Description:FIELD OF THE INVENTION
This invention relates to smart adaptive network switching system.
BACKGROUND OF THE INVENTION
The rapid improvement in mobile network technologies has made 5G a better alternative to 4G, offering faster speeds and lower latency. However, these benefits of 5G come with additional costs in terms of increased power consumption and extreme data usage making 5G not suitable for most tasks that don’t require heavy bandwidth. Most of the people have capped plans for their data and will always be seeking to manage their consumption so that they do not overspend on their bills.
At this time, most current smartphone devices utilize preset static values ensuring the need for users to switch manually between network modes of operation stated as 5G and 4G for example. This form of switching is not quite appropriate in the fast-changing environments where the requirement is that the network type is not static but should rather change to which the user is currently data active and therefore capabilities usage should be high. For instance, there is no need for a 5G network when a user is texting someone or browsing because a 4G would be sufficient. Most smartphones do not offer any form of automated ability to control the switching of different networks as a function of data usage, the tasks at hand, applications running in the device or even preferences of the owner of the device.
This makes it necessary to have the Smart Adaptive Network Switching, that looks at whether the networks should be changed based on the current usage and performs optimization to save and ensure that the current performance needs all the three topics focused on data, battery life and the users convenience and experience when using the device.
Despite the fact that a number of techniques and algorithms have been proposed to improve the efficiency of mobile network resource utilization, most of them employ static or manual selection of the network instead of being adaptive to the kind of task or the data being utilized. Some of the related strategies are outlined below:
1) Manual Network Selection
Most smartphones allow users to manually select between different network types (e.g., 5G, 4G, 3G) through settings. While this gives users control over network selection, it does not address the dynamic nature of data requirements or task-specific needs. Users must manually switch networks based on their usage patterns, which is inefficient and prone to user error.
2) Network-Aware Applications and Services
Some applications appearing on the mobile platforms do have their primary functions as reporting on data usage and trying to resource efficient in network utilization. As an example, Android users can use the Data Saving app, while iOS users can activate Low Data Mode to minimize data usage by restricting irrelevant background operations. But at the same time, it is worth noting that these applications do not autonomically transition from a specific generation of wireless technology to another based on the requirements of performing specific tasks.
3) Automatic Network Switching Based on Signal Strength
Some devices automatically switch between 5G, 4G, and 3G based on signal strength and availability. This approach ensures the best possible signal for the user but does not consider data requirements or power consumption, often leading to unnecessary high-speed 5G connections when lower speeds are sufficient.
4) Battery Saver Modes
Android and iOS have the battery saver modes. These modes make the device to work on the slow data connection; one example is 4G. All the solutions above are passive and based on the saving of power at the cost of being data-inefficient and thus useless for data utilization and network optimization.
5) Carrier and Network Provider Solutions
Some cellular operators and network service providers even provide features or applications to let the user manage or even choose specific types of networks for certain use cases. For example, some networks will automatically use 4G for calls, while services like video streams are given higher priority by using the 5G. Solutions of this kind tend to be limited to only analyzing the optimizations from the network side instead of giving full-time attention to the specific needs of user data or applications.
6) Smart Network Management Systems
The emerging SDN and NFV are characterized as the network management areas that have the potential to make a network more efficient with better resource allocation based on need or traffic patterns. Where these solutions are being used at the infrastructure level, they appear more inclined toward more user-centric solutions that would aptly find its way to better automatic switching between 5G and 4G.
Existing Solution Gaps:
In particular, solutions above pay attention to optimum utilization of mobile networks; however, there is no consideration for dynamic intelligent adaptation between generations of networks based on needs of applications and usage of data. Most of the solutions above require user intervention or do not use any kind of parameters such as signal strength, and battery level but do not take into consideration the general context of data consumption and network performance. In this gap, an adaptive system should switch between 5G, 4G, and other network types in autonomous and real-time operating according to the actual activity and the user's data needs.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
The present invention provides a Smart Adaptive Network Switching system that intelligently selects the most appropriate network type based on real-time monitoring of device activities. The system dynamically transitions between different network generations, such as 5G, 4G, and 3G, depending on task requirements, data consumption, and power efficiency.
