Massive IoT Connectivity Management and Energy Optimization

Abstract

The invention is a system that provides connection management, energy efficiency optimization, and network resource allocation for massive IoT (Internet of Things) devices in 4G (LTE-M, NB-IoT) and 5G (mMTC - massive Machine Type Communications) mobile networks, which includes IoT devices (1) that transmit data by connecting to the network, base stations and gateways that provide access to IoT devices (1) to the network, RAN (eNB / gNB) and IoT Gateway (2), an IoT connection monitoring aggregator (3) that collects the connection status, RRC state transitions, PSM / eDRX activations, RACH attempts and network KPIs of all IoT devices (1) in real time, and an AI-powered device profiling and classification engine (4) that automatically classifies IoT devices (1) according to traffic pattern (periodic / event-driven), data volume, application type, mobility status and battery level and creates an optimal energy profile for each class.Intelligent scheduling and distribution optimizer (5) that predicts periodic connection times and minimizes collisions by automatically distributing thousands of devices (1) connection attempts to RACH resources, time slots and frequency blocks; dynamic resource management module (6) that optimizes RRC connection capacity, paging resources, CE level and uplink grant allocation in real time according to IoT device (1) density and mixed traffic conditions; group coordination manager (7) that groups IoT devices (1) with similar characteristics according to geographical and behavioral criteria and implements group-level collective paging, coordinated sleep cycles and group messaging; QoS-Energy balancing engine (8) that finds the optimal balance point between energy consumption and service quality according to application requirements (criticality, latency tolerance, data volume) and dynamically adjusts PSM / eDRX parameters; historical connection data,It includes a machine learning-based predictive planning and learning engine (9) that predicts future resource needs by analyzing seasonal patterns and traffic trends and continuously improves by learning optimal parameters for different device types, and a management panel and alarm system (10) that visualizes the status of all IoT devices (1), group-based performance metrics, energy saving statistics and resource usage status and generates alarms in case of anomalies.

