parties. The type of priority may influence the context rankings and thus the amount of bandwidth that users may receive for each type of service.

Parameters related to security may translate into various security measures such as data encryption, firewalls, Virtual Private Networks (VPNs), etc. The level of security may be determined by the type of contract.

In some embodiments, rendering and computing tasks may be shared between the different cloud servers, client devices, and/or the super-peer device.

In some embodiments, server zones may include one or more geographically limited zones. For example, a server zone may be an outdoor location (e.g., a park, a sports field, a street, a zoo, etc.) or an indoors location (e.g., a game zone, restaurant, entertainment club, theater, office, etc.).

According to an embodiment, a method for providing real-time responsive and continuous location-based services in three-dimensional space to client devices comprises synchronizing, by one or more master servers comprised by each of one or more mutually connected data centers, a plurality of distributed computing centers, which form a wide area distributed computing center network configured to provide real-time responsive and continuous location-based services in three-dimensional space; wherein the one or more master servers of the one or more data centers synchronize data received from the cloud servers of the plurality of distributed computing centers to maintain consistency and uniformity of digital reality and user data across the cloud servers; and performing, by the master servers, a dynamic network slicing and quality of service management through management of the distributed computing centers.

According to an embodiment, the method for dynamic network slicing and quality of service management comprises assigning each user with a profile selected from one or more of a global profile, a contract-based profile, or a machine-learning-based profile; determining service context parameters and ranking values, priority levels, and security levels of the users depending on the user profiles; allocating bandwidth to each user according to the user profile; and performing dynamic network slicing and QOS management based on context and point of service, but staying within the user ranking values determined by the assigned user profile.

According to an embodiment, the method for dynamic network slicing and quality of service management by the digital reality VRAN further comprises determining the optimum beamforming, steering of antennas, server hopping, antenna hopping, super peer assignment, network functionality needed by client devices, and optimum number of subcarriers and total bandwidth per client devices required to optimize quality of service.

According to an embodiment, server hopping performed by the master server comprises receiving client device location data from antennas; and when users' client devices are located in zones not entirely covered by a cloud server, instructing a cloud server nearest to users to compute and render digital reality data for the client device.

According to an embodiment, antenna hopping performed by the master server comprises receiving client device location data from antennas; and when users' client devices are located in zones not completely covered by an antenna, instructing one or more antennas nearest to users to perform tracking and data provisioning for the client devices.

According to an embodiment, super peer assignment by the master server comprises receiving client device location data from antennas; when users' client devices are located in zones where quality of service and system computing power is not optimized, assigning one or more client devices as super-peer devices for aggregating and distributing digital reality data for peer client devices; and dynamically adjusting the level of computational and rendering operations across the cloud servers, super peer devices, and other peer client devices.

According to an embodiment, the method for dynamic network slicing and quality of service management by the digital reality VRAN is performed based on data from the persistent virtual world system stored in cloud servers and master servers.

The global profile may be a generic profile assigned to average users. Thus, context ranking values (e.g., values assigned to each type of bandwidth-consuming service such as calling, streaming videos, sending and receiving short message services (SMS), downloading/uploading files, and downloading/uploading videos, etc.) are assigned based on a statistical mean of usage from average users. Global profiles may, in some embodiments, be applied geographically (city, state, country, region, etc.) if it is determined that significant geographical differences exist between context ranking values.

A contract-based profile may include adjusted context ranking values for each context parameter according to the terms stipulated in a contract between the user and the service provider. Contract-based profiles may also determine other factors such as priority and security.

Machine-learning-based profiles may determine service context rankings through usage of machine-learning techniques and may optimize network traffic based on a context zone determined by an event. For example, if there is a sports game taking place in a stadium, and users are recording the video and doing live video-streaming, the machine-learning techniques may determine a high context ranking value for that particular group of users and may provide users accordingly with necessary bandwidth. In other embodiments, the machine-learning-based profile may also be used to determine individual users profile and compute a ranking value accordingly.

Specific features, aspects and advantages of the present disclosure will be better understood with regard to the following description and accompanying drawings, where:.

In the following description, reference is made to drawings which show by way of illustration various embodiments.

<FIG> depicts a schematic representation of a wide area distributed computing center network (WADCCN) system <NUM> for enabling real-time responsive location-based cloud computing, rendering, tracking, and communication services in three-dimensional space, according to an embodiment. Three-dimensional space may refer to space in the physical world, a virtual world, which may include virtual, mixed or augmented reality (VR, MR and AR), or both the physical world and a virtual world.

WADCCN system <NUM> may include various distributed computing centers <NUM> (DCCs), one or more of which are connected to data centers <NUM> (DCs) and are synchronized by the DCs <NUM>. In an embodiment, two or more communicatively connected and synchronized DCCs <NUM> form the WADCCN system <NUM>, which may be configured to cover and service large geographical areas. In further embodiments, at least one DCC <NUM> communicatively connected to at least one DC <NUM> form a synchronized distributed computing center (DDC) <NUM>, which may be configured to cover and service smaller geographical areas.

<FIG> depicts an architectural diagram of a synchronized DDC <NUM> that can be included in the WADCCN system <NUM> described in <FIG>, according to an embodiment. The system of <FIG> includes similar elements as those of the system of <FIG>, and therefore contains the same or similar reference numbers.

In <FIG>, a plurality of computing centers <NUM> (CCs) are communicatively connected to each other, forming a DCC <NUM>. The connection between CCs may be a wired or wireless connection. In some embodiments, two or more CCs <NUM> communicatively connected to one or more DCs <NUM> may form a synchronized CC <NUM>. Generally, synchronized CCs <NUM> may be configured to service geographically specific server zones, as will be further explained in <FIG>.

DCs <NUM> may include facilities to house computer systems and associated components, such as telecommunication and storage systems, power supplies, redundant data communication connections, environmental controls, and various security devices. DCs <NUM> may be located in remote areas and may cover relatively large geographical extensions in order to provide service to a plurality of DCCs <NUM>. For example, a DC <NUM> may cover enough area to be able to service and synchronize city, country, or in some cases continental setups of WADCCN system <NUM>.

In some embodiments, DCs <NUM> includes one or more master servers <NUM> with computing resources sufficient to perform application and user data storage and synchronization of data received from different cloud servers <NUM>. Data synchronization includes the process of maintaining the consistency and uniformity of digital reality and user data across the different cloud servers <NUM> from various server zones.

In further embodiments, CCs <NUM>, which house the cloud servers <NUM>, may be smaller versions of DCs <NUM>, and thus may also include facilities to house computer systems and associated components. However, CCs <NUM> may differ from DCs <NUM> in that CCs <NUM> may be located in populated areas to be closer to users and thus provide better service to users (e.g., with reduced latency and an increased quality of service (QOS)).

In some embodiments, between about two and about fifty CCs <NUM> may mutually connect to form a DCC <NUM>. Network connection antennas <NUM> may be configured within, in areas relatively close to CCs <NUM>, DCs <NUM>, and/or areas serviced by the CCs <NUM> in order to provide them with communication services and tracking services.

