SYSTEMS AND METHODS FOR CLOUD-BASED DISTRIBUTION OF ROUTE SPECIFIC NAVIGATION DATA BASED ON REAL-TIME DATA

Systems and methods are disclosed for improving navigation database efficiency by using the cloud to distribute route specific navigation data based on real-time navigation plan data. Methods comprise receiving a request for adaptive navigation dataset at cloud services from a plan loader; querying a real-time data and vehicle history servers for real-time and historical information; obtaining the queried information from the real-time data and vehicle history servers; generating a navigation plan and adaptive navigation dataset; and transmitting the navigation plan and adaptive navigation dataset to the plan loader and/or a navigation database.

TECHNICAL FIELD

Various embodiments of the present disclosure relate generally to the field of navigation and, more particularly, to a system and method for improving the efficiency of a navigation management system by providing adaptive navigation data.

BACKGROUND

All modern aircraft have Flight Management Systems (“FMS”). The FMS and its associated databases are an essential part of modern avionics, and one such database is a navigation database (“NDB”). The NDB contains all the information required for building a flight plan and processing the flight plan once airborne. Three important parameters regulate content selection for NDBs: waypoint count, terminal data and total FMS capacity. The NDB flight plan data is updated via a 28 day AIRAC cycle. An NDB's capacity (memory size) depends largely on both the hardware and software needs of a particular FMS, as well as the large amounts of flight plan related data that is typically transmitted to the FMS NBD. As such, the NDB's capacity and its ability to support AIRAC cycle data updates (which has been increasing in size at an annual rate of 3-8%), has always been a point of contention for the aviation industry. In response to the NBD's capacity issue, the aviation industry has often sought to consistently increase the NBD's capacity. However, significant data increases are expected to continue in the foreseeable future. Therefore, the need to increase the NBD in response to growing data size is anticipated to be an ongoing challenge for the aviation industry.

Another challenge the aviation industry is facing is the delivery and processing of Notices to Airmen (“NOTAM”). Traditionally, pilots receive multiple NOTAMs in paper or electronic form that must be extracted from a larger pool of data, deciphered, and then manually evaluated for relevant information specific to a flight plan. Such a procedure is inefficient and may result in either significant down-time or a pilot's failure to notice critical notifications. Additionally, since NDB data is updated on a 28 day AIRAC cycle, most urgent NOTAMs are not made available in real-time via the NDB, thus increasing pilot work load and safety concerns.

SUMMARY OF THE DISCLOSURE

According to certain aspects of the disclosure, systems and methods are disclosed for improving navigation database efficiency by using cloud infrastructure to distribute route specific navigation data based on real-time navigation plan data.

In one embodiment, a computer-implemented method is disclosed for providing adaptive navigation datasets to plan loaders and vehicle management systems. The method includes: receiving, at a remote server, a request to receive an adaptive navigation dataset associated with a navigation plan; obtaining, by the remote server, real-time and historical information based on the navigation plan; generating, by the remote server, an adaptive navigation dataset based on the navigation plan, and the obtained real-time and historical information; and transmitting the generated adaptive navigation dataset from the remote server to the remote plan loader and/or the navigation database located on the vehicle.

In accordance with another embodiment, a system is disclosed for providing adaptive navigation dataset to plan loaders and vehicle management systems. The system comprises: a memory having processor-readable instructions stored therein; and a processor configured to access the memory and execute the processor-readable instructions, which when executed by the processor configures the processor to perform a plurality of functions, including functions to: receive, at a remote server, a request to receive an adaptive navigation dataset associated with a navigation plan; obtain, by the remote server, real-time and historical information based on the navigation plan; generate, by the remote server, an adaptive navigation dataset based on the navigation plan, and the obtained real-time and historical information; and transmit the generated adaptive navigation dataset from the remote server to the remote plan loader and/or the navigation database located on the vehicle.

In accordance with another embodiment, a non-transitory computer-readable medium is disclosed for providing adaptive navigation dataset to plan loaders and vehicle management systems. A non-transitory, tangible computer readable medium having instructions stored thereon that, in response to instructions by a computer-based system, cause the computer-based system to perform operations comprising: receive, at a remote server, a request to receive an adaptive navigation dataset associated with a navigation plan; obtain, by the remote server, real-time and historical information based on the navigation plan; generate, by the remote server, an adaptive navigation dataset based on the navigation plan, and the obtained real-time and historical information; and transmit the generated adaptive navigation dataset from the remote server to the remote plan loader and/or the navigation database located on the vehicle.