Description

1 TARIFF Massive IoT Connectivity Management and Energy Optimization Technical Area 5 Invention, 4G (LTE-M, NB-IoT) and 5G (mMTC - massive Machine Type Communications) mobile Connectivity management and energy efficiency for mass IoT (Internet of Things) devices in networks. It relates to optimization and network resource allocation. State of the Art 10 Today, smart meters, agricultural sensors, vehicle tracking systems, smart city infrastructures, and In industrial IoT applications, millions of low-power devices can be connected to the network simultaneously. various solutions for the processes of connecting, transmitting data and saving energy. However, there are some significant shortcomings in current IoT connectivity management applications. and there are shortcomings. 15 Current systems use standard PSM (Power Saving Mode) and eDRX for all IoT devices. (Extended Discontinuous Reception) parameters are used, depending on the device type and data transmission. Customization based on frequency and application requirements is not possible. This situation affects both... This leads to unnecessary energy consumption and inefficient use of network resources. Especially in periodic operations such as smart meter reading times, thousands of devices can be checked simultaneously. an attempt to connect to the network (synchronous access storm) RACH (Random Access This leads to channel blockages and connection failures. Current systems address this. It lacks mechanisms to predict and disperse explosions. RRC (Radio Resource Control) connectivity resources, paging capacity, and uplink grant allocation. It is statically configured, but dynamically adapts to sudden changes in IoT device density. It is unable to adapt. This is especially true in hybrid use cases (IoT + smartphone). Traffic leads to resource conflicts. Current approaches either maximize energy or By using very infrequent connections to save energy, they cause latency problems, or By using frequent connections for low latency, the application shortens battery life. It is not possible to dynamically adjust this balance according to the requirements. 30 IoT devices with similar characteristics (smart meters in the same area, soil in the same field) (like sensors) are managed individually, with group-based optimization and collective resource allocation. This is not possible. Critical parameters such as PSM timer, eDRX cycle, and TAU (Tracking Area Update) period. Parameters are adjusted manually and separately for each device type or application. Configuration is required. Existing systems operate reactively, experiencing connection failures or 35 2 Intervention is carried out after resource shortages become apparent. Proactive resource allocation and There are no energy optimization mechanisms. In conclusion, due to the negative aspects described above and the current solutions being the subject of discussion... Due to its shortcomings, it has become necessary to make improvements in the relevant technical field. Purpose of the Invention The invention represents a new breakthrough in this field, unlike the structures used in existing technology. The aim is to create a structure with different technical specifications that bring these elements together. The main purpose of the invention is to provide 4G (LTE-M, NB-IoT) and 5G (mMTC - Massive Machine Type IoT) connectivity. Communications) connecting mass IoT (Internet of Things) devices on mobile networks 10 The goal is to provide management, energy efficiency optimization, and network resource allocation. Another aim of the invention is to categorize IoT devices by data transmission frequency and application type (periodic / event-). (driven), automatically classifying each vehicle according to its mobility status and battery level. The goal is to define optimized PSM and eDRX parameters for the group. This allows the device to... Battery life can be extended by 40-60%. 15 Another aim of the invention is to determine periodic connection times using machine learning algorithms. By anticipating connection attempts from thousands of devices, it automatically processes them in the time and frequency domains. The goal is to distribute it as follows: RACH preamble reduces the collision rate by up to 70% and the connection Minimizes failures; monitors IoT device density in real time RRC connection resources, paging capacity and CE (Coverage Enhancement) levels are 20 It is about dynamically adjusting. In mixed traffic scenarios, both IoT and smartphones are used. It provides optimal resource allocation for its users; geographical proximity, application type, and traffic. Automatically group IoT devices based on their pattern similarities and group them group paging, group messaging, and coordinated sleep-wake cycles at the level of group paging. This approach reduces the signal load by up to 50%; 25% of the application requirements. energy-latency-reliability (according to critical alarms, periodic reports, low-priority telemetry, etc.) finding the optimal equilibrium point in the triangle and adjusting the connection parameters accordingly. The goal is to adjust by analyzing past traffic data and seasonal patterns for future trends. The goal is to predict connection congestion and proactively prepare network resources. This ensures connection quality is maintained even during sudden load increases; different IoT device types and applications 30 learning by experimenting with optimum connection parameters for scenarios and continuously The goal is to improve operational efficiency. It eliminates the need for manual configuration. The aim is to reduce IoT device management costs, increase network capacity by 30-40%, and It minimizes the need for operator intervention; NB-IoT, LTE-M and 5G mMTC between devices. Automatic RAT (Radio Access Technology) selection based on features and coverage status 35 The goal is to make and provide seamless mobility. 