MODULAR EDGE COMPUTING SYSTEM FOR MONITORING AND RESPONDING TO REAL-TIME EVENTS

Abstract

A solar-powered modular edge computing system for real-time event monitoring and response in remote locations is disclosed. The system features modular monitoring units equipped with dual cameras, environmental sensors, and AI-enabled processors for analyzing real-time data and detecting critical events such as vehicle hazards, environmental disasters, and traffic patterns. The units operate within a wireless mesh network, enabling extended coverage through data relay between multiple stations. Event alerts are transmitted wirelessly to cloud infrastructure and user mobile devices for immediate response coordination. The system's intelligence adapts through continuous analysis of historical data, optimizing detection parameters for improved accuracy. This sustainable, autonomous solution addresses challenges in disaster monitoring, environmental surveillance, and traffic management where traditional infrastructure is limited, providing reliable real-time event detection and response capabilities. FIG. 1

Specification

Description:TECHNICAL FIELD
The present disclosure relates generally to the field of autonomous environmental monitoring and event detection systems, and more specifically, to an improved modular edge computing system for remote, real-time monitoring and response. The invention provides a solar-powered, AI-enabled monitoring unit with integrated environmental sensors, dual camera systems, and wireless communication, allowing for continuous, autonomous operation in off-grid locations. The system is particularly useful for applications requiring real-time detection and alerts, such as traffic monitoring in remote areas, disaster prevention, environmental surveillance, and early warning systems for natural hazards like forest fires, landslides, and flash floods.
BACKGROUND
The demand for autonomous, reliable, and real-time environmental monitoring systems has grown significantly in recent years, driven by the need to ensure safety, improve disaster response, and monitor traffic and environmental conditions in remote or hard-to-reach areas. Monitoring remote locations, particularly in mountainous or rural areas, presents significant challenges due to limited power availability and connectivity issues. Traditional monitoring systems often require grid power and stable network connections, making them unsuitable for remote deployments. Traditional monitoring systems often require direct power sources, consistent maintenance, and manual intervention, making them challenging to deploy in off-grid locations. Furthermore, conventional monitoring systems that rely on single-sensor setups or basic processing units often lack the ability to analyze data intelligently or provide actionable insights in real-time. These limitations restrict the effectiveness of conventional systems, particularly in areas where early detection of environmental hazards or real-time traffic monitoring is critical for safety and resource management.
With the rise in natural disasters such as wildfires, floods, and landslides, governments, environmental agencies, and organizations are increasingly seeking advanced solutions to monitor potential risks and alert responders promptly. Real-time monitoring in hilly and remote regions poses unique challenges, as these areas frequently experience poor connectivity, limited power sources, and dynamic weather conditions that make it difficult to maintain conventional monitoring systems. Additionally, in such areas, responding quickly to natural hazards or traffic accidents is essential for saving lives, managing resources, and reducing infrastructure damage. As a result, there is a heightened need for monitoring systems that can operate autonomously, withstand environmental stress, and deliver reliable data for immediate response actions.
Conventional systems generally lack a modular design and often require significant manual configuration, which hinders scalability and flexibility in adapting to various monitoring requirements. These systems also commonly depend on basic wired or wireless communication technologies, which can be unreliable or difficult to establish in isolated regions. In the case of event detection, conventional systems typically rely on threshold-based methods, which may not account for complex environmental variations or provide the level of accuracy required to detect specific events like vehicle movements, forest fires, or sudden floods. This lack of sophisticated event detection limits their ability to proactively alert authorities or provide meaningful insights for pre-emptive measures.
The integration of artificial intelligence (AI) and edge computing capabilities into monitoring systems has shown promise in addressing these challenges by enabling real-time data processing, pattern recognition, and autonomous decision-making at the site of data collection. AI-enhanced edge computing allows systems to analyze data from multiple sources, such as dual cameras and environmental sensors, directly within the monitoring unit. This approach reduces the dependency on centralized servers, minimizes latency, and ensures rapid alert generation, even in locations with intermittent connectivity. Solar power has also become a viable solution for powering autonomous systems in remote areas, offering a sustainable energy source that aligns with environmental goals and enhances system reliability.
