Ornithopter unmanned remote control aerial vehicle for intruder detection and data preservation

Abstract

Embodiments of this disclosure presents a virtual circuit-based blockchain-connected ornithopter unmanned remote control aerial vehicle (URCAV) system for real-time detection, identification, and preservation of data related to intruders or unauthorized individuals. The URCAV, inspired by bird flight mechanics, incorporates dynamic flapping wings and advanced technologies, including optimized IMPROVED-YOLOV7 deep learning algorithms, an ESP32 PSRAM timer camera (OV3660) for real-time video streaming. The system aims to address challenges in intruder detection by providing a lightweight, cost-effective, and efficient solution for remote locations. Exemplary embodiments feature the integration of dense and transition layers of CSPNet into the YOLOV7 approach to obtain IMPROVED-YOLO7, with Gradient Descent utilized for optimization. The proposed system enables swift identification of potential intruders within the monitored area, enhancing security measures in various applications. Through these advancements, the disclosure offers a promising solution for real-time intruder detection and data preservation.

Description

“ORNITHOPTER UNMANNED REMOTE CONTROL AERIAL VEHICLE FOR INTRUDER DETECTION AND DATA PRESERVATION” DESCRIPTION TECHNICAL FIELD

[0001] The disclosed subject matter relates to the field of virtual circuit-based blockchain- connected ornithopter unmanned remote control aerial vehicles. More specifically, the present invention discloses an Ornithopter URCAV (Unmanned Remote Control Aerial Vehicle) configured with a camera module and deep learning algorithms, capable of capturing and processing visual data. This enables swift identification of potential intruders within the monitored area. BACKGROUND

[0002] Intruder security breaches in sensitive locations can have severe consequences, including financial losses, reputational damage, and even physical harm to individuals. Instances such as theft of proprietary information from research facilities or terrorist attacks on military bases highlight the urgent need for robust intrusion detection systems. Traditional security measures often struggle to address these breaches effectively, particularly in remote locations where installing conventional security infrastructure is challenging.

[0003] Some of the known solutions employed for intruder detection in remote areas include fixed CCTV surveillance systems, manned aerial surveillance, ground-based sensors, and satellite surveillance. While these solutions have their merits, they suffer from limitations such as restricted coverage range, high costs, dependency on manned operations, limited accessibility to remote areas, and insufficient real-time monitoring capabilities. Furthermore, they often lack a lightweight, low-cost, and efficient approach to intruder detection, particularly in locations with limited power supply and communication infrastructure.

[0004] Unmanned Remote Control Aerial Vehicles (URCAVs) have emerged as an integral part of modern security and surveillance systems. They offer the potential for real-time monitoring and detection, especially in hard-to-reach areas. However, existing URCAV solutions have theirlimitations. This necessitates the development of alternative technologies to overcome the drawbacks of current approaches.

[0005] To address these challenges, the present invention focuses on designing and developing a virtual circuit-based blockchain-connected ornithopter unmanned remote control aerial vehicle for intruder detection. The Ornithopter URCAV draws inspiration from the flight mechanics of birds, incorporating dynamic wings capable of flapping. By combining the unique flight capabilities of the Ornithopter URCAV with advanced technologies such as the optimized IMPROVED-YOLOV7, ESP32 PSRAM timer camera (OV3660) for real-time video streaming, the invention aims to bridge a significant gap in the existing technology. It provides a lightweight, cost-effective, and efficient system for detecting intruders in remote locations. SUMMARY

[0006] The following invention presents a simplified summary of the disclosure in order to provide a basic understanding to the reader. This summary is not an extensive overview of the disclosure and it does not identify key / critical elements of the invention or delineate the scope of the invention. Its sole purpose is to present some concepts disclosed herein in a simplified form as a prelude to the more detailed description that is presented later.

[0007] Exemplary embodiments of the present disclosure pertain to a virtual circuit-based blockchain-connected ornithopter unmanned remote control aerial vehicle designed for real-time detection, identification, and preservation of data concerning intruders or unauthorized individuals.