DETAILED DESCRIPTION OF EMBODIMENTS

While principles of the present disclosure are described herein with reference to illustrative embodiments for particular applications, it should be understood that the disclosure is not limited thereto. Those having ordinary skill in the art and access to the teachings provided herein, will recognize that the features illustrated or described with respect to one embodiment, may be combined with the features of another embodiment. Therefore, additional modifications, applications, embodiments, and substitution of equivalents, all fall within the scope of the embodiments described herein. Accordingly, the invention is not to be considered as limited by the foregoing description.

Various non-limiting embodiments of the present disclosure will now be described to provide an overall understanding of the principles of the structure, function, and use of methods and systems for providing adaptive navigation dataset to plan loaders and vehicle management systems.

As described above, there is a need in the field of vehicle navigation for systems and methods that improve the efficiency of navigation databases and vehicle management systems by providing route focused navigation plans and adaptive navigation datasets. A navigation plan can be combined with real-time information and vehicle history information to generate an adaptive navigation dataset, which can be used by the vehicle management system to allocate system resources to matters pertinent to an anticipated route that will be traversed. Moreover, as described above, in some cases, vehicle operators and vehicle management systems are only updated periodically, e.g., every 28 days, with critical information route information. Thus, the embodiments of the present disclosure are directed to generating route specific navigation plans and adaptive navigation datasets based on real-time information and historical information.

FIG. 1depicts a block diagram of an example vehicle navigation environment100in which a vehicle management system110and/or an operator130requests132to receive an adaptive navigation dataset (step132). As shown inFIG. 1, the vehicle navigation environment generally comprises the vehicle management system110in communication with a plan loader140via an interface device120.FIG. 1also shows the plan loader140in communication with cloud services160via a network150, such as the Internet or any satellite communications network.FIG. 1also shows cloud services160in communication with a real-time data server170and vehicle history server180. Thus, at a high level, vehicle management system110is provided in communication with real-time data server170and vehicle history server180via interface device120, plan loader140, and cloud services160. In one embodiment, real-time data server170and vehicle history server180are components of cloud services160. Moreover, interface device120and plan loader140should be understood as optional, as vehicle management system110may be provided in communication with cloud services160via any desired method.

In this embodiment shown inFIG. 1, an operator130may use plan loader140to request132to receive an adaptive navigation dataset. As part of the request to receive an adaptive navigation dataset132the operator130may use plan loader140to transmit a navigation plan142to cloud services160via network150. Upon receipt of the request132to receive an adaptive navigation dataset, the cloud services160may obtain real-time information from real-time data server170and vehicle history information from vehicle history server180in order to generate and then transmit a navigation plan and an adaptive navigation dataset166back to the plan loader140. Once the navigation plan and adaptive navigation dataset166are received at the plan loader140, the navigation plan and adaptive navigation dataset166may be manually or automatically uploaded to the vehicle management system110and stored.

The vehicle management system110may have various computing components to manipulate the received navigation plan and adaptive navigation dataset166, such as, a processing unit (e.g. a processor and modules)112and a navigation database114. The vehicle management system110may be a computer, a server, a mobile device (e.g. PDA, mobile phone, or a tablet), etc. The vehicle management system110may receive and transmit data through an interface device120. The interface device120may have ports to receive and transmit data, the ability to write on storage media and/or the ability to communicate with systems and devices wirelessly. The plan loader140may be any device capable of sending and receiving data, for example, a computer or a mobile device (e.g. PDA, mobile phone, or a tablet), etc. The plan loader140may also be a storage medium, for example, removable memory based media (USB memory devices/readers, removable hard drives, flash drives, thumb drives, jump drives, key drives, readable/rewritable DVDs, readable/rewritable CDs, and floppy disks) or memory card (SD, CompactFlash, miniSD, microSD, and xD cards), etc. Cloud services160may comprise one or more servers, computers or mobile devices and may include computing components, such as a processing unit (e.g. a processor and modules)162and a database164. Similarly, the real-time data server170may include computing components, such as a processing unit (e.g. a processor and modules)172and a database174. Additionally, the vehicle history server may include computing components, such as a processing unit (e.g. a processor and modules)182and a database184. Both the real-time data server database174and the vehicle history server database184may store data including, but not limited to, operator notifications191(e.g. airline procedures, terminal information, etc.), route information192(e.g. runway information, route clearances, waypoints, and airport information), diversion plans193, route history data194, real-time information195, and/or maintenance data196, among other data. Further, the data and data transmissions may be encrypted. The network150may be the internet, VPN, LAN, WAN, Airborne Wireless Network (AWN), a vehicle-to-vehicle network, 3G/4G/5G wireless signal, WiMax, CDMA, LTE, satellite uplink or any combination thereof.