3 To achieve the purposes described above, the invention is compatible with 4G (LTE-M, NB-IoT) and 5G (mMTC) technologies. - Massive Machine Type Communications (MSC) mobile networks, Mass IoT (Internet of Things) providing connectivity management, energy efficiency optimization, and network resource allocation for devices. It is a system, and its characteristic is; • IoT devices, which are end-user devices that transmit data by connecting to a network, 5 • RANs (eNB / gNB) are base stations and gateways that enable IoT devices to access the network. and IoT Gateway, • Monitoring the connection status of all IoT devices, RRC state transitions, and PSM / eDRX IoT connectivity that collects activations, RACH initiatives, and network KPIs in real time. monitoring collector, 10 • IoT devices are analyzed based on traffic patterns (periodic / event-driven), data volume, application type, and mobility. Automatically classifies items according to their status and battery level, and provides the optimal solution for each class. AI-powered device profiling and classification engine that creates energy profiles, • RACH predicts periodic connection times and tracks connection attempts from thousands of devices. By automatically distributing resources, time slots, and frequency blocks, it minimizes collisions. Smart scheduling and distribution optimizer that minimizes, • RRC connection capacity and paging based on IoT device density and mixed traffic conditions. Dynamic system that optimizes resources, CE level and uplink grant allocation in real time. resource management module, • 20 that groups IoT devices with similar characteristics according to geographical and behavioral criteria and implements group paging, coordinated sleep cycles, and group messaging at the group level. group coordination manager, • Energy consumption according to application requirements (criticality, latency tolerance, data volume) Finding the optimal balance point between service quality and dynamic PSM / eDRX parameters. QoS - Energy balancing engine, which is set as 25 • by analyzing historical connection data, seasonal patterns, and traffic trends predicting future resource needs and optimal parameters for different device types. machine learning-based predictive planning and learning that continuously improves through learning engine, • Status of all IoT devices, group-based performance metrics, energy savings 30 visualizes statistics and resource usage status and generates alarms in case of anomalies. control panel and alarm system It includes. The structural and characteristic features and all the advantages of the invention are given in the figures below. Thanks to the detailed explanation written with references to the figures, it is clearer than ever. 35 4 This will be understood, and therefore the evaluation should also take these figures and detailed explanations into account. It must be done by taking it. Figures that will help understand the invention. Figure 1 shows the general architecture of the system that is the subject of the invention. 5 Explanation of Part References 1. IoT Devices 2.RAN (eNB / gNB) and IoT Gateway 3. IoT Connection Monitoring Aggregator 4. Device Profiling and Classification Engine 10 5. Intelligent Scheduling and Distribution Optimizer 6. Dynamic Resource Management Module 7th Group Coordination Manager 8. QoS - Energy Balancing Motor 9. Predictive Planning and Learning Engine 15 10. Control Panel and Alarm System Detailed Description of the Invention In this detailed explanation, the preferred configurations of the invention are not merely for better understanding the subject. This will facilitate understanding and will not create any limiting effects. The invention relates to 4G (LTE-M, 20 NB-IoT) and 5G (mMTC - massive Machine Type Communications) mobile networks Connectivity management, energy efficiency optimization, and networking of IoT (Internet of Things) devices. It is a system that provides resource allocation, using mass methods such as smart meters, sensors, and monitoring devices. IoT devices (1), which are IoT equipment, are the base that provides IoT devices (1) access to the network. RAN (eNB / gNB) and IoT Gateway (2), which are stations and gateways, all IoT devices (1) 25 connection statuses, RRC state transitions, PSM / eDRX activations, RACH initiatives IoT connection monitoring aggregator (3) that collects network KPIs in real time, IoT devices (1) traffic pattern (periodic / event-driven), data volume, application type, mobility status, and Automatically classifies energy based on battery level and provides an optimal energy profile for each class. AI-powered device profiling and classification engine (4), periodic connection 30 RACH resources estimate the times and thousands of devices (1) attempt to connect, Minimizing collisions by automatically distributing them across time slots and frequency blocks Smart scheduling and distribution optimizer (5), IoT device (1) density and mixed traffic According to the conditions, RRC connection capacity, paging resources, CE level and uplink grant Dynamic resource management module (6) that optimizes allocation in real time, similar to 35 Grouping IoT devices with features (1) according to geographical and behavioral criteria and group that practices group-level group paging, coordinated sleep cycles, and group messaging Coordination manager (7), application requirements (criticality, delay tolerance, data (based on volume) and finding the optimal balance point between energy consumption and service quality. QoS-Energy balance engine (8) which dynamically adjusts PSM / eDRX parameters, past By analyzing connection data, seasonal patterns, and traffic trends, we can identify future resources. By anticipating your needs and learning the optimal parameters for different device types, it continuously machine learning-based predictive planning and learning engine (9) that improves all IoT status of devices (1), group-based performance metrics, energy saving statistics and a management panel that visualizes resource usage status and generates alarms in case of anomalies. and includes an alarm system (10). 