[0008] The objective of the present disclosure is to introduce a system capable of real-time detection and identification of intruders or unauthorized individuals. This is achieved by employing cutting-edge technologies, including deep learning algorithms (such as optimized IMPROVED-YOLOV7), an ESP32 PSRAM timer camera (OV3660). These components enable the system to capture and process visual data, facilitating swift identification of potential intruders within the monitored area.

[0009] Another objective of the present disclosure is to facilitate the preservation of data related to intruders or unauthorized individuals. By incorporating blockchain technology, the Ornithopter URCAV will securely store and timestamp intrusion-related information, ensuring data integrity and facilitating subsequent analysis, investigation, or legal proceedings.

[0010] Another objective of the present disclosure is to overcome the limitations of existing intruder detection solutions. By offering a lightweight and low-cost alternative, the Ornithopter URCAV provides enhanced coverage range, cost-effectiveness, and accessibility to remote locations. It mitigates the dependence on manned operations and overcomes challenges posed by rugged terrains or obstructed line-of-sight scenarios.

[0011] Another objective of the present disclosure is to provide a portable and efficient surveillance system suitable for remote locations. The Ornithopter URCAV's compact design ensures ease of transportation and deployment, enabling rapid deployment in areas where traditional security measures are impractical or insufficient.

[0012] Another objective of the present disclosure is to integrate advanced technologies into the Ornithopter URCAV system. This includes the incorporation of the ESP32 PSRAM timer camera (OV3660) for efficient control, communication and for visual data capture, deep learning algorithms for real-time intrusion detection and identification, and blockchain for secure data preservation.

[0013] Another objective of the present disclosure is to offer a cost-effective and versatile solution for intruder detection. By leveraging lightweight materials, efficient flight mechanics, and advanced technologies, the Ornithopter URCAV provides an affordable alternative to expensive manned aerial surveillance or satellite-based systems. Its versatility allows for deployment in various remote locations, regardless of power supply and communication infrastructure limitations.

[0014] In an exemplary embodiment of the present disclosure, an Ornithopter URCAV integrating with an ESP32 PSRAM timer camera (OV3660). The ESP32 microcontroller serves as the central control unit, camera interface, and IP streaming platform.

[0015] Another exemplary embodiment of the present disclosure is to equip the Ornithopter URCAV with dynamically flapping wings, reduces the detection rate by mimicking bird-like flight patterns and behavior.

[0016] Another exemplary embodiment of the present disclosure is to utilize the ESP32 PSRAM timer camera (OV3660) module for real-time video streaming and image processing. The URCAV captures live video footage of the monitored area, which is wirelessly transmitted for further analysis.

[0017] Another exemplary embodiment of the present disclosure employs the optimized IMPROVED-YOLOV7 with CSPNet as the deep learning algorithm for object detection and classification. A custom dataset of suspected or intruder face images is collected and annotated for training the model.

[0018] Another exemplary embodiment of the present disclosure involves incorporating dense layers and transition layers of CSPNet into the YOLOV7 approach to obtain IMPROVED- YOLOV7.

[0019] Another exemplary embodiment of the present disclosure employs the Gradient Descent approach to optimize IMPROVED-YOLOV7.

[0020] Another exemplary embodiment of the present disclosure incorporates Deblur GANv2 to remove motion blur from the captured aerial images, enhancing the quality of the extracted frames for object detection.

[0021] Another exemplary embodiment of the present disclosure is to analyze the video stream on-board the URCAV using the optimized IMPROVED-YOLOV7 model for real-time intruderdetection. Once an intruder or suspicious object is detected, the system generates alerts, such as sending notifications to a terminal or utilizing platforms like Telegram.

[0022] Another exemplary embodiment of the present disclosure leverages blockchain technology for data preservation, ensuring the integrity and immutability of intrusion-related information. Intruder or suspicious images are maintained over an IPFS-based Ethereum blockchain, providing a secure and tamper-proof record of detected intrusions.