Therefore, in light of the foregoing discussion, there exists a need to overcome the aforementioned drawbacks associated with conventional monitoring systems by developing an advanced, modular edge computing solution. The present invention provides a modular edge computing system that leverages solar power, AI-enabled processing, and a versatile wireless communication system. The system is designed to operate autonomously and sustainably in remote locations. Equipped with dual camera systems and a range of environmental sensors, the system can monitor critical environmental and safety parameters. Using AI, it can process data in real-time and generate alerts for various events, including natural disasters and traffic safety hazards. The system can relay information through a cloud computing device or a wireless mesh network, thereby offering a flexible and resilient communication solution. The present disclosure invention provides an effective and scalable approach to real-time monitoring and response in remote or hard-to-reach locations, ensuring enhanced safety and environmental management.
SUMMARY
The present invention provides a modular edge computing system (100) for monitoring and responding to real-time events, particularly designed for autonomous operation in remote locations using solar power. The system overcomes the challenges of power availability, real-time processing, and network connectivity in remote areas while providing intelligent monitoring capabilities.
The present disclosure provides a solution to the existing problems encountered in remote monitoring systems. Current systems suffer from limited monitoring capabilities in remote locations due to power infrastructure dependencies and inability to effectively detect and respond to real-time events in mountainous and rural areas. There is a significant lack of integrated systems that can autonomously monitor multiple environmental and safety parameters, coupled with inefficient data processing and alert generation capabilities. Additionally, poor communication infrastructure hampers the transmission of critical alerts from remote locations, while the inability to form networks between multiple monitoring units limits coverage. These systems also require manual monitoring in hazardous or difficult-to-access areas, resulting in delayed response times in critical situations like vehicle accidents, forest fires, and natural disasters.
One or more objectives of the present disclosure is achieved by the solutions provided in the enclosed independent claims. Advantageous implementations of the present disclosure are further defined in the dependent claims.
In one aspect, the present disclosure provides a modular edge computing system (100) specifically engineered for autonomous, solar-powered, and remote monitoring with the capacity to detect and respond to real-time events. This system is constructed around a solar-powered monitoring unit (102) mounted on an independent pole structure, which comprises a solar panel (104) connected to a battery system (106), a dual camera system (108), a set of environmental sensors (112), and a wireless communication module (114). At its core, an AI-enabled processing unit (110) is responsible for interpreting data collected from the cameras and environmental sensors, identifying predefined events, and transmitting alert signals to relevant endpoints through the wireless communication module.
The system further incorporates cloud connectivity, allowing data to be transmitted to a cloud computing device (116) and accessed by user handheld devices (118), where alerts and real-time monitoring data can be displayed on a graphical interface (120). Designed for distributed environments, the system's modular units (102) can form a wireless mesh network, relaying data efficiently between units and aggregating it for seamless transmission to the cloud computing device (116). The event detection capabilities are customizable and cover a range of critical criteria, including vehicle detection on hazardous terrain, forest fire and landslide detection, water hazards, and other disaster management scenarios. The system's environmental sensors (112) are calibrated to detect temperature (122), humidity (124), and local weather patterns (126), ensuring robust environmental monitoring. Beyond real-time alerts, the processing unit (110) also facilitates traffic monitoring by tracking vehicle passages and analyzing traffic patterns, transmitting this data to the cloud computing device (116) for further analysis.
The system includes a method for event detection, which entails collecting data from the dual camera system and environmental sensors, processing this data with AI algorithms, detecting specific events, and generating alert signals. The method also allows for enhanced event detection through cross-verification of multiple data sources, real-time analysis, and alert generation. Historical monitoring data can be stored, analyzed for trend patterns, and used to update event detection parameters, allowing the system to customize alert responses based on specific applications or environmental conditions. A computer program product which includes a non-transitory computer-readable medium with executable instructions that enable the system to carry out the monitoring method. The modular edge computing system thereby provides an adaptable, autonomous solution for comprehensive monitoring and alert management in remote, challenging environments, demonstrating substantial utility for disaster management and environmental monitoring applications.