Notably, while the navigation database114can be updated on the periodic or even irregular cycle, it should be understood that the navigation plan and adaptive navigation dataset166could be uploaded upon any operator request132to receive adaptive navigation dataset166or it could be transmitted to the plan loader140and vehicle management system110dynamically as needed. Additionally, the cloud services processing unit162, real-time data server processing unit172, and the vehicle history server processing unit182could include information selection modules, such that only relevant information is included in the navigation plan and adaptive navigation dataset166during a dynamic transmission. The information selection modules may select information according to various criteria, triggers, machine learning algorithms or any combination thereof. For example, an operator request132to receive adaptive navigation dataset, may include information unique to the vehicle being operated (e.g. a unique vehicle identification number), information about the vehicle management system110(e.g. NDB capacity, NDB memory usage information, information regarding what type of information is currently stored in the NDB), information regarding the vehicle operator130(e.g. operator unique identification number, operator preferences, operator health information, etc.), information regarding the passengers or cargo being carried in the vehicle, and a navigation plan, such that cloud services160can cross analyze this information with the information received from the real-time data server170and vehicle history server180to select a navigation plan and adaptive navigation dataset166that will align with the preferences of the operator130and meet the capacity constraints of the navigation database114. Further, cloud services160may transmit the navigation plan and adaptive navigation dataset166at certain time intervals as a consideration of the network150bandwidth or vehicle management system110resources.

As illustrated inFIG. 2, the above-mentioned devices and systems can be directed to an aircraft management environment200.FIG. 2depicts a block diagram of an example flight management system210, wherein an operator230(e.g., a pilot) requests232to receive an adaptive navigation dataset. In this embodiment, as part of the request232to receive an adaptive navigation dataset, the operator230(e.g. a pilot, flight crew or maintenance crew) uses the plan loader240to transmit a navigation plan242to cloud services via a network250. Upon receipt of the request232to receive an adaptive navigation dataset, the cloud services260may obtain real-time information from a real-time data server270and vehicle history information from a vehicle history server280, in order to generate and then transmit a navigation plan and an adaptive navigation dataset266back to the plan loader240. Once the navigation plan and adaptive navigation dataset266are received at the plan loader240, the navigation plan and adaptive navigation dataset266may be manually or automatically uploaded to the flight management system210.

The flight management system210will have various computing components to manipulate the received navigation plan and adaptive navigation dataset266, such as a processing unit212(e.g. a processor and modules) and a navigation database214. The flight management system may be a computer, a server, a mobile device (e.g. PDA, mobile phone, or a tablet), etc. The flight management system210may receive and transmit data through an interface device220. The interface device220may have ports to receive and transmit data, the ability to write on storage media, and/or the ability to communicate with systems and devices wirelessly. The plan loader240may be any device capable of sending and receiving data, for example, a computer or a mobile device (e.g. PDA, mobile phone, or a tablet), etc. The plan loader240may also be a storage medium, for example, removable memory based media (USB memory devices/readers, removable hard drives, flash drives, thumb drives, jump drives, key drives, readable/rewritable DVDs, readable/rewritable CDs, and floppy disks) or memory card (SD, CompactFlash, miniSD, microSD, and xD cards), etc.

Cloud services260may be a server, a computer or mobile device and it may include computing components, such as a processing unit262(e.g. a processor and modules) and a database264. Similarly, the real-time data server270may include computing components, such as a processing unit272(e.g. a processor and modules) and a database274. Additionally, the vehicle history server may include computing components, such as a processing unit282(e.g. a processor and modules) and a database284. Both the real-time data server database274and the vehicle history server database284may store data including, but not limited to, operator notifications291(e.g. airline procedures, terminal information, etc.), route information292(e.g. runway information, route clearances, waypoints, and airport information), diversion plans293, flight route history data294, real-time information295, and maintenance data296. The network250may be the Internet, VPN, LAN, WAN, Airborne Wireless Network (AWN), a vehicle-to-vehicle network, 3G/4G/5G wireless signal, WiMax, CDMA, LTE, VHF/HF/Datalink satellite uplink or any combination thereof.