[0023] According to the non limiting exemplary embodiments of the present disclosure focuses on developing a lightweight and low-cost system, making it suitable for deployment in remote areas with limited power supply and communication infrastructure. By utilizing cost-effective components and efficient technologies, the system addresses the challenges of traditional security measures in remote environments. The integration of an Ornithopter URCAV with advanced technologies like the ESP32 PSRAM timer camera (OV3660), deep learning algorithms (such as optimized IMPROVED-YOLOV7 with CSPNet), and blockchain to provide a versatile, cost-effective, and real-time solution for intruder detection in remote areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] FIG.1 is a diagram depicting proposed system architecture.

[0025] FIG. 2 is a diagram depicting virtual circuit based Ornithopter unmanned remote control aerial vehicle simulator architecture.

[0026] FIG. 3 is a diagram depicting multiple functional components of the Ornithopter unmanned remote control aerial vehicle.

[0027] FIG.4 is a diagram depicting top view of the Ornithopter unmanned remote control aerial vehicle.

[0028] FIG. 5 is a diagram depicting bottom view of the Ornithopter unmanned remote control aerial vehicle.

[0029] FIG.6 is a diagram depicting the results of face detection by ESP32 microcontroller with OV3660 camera.

[0030] FIG. 7 is a flow diagram depicting a method for real-time detection, identification, and preservation of data related to intruders or unauthorized individuals using an ornithopter unmanned remote control aerial vehicle.

[0031] FIG.8 is a screen diagram depicting framework for registration verification.

[0032] FIG.9 is a screen diagram depicting the framework for Firebase data storage of registration details.

[0033] FIG. 10 is a screen diagram depicting the dApp (Distributed Application) for uploading the IDS (Intrusion Detection System) data information in Ganache, which stores it as transactions in a block.

[0034] FIG. 11 is a screen diagram depicting the block information in Ganache, which includes the total number of blocks and transactions per block.

[0035] FIG. 12 is a screen diagram depicting the transaction details in Ganache, which displays the hash value for the transaction between the seller and buyer addresses.

[0036] FIG.13 is a flow diagram depicting a method for preserving the IDS (Intrusion Detection System) data in a Blockchain. DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

[0037] It is to be understood that the present disclosure is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The present disclosure is capable of other embodiments and of being practiced or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.

[0038] The use of “including”, “comprising” or “having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced item. Further, the use of terms “first”, “second”, and “third”, andso forth, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another.

[0039] Referring to FIG. 1 is a diagram illustrating a possible virtual circuit-based architecture for a simulator of an Ornithopter unmanned remote control aerial vehicle (URCAV). The diagram showcases several labeled components that may be involved in the simulation of the URCAV and its intrusion detection system (IDS). Label 100 represents the overall image, potentially depicting the Ornithopter URCAV capturing aerial images. The URCAV consists of two wings, labeled as the right wing 102 and the left wing 104, which enable the flapping motion required for flight. Label 106 represents a component named ESP32 microcontroller with OV3660 camera, which could serve as a central control unit and camera module for real-time video streaming and image processing within the simulation. Power to the various components of the Ornithopter URCAV may be supplied by a Lithium Ion battery 108, ensuring uninterrupted operation during flight and data capture. Label 110 indicates a Dh90503 receiver, which may house the necessary electronics and circuitry for flight control and the functioning of the IDS. The movement and stability of the Ornithopter's tail may be controlled by a Coreless Motor 112, allowing for precise maneuvering and flight control.

[0040] For intrusion detection purposes, label 114 represents a component referred to as optimized IMPROVED-YOLOV7 (IDS), which may employ deep learning algorithms to analyze live video footage captured by the camera module and identify potential intruders or suspicious objects. The IDS data could potentially be stored in a blockchain, providing secure and tamper-proof record-keeping. A dedicated Blockchain Server 116 may manage and maintain the blockchain, ensuring data integrity and immutability. In summary, the diagram presents a possible virtual circuit-based architecture for simulating an Ornithopter URCAV. It features labeled components such as the wings, ESP32 microcontroller with OV3660 camera, Lithium Ion Battery, Dh90503 receiver, Coreless Motor, optimized IMPROVED-YOLOV7 (IDS), connecting rods, carbon rods, bamboo sticks, coreless motor with propeller spur gears , gear box and a potential blockchain-based storage system. These components, if implemented, could contribute to the simulation of the URCAV's flight dynamics and real-time intrusion detection capabilities.