In an embodiment, for servicing devices located outdoors, antennas <NUM> may include millimeter wave (mmW)-based antenna systems or a combination of mmW-based antennas and sub <NUM> antenna systems, such as through 5th generation wireless systems communication (<NUM>). In other embodiments, antennas <NUM> may include other types of antennas, such as <NUM> antennas, or may be used as support antennas for the mmW/sub GHz antenna systems.

In embodiments whereby antennas <NUM> are servicing indoors, the antennas <NUM> may use wireless local area networking (WiFi), providing data at, for example, <NUM>.

For outdoor antenna systems in the current disclosure, the mmW band, also called the extremely high frequency band, is employed. The millimeter-wave band spans from <NUM> to <NUM>; however, neighboring super-high frequencies from about <NUM> to <NUM> may also be included, since these waves propagate similarly to mmW. Mmw-based antennas or combinations of mmW-based antennas and sub GHz antenna systems, because of the extremely high frequency of mmW, are highly reflective, being easily blocked by walls or other solid objects, and may suffer significant attenuation passing through foliage or in tough weather conditions. Therefore, the antennas <NUM> may include small, mmW transceivers arranged in a grid pattern, which can help to magnify the collective energy, increase gain, and decrease power losses without increasing transmission power. Techniques such as multiple-input, multiple-output, or MIMO, may be used to separate beams at several devices simultaneously or send multiple data streams to a single device, thereby increasing QOS. Additionally, the antennas may cover relatively small areas between about <NUM> meters to about <NUM> to ensure accurate propagation of the millimeter waves. The antennas <NUM>, leveraging both sub <NUM> and mmW frequency space, may provide ubiquitous or very broad coverage and network capacity to elements of synchronized CC <NUM>.

<FIG> depict illustrative diagrams of a cloud server <NUM> according to embodiments of the current disclosure. The system of <FIG> may include similar elements as those of the systems of <FIG>, and may therefore contain the same or similar reference numbers.

<FIG> depicts operating systems <NUM> embedded within the cloud servers <NUM>, according to an embodiment. The operating systems <NUM> stored in the cloud servers <NUM> include a set of software comprising data and instructions that, when executed by a processor of the cloud servers <NUM>, enable a real-time responsive and continuous location-based cloud computing, rendering, tracking, and communication services of client devices in three-dimensional space with synchronized DCCs. The operating systems <NUM> may include a network operating system <NUM> (NOS), a digital reality virtual radio access network (VRAN) <NUM>, and an experience operating system (OS) <NUM>. Operating systems <NUM> within the cloud servers <NUM> may be communicatively connected to the one or more antennas <NUM> through the NOS <NUM>.

The NOS <NUM> includes a set of data and instructions that provide functions for connecting the antennas <NUM> to the cloud servers <NUM>. The NOS <NUM> additionally includes functions for connecting the antennas <NUM> to client devices <NUM> such as digital reality devices <NUM> or other connected computing devices. Digital reality devices <NUM> may include head-mounted display devices, see-through devices, and smart contact lenses, for example. Aside from digital reality devices, client devices may include any type of suitable computing devices that may connect to the cloud server <NUM> and to each other, such as Internet of Things devices. Non-limiting examples of connected computing devices include vehicles (e.g., cars, trains, planes, drones, amusement rides, etc.), mobile devices (e.g., cell phones, notebook computers, tablet computers, etc.), wearable devices (e.g., shoes, gloves, hats, rings, clothes, etc.), projectiles with embedded computing and communication hardware (e.g., sports balls, frisbees, boomerangs, other toys), and other physical objects with embedded computing and communication hardware such as train tracks, streets, street lights, buildings, and the like.

In an embodiment, the NOS <NUM> employed in the cloud server <NUM> may be an Open Network Automation Platform (ONAP), which is an open source software platform that delivers capabilities for the design, creation, orchestration, monitoring, and life cycle management of virtual network functions (VNFs), carrier-scale Software Defined Networks (SDNs) included in the VNFs, and higher-level services that combine the above functions. ONAP provides for automatic, policy-driven interaction of these functions and services in a dynamic, real-time cloud environment, achieving both faster development and greater operational automation.

Typically, baseband processors that manage radio functions are located at the radio sites (e.g., at radio or network antennas). Implementing the digital reality VRAN <NUM> of the current disclosure involves moving the baseband processors, originally located at the radio sites, to CCs and DCs, and are implemented using virtual machines (VMs) on the cloud servers <NUM> and master servers, decentralizing the functions at the radio sites and virtualizing network functions in the radio access network (RAN). VMs are software-based emulations of a computer system, which are based on computer architectures and provide functionality of a physical computer.

When antennas <NUM> provide mmW radio signals that enable data connection between the different elements of the system and digital reality devices <NUM>, the network signals may not propagate efficiently at longer distances and may be excessively absorbed by the atmosphere, rain, and vegetation at these distances, due to the extremely high frequencies of the millimeter waves. Thus, by employing several CCs in densely populated areas, the digital reality VRAN <NUM> is allowed to perform an efficient scaling and pooling of network resources in the proximity of users, bringing network signals closer to users and avoiding mentioned drawbacks of mmW spectrum signals.

The experience OS <NUM> includes data and instructions used by the digital reality VRAN <NUM> to determine the processing required to control and provide network signals to client devices for QOS management. In some embodiments, the experience OS <NUM> receives, stores, processes, and provides data and instructions to the digital reality VRAN <NUM> related to the number, position, and orientation, and metadata of client devices that are to be serviced by an antenna <NUM> as well as the context that affects each client device <NUM> and each antenna <NUM> and which may have an effect on the QOS management.

<FIG> depicts a cloud server <NUM> of the current disclosure connected to client devices <NUM> via a network. The cloud server <NUM> includes memory <NUM> and at least one processor <NUM>. The memory <NUM> stores a persistent virtual world system <NUM> in a database or data structure and includes a plurality of virtual replicas <NUM>, such as virtual replicas A, B, C, and D, corresponding to respective real world elements. The virtual replicas <NUM> are communicatively connected to the real world entities via sensors connected to the client devices <NUM>. The memory <NUM> may further stores purely virtual objects not available in the real world, as well as applications which may also be represented by virtual objects.

According to an embodiment, the memory <NUM> may further include a replica editor <NUM> which may include software and hardware configured to enable users to model and edit the virtual replicas <NUM> of the real world entities, the purely virtual objects, and the applications. The replica editor <NUM> may be, for example, a computer-aided drawing (CAD) software that may store data and instructions necessary to input and edit virtual replicas <NUM>, the purely virtual objects, and the applications. The replica editor <NUM> may enable the input of explicit data and instructions <NUM> that relate to each virtual replica <NUM>, the purely virtual objects, and the applications, which refer to data and instructions that describe their shape, location, position and orientation, physical properties, and expected functioning. In an embodiment, explicit data and instructions <NUM> may not be obtained by the sensors of client devices <NUM> but may instead need to be input digitally through the replica editor <NUM>, such as priority data, building materials, wall thicknesses, electric installations and circuitry, water pipes, fire extinguishers, emergency exits, window locations, machine performance parameters, machine sensor and valve locations, etc. "Instructions" refers to code (e.g., binary code) that can be executed by the processor <NUM>. In the context of the persistent virtual world system, instructions represent, on the virtual replica <NUM>, the behavior of the real world entity.