Notably, while the navigation database214can be updated on the AIRAC 28 day cycle, it should be understood that the navigation plan and adaptive navigation dataset266could be uploaded upon any operator request232to receive an adaptive navigation dataset or it could be transmitted to the plan loader240and vehicle management system210dynamically as needed. Additionally, the cloud services processing unit262, real-time data server processing unit272, and the vehicle history server processing unit282could include information selection modules, such that only relevant information is included in the navigation plan and adaptive navigation dataset166during an dynamic transmission. The information selection modules may select information according to various criteria, triggers, machine learning algorithms or any combination thereof. For example, an operator request232to receive an adaptive navigation dataset, may include information unique to the aircraft being operated (e.g. a unique flight identification number), information about the flight management system (e.g. NDB capacity, NDB memory usage information, information regarding what type of information is currently stored in the NDB)210, information regarding the vehicle operator230(e.g. operator unique identification number, operator preferences, operator health information, etc.), information regarding the passengers or cargo being carried in the aircraft, and a flight navigation plan, such that cloud services260can cross analyze this information with the information received from the real-time data server and vehicle history server to select navigation plan and adaptive navigation dataset266that will align with the preferences of the operator230and meet the capacity constraints of the navigation database214. Further, cloud services260may transmit the navigation plan and adaptive navigation dataset266at certain time intervals as a consideration of the network250bandwidth or flight management system210resources.

FIG. 3illustrates an alternative flight navigation environment300that is similar to the flight navigation environment200inFIG. 2, except inFIG. 3the flight management system310is in direct communication with cloud services360. In an ideal embodiment, the flight management system310would transmit a request342to receive adaptive navigation dataset to cloud services360. Upon receipt of the request342to receive an adaptive navigation dataset, cloud services360may obtain real-time information from a real-time data server370and vehicle history information from a vehicle history server380, in order to generate and then transmit a navigation plan and an adaptive navigation dataset366back to the flight management system310. Once the navigation plan and adaptive navigation dataset366are received at the flight management system310, it may be stored in the flight management system navigation database314.

The flight management system310may have various computing components to manipulate the received navigation plan and adaptive navigation dataset266, such as, a processing unit312(e.g. a processor and modules) and a navigation database314. The vehicle management system may be a computer, a server, a mobile device (e.g. PDA, mobile phone, or a tablet), etc. Cloud services360may be a server, a computer or mobile device and it may include computing components, such as a processing unit362(e.g. a processor and modules) and a database364. Similarly, the real-time data server370may include computing components, such as a processing unit372(e.g. a processor and modules) and a database374. Additionally, the vehicle history server380may include computing components, such as a processing unit382(e.g. a processor and modules) and a database384. Both the real-time data server database374and the vehicle history server database384may store data including, but not limited to, operator notifications391(e.g. airline procedures, terminal information, etc.), route information392(e.g. runway information, route clearances, waypoints, and airport information), diversion plans393, route history data394, real-time information395, and maintenance data396. The network350may be the Internet, VPN, LAN, WAN, Airborne Wireless Network (AWN), a vehicle-to-vehicle network, 3G/4G/5G wireless signal, WiMax, CDMA, LTE, VHF/HF/Datalink satellite uplink or any combination thereof.

FIG. 4depicts a flow diagram of a method400for processing a request to receive adaptive navigation dataset132. Once the request to receive adaptive navigation dataset132is received at cloud services160(Step402), cloud services160obtains real-time and historical information based on the navigation plan142sent along with the request to receive adaptive navigation dataset132from a real-time data server170and a vehicle history server180(Step404). Cloud services160then generates a navigation plan and an adaptive navigation dataset166, based on the navigation plan142and the real-time and historical information190(Step406). The cloud services160then transmits the generated navigation plan and adaptive navigation dataset166to the vehicle management system110(Step408).

