Managing operation of applications on mobile computing devices

Abstract

Embodiments of the present disclosure generally relate to managing operation of applications on mobile computing devices. A method (100) for managing operation of at least one application (1) on a mobile computing device (2), wherein the application (1) is capable of operating in a plurality of different operation modes (3a-3c), and the method (100) comprises the steps of: • obtaining (110) a current value and / or a predicted future value of at least one performance indicator (4a) of at least one network (4) to which the mobile computing device (2) is attached; • mapping (120), based on a ruleset (5), the value of the at least one performance indicator (4a) to at least one optimal operation mode (3*) of the application (1) and / or to at least one action (3**); and • causing (130) the application (1) to switch to the optimal operation mode (3*), and / or initiating the action (3**); and / or prompting a user to switch the application (1) to the optimal operation mode (3*) or to initiate the action (3**).

Description

Technical Field

[0001] This invention relates to managing the operation of applications on mobile computing devices, and more particularly to variable availability adapted for network connectivity. Background Technology

[0002] Mobile computing devices with appropriate applications enable workers in industrial plants to access critical information anytime, anywhere within the plant. For example, a machine's manual can be viewed directly in the workplace. Furthermore, real-time information from the plant's distributed control system (DCS) can also be provided. Information from the construction site can also be fed back and analyzed to facilitate equipment repair and maintenance. For instance, US9,208,555B1 discloses a method for inspecting electrical equipment by capturing images of the equipment using a mobile device and comparing these images with nominal condition images of the corresponding equipment in a database.

[0003] Such applications rely on network connectivity. If network performance unexpectedly degrades to low bandwidth or is completely lost, the application may fail to function as expected. However, it is difficult to guarantee a high level of network connectivity at every location within a factory. Especially in the indoor environment of a factory floor, the probability of connectivity "weaknesses" is high. Eliminating all these "weaknesses" requires significant effort. Summary of the Invention

[0004] Therefore, one objective of this invention is to manage the operation of applications on mobile devices so as to minimize the impairment of mobile device functionality caused by network performance degradation.

[0005] This objective is achieved by the method described according to the independent claim. Further advantageous embodiments are described in detail in the corresponding dependent claims.

[0006] This invention provides a method for managing the operation of at least one application on a mobile computing device. The mobile computing device may be, for example, a smartphone, tablet, or laptop. The mobile computing device is connected to at least one network, which may be a wireless network, such as a wireless LAN network, WLAN, or Public Land Mobile Network (PLMN), such as a 4G or 5G network according to the corresponding 3GPP specifications. The application is capable of operating in multiple different operating modes. Specifically, these operating modes are selected such that the application requires different levels of network bandwidth, network latency, and / or responsiveness of entities communicated via the network. That is, the selection of a particular operating mode may cause the application to request corresponding levels of network bandwidth, network latency, and / or responsiveness of entities communicated via the network from multiple different levels.

[0007] In this method, the current value and / or predicted future value of at least one performance metric of the network are obtained. Based on a rule set, the value of at least one performance metric is mapped to at least one optimal operating mode and / or at least one action to be performed by the application, so that the connectivity provided by the network is consistent with the connectivity requirements of the application.

[0008] The application is prompted to switch to the optimal operating mode and / or a determined action is initiated. Alternatively or in combination, the user may be prompted to switch the application to the optimal operating mode or initiate an action.

[0009] In this approach, available network performance is considered a given condition, and the balance between functionality on one hand and network connectivity requirements on the other is dynamically adjusted. In each case with a given amount of available network performance, as many functions as possible can be implemented. How to accurately adjust network connectivity requirements is delegated to the specific application.

[0010] This differs from the "low data mode" or "connection-as-metered" features of smartphone and tablet operating systems (such as Apple's iOS or Android) and desktop operating systems (such as Microsoft Windows). These features suppress data usage when mobile computing devices are connected to a network, where such usage is budgeted and / or priced by time and / or capacity, regardless of actual available performance. This is designed for consumers who want to save on mobile data usage costs. However, in the context of this invention, the application on the mobile computing device is for work use, and what needs to be optimized is the functionality of the application given the available network performance. Specifically, the method according to the invention aims to adapt to changes in available performance that have occurred or are expected to occur, while the mobile computing device remains connected to the same wireless network, such as a wireless LAN network deployed in an industrial plant or a 5G network available on the plant. Examples of plants from which the invention can be used include chemical plants, ports, mines, ships, and factories.