Beneficially, the modular edge computing system (100) achieves significant technical advantages through its innovative integration of solar power, artificial intelligence, and sensor technologies. The solar-powered design of the monitoring unit (102), equipped with a battery system (106), ensures a continuous, self-sustaining power source, which eliminates dependency on external power infrastructure and enhances deployment flexibility in isolated locations. This capability is especially valuable for long-term, unattended operation, making the system suitable for locations that may be logistically challenging to access or maintain.
The integration of dual camera systems (108) and a suite of environmental sensors (112), coupled with an AI-enabled processing unit (110), results in sophisticated real-time data processing. This configuration enables the system to detect predefined events based on image data and environmental metrics with high accuracy, significantly reducing false alarms and enhancing response efficacy. The on-board AI processing further allows for localized data interpretation, reducing the need for constant data transfer to central servers, thereby conserving bandwidth and supporting faster response times.
The wireless communication module (114) and the cloud computing device (116) enable reliable, scalable communication pathways, allowing the system to transmit alert signals and monitoring data to a central interface where real-time data can be accessed and displayed on user handheld devices (118). This provides a seamless and effective way to manage alerts and monitor conditions from a distance, with minimal latency, enhancing user responsiveness and decision-making.
Moreover, the capability for the monitoring units (102) to form a wireless mesh network provides significant technical advantages in network resilience and data relay efficiency. This feature allows each unit to communicate directly with others, creating a robust, decentralized communication structure that reduces single points of failure and facilitates coverage over larger geographic areas without the need for extensive infrastructure.
The system's predefined event detection criteria, which include hazard and disaster-related conditions, add another layer of technical utility. By leveraging environmental and image-based data, the system can autonomously detect and respond to critical situations, such as detecting vehicles in dangerous zones, identifying signs of forest fires or landslides, and monitoring water hazards. This capability addresses high-risk scenarios in real time, providing actionable data for emergency responses.
The historical data storage and analysis functions further enhance the system's adaptability and precision over time, allowing it to analyze past events, adjust event detection parameters, and customize alert generation based on evolving environmental conditions. These technical effects make the system highly adaptable, reducing false alerts while ensuring accurate, targeted responses.
In one implementation, the modular edge computing system (100) incorporates several enhancements that expand its functionality and adaptability for complex, real-world applications. The addition of a cloud computing device and a user handheld device extends the system's monitoring reach, enabling seamless communication with remote cloud platforms and real-time data visualization on portable devices. This connectivity allows users to receive alerts and monitor environmental conditions from virtually any location, thereby improving accessibility and responsiveness in critical situations.
A wireless mesh network configuration enables multiple solar-powered monitoring units to interconnect and relay data across extended areas. This networked setup strengthens communication resilience, as each unit can autonomously establish connections with nearby units, share data, and collectively transmit information to the cloud computing device. This decentralized communication model minimizes reliance on a single point of connection, making the system particularly robust and well-suited for vast or remote geographic areas where traditional infrastructure may be limited or unreliable.
The system's event detection capabilities are fine-tuned to address a variety of high-risk scenarios, including vehicle detection on dangerous turns in hilly areas, forest fire detection, landslide warnings, water hazards on roads, and general disaster management monitoring. By embedding these specific criteria, the system can autonomously monitor environmental hazards and generate timely alerts, providing proactive support in disaster-prone regions where rapid response is essential.
The environmental monitoring scope is enhanced by integrating specialized sensors, such as temperature, humidity, and weather sensors, as well as real-time traffic pattern analysis. The temperature and humidity sensors allow for granular weather pattern detection, which is vital for early warning systems in fire-prone or flood-risk areas. Additionally, the processing unit is configured to analyze vehicle movement, track traffic flow, and transmit this data for advanced traffic management and safety assessments.
Together, these enhancements refine the modular edge computing system by adding cloud-based accessibility, networked connectivity, targeted environmental monitoring, and robust traffic analysis. These features improve the system's adaptability, enabling it to address a wide range of monitoring scenarios while providing reliable, real-time data for user decision-making.
BRIEF DESCRIPTION OF THE DRAWINGS
The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.
Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:
FIG. 1 illustrates a solar-powered monitoring unit of the modular edge computing system (100), in accordance with an embodiment of the present disclosure; and
FIG. 2 is a flowchart illustrating a method (200) for real-time event monitoring and response of the modular edge computing system, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS
The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognize that other embodiments for carrying out or practicing the present disclosure are also possible.
The present invention provides a modular, autonomous edge computing system designed for real-time environmental monitoring and event detection, particularly in remote and off-grid locations. The system is configured to operate independently through solar power and integrates advanced artificial intelligence (AI) and multi-sensor technology to detect specific environmental and situational events.
FIG. 1 illustrates a solar-powered monitoring unit of the modular edge computing system (100), in accordance with an embodiment of the present disclosure.
In one embodiment, the present disclosure provides a modular edge computing system (100) designed to autonomously monitor and respond to real-time events in remote locations, powered by solar energy. The system operates without reliance on conventional power infrastructure, making it well-suited for use in environments where access to power or communication networks is limited. The system is self-sustaining, capable of continuous operation in isolated areas, and optimized for real-time data processing and event detection.
The system (100) comprises a solar-powered monitoring unit (102) that is mounted on an independent pole structure. The monitoring unit (102) is equipped with a solar panel (104) that captures sunlight and converts it into electrical energy, which is stored in an associated battery system (106). This energy storage ensures that the monitoring unit remains operational even in low-light conditions or at night, providing uninterrupted performance. The combination of solar power and battery storage enables the system to function autonomously for extended periods in remote locations, eliminating the need for external power sources.
The monitoring unit (102) is equipped with at least one dual camera system (108) that is operatively connected to a processing unit (110). The cameras capture high-resolution visual data of the monitored environment, enabling the system to analyze and interpret images in real-time. The dual camera setup provides redundancy and enhances the system's ability to capture more detailed or wide-angle views of the area. The processing unit (110), powered by artificial intelligence (AI) algorithms, is responsible for processing the visual data received from the cameras and correlating it with environmental data collected from a plurality of environmental sensors (112). These sensors are connected to the processing unit (110) and measure various environmental parameters, such as temperature, humidity, air quality, and other factors that may influence the detection of specific events or conditions.
The AI-enabled processing unit (110) is designed to analyze the collected data in real time, enabling the system to identify and classify potential events based on predefined detection criteria. These events could include detection of vehicles in hazardous zones, forest fires, landslides, or other environmental threats. Once an event is detected, the processing unit generates alert signals to notify relevant authorities or users about the detected issue. These alert signals are transmitted through the system's wireless communication module (114), which facilitates communication with remote devices such as cloud platforms or user handheld devices. This wireless communication module ensures that alerts are promptly transmitted, even in areas where traditional communication networks may not be readily available.
In another embodiment, the system (100) includes a cloud computing device (116) that is operatively connected to the wireless communication module (114). This cloud computing device allows the system to send and receive data between the monitoring unit and remote servers or cloud platforms. The cloud computing device plays a critical role in ensuring that data collected by the system is stored and processed centrally, making it accessible for analysis and action from any location. In addition to this, the system is further enhanced by the inclusion of a user handheld device (118), which is configured to receive alert signals transmitted via the cloud computing device. The handheld device allows users to stay informed of real-time conditions, providing access to monitoring data and alerts through a graphical user interface (120). This ensures that users can efficiently monitor the status of the system and respond promptly to emerging events, even when situated far from the monitored location.
Moreover, the system's wireless communication capabilities are expanded by the formation of a wireless mesh network. In this configuration, multiple solar-powered monitoring units (102) are interconnected to relay data between each other. This mesh network structure enables the monitoring units to communicate with nearby devices, thereby creating a decentralized network of units that can share data and extend the system's reach. The wireless communication module (114) is responsible for establishing communication links between these units, relaying collected data, and transmitting aggregated data to the cloud computing device (116) for centralized processing. This enhances the robustness and scalability of the system, enabling it to cover larger areas without requiring additional infrastructure.
In another embodiment, system's event detection capabilities are configured to identify a range of predefined events. These include detecting vehicles on sharp turns in hilly regions, generating warnings for approaching vehicles, as well as identifying potential environmental hazards such as forest fires, landslides, and water rapids on roads. The system can also monitor disaster management conditions, providing timely alerts for situations that require immediate attention. The predefined event detection criteria are tailored to address specific risks relevant to the environment in which the system operates.