FIG. 5depicts a flow diagram of an exemplary method500for processing a request to receive adaptive navigation dataset132. Once the request to receive adaptive navigation dataset132is received at cloud services160(Step502), cloud services160queries the cloud services database164for adaptive navigation data (Step504). Cloud services160additionally queries the real-time data server170and vehicle history server180for real-time and historical information190(Step506). Cloud services160then analyzes the navigation plan142based on the queried real-time and flight history information190retrieved from the real-time data server170and vehicle history server180(Step508). Cloud services160then generates a navigation plan and adaptive navigation dataset166, based on the analysis (Step510). The cloud services160then transmits the generated navigation plan and adaptive navigation dataset166to the vehicle management system110(Step512).

It should be understood by one having ordinary skill in the art, that in an ideal embodiment, the aforementioned steps will occur prior to the departure of a vehicle (e.g. pre-flight). However, some steps may be executed at any time while the vehicle is en route to its designated point of interest. Therefore, many steps can occur outside of the AIRAC cycle, for example, by the second, minute, hourly, daily, weekly, etc. Additionally, it will be understood that the method400is flexible and is merely illustrative. For example, the arrangement of steps is for illustrative purposes only and is not meant to limit the method400in any way; as such, it should be understood that the steps can proceed in any order and additional or intervening steps can be included without detracting from embodiments of the invention.

It should also be understood that real-time and flight history information190is not limited to the examples conveyed in environment100. Additional information that may be obtained, can include, but is not limited to:

Operator Notification

NOTAM: Notice to Airmen

Runway information

Landing information

Takeoff information

Equipment in use at airport

Temporary flight restrictions

Inoperable lights on buildings and runways

Military exercises

Geographical location of hazard or obstruction

Route Information

STAR (Standard Terminal Arrival Route) instructions

Flight levels

Operating certificate information

Traffic advisories

Aircraft identification information

Estimated time enroute

Fuel onboard

Crew information

Total number of people onboard vehicle (e.g. aircraft, spaceship, etc.)

Mean sea level information

Route forecast information (e.g. weather information)

Payload information

Takeoff weight

Landing weight

Waypoint information

Terminal data

FMS information (e.g. navigation database memory capacity)

Navigation plan

Diversion Plan

Alternate airfield information

Equitime point information

Point of interest information

Groundspeed information

Airspeed information

Angle between point of interest and alternate airfield

Drift information

Route History Data

Schedule information

Vehicle connection information

Time in flight information

Airport IATA code

ICAO code

Latitude and longitude information

Real-Time Information

Required are navigation (RNAV) information

Required Navigation Performance (RNP) information

Localizer performance with vertical guidance (LPV) information

Maintenance Data

Record of maintenance for vehicle

Date maintenance was performed

Description of the type of maintenance performed

Manufacturer information

Services letters

Work order information

Troubleshooting information

Vehicle unique identifier information

Predictive maintenance analytics data

This real-time and flight history information190may be stored in the cloud services160, the real-time data server170and/or the vehicle history server180. The real-time and flight history information190and the searches/queries may be stored in a non-conventional way, such that a request to receive adaptive navigation dataset132that are commonly received at the cloud services160or request to receive adaptive navigation dataset132that are consistently requested from a specific operator130can be readily available, for example, by being stored in cloud services160cache (not illustrated). In this instance, machine learning algorithms may obtain real-time and flight history information190in advance and in anticipation of a future request to receive adaptive navigation dataset132. By having the real-time and flight history information190readily available, cloud services allows the vehicle navigation environment100to operate more efficiently. Further, searches/queries may be grouped together based on common data points. In the example below there are four queries:

Query 1: request to receive adaptive navigation dataset for region A+Navigation Plan for route A

Result 1: Navigation Plan and Adaptive Navigation Dataset for region A+Navigation Plan for route A+Real-time Information for route A returned

Query 2: request to receive adaptive navigation dataset for region A+Navigation Plan for route B

Result 2: Navigation Plan and Adaptive Navigation Dataset for region A+Navigation Plan for route B+Real-time Information for route B returned

Query 3: request to receive adaptive navigation dataset for region C+Navigation Plan for route C

Result 3: Navigation Plan and Adaptive Navigation Dataset for route C+Navigation Plan for route C+Real-time Information for route C returned

Query 4: request to receive adaptive navigation dataset for region C+Navigation Plan for route D

Result 4: Navigation Plan and Adaptive Navigation Dataset for route C+Navigation Plan for route D+Real-time Information for route D returned

Here, all four queries are different; however, queries 1 and 2 may be grouped based on the commonality of the need for data pertaining to region A. Similarly, queries 3 and 4 may be grouped together based on the commonality of the need for data pertaining to region C. Machine learning algorithms can leverage real-time and historical data pertaining to request to receive adaptive navigation dataset132and the resulting searches/queries to provide relevant navigation plan and adaptive navigation dataset166faster and more efficiently by keeping such information stored in cache.