[0011] Furthermore, the functionality of applications and the network as a whole becomes more reliable. If an application repeatedly attempts demanding network activities and fails due to insufficient network performance, this will consume system resources and cause unnecessary battery drain on mobile computing devices. Additionally, network connectivity may be overwhelmed by useless communication, causing other aspects of the application to fail as well. For example, if an application repeatedly attempts to pull high-bandwidth video streams, even if network bandwidth is insufficient, this traffic may crowd out low-bandwidth but critical traffic, such as measurement data streams.

[0012] Specifically, performance metrics may include available throughput, latency, and / or the available feature set. For example, the available feature set may differ when a mobile device moves from an area where a mobile network provides 5G connectivity to an area where the same mobile network only provides 4G coverage.

[0013] For example, performance metric values ​​can be obtained through measurement. For instance, a mobile computing device can send a test data stream to an access point of a wireless network, receive the test data stream from the access point, and measure throughput and / or latency. However, directly measuring performance metrics is not the only option. In a further advantageous embodiment, obtaining the current value and / or predicted future value of at least one performance metric involves finding the value of the performance metric based at least in part on the location of the mobile computing device. For example, in an industrial plant, given a certain spatial distribution of wireless access points on the one hand, and buildings and equipment that absorb and reflect radio frequency waves on the other hand, the distribution of available network performance varies considerably in space, but remains relatively constant at any given point in space. Therefore, once the spatial distribution has been investigated, it can be later invoked to determine the performance metric based on the location of the mobile computing device.

[0014] Therefore, in a further advantageous embodiment, the method further includes monitoring at least one performance metric during the movement of the mobile computing device to investigate the relevance of the at least one performance metric to the location of the mobile computing device. For example, this monitoring can be performed during normal movement of the mobile computing device, for example, within an industrial plant. However, the movement path of the mobile computing device can also be specifically designed as an "investigation round," for example, suitable for typical maintenance rounds or intended to simply cover all walkable or maintainable spaces.

[0015] Connectivity is not limited to a single network. Instead, multiple applications on a mobile computing device can connect to different networks. For example, voice calls can be made via public terrestrial mobile networks, while large amounts of data can be transmitted via the local wireless LAN of an industrial plant operator. Furthermore, in 5G networks, different applications can use different network segments to ensure they do not compete with each other for the same scarce resources.

[0016] Furthermore, the approach is not limited to a single application. Instead, a management entity can manage requests from multiple applications, each connected to the network separately for different purposes (such as voice, video, real-time data, etc.), with varying availability requirements. This ensures high availability for critical real-time data or voice, without wasting network resources on video in poorly covered areas, where video might be secondary to the work being performed using mobile computing devices.

[0017] In another particularly advantageous embodiment, obtaining predicted future values ​​of the performance metric includes:

[0018] • Obtain the predicted future location of mobile computing devices; and

[0019] • Obtain future values ​​of performance metrics based on predicted future locations.

[0020] Location changes are a primary driver of network performance and availability variations. On the other hand, in industrial environments, location is highly predictable. The movement of workers carrying mobile devices is far less random than the movement of consumers in public places. Instead, much of the movement can be explained by fixed maintenance shifts in an industrial plant or pre-arranged work orders for specific equipment. Therefore, once a mobile computing device is detected moving along a path, further movement along that path is fairly predictable and can be used to predict changes in available network performance. For example, outside an industrial plant, a mobile computing device traveling along a road or railway is likely to be detected. If the road or railway passes through a tunnel, operating modes can be switched and / or other actions can be initiated in time before the mobile computing device disappears into the tunnel and network connectivity is lost.

[0021] Being able to predict these changes is highly advantageous because it allows users to prepare for them. For example, mobile computing device users can be notified in advance before an application switches to an operating mode with a lower expected user experience. In this way, users can perform tasks requiring higher network performance before the higher network performance becomes unavailable. The user's workflow is not interrupted. Furthermore, changes in network performance do not surprise users and are therefore not perceived as a lack of application reliability. If users are informed in advance that network connectivity is poor in a particular area and certain features of an application will be unavailable, this will be considered a limitation of the system. Within such limitations, the application will be considered reliable; all applications and systems have limitations. However, if a feature becomes unavailable unexpectedly, the application will be considered unreliable because it fails without an apparent reason. The result may be that workers stop using the application and switch to temporary solutions they perceive as more reliable. It's not good if workers in a factory do things differently than people expect them to; work procedures and tools exist for a reason. For example, an application that workers are supposed to use daily can also be used to distribute important information for the day to workers to ensure everyone reads it. However, if a worker refuses to use the application because "it's unreliable anyway," he will also miss important information.