[0041] According to the exemplary embodiment, the proposed intruder detection system may incorporate a comprehensive approach to potentially achieve effective surveillance and intrusion detection in remote areas. The system comprises three key modules: a Virtual Circuit based Ornithopter URCAV Simulator, the potential Development of an Intruder Detection system using optimized IMPROVED-YOLOV7, and the integration of Blockchain for potential data storage.

[0042] In the Virtual Circuit based Ornithopter URCAV Simulator module, the URCAV is designed with various components such as gearboxes for potential wing and tail control, a Dh90503 receiver for potential remote operation, a Lithium Ion battery, and other necessary tools. The architecture diagram provides a potential overview of the connections and operations within the URCAV circuit. This module combines both hardware and software expertise to potentially ensure proper flight and control of the URCAV in the target environment.

[0043] Moving to the potential Development of the Intruder Detection system, the system may leverage advanced technologies for potential efficient surveillance. An Ornithopter URCAV is integrated with a potential ESP32 microcontroller with OV3660 camera. The camera captures real-time video footage of the surveillance area, which may be processed using deep learning algorithms, such as IMPROVED-YOLOV7 including dense layer and transition layer of CSPNet along with Gradient Descent optimization for effective facial feature extraction, real- time intruder detection, and localization. This algorithm enables potential real-time object detection and classification, allowing the system to potentially identify intruders or suspicious objects within the captured video frames. When a potential intruder or suspicious object is detected, alerts may be generated. These alerts can potentially be sent to a terminal for potential immediate action or communicated through platforms like Telegram.

[0044] Additionally, the system may incorporate the use of Blockchain for potential data storage. This integration aims to potentially ensure the integrity, transparency, and potential immutability of the captured surveillance data. When a potential intruder or suspicious activity is detected, therelevant data, including potential images or video footage, may be securely stored in a blockchain network. The decentralized and distributed nature of the blockchain may provide a tamper-proof and potentially reliable record of the detected intrusions. Authorized personnel may potentially access and review the stored data for potential investigations or post-analysis purposes.

[0045] By potentially combining the functionalities of the Virtual Circuit based Ornithopter URCAV Simulator, the Intruder Detection system using optimized IMPROVED-YOLOV7, and the potential Blockchain integration, the proposed system offers a comprehensive solution for potential remote area surveillance and intruder detection. It may address the challenges of potentially effectively monitoring and securing remote locations where traditional security measures may be limited. The system may potentially ensure reliable data collection, potential real-time detection, and secure storage, enhancing overall security measures and facilitating investigations in case of potential intrusions or suspicious activities.

[0046] Referring to FIG. 2 is a diagram illustrating the potential components of an Ornithopter unmanned remote control aerial vehicle (URCAV). The diagram showcases labeled parts that may contribute to the functionality of the URCAV. Label 200 represents the overall image of the Ornithopter URCAV, potentially providing an overview of its structure and design. The right wing of the URCAV is labeled as 202, while the left wing is labeled as 204. These wings may enable the flapping motion that allows the Ornithopter to potentially achieve flight. Label 206 corresponds to the component named ESP32 microcontroller with OV3660 camera, which may serve as a central control unit and camera module for capturing real-time video footage. Power to the various components of the URCAV may be supplied by a Lithium-Ion Battery, labeled as 208, potentially ensuring uninterrupted operation during flight. Label 210 represents the Dh90503 Receiver, which may house the necessary electronics and circuitry for controlling the URCAV's potential flight dynamics and operations.

[0047] The movement and stability of the URCAV's tail may be controlled by a Coreless Motor, labeled as 212, potentially enabling precise maneuvering and control. In summary, the diagramshowcases the potential components of an Ornithopter URCAV, including the overall structure (200), right wing (202), left wing (204), ESP32 microcontroller with OV3660 camera (206), Lithium-Ion Battery (208), Dh90503 Receiver (210), Coreless Motor (212), Connecting Rods (214), Carbon Rods (216), Bamboo Sticks (218), Coreless Motor with Propeller (220), Spur Gears (222), and Gear Box (224). These components, if implemented, may work together to facilitate the potential flight and control of the Ornithopter URCAV.