The invention employs a real-time monitoring mechanism that continuously tracks data usage, battery status, and active applications. The system intelligently determines the optimal network type for each task, ensuring efficient resource utilization without user intervention. For instance, data-heavy applications such as video streaming and gaming are allocated high-speed 5G networks, while lower-bandwidth tasks such as messaging and browsing use 4G or 3G networks.
The invention enhances energy efficiency by prioritizing lower-power networks when the device’s battery level is low. It also enables users to set customizable preferences, such as data limits and battery-saving configurations, for tailored optimization. The system ensures a seamless user experience by transitioning between networks without disrupting ongoing activities.
The system can be implemented as a mobile application or an integrated system feature within mobile operating systems. It supports various configurations, including app-based solutions for non-rooted devices and deeper system integration for rooted devices, allowing granular control over network management.
Additionally, the invention includes learning and adaptation capabilities, where the system analyzes user behavior over time to refine network-switching logic. Machine learning techniques may be utilized to enhance decision-making accuracy and improve user experience based on historical data.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The proposed invention aims to develop a Smart Adaptive Network Switching system that automatically switches between mobile network generations (e.g., 5G, 4G) based on real-time data usage and task requirements. This intelligent system will optimize both data consumption and battery life, ensuring the most efficient network is used for each specific task.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: ARCHITECTURE OF SMART ADAPTIVE NETWORK SWITCHING
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The Smart Adaptive Network Switching system consists of multiple components, including a real-time monitoring module, network decision engine, task-based optimization module, user customization interface, and seamless transition mechanism. The system works as follows:
The real-time monitoring module continuously collects data from the device, including battery level, network availability, active applications, and data consumption patterns. This data is fed into the network decision engine, which processes and determines the most suitable network type based on predefined algorithms.
The task-based optimization module categorizes ongoing activities into different task types, such as high-bandwidth applications (e.g., video streaming, online gaming) and low-bandwidth tasks (e.g., browsing, texting). Based on this categorization, the system assigns the most appropriate network.
The user customization interface allows users to set preferences, including data usage limits, battery-saving priorities, and task-specific network choices. Users may choose to prioritize cost savings by favoring 4G over 5G, or battery efficiency by limiting high-speed connections when battery levels are critically low.
The seamless transition mechanism ensures smooth network switching without interrupting ongoing activities. The system implements predictive algorithms to preemptively adjust network selection without causing connection disruptions. If a user is engaged in a video call, for example, the system will ensure a stable network handoff to prevent disconnection.
The invention is implemented as a software application that interfaces with mobile operating systems. It may also be integrated as a native feature in smartphone firmware for deeper optimization. The system supports cloud-based analytics for advanced data processing, allowing users to review insights into their network usage and efficiency gains.
For rooted devices, custom scripts or ROM modifications can provide fine-grained control over network switching. Additionally, automation tools like Tasker may be integrated to enable advanced trigger-based network adjustments.
The invention further includes an alert and notification system that informs users of network changes, data thresholds, and battery optimization status. For example, a notification may indicate that the device has switched to 4G to conserve data or that 5G is activated for optimal performance in high-bandwidth applications.
The present invention aims to develop a Smart Adaptive Network Switching system that automatically switches between mobile network generations (e.g., 5G, 4G) based on real-time data usage and task requirements. This intelligent system will optimize both data consumption and battery life, ensuring the most efficient network is used for each specific task.
Key Features:
1. Real-Time Monitoring: The system will continuously monitor the device's data usage, battery status, and active applications. It will assess the requirements of tasks such as browsing, streaming, gaming, or messaging to determine the optimal network type.