[0022] Predicting movement paths and using them to predict network performance is also useful for introducing switching hysteresis during operating mode transitions. This avoids frequent switching of operating modes, which is considered undesirable and could potentially lead to data loss (e.g., if features stop working while the user is inputting data).

[0023] However, movement paths are not the only quantity for predicting network performance. For example, any other quantity or combination of quantities from the environment in which the network is deployed can be fed into a machine learning model, which then outputs a prediction of network performance. This is particularly useful in situations where network performance at any given location fluctuates more complexly over time. Given training values ​​for quantities related to network performance and corresponding "ground truth" values, a machine learning model can learn which influencing factors, individually or jointly, affect network performance. For example, the performance of a wireless LAN might be impaired by operating devices that generate significant electromagnetic noise, such as microwave ovens or spark erosion machines.

[0024] In one exemplary embodiment, the application is selected to include the display of real-time measurement data. Switching from a first operating mode to a second operating mode causes the application to replace parts of the real-time measurement data with interpolation. For example, in the first operating mode, the real-time updated graph can scroll smoothly on the screen. In the second operating mode, the curve can be drawn progressively using interpolation on a static background. The advantage of doing this is that it does not cause scrolling interruptions compared to simply reducing the update rate of the measurement data.

[0025] In a further exemplary embodiment, the application is selected to include a map view display of data obtained from multiple devices in an industrial plant within a radius of interest around the mobile device. Switching from a first operating mode to a second operating mode then reduces this radius. For example, in the first operating mode, the map may show data from all devices whose locations are within the map's coverage area. In the second operating mode, the map may show only data from devices whose locations are directly adjacent to the mobile computing device. In a third operating mode, the map may show only data from devices whose locations are precisely where the user taps or clicks on the map display.

[0026] In a further exemplary embodiment, the application is selected to include displaying a real-time video stream. Switching from a first operating mode to a second operating mode then allows the compression level of the video stream to be adapted. In this way, the video stream can remain operational, ensuring that the most important aspects of the video stream can still be seen even during periods of bandwidth shortage. Specifically, the compression level can be adapted to network bandwidth in a preemptive manner (i.e., based on network bandwidth prediction), which in turn can be based on the location of the mobile device. That is, the bandwidth requirement of the video stream can be reduced before the available network bandwidth actually decreases. This avoids stream interruptions and network congestion. Specifically, unlike video streaming for consumer entertainment, real-time streaming for industrial purposes needs to be as close to real-time as possible. Therefore, it is not possible to pre-buffer content and responsively change the video encoding to compensate for network bandwidth shortages. These and other remedies increase the latency of the video stream. In industrial applications, especially if any real-time operation on a plant needs to be coordinated based on a real-time video stream, such latency is undesirable. For example, if work is performed on electrical equipment, the switching states visible in the video stream and the equipment power supply must be up-to-date. If bandwidth or latency drops below the available threshold, unlike in the consumer scenario, video quality will not only deteriorate further, but the video may also stop completely, and in the next lower operating mode, live video streaming will be replaced by a completely different method. For example, in situations where work is performed on electrical equipment, if the switch status is necessary information, this information can be extracted in real time from the local video, resulting in higher local power consumption on mobile devices, but much higher efficiency for real-time transmission over the network. This switch will notify users, allowing them to prepare their working methods in advance.

[0027] In a further advantageous embodiment, the application is selected as a human-machine interface (HMI) that displays at least a portion of the distributed control system (DCS) of an industrial plant. For example, the level of detail of the information obtained by the application can be adjusted based on the available network bandwidth for different operating modes. In this way, the amount of information presented may vary, but the user can ensure that the information actually presented is up-to-date and accurate. For example, if a section of pipe is to be temporarily dismantled for maintenance work, accurate information is needed regarding whether the pipe is filled with a hot medium and / or whether it is pressurized at the time dismantling is about to begin. This display of status information from the DCS can, for example, be combined with a display of step-by-step instructions for performing planned maintenance.

[0028] In a further advantageous embodiment, actions to align network connectivity provisioning with application connectivity requirements include network reconfiguration to improve network connectivity for the application. Specifically, such reconfiguration may include transferring network resources from other network activities and / or from other mobile devices to the application and / or to the mobile device hosting the application.

[0029] For example, if a critical remediation task is pending and that task requires augmented reality (AR) coverage, it may be worthwhile to temporarily divert network resources from other activities to allow AR coverage to continue, even if network coverage is poor. For instance, if the network is 5G and a segment booked from a public terrestrial mobile network operator is fully utilized, another segment can be booked to allow the critical remediation task to proceed. It is also possible to temporarily reduce the capacity of other mobile devices in the area, with advance notice of the planned outage and estimated recovery time (in which case, for example, maintenance schedules for affected users could be updated). Alternatively, resources can be temporarily diverted from other non-mobile applications (such as video), intentionally reducing the resolution or frame rate of nearby devices. In networks with managed network equipment (such as switches, routers, and bridges), the configuration of these devices can be adapted to prioritize AR coverage. This is more applicable than ever for software-defined networks.