In some embodiments, a virtual replica <NUM> includes one or more of 3D world and building data, such as SLAM or derivate-mapping based data; 3D geometry data; 3D point cloud data; or geographic information system data representing real-world structural properties that may serve to model a 3D structure for the virtual replicas <NUM>.

As way of example, a virtual replica <NUM> of an elevator may include data and instructions representing the geometry, materials, and physics, along with the mechanics and functioning of the elevator. The functioning, such as the movement from one floor to another, may be updated in real time in the persistent virtual world system <NUM> as the elevator moves in real life. Likewise, if the elevator includes sufficient computing and communication hardware and connected electromechanical components that allow the elevator to be controlled responsive to communications from the persistent virtual world system <NUM>, the elevator may be indirectly manipulated in real life by manipulating the virtual replica <NUM>.

Modeling techniques for converting real world entities into virtual replicas <NUM> with explicit data and instructions <NUM> and making them available in the persistent virtual world system <NUM> may be based on readily-available CAD models of the real world entities. For example, machine owners may provide an administrator of the persistent virtual world system <NUM> or may input by themselves the already-existing digital CAD models of their machines. Similarly, building owners may provide building information models (BIM) with building details to be stored in the persistent virtual world system <NUM>, which may include information that may not be visible or easily obtainable via sensing mechanism. In these embodiments, the owners of these real world entities may be responsible for adding the virtual replicas <NUM> into the persistent virtual world system <NUM>, which may be achieved, for example, through incentive systems or by legal requirements. In some embodiments, the administrators of the persistent virtual world system <NUM>, and even government officials, may collaborate with owners of real world entities for inputting the real world entities into the persistent virtual world system <NUM> and therefore realizing a faster and more thorough creation of the persistent virtual world system <NUM>.

In other embodiments, radar-imaging, such as synthetic-aperture radars, real-aperture radars, Light Detection and Ranging (LIDAR), inverse aperture radars, monopulse radars, and other types of imaging techniques may be used to map and model real world entities before integrating them into the persistent virtual world system <NUM>. Utilizing these technical solutions may be performed especially in cases where the original models of the structures are not available, or in cases where there is missing information or there is a need to add additional information to the virtual world entities which is not provided by the CAD models.

The explicit data and instructions <NUM> input through the replica editor <NUM> may include, apart from the shape and other properties of a real world entity, descriptive data and instructions that detail the expected functioning of the real world entity. For example, the explicit data and instructions <NUM> of a building may include the shape and properties of the building (e.g., 3D shapes, thickness of walls, location of fire alarms, materials used for each segment, location of windows, location of electric lines and water pipes, etc.), along with descriptive data and instructions that detail how much bandwidth a building is designed to consume, the number of people that the building may allow, how many people should circulate daily, etc..

Independent of the modeling techniques used for creating the virtual replicas <NUM>, the information of each virtual replica <NUM> should provide enough details about each corresponding real world entity so that a highly accurate virtual replica <NUM> of each real world entity is available. The virtual replicas <NUM> are then enriched and updated through multi-source sensory data <NUM>. Thus, each virtual replica <NUM> includes data <NUM> and instructions <NUM> that serve to describe the real appearance and behavior of each real world entity.

The multi-source sensory data <NUM> may also include contextual data, such as micro-context including micro-context A, B, C, and D (not shown) from real-world elements, and macro-context (not shown). This data is then transferred to the persistent virtual world system <NUM> to become, respectively, a virtual micro-context <NUM> including corresponding digital micro-contexts A, B, C, and D, and a virtual macro-context <NUM>, which are updated in real-time based on the multi-source sensory data <NUM> obtained by sensors of the client devices <NUM>. The virtual micro-context <NUM> and virtual macro-context <NUM> also include data <NUM> and instructions <NUM> that serve to describe the respective real-world appearance and behavior of elements within the micro-context <NUM> and macro-context <NUM>.

Including a persistent virtual world system <NUM> along with sensory and explicit data and instructions that mimic the appearance and behavior of the real world along with the context corresponding to each entity and to the system as a whole may assist in the adjustment of QOS for each client device <NUM>, as the management of QOS and provision of services such as real-time tracking, communication, rendering, and computing may be directly linked to this data and instructions embedded in each virtual replica <NUM>. Thus, in some embodiments, the network slicing and quality of service management is based on data from the persistent virtual world system <NUM>.

<FIG> depict diagrams <NUM> including operational components <NUM> of the client devices <NUM> (e.g., digital reality devices <NUM> or other connected computing devices) that may be serviced by the antennas <NUM>, according to an embodiment. The systems of <FIG> may include similar elements as those of the systems of <FIG>, and may therefore contain the same or similar reference numbers.

<FIG> shows an embodiment where the operational components <NUM> of the client devices <NUM> include a power source <NUM>, a memory <NUM>, sensors <NUM> and a mmW transceiver <NUM>, all operatively connected to a processor <NUM>.

The power source <NUM> is configured to provide power to the client devices. In one embodiment, the power source <NUM> may be a battery. The power source <NUM> may be built into the client devices or removable from the client devices, and may be rechargeable or non-rechargeable. In one embodiment, the client devices may be repowered by replacing one power source <NUM> with another power source <NUM>. In another embodiment, the power source <NUM> may be recharged by a cable attached to a charging source, such as a universal serial bus ("USB") FireWire, Ethernet, Thunderbolt, or headphone cable, attached to a personal computer. In yet another embodiment, the power source <NUM> may be recharged by inductive charging, wherein an electromagnetic field is used to transfer energy from an inductive charger to the power source <NUM> when the two are brought in close proximity, but need not be plugged into one another via a cable. In another embodiment, a docking station may be used to facilitate charging.

The memory <NUM> may be implemented as computing hardware and software adapted to store application program instructions and to store multi-source sensory data captured by the sensors <NUM>. The memory <NUM> may be of any suitable type capable of storing information accessible by the processor <NUM>, including a computer-readable medium, or other medium that stores data that may be read with the aid of an electronic device, such as a hard-drive, memory card, flash drive, ROM, RAM, DVD or other optical disks, as well as other write-capable and read-only memories. The memory <NUM> may include temporary storage in addition to persistent storage.

The sensors <NUM> may include one or more of an Inertia Measuring Unit (IMU), accelerometers, and gyroscopes, amongst others. The IMU is configured to measure and report the velocity, acceleration, angular momentum, speed of translation, speed of rotation, and other telemetry metadata of the client device by using a combination of accelerometers and gyroscopes. In one embodiment, accelerometers within the IMU may include a tri-axial accelerometer that is capable of measuring acceleration in three orthogonal directions. In other embodiments one, two, three, or more separate accelerometers may be included within the IMU. In other embodiments, additional accelerometers and gyroscopes may be included separate from the IMU.

The mmW transceivers <NUM> may allow the client device to receive mmW from the antennas and to send the data back to antennas when interacting with digital reality content. The mmW transceivers <NUM> may also enable positional tracking of the client device. The mmW transceiver <NUM> may be a two-way communication mmW transceiver <NUM>.

In an embodiment, combining the capabilities of the sensors <NUM> (e.g., IMU, accelerometers, and gyroscopes) with the positional tracking provided by the mmW transceivers <NUM> may enable sub-centimeter or sub-millimeter positional and orientational tracking, which may increase accuracy when tracking the real-time position and orientation of the client devices and may improve the general user experience.