2. Dynamic Network Switching: Based on the monitored data, the system will automatically switch between available network types (5G, 4G, or 3G) without user intervention. For example, for data-heavy tasks, the system will prioritize 5G, while for low-bandwidth tasks, it will switch to 4G or even 3G to save data and power.
3. Task-Based Network Optimization: The system will identify the specific task being performed (e.g., video streaming, messaging, browsing) and match it with the most appropriate network. High-speed tasks will utilize 5G, while simpler tasks will be handled by slower, more energy-efficient networks.
4. Data and Power Efficiency: By intelligently switching networks, the system ensures that data is used efficiently according to the task and that the device conserves battery life by avoiding unnecessary high-speed connections when they are not required.
5. User Preferences & Settings: Users can configure preferences within the app, such as setting data usage limits or battery-saving priorities. The system will use these preferences to further personalize network switching behavior.
6. Seamless User Experience: The switching process will be seamless, ensuring minimal disruption to ongoing tasks, such as avoiding disconnections during calls or buffering during media playback.
Workflow of Smart Adaptive Network Switching:
1. App-Based Implementation (For Non-Rooted Devices)
1.1 App Installation: A custom mobile application may be installed on the device, designed to monitor data usage, task types, and network conditions. The app may request access to data usage statistics, battery status, active applications, and permission to manage network connections, which may require specific system-level permissions on devices such as Android.
1.2 User Configuration: Upon installation, users configure preferences within the app to define the behavior of network switching based on the following criteria:
Data Usage Limits: Users can set thresholds (e.g., switch to 4G after 1 GB of data usage).
Battery-Saving Preferences: Users may configure the system to prioritize network switching to a lower network (e.g., 4G or 3G) when battery levels are low.
Task-Based Preferences: Network priority can be set based on tasks (e.g., prioritizing 5G for streaming or gaming, 4G for browsing).
2. Built-In System Feature (For Android / iOS Devices)
2.1 Automatic Network Switching: An integrated feature within the mobile device's settings, such as under Mobile Networks, may be activated to enable Smart Network Switching. This feature allows automatic selection of the optimal network (5G, 4G, or 3G) based on real-time data usage, battery status, and the type of task being performed.
2.2 Task-Based Profiles: Users may select from predefined profiles or configure custom profiles for specific scenarios, including:
Data Saver Mode: Network switching to 4G or 3G for non-data-intensive tasks.
High-Speed Mode: Automatic switching to 5G for tasks such as video streaming or online gaming.
Battery Saver Mode: Prioritize 4G or 3G when battery levels are low to extend battery life.
3. Rooted Android Devices (Advanced Users)
3.1 Custom ROMs or Scripts: For advanced users with rooted devices, custom ROMs or scripts may be utilized to enable fine-grained control over network switching. These modifications allow for highly customized automation of network management, including more advanced rules for network transitions based on data consumption, app usage, and battery level.
3.2 Automated Task Management Apps: Advanced automation apps such as Tasker can be configured to automate network switching based on specific triggers, such as opening particular applications or reaching preset data thresholds. This provides additional flexibility and control for the user.
4. Notifications & Alerts
4.1 Network Switch Notifications: The system may send notifications to the user when a network switch occurs, informing them of the change and providing details regarding data savings or battery improvements.
4.2 Data Usage Alerts: Users may be alerted if their data usage is approaching the configured limit, with the system notifying the user of an impending network switch to save data (e.g., from 5G to 4G or 3G).
5. User Customization Options
5.1 Learning & Adaptation: The system may be designed to learn the user’s behavior over time, adapting network-switching logic based on task patterns, app preferences, or habitual data usage. For example, it may avoid switching to 5G when the user is connected to Wi-Fi or when specific apps are used.
5.2 Override Capabilities: Users can disable or override automatic network switching during specific scenarios, such as when making calls or engaging in activities that require a stable, uninterrupted connection (e.g., streaming video or conducting video calls).