[0030] In a further advantageous embodiment, actions to keep network connectivity supply and application connectivity needs aligned include having the application preload data it might need during periods of good network performance, while preventing the loading of such data during periods of poor network performance. For example, large manuals or instructional videos required for work on certain devices can be preloaded so that work doesn't fail due to the lack of a manual. Furthermore, any other data that a mobile device might access (such as web pages or map data) can be cached, making it immediately available upon user request. Since available memory on mobile computing devices is often underutilized, reserving some memory for caching when network bandwidth is ample will not impair normal activity on mobile computing devices.

[0031] The method can be implemented wholly or partially by a computer. The invention therefore also relates to one or more computer programs having machine-readable instructions that, when executed on one or more computers and / or computing instances, cause the one or more computers to perform the method. In this context, virtualization platforms, hardware controllers, network infrastructure devices (such as switches, bridges, routers, or wireless access points) capable of executing machine-readable instructions, and terminal devices in the network (such as sensors, actuators, or other industrial field devices) are also considered computers.

[0032] The present invention therefore also relates to a non-transitory storage medium and / or a downloadable product having one or more computer programs. The downloadable product is a product that can be sold in an online store and is immediately available through download. The present invention also provides one or more computer programs and / or one or more non-transitory machine-readable storage media and / or downloadable products for one or more computers and / or computing instances. Attached Figure Description

[0033] In the following text, the invention is illustrated using diagrams without limiting its scope. The diagrams show:

[0034] Figure 1 An exemplary embodiment of a method 100 for managing the operation of at least one application 1;

[0035] Figure 2 : An exemplary setup for performing method 100. Detailed Implementation

[0036] Figure 1 This is a schematic flowchart illustrating an exemplary embodiment of a method 100 for managing the operation of at least one application 1 on a mobile computing device 2. The mobile computing device 2 is connected to at least one network 4. Application 1 can operate in several different operating modes 3a-3c.

[0037] In step 110, the current value and / or predicted future value of at least one performance metric 4a in network 4 are obtained.

[0038] According to box 111, performance metric 4a can be found at least in part based on the location 2a of mobile computing device 2.

[0039] According to box 112, the future location 2a' of mobile computing device 1 can be predicted. Based on this predicted future location 2a', the future value of performance metric 4a can be obtained.

[0040] In step 120, based on rule set 5, the value of at least one performance metric 4a is mapped to at least one optimal operating mode 3* and / or at least one action 3** of application 1.

[0041] According to box 121, action 3** may include reconfiguring network 4 to improve the network connectivity of the application.

[0042] According to box 122, action 3** may include enabling application 1 to preload the data it may need during periods of good network performance, and not to load the data during periods of poor network performance.

[0043] In step 130, application 1 is switched to optimal operating mode 3*, and / or action 3** is initiated. This will affect application 1 and / or network 4. Alternatively or in combination, the user may be prompted to switch application 1 to optimal operating mode 3*, or to initiate action 3**.

[0044] According to box 131, the user of mobile computing device 2 can be informed in advance before switching application 1 to operating modes 3a-3c, which are expected to have a lower level of user experience.

[0045] Figure 2 This is a block diagram of example settings for performing method 100.

[0046] Application 1 runs on mobile computing device 2. Application 1 interacts with industrial process 7 via a network. To this end, application 1 includes a network-aware front-end 1a capable of communicating with application back-end 1b via network 4, and application back-end 1b in turn communicates with process 7. This enables task support logic 1c to communicate bidirectionally with application back-end 1b.

[0047] The throughput of this communication is related to the performance of network 4, which is characterized by at least one performance metric 4a. Based on the current operating modes 3a-3c of application 1, application 1 is configured to communicate with application backend 1b in a more or less demanding manner on network 4.

[0048] The network-aware front-end 1a queries a database based on the location 2a of the mobile computing device 2. In the database, the value of performance metric 4a is stored in association with location 2a. That is, given location 2a, the database returns the value of performance metric 4a. Based on this performance metric 3a, the optimal operating mode 3* is determined. Figure 2 In the example shown, this optimal operating mode is communicated to user 6 via user interface 1d on screen 2b of mobile computing device 2. User 6 is then prompted to switch application 1 to optimal operating mode 3*.