Additionally, searches/queries may be rewritten once a request to receive adaptive navigation dataset132is received at cloud services160. Could services160may crowdsource navigation plan142information and real-time and flight history information190, and analyze such information to assess common requests for data among an array of vehicles to determine a spike in a request for certain information, then rewrite queries to the real-time data server170and vehicle history server180. For example, upon receipt of a request to receive adaptive navigation dataset132, cloud services160may query the real-time data server170for real-time and flight history information190. In analyzing the query from cloud services160, the real-time data server170may detect a spike in queried information (e.g. a query for weather information pertaining to a particular route and point of interest) and in response, provide cloud services160with the result of the its initial query and a new rewritten query that cloud services160should use in obtaining real-time and flight history information190from the vehicle history server180. Determining, whether there is a spike in searches/queries for specific information can be a function of time and a number of queries exceeding a certain threshold for a specific type of information. For example, cloud services160, the real-time data server170or vehicle history server170may detect that over a twenty-four hour period, there have been fifty requests for real-time weather related information pertaining to routes where the ultimate destination is Chicago. If in this example, the threshold for detecting a spike in queries, is the receipt of twenty-five queries within a (potentially predetermined) twenty-four hour period, cloud services160, the real-time data server170or vehicle history server170may rewrite queries for routes with the end destination of Chicago to ensure that real-time information regarding weather is included in the returned navigation plan and adaptive navigation dataset166. Alternatively, queries may be rewritten as a function of the cloud services160detecting that certain real-time and flight history information190needs to be more heavily weighted. Could services160may crowdsource navigation plan142information and real-time and flight history information190, and analyze such information to assess keywords, sounds and images, for urgent/critical information, then rewrite queries to the real-time data server170and vehicle history server180. For example, cloud services160, the real-time data server170or vehicle history server170may detect a NOTAM with keywords pertaining to an unexpected military exercise occurring in close proximity to Washington, D.C. In this example, cloud services160may assign instructions to its own system and the real-time data server and the vehicle history server180to weight information pertaining to operator notifications more heavily and rewrite queries associated with Washington, D.C. as a point of interest.

In one embodiment the transmitted navigation plan and adaptive navigation dataset166is generated based partially on the capacity of the vehicle management system navigation database114. Cloud services160can prioritize certain data blocks in the transmission of a navigation and adaptive navigation dataset166. Further, the vehicle management system160, cloud services160, real-time server170and vehicle history server180may have processors and/or modules, which provide instructions which decide what type of information is stored in cache, what type of information to store in a database and predict what of data may be queried next.

Throughout this disclosure, references to components or modules generally refer to items that logically can be grouped together to perform a function or group of related functions. Like reference numerals are generally intended to refer to the same or similar components. Components and modules can be implemented in software, hardware, or a combination of software and hardware. The term “software” is used expansively to include not only executable code, for example machine-executable or machine-interpretable instructions, but also data structures, data stores and computing instructions stored in any suitable electronic format, including firmware, and embedded software. The terms “information” and “data” are used expansively and includes a wide variety of electronic information, including executable code; content such as text, video data, and audio data, among others; and various codes or flags. The terms “information,” “data,” and “content” are sometimes used interchangeably when permitted by context.

It should be noted that although for clarity and to aid in understanding some examples discussed herein might describe specific features or functions as part of a specific component or module, or as occurring at a specific layer of a computing device (for example, a hardware layer, operating system layer, or application layer), those features or functions may be implemented as part of a different component or module or operated at a different layer of a communication protocol stack. Those of ordinary skill in the art will recognize that the systems, apparatuses, devices, and methods described herein can be applied to, or easily modified for use with, other types of equipment, can use other arrangements of computing systems such as client-server distributed systems, and can use other protocols, or operate at other layers in communication protocol stacks, than are described.

It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.