WORKING OF SMART ADAPTIVE NETWORK SWITCHING:
Application Download and Installation
The user can download the Smart Adaptive Network Switching app from a trusted platform (e.g., Google Play Store or Apple App Store) and installs it on their mobile device.
Permissions Request
The app sends a notification or displays an in-app prompt requesting the necessary permissions. These may include:
o Data Usage Access: To monitor real-time data consumption.
o Battery Status Access: To assess battery levels for optimization decisions.
o Application Activity Monitoring: To identify active tasks for task-based network switching.
o Network Management Permissions: To enable automated switching between network types (e.g., 5G, 4G, 3G).
Threshold Trigger Notifications
• When predefined thresholds for data usage or battery saving are reached, the system sends alerts notifying users of changes and actions taken.
• Example Threshold Notifications:
o "Data Limit Reached: You have used 80% of your data. Switched to 4G to conserve remaining data."
o "Battery Saver Active: Low battery detected. Prioritizing energy efficiency by switching to 4G."
The Smart Adaptive Network Switching system introduces a unique combination of features and capabilities that set it apart from existing technologies. The novel aspects include:
Real-Time Network Adaptation: Unlike traditional manual network selection, the system dynamically and automatically switches between network generations (e.g., 5G, 4G, 3G) based on real-time analysis of task requirements, data usage, and battery levels.
Task-Based Network Optimization: The invention integrates task-aware intelligence to prioritize network selection. For instance, high-speed networks like 5G are allocated for streaming or gaming, while low-data tasks like messaging use 4G or 3G, ensuring optimal utilization of network resources.
Energy Efficiency: The system conserves device battery life by avoiding unnecessary use of power-intensive networks (e.g., 5G) for low-bandwidth tasks, which is not a feature in conventional systems.
User-Centric Customization: Users are provided with the ability to set preferences such as data usage thresholds, task-based priorities, and battery-saving modes, making the system adaptable to individual needs and habits.
Seamless Transition with Minimal Disruption: The system ensures seamless network switching, avoiding disruptions during ongoing tasks like calls or media streaming, which is a significant improvement over existing methods.
Machine Learning Integration: The system learns user behavior over time and adapts its switching logic based on patterns and preferences, enhancing accuracy and efficiency.
Comprehensive Monitoring: By monitoring multiple parameters (data usage, task type, battery status, network availability), the system ensures holistic decision-making, which is lacking in traditional network management solutions.
Load Balancing and Network Congestion Mitigation: The invention helps reduce network congestion by dynamically managing load distribution across available networks, thereby enhancing the overall efficiency of mobile networks.
, C , Claims:1. A smart adaptive network switching system comprising:
a real-time monitoring module configured to collect data related to network availability, device battery level, active applications, and data consumption;
a network decision engine configured to process the collected data and determine an optimal network type based on predefined algorithms;
a task-based optimization module configured to assign network types based on bandwidth requirements of ongoing tasks;
a seamless transition mechanism ensuring uninterrupted network switching without disrupting ongoing activities;
a user customization interface enabling users to configure data limits, battery-saving settings, and task-specific network preferences; and
a predictive algorithm that preemptively adjusts network selection based on anticipated user activities.
2. The system as claimed in claim 1, wherein the real-time monitoring module further tracks historical network usage patterns to improve network switching decisions.
3. The system as claimed in claim 1, wherein the network decision engine employs machine learning techniques to enhance accuracy over time.
4. The system as claimed in claim 1, wherein the task-based optimization module prioritizes network switching based on application-specific data consumption trends.
5. The system as claimed in claim 1, wherein the seamless transition mechanism minimizes packet loss and latency during network switching.
6. The system as claimed in claim 1, wherein the user customization interface allows real-time manual override of automated network switching decisions.
7. The system as claimed in claim 1, wherein the predictive algorithm utilizes AI-driven analysis of user behavior to optimize network transitions.
8. The system as claimed in claim 1, wherein the system integrates with cloud-based analytics for remote configuration and insights into network usage patterns.