[0048] Referring to FIG. 3 is a diagram presenting the components of a remote-controlled Ornithopter unmanned aerial vehicle (URCAV). The diagram displays labeled parts that contribute to the functionality of the URCAV. Label 300 represents the overall image of the remote-controlled Ornithopter URCAV, providing an overview of its structure and design. The Spur gear, labeled as 302, potentially plays a role in transmitting rotational motion within the URCAV's mechanical system. Label 306 corresponds to the component named Coreless motor, which may provide the necessary power and propulsion for the URCAV's flight. Carbon Rods, labeled as 306, may be utilized as structural elements to provide stability and support to the URCAV's overall framework. The Receiver Board, labeled as 308, may serve as a component for receiving and processing signals from the remote controller, enabling wireless control of the URCAV. Label 310 represents the ESP32 PSRAM Timer Camera (OV3660), which potentially functions as a camera module and a communication interface for transmitting real-time video footage. The Propellers, labeled as 312, may be responsible for generating the necessary lift and thrust required for the URCAV's flight. A 3 channel Remote controller, labeled as 314, may provide the means for a user to wirelessly control the URCAV's movements, such as throttle, yaw, and pitch.

[0049] In summary, the diagram showcases the components of a remote-controlled Ornithopter URCAV, including the overall structure (300), Spur gear (302), Coreless motor (306), Carbon Rods (306), Receiver Board (308), ESP32 PSRAM Timer Camera (OV3660) (310), Propellers (312), and a 3 channel Remote controller (314). These components, if implemented, may work together to facilitate the controlled flight and maneuverability of the remote-controlled Ornithopter URCAV.

[0050] Referring to FIG. 4 is a detailed top view diagram of the Ornithopter unmanned remote control aerial vehicle (URCAV). This view focuses on the topside of the URCAV, showcasing its specific features and components.

[0051] Referring to FIG. 5 is a detailed bottom view diagram of the Ornithopter unmanned remote control aerial vehicle (URCAV). This view focuses on the underside of the URCAV, showcasing its specific features and components.

[0052] Referring to FIG. 6 showcases the results of face detection achieved through the combined capabilities of the ESP32 microcontroller with OV3660 camera. This diagram provides a visual representation of how the system may detect and locate human faces within captured images. The diagram demonstrates the capabilities of the face detection algorithm implemented in the ESP32 microcontroller with OV3660 camera. It illustrates how faces within the images are potentially identified and outlined, suggesting the presence and potential location of detected faces. The integration of advanced image processing and computer vision techniques within the ESP32 microcontroller with OV3660 camera may enable the system to recognize and analyze human facial features in real-time.

[0053] Referring to FIG. 7 is a flow diagram 700 depicting a method for real-time detection, identification, and preservation of data related to human intrusions using an ornithopter unmanned aerial vehicle. The method may commence at step 702, the method involves designing the Virtual Circuit-based Ornithopter URCAV Simulator. This step includes incorporating various functional components, such as wings, ESP32 PSRAM Timer Camera (OV3660), Lithium-Ion Battery, Dh90503 Receiver, and Coreless Motor. The goal is to ensure proper functionality and connectivity, which are essential for achieving realistic flight dynamics and maneuverability in the simulation. At step 704, the method involves ensuring proper functionality and connectivity to enable realistic flight dynamics and maneuverability in the simulation.

[0054] Moving to step 706, the method focuses on integrating an Ornithopter URCAV with an ESP32 microcontroller with OV3660 camera. This integration allows for real-time video streaming and image capture, providing valuable data for the subsequent steps. The connectivity between the URCAV and the camera module enables the system to capture visual information from the environment.

[0055] At step 708, deep learning algorithms are implemented to analyze the live video footage captured by the URCAV's camera module. These algorithms, including optimized IMPROVED- YOLOV7 with CSPNet, may be employed to detect potential intruders or suspicious objects. The integration of these algorithms enhances the system's ability to identify and classify objects in real-time, contributing to effective intrusion detection.