Tracking of the client devices may be performed employing different techniques. For example, tracking may be performed by employing time of arrival (TOA) tracking technique, which uses information gathered from three or more antennas. The client device then sends out a signal that is received by all of the antennas within range. Then, each antenna measures the amount of time it has taken to receive the signal from the time the signal was sent, triangulating the position of the client device. In other embodiments, tracking of client devices may be performed by using an angle of arrival (AOA) technique which, instead of using the time it takes for a signal to reach three base stations like TOA does, uses the angle at which a client device signal arrives at the antennas. By comparing the angle-of-arrival data among multiple antennas (at least three), the relative location of a client device can be triangulated. In further embodiments, other tracking techniques may be employed (e.g., visual imaging, radar technology, etc.).

In other embodiments, global navigation satellite systems (GNSS), which refers collectively to multiple satellite-based navigation systems like GPS, BDS, Glonass, QZSS, Galileo, and IRNSS, may be used for enabling positioning of devices. Employing signals from a sufficient number of satellites and techniques such as triangulation and trilateration, GNSS can calculate the position, velocity, altitude, and time of devices. In an embodiment, the external positioning system is augmented by assisted GNSS (AGNSS) through the architecture of existing cellular communications network, wherein the existing architecture comprises <NUM>. In other embodiments, the AGNSS tracking system is further supported by a <NUM> cellular communications network. In indoor embodiments, the GNSS is further augmented via radio wireless local area networks such as Wi-Fi, preferably, but not limited to, providing data at <NUM>. In alternative embodiments, the GNSS is augmented via other techniques, such as via differential GPS (DGPS), satellite-based augmentation systems (SBASs), real-time kinematic (RTK) systems. In some embodiments, tracking of devices is implemented by a combination of AGNSS and inertial sensors in the devices.

The processor <NUM> may be implemented as computing hardware and software configured to receive and process instructions. For example, the processor <NUM> may be configured to provide imaging requests, receive imaging data, process imaging data into environment or other data, process user input data and/or imaging data to generate user interaction data, perform edge-based (on-device) machine learning training and inference, provide server requests, receive server responses, and/or provide user interaction data, environment data, and content object data to one or more other system components. For example, the processor <NUM> may receive user input data from an I/O module (not shown) and may respectively implement application programs stored in the memory <NUM>. In other examples, the processor <NUM> may receive multi-source sensory data from sensors <NUM> captured from the real world, or may receive an accurate position and orientation of the client device <NUM>, and may prepare some of the data before sending the data to a server for further processing.

<FIG> shows an embodiment where the client devices <NUM> include a power source <NUM>, a memory <NUM>, and a coupled sensor/mmW transceiver <NUM>, all operatively connected to a processor <NUM>. The functionality of the operational components <NUM> may be the same as described in <FIG>.

In some embodiments, a tracking module (not shown) may be implemented by combining the capabilities of the IMU, accelerometers, and gyroscopes with the positional tracking provided by the mmW transceivers <NUM> and the accurate tracking, low-latency and high QOS functionalities provided by mmW-based antennas may enable sub-centimeter or sub-millimeter positional and orientational tracking, which may increase accuracy when tracking the real-time position and orientation of a client device <NUM>.

In other embodiments, one or more operational components <NUM> may be omitted, or one or more additional components may be added.

<FIG> depict architectural diagrams of a synchronized CC <NUM> which may be configured for enabling real-time responsive and continuous location-based services in three-dimensional space through server hopping and antenna hopping, according to an embodiment. The systems of <FIG> may include similar elements as those of the systems of <FIG>4B, and may therefore contain the same or similar reference numbers. In a synchronized CC <NUM>, a user <NUM> may receive and interact with digital reality content <NUM> retrieved from one or more sources, such as one or more digital reality applications <NUM>. The digital reality data that generates the digital reality content <NUM> is computed and rendered by a cloud server and viewed by a user <NUM> through, for example, a digital reality device <NUM>. The digital reality device <NUM> is continuously tracked by one or more antennas.

In some embodiments, the digital content provided by the one or more digital reality applications <NUM> may include at least one of the following: image data, 3D geometries, video data, audio data, textual data, haptic data, or a combination thereof. In these embodiments, one or more parts of the digital content to be provided to the at least one user <NUM> may include augmented reality (AR), virtual reality (VR), or mixed reality (MR) digital content. If a user <NUM> views the digital content as AR digital content, the AR digital content includes physical, real-world environment elements augmented by computer-generated sensory input such as sound, video, graphics, or GPS data. Augmentation techniques are typically performed in real-time and in semantic context with environmental elements, such as overlaying supplemental information or virtual objects in the real world. The AR digital content allows information about the surrounding real world of the user <NUM> or virtual objects overlay in the real world to become interactive and digitally manipulable. If a user <NUM> views the digital content as VR digital content, the VR digital content may include virtual elements that are used to replace the real world with a simulated one. If a user <NUM> views the digital content as MR digital content, the MR digital content may include a mixture of augmented physical, real-world environment elements interacting with virtual elements. For example, an MR experience may include a situation where cameras capture real humans. Subsequently, suitable computer software creates a 3D mesh of the humans that is then inserted into a virtual world and is able to interact with the real world.

According to an embodiment, the cloud server may be a remote server with computing resources sufficient to carry out heavy load applications, such as rendering digital content from one or more digital reality applications <NUM>. The cloud server may be configured to provide a combined single data stream to at least one digital reality device <NUM>. In some embodiments, the cloud server communicatively connects to the digital reality device <NUM> and to the digital reality applications <NUM> through a wireless systems communication, including but not limited to mmW-based wireless systems communication and/or wireless local area networking (Wi-Fi). Mmw-based connection may allow for low latency (e.g., <NUM> to about <NUM> millisecond end-to-end (E2E) latency) and about high downlink speeds (e.g., <NUM> to about <NUM> Gbps downlink speeds) to end points in the field, complying with parameters necessary for executing the typically highly-interactive digital reality applications. This may result in high-quality, low latency, real-time digital content streaming.

Cloud computing networks, which include the cloud servers, implement a computing environment that is run on an abstracted, virtualized infrastructure that share resources such as CPU, memory, and storage between applications. Typically, a cloud computing environment implements a distributed computing architecture of distributed data storage and other content via software and services provided over a network or the Internet. Using a cloud computing network, access to computing power, computer infrastructure, applications, and business processes can be delivered as a service to a user on demand. Cloud servers, as described with reference to <FIG>, may be located in facilities such as CCs.

In some embodiments, the synchronized CC <NUM> may be implemented in a cloud to edge infrastructure that may display distributed computing capabilities employing public or private clouds, fog servers, and edge devices and systems, such as enterprise systems, mobile platforms, and user devices, all of which may connect through a network. Using a cloud to edge computing network, access to computing power, computer infrastructure (e.g., through so-called infrastructure as a service, or IaaS), applications, and business processes can be delivered as a service to users via client devices on demand. This way, resources including physical servers and network equipment enable a shared storage and computing that may be dynamically allocated depending on factors such as the distance of the user to the resources and the network and computational demand from the users.