Furthermore, environmental sensors (112) integrated into the system further improve its ability to monitor conditions in real time. These sensors include temperature (122), relative humidity (124), and weather monitoring sensors (126) for detecting local weather patterns. The collected data from these sensors helps the system to assess environmental changes, identify trends, and detect hazards such as extreme temperatures or high humidity that may indicate fire risk or other environmental concerns.
In addition to environmental monitoring, the processing unit (110) is configured to track vehicles passing through the monitored regions. The system can generate vehicle passage data, analyze traffic patterns, and use this data to assess potential traffic-related events. This capability supports the system's broader safety monitoring objectives, such as monitoring vehicle flow and detecting potential accidents or congestion. The vehicle passage data is transmitted to the cloud computing device (116) for further analysis and reporting, enabling more effective traffic management and timely responses to traffic-related events.
FIG. 2 is a flowchart illustrating a method (200) for real-time event monitoring and response of the modular edge computing system, in accordance with an embodiment of the present disclosure.
The method for monitoring and responding to real-time events is designed to efficiently process data from various sources and provide timely alerts based on predefined event detection criteria. At the step (202), method begins by receiving real-time data from the dual camera system and the environmental sensors. These data sources provide critical information regarding the monitored environment, including visual data captured by the cameras and environmental parameters such as temperature, humidity, and weather conditions measured by the sensors. At the step (204), captured real-time data by the cameras and sensor is then processed by the artificial intelligence-enabled processing unit, which leverages advanced algorithms to analyze the data in real time.
At the step (206), once the data is processed, the system detects predetermined events based on specific patterns or thresholds that have been predefined in the system's configuration. These events could range from detecting vehicles in hazardous conditions to identifying environmental threats like fires, landslides, or flooding. At the step (208), upon detecting such events, the system generates alert signals that are triggered by the AI algorithms. These alerts are then transmitted through the wireless communication module to ensure they reach the necessary recipients or systems at the step (210).
At the step (212), generated alert signals are displayed on a user handheld device, which allows users to receive real-time updates on the status of the monitored area. The graphical user interface on the device presents these alerts in an easily understandable format, allowing users to take immediate action based on the event information. The handheld device serves as an essential tool for operators to monitor and respond to critical situations remotely. The process of detecting predetermined events involves analyzing image data using artificial intelligence algorithms, which enables the system to identify visual cues or patterns that suggest the occurrence of an event. The analysis is further supported by the processing of environmental sensor data, such as temperature and humidity readings. To ensure accuracy, the system correlates data from multiple sources, including the camera system and environmental sensors, to verify the occurrence of the event. The multi-source data correlation enhances the reliability of event detection and helps reduce false positives. Additionally, the method can further include storing historical monitoring data and analyzing patterns in this stored data. This historical data can be invaluable for refining the system's event detection parameters. By analyzing stored data, the system can identify emerging trends, detect long-term changes, and adapt its event detection criteria to better address evolving conditions. The ability to update event detection parameters based on the analysis of historical data ensures that the system remains responsive to changing environments and user requirements. Furthermore, the method allows for the customization of alert generation, enabling the system to tailor alerts to specific application needs, ensuring that alerts are not only timely but also contextually relevant.
A computer program product, as described, consists of a set of computer-executable instructions stored in a non-transitory computer-readable medium. The product is specifically designed to perform the operation when the instructions are executed by a processor. In essence, the computer program product is a software solution that enables the system to carry out all the necessary operations for monitoring and responding to real-time events, as described. The computer program product includes a set of coded instructions that are stored on a medium such as a hard drive, memory device, or any other type of data storage device capable of retaining data.
Upon execution by a processor, the computer program product causes the system to perform the method. It includes receiving real-time data from various sensors and camera systems, processing data using artificial intelligence algorithms, detecting predetermined events, generating alert signals based on these events, transmitting the alerts via wireless communication, and displaying them on user handheld devices. The computer program product is an essential component of the system because it facilitates the operation of the hardware and sensors, ensuring that the real-time monitoring, event detection, and alert generation processes are carried out efficiently and accurately. This embodiment allows for a software-based implementation of the method that can be easily deployed, updated, and maintained as needed, providing flexibility and adaptability for various monitoring applications.
Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non-exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural. The word "exemplary" is used herein to mean "serving as an example, instance or illustration". Any embodiment described as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments or to exclude the incorporation of features from other embodiments. The word "optionally" is used herein to mean "is provided in some embodiments and not provided in other embodiments". It is appreciated that certain features of the present disclosure, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable combination or as suitable in any other described embodiment of the disclosure. , Claims:CLAIMS
I/We claim:
1. A modular edge computing system (100) for monitoring and responding to real-time events, wherein said system (100) is configured to operate autonomously in remote locations using solar power, said system comprising:
? a solar-powered monitoring unit (102) mounted on an independent pole structure, wherein the solar-powered monitoring unit (102) comprises:
? a solar panel (104) and an associated battery system (106);
? at least one dual camera system 108 operatively connected to a processing unit (110);
? a plurality of environmental sensors (112) operatively connected to said processing unit (110); and
? a wireless communication module (114);
? an artificial intelligence enabled at least one processing unit (110) configured to:
i. process data received from said dual camera system 108 and said environmental sensors (112);
ii. generate alert signals based on predefined event detection criteria; and
iii. transmit said alert signals through said wireless communication module (114);
? wherein said processing unit is configured to:
? process data received from said dual camera system and said environmental sensors;
? generate alert signals based on predefined event detection criteria; and
? transmit said alert signals through said wireless communication module;
? wherein said system (100) is configured to operate autonomously in remote locations using solar power.
2. The system (100) as claimed in claim 1, further comprising:
? a cloud computing device (116) operatively connected to said wireless communication module (114); and
? a user handheld device (118) configured to:
? receive said alert signals from said cloud computing device (116); and
? display real-time monitoring data and alerts on a graphical user interface (120).
3. The system (100) as claimed in claim 1, wherein plurality of said solar-powered monitoring units 102 are configured to form a wireless mesh network for data relay between said units, and wherein said wireless communication module (114) is adapted to:
? establish communication with nearby monitoring units 102;
? relay data between said monitoring units 102; and
? transmit aggregated data to said cloud computing device (116).
4. The system (100) as claimed in claim 1, wherein said predefined event detection criteria comprises at least one of:
? detection of vehicles on sharp turns in hilly regions;
? generation of approaching vehicle warnings;
? detection of forest fires;
? detection of landslides;
? detection of water rapids on roads; and
? monitoring of disaster management conditions.
5. The system (100) as claimed in claim 1, wherein said environmental sensors (112) comprise:
? a temperature sensor (122);
? a relative humidity sensor (124); and
? weather monitoring sensors (126) for local weather pattern detection.
6. The system (100) as claimed in claim 1, wherein said processing unit (110) is further configured to:
? track vehicles passing through monitored regions;
? generate vehicle passage data;
? analyze traffic patterns in real-time; and
? transmit said vehicle passage data to said cloud computing device.
7. A method (200) for monitoring and responding to real-time events, the method comprises:
? receiving real-time data from said dual camera system and said environmental sensors;
? processing said real-time data using said artificial intelligence enabled at least one processing unit;
? detecting predetermined events based on a processed data;
? generating alert signals based on detected events;
? transmitting said alert signals through said wireless communication module; and
? displaying said alert signals on a user handheld device (118).
8. The method (200) as claimed in claim 7, wherein the step of detecting predetermined events comprises:
? analyzing image data using artificial intelligence algorithms;
? processing environmental sensor data;
? correlating multiple data sources for event verification; and
? generating real-time alerts for detected events.
9. The method (200) as claimed in claim 7, further comprising the steps of:
? storing historical monitoring data;
? analyzing patterns in stored data;
? updating event detection parameters based on analyzed patterns; and
? customizing alert generation based on specific application requirements.
10. A computer program product comprising computer executable instructions embodied in a non-transitory computer-readable medium, wherein said computer program product is adapted to perform the method as claimed in claim 7 when said instructions are executed by a processor.

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