[0049] User interface 1d also displays information about process 7 to user 6 and allows user 6 to interact with process 7. Therefore, user interface 1d is the connecting element between user 6 and process 7.

[0050] List of reference numerals in the attached diagram:

[0051] 1 application

[0052] Application 1a Network Awareness Front End

[0053] 1b Application 1 Backend

[0054] Task support logic in application 1c

[0055] User interface of 1d application 1

[0056] 2 Mobile computing devices

[0057] 2a Location of mobile computing device 2

[0058] 2a' Predicted future location of mobile computing device 2

[0059] 2b Mobile computing device 2's display

[0060] 3a-3c Application 1 Operation Mode

[0061] 3* Best Operating Mode

[0062] 3** Actions to match connection needs with network performance

[0063] 4 Networks

[0064] 4a network 4 performance metrics 5 rule set used to determine optimal operating mode 3*, action 3** 6 user

[0065] 7 Industrial Processes

[0066] 100 Methods for managing the operation of application 1

[0067] 110 Obtain the current and / or predicted values ​​of performance metric 4a. 111 Find performance metric 4a based on position 2a.

[0068] 112 Obtain the predicted future location 2a'

[0069] 113 Based on the predicted position 2a', the optimal operating mode 3* and action 3** are determined using the predicted performance index 4a120.

[0070] 121 Reconfigure the network 4

[0071] 122 causes preloading data; 130 causes switching to operation mode 3* and initiating action 3**.

[0072] 131 Before switching to the optimal operating mode 3*, it is recommended that users 6

Claims

1. A method (100) for managing the operation of at least one application (1) on a mobile computing device (2), wherein the application (1) is capable of operating in multiple different operating modes, and the method (100) includes the following steps: Obtain the predicted future value of at least one performance metric (4a) of at least one network (4) to which the mobile computing device (2) is attached; Based on the rule set (5), the value of the at least one performance metric (4a) is mapped to at least one optimal operating mode (3) of the application (1). ) and / or at least one action (3 This ensures that the connectivity provided by the network aligns with the connectivity requirements of the application. as well as This causes the application (1) to switch to the optimal operating mode (3). ), and / or initiate the aforementioned action (3) ); The application (1) mentioned therein is selected to include: Displaying real-time measurement data, and switching from the first operating mode to the second operating mode causes the application (1) to replace a portion of the real-time measurement data with interpolation; and / or A map view showing data acquired from multiple devices in an industrial plant within a radius of interest around the mobile computing device (2), and switching from a first operating mode to a second operating mode causes the radius to decrease; Obtaining the predicted future value of the performance indicator (4a) includes: Obtain the predicted future location (2a') of the mobile computing device (2); and Based on the predicted future position (2a'), the future value of the performance index (4a) is obtained.

2. The method (100) according to claim 1, wherein the performance index (4a) includes: Available throughput; Delay; and / or Available feature sets.

3. The method (100) according to any one of claims 1 to 2, wherein the selection of the operating mode results in the application (1) requiring corresponding levels of network bandwidth, network latency and / or the responsiveness of entities it contacts via the network (4) among a plurality of different levels.

4. The method (100) according to any one of claims 1 to 2, further comprising: The at least one performance metric (4a) is monitored during the movement of the mobile computing device (2) to investigate the correlation between the at least one performance metric (4a) and the position (2a) of the mobile computing device (2).

5. The method (100) according to any one of claims 1 to 2, wherein obtaining the current value and / or predicted future value of the at least one performance metric (4a) comprises: The value of the performance metric is found at least in part based on the location (2a) of the mobile computing device (2).

6. The method (100) according to any one of claims 1 to 2, wherein the application is selected to include: Displays at least part of the human-machine interface of a distributed control system (DCS) in an industrial plant.

7. The method (100) according to any one of claims 1 to 2, wherein the action (3) This includes reconfiguring the network (4) to improve the network connectivity of the application.

8. The method (100) according to claim 7, wherein the reconfiguration of the network (4) specifically includes: Transferring network resources from other network activities and / or from other mobile devices to the application and / or to the mobile computing device (2).

9. The method (100) according to any one of claims 1 to 2, wherein the action (3) )include: This allows the application (1) to preload data that it might need during periods of better network performance but cannot load during periods of poor network performance.

10. A non-transitory machine-readable storage medium having one or more computer programs, the one or more computer programs including machine-readable instructions that, when executed by one or more computers or computing instances, cause the one or more computers or computing instances to perform the method (100) according to any one of claims 1 to 9.

11. A computer having a non-transitory machine-readable storage medium according to claim 10.

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