In <FIG>, a user <NUM> is located in a server zone such as server zone A <NUM>. In the server zone A <NUM>, the user <NUM> is able to receive, view, and engage, via the digital reality device <NUM>, with digital reality content <NUM> retrieved by cloud server A <NUM>, located in CC A <NUM>, from one or more digital reality applications <NUM>. Any transfer of data to and from the digital reality device <NUM> is performed through the antennas, such as one or more antennas A <NUM>, in areas covered by the antennas A <NUM> and as determined by the position of the digital reality devices <NUM>.

The one or more antennas A <NUM>, which in this example are continuously tracking <NUM> user digital reality devices <NUM>, may send positional and orientational data of the digital reality devices <NUM> to a master server <NUM> located in a DC <NUM>. Generally, the master server <NUM> may send instructions to a respective cloud server to compute and render digital reality content <NUM> depending on which server zone the user <NUM> is located in. For example, as viewed in <FIG>, the user <NUM> is still located in server zone A <NUM>, therefore the master server <NUM> instructs cloud server A <NUM> to compute and render the digital reality content <NUM> for the user <NUM>.

In some embodiments, server zones may include one or more geographically limited zones that may be serviced by one or more cloud servers and by one or more antennas. For example, a server zone may be an outdoor location (e.g., a park, a sports field, a street, a zoo, etc.) or an indoors location (e.g., a game zone, restaurant, entertainment club, theater, office, etc.) serviced by antenna A <NUM> and by cloud server A <NUM>.

In <FIG>, as the user <NUM> moves closer to server zone B <NUM>, the one or more antennas A <NUM> instruct the master server <NUM> to prepare to start retrieving and synchronizing digital reality data from the digital reality application <NUM>, and to thereafter send the synchronized digital reality data to cloud server B <NUM> located in CC B <NUM> as soon as the user <NUM> moves into server zone B <NUM>. It is important to note in this embodiment that, as long as the user <NUM> is still located in server zone A <NUM> in areas where QOS is suitable, then the user <NUM> may still receive digital reality content <NUM> from cloud server A <NUM>.

In <FIG>, when the user <NUM> is in-between server zones, such as between server zone A <NUM> and server zone B <NUM>, the master server <NUM> may start retrieving part of digital reality data from cloud server A <NUM>, synchronizing the retrieved data, and sending the data to cloud server B <NUM>. In this embodiment, since the user <NUM> is not fully in server zone A <NUM> nor is the user in server zone B <NUM>, the computational and rendering tasks may be partially carried out by both cloud server A <NUM> and cloud server B <NUM>, maximizing QOS for the user <NUM>.

In <FIG>, when the user <NUM> has moved fully into server zone B <NUM>, synchronized CC <NUM> completely performs a server hopping from cloud server A <NUM> to cloud server B <NUM>. More specifically, when the user <NUM> has moved completely into server zone B <NUM>, the master server <NUM> starts retrieving all digital reality data from digital reality application <NUM> via cloud server A <NUM>, synchronizes the digital reality data, and sends the data to cloud server B <NUM>. Cloud server B <NUM> then computes and renders the digital reality data so that the user <NUM> may keep receiving the digital reality content <NUM> when located in server zone B <NUM>.

In <FIG>, in case of the user <NUM> moving close or into a server zone C <NUM> in areas that may be partially covered by one or more antennas A <NUM> and one or more antennas B <NUM>, then the server zone servicing is performed partially by both antennas A <NUM> and antennas B <NUM>. Additionally, the digital reality content <NUM> is retrieved and synchronized by the master server <NUM> from cloud server A <NUM> and is partially rendered and computed by both cloud server B <NUM> from CC B <NUM> and cloud server C <NUM> from a CC C <NUM>.

Afterwards, in <FIG>, in case of the user <NUM> moving fully into server zone C <NUM>, e.g., in an area covered solely by the one or more antennas B <NUM> and by the cloud server C <NUM>, then the server zone servicing is performed only by the one or more antennas B <NUM>, and the digital reality data computing and rendering is performed solely by cloud server C <NUM>.

In some embodiments, synchronized CC <NUM> may include additional, fewer, or differently arranged components than those described above.

<FIG> depict an architectural diagrams illustrating a embodiments of a CC <NUM> configured to enable continuous location-based computing and rendering services through assignment of super-peer devices. The system of <FIG> may include similar elements as those of the systems of <FIG>, and may therefore contain the same or similar reference numbers.

Generally, computing and rendering through super-peer devices may decrease hardware and network demands for antennas and cloud servers and increase the QOS and service capacity of the system. Utilizing super-peer devices may as well improve security of the system, since access to cloud servers may be performed by only one or a low number of devices, minimizing cyber-threats to the cloud servers that may be increased from access by more devices.

In some embodiments, computing and rendering through super-peer devices may be performed as a way to improve QOS when the QOS is below a certain threshold. In other embodiments, computing and rendering through super-peer devices may be performed as a way to optimize the computing power and system bandwidth within a synchronized CC <NUM> without being necessarily tied to specific QOS thresholds.

In <FIG>, four peers, peer A <NUM>, peer B <NUM>, peer C <NUM>, and peer D <NUM>, receive and interact with digital reality content <NUM> retrieved from one or more sources, such as one or more digital reality applications <NUM>. The digital reality data that generates the digital reality content <NUM> is computed and rendered by a cloud server, such as cloud server A <NUM>, and viewed by the peers through a digital reality device <NUM> used by each peer. The digital reality device <NUM> is continuously being tracked <NUM> by one or more antennas <NUM>.

The one or more antennas <NUM>, which are constantly tracking <NUM> digital reality devices <NUM>, may send location data of the digital reality devices <NUM> to the master server <NUM> located in a DC <NUM>. The master server <NUM> may send instructions to a respective cloud server, to perform computations and rendering of digital reality content <NUM> depending on which server zone the user <NUM> is located in. For example, as viewed in <FIG>, the peers are still located in server zone A <NUM>, therefore the master server <NUM> instructs cloud server A <NUM> to compute and render the digital reality content <NUM> for the peers.

In <FIG>, the four peers have changed location. More specifically, peer B <NUM>, peer C <NUM>, and peer D <NUM> are now farther away from the cloud server A <NUM>, whereas peer A has remained in a location relatively close to the cloud server A <NUM>. At that moment, the master server <NUM> assigns peer A as a super peer <NUM>. The super peer <NUM> then starts aggregating and distributing the digital reality content <NUM> to peer B <NUM>, peer C <NUM>, and peer D <NUM>. Likewise, any input received from peer B <NUM>, peer C <NUM>, and peer D <NUM> gets sent back to the cloud server A <NUM> through the super peer <NUM>. The cloud server A <NUM> then sends the data to the digital reality application, which receives the input data to control application execution or to update the current operating state of the application, and then sends the output data back to the super peer <NUM> through the cloud server A <NUM>. The super peer <NUM> then aggregates the data and sends it to the respective peers.