[0056] Proceeding to step 710, the system may be configured to generate alerts upon detecting intrusions or suspicious activities. These alerts can enable immediate action or communication through various communication platforms. By promptly notifying relevant personnel, the system enhances the potential for swift response and mitigation of potential security threats.

[0057] At step 712, the method involves establishing a blockchain network for secure and tamper-proof storage of data related to detected intrusions or suspicious activities. This integration aims to ensure the integrity, transparency, and potential immutability of the captured surveillance data. The decentralized and distributed nature of the blockchain provides a reliable record of the detected intrusions.

[0058] Step 714 focuses on storing relevant data, such as images or video footage, within the blockchain network. This storage process ensures the integrity and transparency of the data, making it resistant to tampering or unauthorized modifications. Storing the data within the blockchain network enhances the system's ability to maintain a reliable and tamper-proof record of detected intrusions or suspicious activities.

[0059] Finally, at step 715, the method involves managing and maintaining the blockchain network with a dedicated Blockchain Server. This server ensures authorized access for investigations or post-analysis purposes. It provides the necessary infrastructure to access and review the stored data, contributing to potential investigations and analysis of detected intrusions or suspicious activities. By following this method, the proposed system aims to achieve effective surveillance and intrusion detection in remote areas. The combination of the Virtual Circuit- based Ornithopter URCAV Simulator, Intruder Detection System, and Blockchain integration provides a comprehensive solution for remote area surveillance, ensuring reliable data collection, real-time detection, and secure storage.

[0060] Referring to FIG. 8, this screen diagram (800) depicts a framework for registration verification within the proposed system. The main aim of this framework is to potentially facilitate the trusted sharing of data among entities in the communication network by utilizing blockchain technology.

[0061] During the initialization phase of the system, the proposed blockchain-based framework may incorporate a registration process, which could play a crucial role in establishing connections solely with authorized entities. In order to potentially join the blockchain network, a user, referred to as 'P,' may be required to undergo registration with administrators. This registration process may grant the user 'P' a unique identity represented by the identity IDi, and they may also be assigned a secure password PWi, as illustrated in Fig 8.

[0062] Through this registration procedure, the user 'P' may be authorized to participate in the blockchain network, potentially ensuring that only trusted and authenticated entities gain access to the shared data. By leveraging blockchain technology in the registration verification process, the system may enhance overall security and integrity in the communication network, fostering a potentially reliable and decentralized environment for data sharing and collaboration.

[0063] Referring to FIG.9, this screen diagram (900) illustrates the framework for Firebase data storage of registration details. The registration process involves creating a mapping list, denotedas { IDi, PWi }, which represents a unique identity IDi and a corresponding password PWi for each user. Upon completion of the registration process, this mapping list { IDi, PWi } is forwarded as a transaction to the Firebase database using the IPFS (Interplanetary File System) technology, ensuring secure and distributed storage of the data.

[0064] Each transaction is recorded and maintained as individual records within the Firebase database, as demonstrated in Fig 9. This approach guarantees the transparency and immutability of the registration data, promoting a trustful environment for users in the framework or blockchain system. The registration page collects essential details from users, and based on this information, the identity of framework or blockchain users is subsequently verified. This verification process ensures that only authorized and authenticated users gain access to the system, enhancing overall security and data integrity within the framework. The combination of Firebase data storage and IPFS technology contributes to the reliability and resilience of the registration verification system, enabling seamless and trustworthy data sharing among the entities in the network.

[0065] Referring to FIG. 10, this screen diagram (1000) presents a dApp (Distributed Application) thoughtfully designed to potentially facilitate the secure uploading of IDS (Intrusion Detection System) data information in Ganache, a blockchain development and testing environment. Building on the previously mentioned registration verification framework, which stores the mapping list { IDi, PWi } as transactions in the Firebase database using IPFS, the dApp showcased in this figure plays a pivotal role in handling IDS data uploads to the Ganache blockchain.