10 The system operates in the following steps: IoT devices (1) perform various connection activities (attach, RRC) over the RAN network (2). Connection establishment, data transfer, detach, PSM / eDRX transitions). Connection monitoring The collector (3) keeps detailed activity records for each device: RRC state transition times (idle→connected→idle), RACH preamble transmission count and results, uplink / downlink 15 packet count and sizes, PSM timer activations, paging window in eDRX cycles Number of cells, TAU periods, battery level reports, and reasons for attaching / detaching. Also, cell count. Network parameters such as RACH resource usage rate, paging load, and RRC connection count at this level. KPIs are also collected simultaneously. The device profiling engine (4) analyzes the activity history of each IoT device (1) over the last 7-30 days. It characterizes traffic by analyzing data transmission frequency histograms. Periodicity is determined through this analysis. This is done (e.g., once an hour, 4 times a day, random event-driven). Packet size distribution (small). Sensor data vs. major firmware update) is determined. Mobility status TAU frequency and cell It is calculated from the number of changes. Using this data, each device is automatically assigned a profile. Assigned: Ultra-Low Power (for 25-bit systems that send data only a few times a month and require years of battery life) (devices), Low Power Periodic (sensors that send daily / weekly regular reports), Moderate Activity (tracking devices that send data several times a day), Event-Driven (only specific events) alarm systems that connect when an event occurs), Mobile IoT (continuous moving vehicle monitoring) (devices) such as classes. Machine learning models use clustering algorithms (K- (means, DBSCAN) optimizes it. 30 For each device class, the QoS-Energy Balancing Engine (8) calculates the optimal connection parameters. For Ultra-Low Power devices (1), a very long PSM timer (e.g., T3412=24 hours, T3324=0) is required. A very long eDRX cycle (e.g., 10.24 minutes) is set. PSM for Event-driven devices (1) Fast paging is enabled by disabling eDRX but setting a short eDRX cycle (e.g., 20.48 seconds). Responsiveness is ensured. 35 for critical applications (emergency buttons, medical devices). Minimum delay settings (short active timer, frequent paging) are applied. The motor adjusts each parameter. 6 It predicts the effect of battery consumption after the change using a mathematical model: PowerConsumption = IdlePower × PSM_duration + ActivePower × (Data_tx_time + RRC_connection_time) + PagingPower × (eDRX_wakeup_frequency). Minimize this equation parameters will be optimized in a way that will both satisfy and meet QoS requirements. It is done. 5 Group coordination manager (7), devices with similar profiles (1) geographically and temporally It groups them according to criteria. For example, all smart electricity meters located in the same neighborhood. It forms a group. A Group-ID is assigned to each group, and coordination is carried out at the group level. mechanisms are activated. Coordinated paging is applied to devices (1) in the same group: Signaling overhead is reduced by sending a single paging message to the entire group. Within the group, there are 10... The connection times of the devices (1) are synchronized (all within the same 15 minute window (connected) but a small random offset (0-30 seconds) is assigned to each device to prevent collisions. Group-based firmware updates are performed using multicast. Predictive planning engine (9) analyzes past connection data to determine periodic connection It predicts the times. For example, smart meters automatically read the meter at 02:00 every day. If it is detected that it has done so, this information is transmitted to the timing optimizer (5). The optimizer, as expected To prevent 10,000 devices (1) from attempting to connect simultaneously, it implements the following strategies: (i) Time- domain spreading: spreading into a 30-minute window between 02:00-02:30, (ii) RACH resource expansion: increase the RACH preamble count from 54 to 64 before connection time, (iii) Randomization injection: adding a random delay of 0-1800 seconds to each device, (iv) 20 Priority-based scheduling: prioritizing critical devices. The system uses real-time RACH (Railway, Emergency, and Air Quality). By performing collision monitoring, if the collision rate exceeds 10%, the distribution parameters are dynamically adjusted. It expands as follows. Dynamic resource management module (6) monitors instantaneous IoT device (1) activity and total network load. It monitors constantly. During normal times (night, weekend) RRC connection 25 allocated to IoT devices (1) The data quota is kept low (20%), with 80% allocated to smartphone traffic. However, IoT peak... During these hours (morning meter reading times), this ratio is dynamically reversed: to IoT. 60% for smartphones, 40% for mobile devices. Paging capacity is adjusted similarly. Coverage Enhancement level distribution is also optimized: in remote areas experiencing coverage problems CE Mode A / B is assigned to devices (1), while devices with good coverage (1) are in normal mode 30 It is activated. In this way, limited CE resources are used efficiently. The module is for load balancing. When needed, some IoT devices (1) can offload to neighboring cells (adjacent cell handover) (or via re-selection parameter tuning). The machine learning engine (9) monitors the results of each parameter configuration applied. and records performance metrics. Using a reinforcement learning algorithm, the optimal 35 policy is learned: State = {device_class, traffic_pattern, network_load, time_of_day}, Action = 7 {PSM_timer, eDRX_cycle, TAU_period, RACH_parameters}, Reward = weighted_sum(battery_life_improvement, connection_success_rate, latency_reduction, resource_efficiency). The policy is continuously monitored by observing the behavior of thousands of devices (1) every day. Improvements are made. New parameter sets are tested in a controlled manner with A / B testing: devices (1) The new parameter is applied to 10% of the participants, the remaining 90% serve as a control group, and the results are compared. 5 If the new parameters yield better results, they will be rolled out to all devices (1). The admin panel (10) provides real-time dashboards: (i) Device groups and profiles distribution, (ii) Group-based energy saving rates (comparative: before optimization vs. (i) RACH collision rate and time-domain distribution, (iv) Connection success rates phase based on, (v) Resource usage graphs (RRC connections, paging load, CE mode 10 (distribution). The alarm system automatically generates notifications in the following situations: RACH collision rate > 15%, Setup failure rate > 5%, Unexpected connection storm detected, A group of devices If the average battery level drops to a critical level, the Learning engine's (9) confidence score will be determined. if it falls below a threshold. Recommended action plan for each alarm (RACH resource increase, Parameter changes (such as the need for a device firmware update) are offered automatically. 15