In some embodiments, the cloud server, such as cloud server A <NUM>, the super peer <NUM> and the other peers (peer B <NUM>, peer C <NUM>, and peer D <NUM>), all partially perform certain computational and rendering tasks for the digital reality data that is viewed as digital reality content <NUM>. For example, the cloud server A <NUM> may receive the digital reality data from the digital reality applications <NUM>, perform certain rendering and computational tasks, and send the pre-rendered data to the super peer <NUM>. The super peer <NUM> may thereafter perform other lightweight computational and rendering tasks, and may aggregate and distribute the data to the other peers, which may finally perform final, lightweight computations and rendering operations on the digital reality data.

In other embodiments, heavier or lighter computational and rendering operations may be performed by one or more of the cloud servers, the super peer <NUM>, and the rest of the peers. In these embodiments, heavier computational and rendering operations by one or more elements may translate into lighter computational and rendering operations by the other elements. Likewise, lighter computational and rendering operations by one or more elements may translate into heavier computational and rendering operations by the rest of the elements. For example, heavier computational and rendering operations performed by the cloud server may translate into lighter computational and rendering operations to be performed by the super peer <NUM> and by the rest of the peers.

In <FIG>, the four peers: peer A <NUM>, peer B <NUM>, peer C <NUM>, and peer D <NUM>, have completely moved into server zone B <NUM>, disconnecting the super peer. In this case, the four peers are close enough to the cloud server B <NUM> and can suitably receive and engage with digital reality content <NUM> computed and rendered by cloud server B <NUM>.

<FIG> depict block diagrams of a method <NUM> for enabling a real-time responsive and continuous location-based services in three-dimensional space when server-hopping or super peer devices may be required, according to an embodiment. The method <NUM> may be implemented in a system such as the systems detailed in <FIG>.

As seen in <FIG>, method <NUM> may begin when one or more users enter a first server zone (e.g., a server zone A <NUM> from <FIG>) as seen in step <NUM>. Entering a first server zone may involve physically moving into a geographical zone that is covered by the cloud server of the first server zone. Then, in step <NUM>, one or more antennas start tracking the one or more user digital reality devices in the first server zone.

Afterwards, in step <NUM>, the one or more antennas may signal the master server in the first server zone to instruct the cloud server of the first server zone for retrieving digital reality data from the digital reality application. The cloud server of the first server zone then retrieves the digital reality data from the digital reality application in the first server zone and performs data computations and rendering tasks on the digital reality data, as seen in step <NUM>. Subsequently, the cloud server of the first server zone sends the computed and rendered digital reality data to the one or more digital reality devices in the first server zone, as seen in step <NUM>.

The cloud server of the first zone then proceeds by receiving input data from users via the one or more digital reality devices and then by sending the input data to the digital reality application, as seen in step <NUM>, updating the application. Then, in check <NUM>, method <NUM> checks whether the system bandwidth can be optimized or if the QOS can be improved. Several decisions may be taken thereafter depending on the QOS of the system and bandwidth optimization rules. For example, the method <NUM> can determine whether to compute and render <NUM> from a combination of a first and second server zone cloud servers, to compute and render from super-peer devices, or to compute and render from a cloud sever of a second server zone, as viewed in checks <NUM>, <NUM>, and <NUM> and continuing with connectors A <NUM>, B <NUM> and C <NUM>, respectively.

If the system bandwidth cannot be optimized and/or the QOS cannot be further improved, then the method <NUM> may go back to step <NUM> whereby the cloud server in the first server zone retrieves digital reality data from the digital reality application and makes data computations and rendering of the digital reality data. Thereafter, the process may continue until reaching check <NUM> again.

In <FIG>, method <NUM> determines to compute and render from a combination of a first and second server zone cloud servers. This may be performed when users are located in areas where coverage of only one cloud server may not be sufficient to provide suitable QOS, for example, when the users are located in-between server zones.

Starting with connector A <NUM>, the master server retrieves part of the digital reality data from the cloud server of the first server zone, synchronizes the data, and sends the synchronized data to the cloud server of the second server zone, as seen in step <NUM>. Then, in step <NUM>, the cloud server of the second server zone retrieves the synchronized digital reality data from the master server. In step <NUM>, the cloud servers of the first and second server zones perform data computations and rendering of the digital reality data, and, in step <NUM>, send the computed and rendered digital reality data to the digital reality devices of users who are in-between server zones.

The cloud servers of the first and second server zones then receive input data from users via the digital reality devices and thereby update the digital reality application in the first server zone, as seen in step <NUM>. Method <NUM> then verifies in check <NUM> whether the system bandwidth can be further optimized or the QOS further improved. In negative case, the method <NUM> goes back to step <NUM> and may continue the process. Otherwise, method <NUM> checks whether to render and compute <NUM> from super-peer devices or to render or compute from the cloud server of the second server zone, as seen in checks <NUM> and <NUM>, respectively.

In <FIG>, method <NUM> determines to compute and render digital reality data from super-peer devices.

Starting with connector B <NUM>, the master server retrieves, from the antennas, locations of digital reality devices and assigns a super-peer device based on bandwidth and QOS management rules, as seen in step <NUM>. Then, in step <NUM>, the master server retrieves the digital reality data from the cloud server of first server zone, synchronizes the data, and sends the synchronized data to the super-peer device. The super-peer device then performs data computations and rendering of the digital reality data, as viewed in step <NUM>, and then sends the computed and rendered digital reality data to the other peer devices of the first server zone, as viewed in step <NUM>. The super-peer device receives input data from the peer devices and thereby updates the digital reality application, as seen in step <NUM>.

Method <NUM> then verifies in check <NUM> whether system bandwidth can be further optimized or the QOS further improved. In negative case, the method <NUM> goes back to step <NUM> and may continue the process. Otherwise, method <NUM> checks whether to compute and render <NUM> from cloud server of second server zone, or to compute and render <NUM> from first and second server zone cloud servers, as seen in checks <NUM> and <NUM>, respectively.

In <FIG>, method <NUM> determines to compute and render digital reality data by the cloud server of the second server zone. This may be performed when one or more users who are receiving and interacting with digital reality content move fully into the second server zone, so the QOS may be highest when computing and rendering from the cloud server of the second server zone.

Starting in connector C <NUM>, the master server retrieves digital reality data from the cloud server of the first server zone, synchronizes the data, and sends the synchronized data to the cloud server of the second server zone, as seen in step <NUM>. The cloud server of the second server zone then retrieves the synchronized digital reality data from the master server and makes data computations and rendering of the digital reality data, as seen in step <NUM>. Afterwards, the cloud server of the second server zone sends the rendered digital reality data to the one or more digital reality devices of users in the second server zone, as seen in step <NUM>. Then, in step <NUM>, the cloud server of the second server zone receives input data from the one or more users via the one or more digital reality devices and thereby updates the digital reality application in the first server zone via the master server.

Method <NUM> then verifies in check <NUM> whether system bandwidth can be further optimized or the QOS further improved. In negative case, the method <NUM> goes back to step <NUM> and may continue the process. Otherwise, method <NUM> checks whether to compute and render from a combination of first and second server zone cloud servers, or from super-peer devices, as seen in checks <NUM> and <NUM>, respectively.

<FIG> depicts a block diagram of a method <NUM> for enabling real-time responsive and continuous location-based services in three-dimensional space when antenna-hopping may be required, according to an embodiment. In some embodiments, antenna-hopping may be required when users have moved to toward areas within server zones where data from antennas may be weaker.

Method <NUM> may start when one or more users receive and interact with rendered digital reality data via the digital reality devices in server zones serviced by a first antenna (e.g., server zone A <NUM> and/or server zone B <NUM> in <FIG>), as seen in step <NUM>. Then, in step <NUM>, the one or more users move close to or into a server zone not completely covered by a first antenna (e.g., server zone C <NUM> in <FIG>). In step <NUM>, the first and/or second antennas send user locations to the master server. Then, the master server instructs the user digital reality devices to connect to the first and second antennas, as seen in step <NUM>.

Subsequently, as the user moves closer to the third server zone, the master server prompts the first antenna to disconnect from the digital reality device, as seen in step <NUM>. Finally, the second antenna services the third server zone alone, as seen in step <NUM>. In this case, the digital reality devices are solely connected to the second antenna and have completely been disconnected from the first antenna.

<FIG> depicts a diagram of flat network bandwidth slicing <NUM>, according to prior art.

Typically, <NUM> bandwidth (i.e., the width of frequencies users may send and receive on), which uses the Long-Term Evolution (LTE) standard for high-speed wireless communication, is critical in supporting high speed and a high number of users.

A method typically adopted for multiplexing users in wireless communication is called orthogonal frequency-division multiple access (OFDMA). In OFDMA, simultaneous access by plural mobile stations, or client devices, is realized by assigning a subset of many subcarriers predefined by the OFDMA scheme to each client device. In the OFDMA scheme, it is necessary to perform assignment of subcarriers to be used for data communication before data transmission is performed. For example, in a cellular wireless system adopting the OFDMA scheme, a base station (BS), such as antennas <NUM>, determines subcarrier assignments and signals subcarrier assignment information to client devices through a dedicated control information channel.

For data transmission on downlink (i.e., from an antenna to a client device) the antenna first assigns subcarriers to each client device, depending on the amount of data to be transmitted to the client device. Subcarrier assignment information is signaled from the antennas to client devices simultaneously with or before data transmission through a control information channel. Using the subcarriers assigned to each client device, the antennas transmit data to each client device.

For data transmission on uplink (i.e., from a client device to an antenna), each client device first signals a data transmission request and information about the amount of data to be transmitted to the antenna. The antenna assigns subcarriers to each client device based on the data transmission request from the client device. Subcarrier assignment information is signaled from the antenna to the client device through the control information channel. After that, each client device knows the subcarriers on which it is allowed to transmit data from the subcarrier assignment information signaled by the antenna <NUM> and transmits data based on this information.

In this way, in OFDMA, information on subcarrier assignments to each client device, determined by the antennas, is shared across the antennas and each client device, which thereby realizes data communication in which adaptive bandwidth allocation is performed depending on the amount of transmission.

Despite the benefits of OFDMA, usage of the whole frequency spectrum may not be optimized with the current bandwidth allocation. For example, in some occasions, users may be engaged in bandwidth consuming activities while other users may not, but still, all users may at all times be receiving a similar, not necessarily optimal amount of data and bandwidth frequencies. <FIG> illustrates this example showing four users, user A <NUM>, user B <NUM>, user C <NUM>, and user D <NUM>. Each user has been assigned specific subcarriers by antennas <NUM> that allow them to receive data without mutually interfering with each other's subcarriers. Nevertheless, users A <NUM> and B <NUM> may not be using their client device while users C <NUM> and D <NUM> may be doing so, and yet, each user may receive a similar bandwidth slice <NUM>. This may be due to an insufficiently adaptive sub-carrier assignment because these assignments prioritize the number of client devices connected to the antennas rather than the amount of data required, in addition to disregarding other factors, as may be further detailed in <FIG>.

<FIG> depicts dynamic network slicing and QOS management parameters <NUM>, according to an embodiment.

Dynamic network slicing and QOS management is referred herein as the ability to tailor a set of functions for use of the network for each client device. For example, dynamic network slicing and QOS management may determine the optimum beamforming, steering of antennas, server hopping, antenna hopping, super peer assignment, network functionality needed by client devices, and optimum number of subcarriers and total bandwidth per client devices required to optimize QOS. Dynamic network slicing and QOS management is performed by the digital reality VRAN <NUM> in the cloud server described in reference to <FIG>, and may use data and instructions from the experience OS (e.g., experience OS <NUM> of <FIG>) that may be required to perform these functions.

According to an embodiment, the dynamic network slicing and QOS management is performed by using machine learning algorithms. Generally, during machine learning, a programmer provides a computer with a set of sample data and a desired outcome, and the computer generates its own algorithm on the basis of those data that it can apply to any future data. Thus, in the current disclosure, a set of dynamic network slicing and QOS management parameters <NUM> along with data sets corresponding to each parameter and desired outcomes may be provided for training the machine learning algorithms. These algorithms may go through numerous iterations during training in order to generate trained machine learning models that may be used when performing dynamic network slicing and QOS management. The training and inference may be performed by a processor in the cloud server <NUM>.

As shown in <FIG>, these dynamic network slicing and QOS management parameters <NUM> may include, amongst others, a point of service <NUM>, context <NUM>, priority <NUM>, and security parameters <NUM>.

Point of service <NUM> refers to the location of a client device as related to the distance between the client device and an antenna. More specifically, the farther a client device goes from the antenna, because of signal attenuation, the more subcarriers that the user may need to be assigned in order to compensate for this attenuation.

Context <NUM> includes data related to the direct or indirect environment of a client device and an antenna, and which may include micro-context and macro-context, as described with respect to <FIG>. For example, if a client device is receiving network signals inside of a building, relevant micro-context data may include building materials, wall thicknesses, and window locations, as well as data on the structures around the antennas, all of which may be relevant during processing by the digital reality VRAN to performing dynamic network slicing and QOS management. Further in this example, the digital reality VRAN may determine how to direct network signals such that the QOS may be optimized, preventing or diminishing to the least possible any interception from structures (e.g., buildings) around the antennas or around the client devices, and directing the signals through less reflective materials such as windows or thinner walls in order to reach a client device with an optimized QOS, instead of sending the network signals through thicker walls or walls made of materials where network signals may have difficulties penetrating.

The term "service context" refers to the actual applications being used by a user or users in the vicinity. As each application consumes bandwidth, service context may provide the cloud server with valuable information required to assess provisioning of network signals to each client device.

Priority <NUM> refers to the relative importance that certain users or entities may have with respect to the service providers, which may be determined by the type of contract agreed by the different parties. The type of priority may influence the context rankings and thus the amount of bandwidth that users may receive for each type of service.

Security parameters <NUM> relate to a level of protection that may be required for specific users to defend against cyber-risks. Security parameters <NUM> may translate into various security measures such as data encryption, firewalls, Virtual Private Networks (VPNs), etc. The level of security may, like priority <NUM>, be determined by the type of contract.

<FIG> shows sample service context parameters <NUM> that may be taken into account when applying dynamic network slicing and QOS management, according to an embodiment. Service context parameters <NUM> include several parameters <NUM> with a respective ranking <NUM> that may be defined by a user profile, as will be further explained in <FIG>. For example, the more bandwidth a specific application requires, the higher the ranking <NUM>, translating into a greater amount of subcarriers allocated for these users. Likewise, users generally employing applications requiring lower bandwidths are provided lower amount of subcarriers.