Claims

8 REQUESTS 1. 4G (LTE-M, NB-IoT) and 5G (mMTC - massive Machine Type Communications) mobile Connectivity management and energy efficiency for mass IoT (Internet of Things) devices in networks. It is a system that provides optimization and network resource allocation, and its features include: IoT devices are end-user devices that transmit data by connecting to the network (1), 5 RANs are base stations and gateways that enable IoT devices (1) to access the network. (eNB / gNB) and IoT Gateway (2), (1) connection status of all IoT devices, RRC state transitions, PSM / eDRX IoT connectivity that collects activations, RACH initiatives, and network KPIs in real time. monitoring collector (3), 10 IoT devices (1) traffic pattern (periodic / event-driven), data volume, application type, mobility Automatically classifies items according to their status and battery level, and provides the optimal solution for each class. AI-powered device profiling and classification engine that creates energy profiles (4), predicts periodic connection times and thousands of devices (1) connection attempts RACH automatically distributes collisions across time slots and frequency blocks, saving 15 time slots. Smart timing and distribution optimizer that minimizes (5), RRC connection capacity according to IoT device (1) density and mixed traffic conditions, Optimizing paging resources, CE level, and uplink grant allocation in real time. dynamic resource management module (6), Grouping IoT devices with similar characteristics (1) according to geographical and behavioral criteria 20 separating and group-level collective paging, coordinated sleep cycles, and group messaging. implementing group coordination manager (7), energy consumption according to application requirements (criticality, latency tolerance, data volume) Finding the optimal balance point between service quality and dynamic PSM / eDRX parameters. QoS-Energy balance engine (8), which sets as 25 By analyzing past connection data, seasonal patterns, and traffic trends, we can predict the future. by estimating resource requirements and learning the optimal parameters for different device types continuous improving machine learning based predictive planning and learning engine (9), • status of all IoT devices (1), group-based performance metrics, energy savings 30 that visualizes statistics and resource usage status and generates alarms in case of anomalies control panel and alarm system (10) It includes.

2. The system is compliant with Request 1 and its features are; IoT devices (1) data transmission frequency, application type (periodic / event-driven), automatically switches to 35 based on mobility status and battery level. 9 A device that classifies groups and determines optimized PSM and eDRX parameters for each group. It includes a profiling and classification engine (4).

3. The system compliant with Request 1 is characterized by its periodic connection with machine learning algorithms. By predicting their timing, thousands of devices (1) attempt to connect, time and frequency 5 It includes an intelligent scheduling and distribution optimizer (5) that automatically distributes in the domain.

4. It is a system that complies with Request 1, and its feature is to monitor the density of IoT devices (1) in real time. RRC connection resources, paging capacity and CE (Coverage Enhancement) levels. It includes a dynamic resource management module (6) that adjusts dynamically. 10 5. The system complies with Request 1, and its characteristics are: geographical proximity, application type, and traffic pattern. Automatically groups IoT devices (1) according to their similarities and at the group level groups that practice group paging, group messaging, and coordinated sleep-wake cycles It includes the coordination manager (7). 15 6. The system complies with System 1, and its features include; according to application requirements (critical alarm, (Periodic reporting, low-priority telemetry, etc.) optimal in the energy-latency-reliability triangle. QoS - Energy balance, which finds the balance point and adjusts the connection parameters accordingly. It includes the engine (8). 20 7. The system complies with System 1 and its feature is that it analyzes historical traffic data and seasonal patterns. by predicting future connection density and proactively allocating network resources. It includes a predictive planning and learning engine (9) that prepares.

8. It is a system that complies with Request 1, and its feature is that it is suitable for different IoT device (1) types and application scenarios. Predictive technology that learns by experimenting with optimum connection parameters and continuously improves. It includes planning and learning engine (9).

9. System compliant with Request 1, its feature is; device (1) 30 between NB-IoT, LTE-M and 5G mMTC. Automatic RAT (Radio Access Technology) selection based on features and coverage status. predictive planning and learning engine that does and provides seamless mobility (9) It includes.

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