Examples of parameters <NUM> may include calling <NUM>, streaming videos <NUM>, sending and receiving short message service (SMS) <NUM>, downloading/uploading files <NUM>, and downloading/uploading videos <NUM>, with an example ranking <NUM> of C <NUM>, A <NUM>, E <NUM>, D <NUM>, and B <NUM>, assigned respectively to each parameter <NUM>.

Rankings <NUM> assigned to each parameter <NUM> determine the network slice and thus, the bandwidth, to be assigned to each user according to the user profile. In the example of <FIG>, each parameter <NUM> has been assigned a letter of the alphabet, with an ascending order representing a lower bandwidth allocated for each parameter <NUM> depending on the nature of the parameter <NUM>. For example, as seen in <FIG>, a ranking <NUM> of A <NUM> indicates a higher amount of bandwidth allocated to the user for the category of video streaming videos <NUM>; a ranking <NUM> of B <NUM> indicates lower amount of bandwidth allocated to the user for the category of downloading/uploading videos <NUM>, but higher than categories C-D; a ranking <NUM> of C <NUM> indicates a lower amount of bandwidth allocated to the user for the category of calling <NUM> when compared to categories A-B, but higher than categories D-E; a ranking <NUM> of D <NUM> indicates a lower amount of bandwidth allocated to the user for the category of downloading/uploading files <NUM> when compared with categories A-C, but higher than category E; and a ranking <NUM> of E <NUM> indicates a lower amount of bandwidth allocated to the user for sending and receiving SMSs <NUM>.

<FIG> shows sample profile types <NUM> used for determining the service context ranking <NUM> of <FIG>, according to an embodiment.

In the example of <FIG>, profile types <NUM> include a global profile <NUM> (e.g., the profile including parameters <NUM> and the ranking <NUM> of <FIG>); a contract-based profile <NUM>; and a machine-learning-based profile <NUM>.

The global profile <NUM> may be a generic profile used for average users. Thus, the service context ranking <NUM> may be obtained through statistical data reflecting the usage rate of each context parameter. In some embodiments, a global profile <NUM> may also be applied geographically (city, state, country, region, etc.). More specifically, ranking values for context parameters may be calculated geographically, and if it is determined that significant differences exist between the values, then a different geographical global profile <NUM> may be determined and applied.

The contract-based profile <NUM> may determine the adjustment of the context ranking values for each context parameter according to the terms stipulated in a contract between the user and the service provider. Thus, for example, a low-data user that may not engage frequently in video-streaming, uploading/downloading videos, or downloading/uploading files, may decide to have more bandwidth (and thus higher context ranking values) for calling and texting.

The contract-based profile <NUM> may also determine other dynamic network slicing and QOS management parameters <NUM> described in <FIG>, such as priority <NUM> and security <NUM>. For example, high-ranking government officials may be provided a higher priority <NUM> and security <NUM> than a global profile user, and thus may be provided with higher amounts of bandwidth for each context parameter as well as higher security measures to prevent endangering potential high-level information.

The machine-learning-based profile <NUM> may determine context rankings through usage of machine-learning techniques through, for example, instructions and data implemented in the digital reality VRAN <NUM> of <FIG>, and may optimize network traffic based on a context zone determined by an event. For example, if there is a sports game taking place in a stadium, and the majority of users are recording the video and doing live video-streaming, the machine-learning techniques may determine a high service context ranking value for that particular group of users and may provide users accordingly with necessary bandwidth. In other embodiments, the machine-learning-based profile <NUM> may also be used to determine individual users' profile and compute a ranking value accordingly.

<FIG> shows a diagram of an exemplary dynamic slicing <NUM> of the current disclosure, according to an embodiment.

In <FIG>, user A <NUM>, user B <NUM>, user C <NUM>, and user D <NUM> receive <NUM>% bandwidth <NUM>, <NUM>% bandwidth <NUM>, <NUM>% bandwidth <NUM>, and <NUM>% bandwidth <NUM>, respectively, from one or more antennas <NUM>. Thus, although <NUM>% of the bandwidth is still allocated to all users in the system, each user receives an amount of bandwidth according to the user's service context, point of service, priority, and security, as stated with reference to <FIG>.

<FIG> shows a block diagram of a method <NUM> for optimizing bandwidth and QOS in a synchronized CC, according to an embodiment. Method <NUM> may be implemented in a system, such as the systems described in <FIG> and <FIG>. More specifically, method <NUM> may be implemented in the digital reality VRAN portion of the cloud servers, as described with reference to <FIG>.

Method <NUM> may begin in step <NUM> by synchronizing, by one or more mutually connected data centers, a plurality of distributed computing centers, forming a wide area distributed computing center network configured to provide real-time responsive and continuous (or substantially continuous, allowing for occasional network latency problems or service interruptions) location-based services in three-dimensional space. The method continues in step <NUM> by performing, by the master servers, dynamic network slicing and quality of service management through management of the distributed computing centers.

<FIG> depicts a block diagram of a method <NUM> for performing dynamic network slicing and quality of service management, according to an embodiment.

Method <NUM> may begin in step <NUM> by assigning users with a profile (e.g., a global profile <NUM>, a contract-based profile <NUM>, or a machine-learning-based profile <NUM> of <FIG>). User profile information as well as the service context parameters and ranking values, priority levels, and security levels are stored as a set of data and instructions in the experience OS of the cloud server.

In step <NUM>, the method <NUM> continues by determining service context parameters and ranking values, priority levels, and security levels of the users depending on the user profiles. Then, in step <NUM>, the method continues by allocating bandwidth to each user according to the user profile (e.g., according to the service context parameters that are based on the user profile). Finally, method <NUM> may end in step <NUM> by dynamically managing network slicing and QOS based on context and point of service while staying within the user ranking values determined by the assigned user profile.

According to an embodiment, the dynamic network slicing and QOS management includes performing, at the digital reality VRAN of the cloud server, computations required to achieve an optimum beamforming, steering of antennas, server hopping, antenna hopping, super peer assignment, providing the correct network functionality needed by client devices, and optimum number of subcarriers and total bandwidth per client devices required to optimize QOS.

Claim 1:
A wide area distributed computing center network system (<NUM>) characterized in that the system (<NUM>) comprises:
one or more data centers (<NUM>), each data center (<NUM>) comprising one or more master servers (<NUM>), communicatively connected to each other and to a plurality of distributed computing centers (<NUM>), each of the plurality of distributed computing centers (<NUM>) comprising cloud servers (<NUM>, <NUM>), wherein the one or more master servers (<NUM>) of the one or more data centers (<NUM>) are configured to synchronize data received from the cloud servers (<NUM>, <NUM>) of the plurality of distributed computing centers (<NUM>) to maintain consistency and uniformity of digital reality and user data across the cloud servers (<NUM>, <NUM>), wherein the distributed computing centers (<NUM>) form a wide area distributed computing center network configured to provide real-time responsive location-based services in three-dimensional space; and
wherein the one or more master servers (<NUM>) of each of the one or more data centers (<NUM>) is/are configured to perform dynamic network slicing and quality of service management through management of the distributed computing centers (<NUM>).