[0066] The user-friendly dApp allows potentially authorized entities, verified through the registration process, to securely upload their IDS data to the Ganache blockchain. By leveraging the inherent decentralized nature of blockchain technology, the IDS data may be distributed across multiple nodes, offering enhanced redundancy and data integrity. Whenever a user initiates the upload process through the dApp, the IDS data is encapsulated into a transaction and may be broadcasted to the Ganache network. Each data upload represents a potential transactionwithin a block, contributing to the blockchain's immutable ledger. This organization of data into blocks enhances the potential security and integrity of the IDS information, making it resilient to tampering and unauthorized access.

[0067] By ingeniously integrating the registration verification framework, Firebase data storage, IPFS technology, and the dApp for IDS data uploads, the proposed system establishes a potentially robust and trustworthy infrastructure for data management and sharing within the communication network. This cohesive ecosystem ensures that potentially authenticated users may have the privilege to upload their IDS data to the blockchain, fostering an environment of trust and confidentiality for sensitive information. Moreover, the decentralized nature of Ganache may further reinforce the potential resilience of the system, offering safeguards against single points of failure and potential cyber threats. The integration of these components, as depicted in FIG. 10, exemplifies a thoughtful and potentially secure approach to handling and storing IDS data within a blockchain network. The proposed system has the potential to foster a reliable and secure environment for communication and collaboration among entities, contributing to a more cohesive and confident network architecture.

[0068] Referring to FIG. 11, this screen diagram (1100) provides insightful glimpses into the block information managed within Ganache, the blockchain development and testing environment. The diagram thoughtfully illustrates the block information, offering essential details that may contribute to the integrity and transparency of the blockchain. It potentially includes crucial information, such as the total number of blocks present within the Ganache blockchain and the number of transactions contained within each individual block. The total number of blocks potentially reflects the chronological sequence of data entries, forming a chain of interconnected blocks. Each block may encapsulate multiple transactions, and the chain ensures the chronological order and potential immutability of data recorded within the blockchain. By depicting the number of transactions per block, FIG. 11 potentially provides insights into the efficiency and activity levels within the Ganache blockchain. This information enables a comprehensive assessment of the blockchain's potential performance and scalability, allowing stakeholders to potentially monitor and optimize the system as needed.

[0069] The utilization of Ganache as the underlying blockchain technology may enhance the security and reliability of the system. Its potential decentralized nature ensures that transactions and blocks are potentially distributed across multiple nodes, thereby reducing the risk of data loss or manipulation. FIG. 11 serves as a valuable reference point for potentially understanding the block information within Ganache. As part of the overall proposed system, it may complement the registration verification framework and the dApp for IDS data uploads, potentially contributing to a cohesive and resilient ecosystem for data management and sharing. The potential transparent and traceable nature of the Ganache blockchain, as depicted in this diagram, may foster a sense of trust and accountability among the entities in the communication network, potentially promoting a secure and collaborative environment for all participants.

[0070] Referring to FIG.12, this screen diagram (1200) thoughtfully provides important insights into the transaction details within Ganache, the blockchain development and testing environment. The diagram potentially showcases the transaction details, with a particular emphasis on the hash value associated with each transaction. In the context of blockchain technology, a hash value serves as a unique identifier that may represent the integrity and immutability of a transaction. It is generated using a cryptographic hashing algorithm, and any alteration to the transaction data may result in a completely different hash value. This feature may enhance tamper-resistance and data security within the system. By displaying the hash value for the transaction between the seller and buyer addresses, FIG. 12 offers a means of potentially verifying the authenticity and accuracy of each transaction. It may allow participants and stakeholders within the communication network to potentially trace and validate the origin of data exchanged between entities, thereby fostering transparency and trust in the system.

[0071] As transactions are potentially securely recorded and stored within the Ganache blockchain, the hash values may play a crucial role in maintaining the integrity of the data throughout the network. Any attempt to potentially alter or manipulate a transaction would be evident through a change in the hash value, alerting the network to possible malicious activities.

[0072] This level of transparency and security, as depicted in FIG.12, may reinforce the overall reliability of the proposed system. It complements the registration verification framework, the dApp for IDS data uploads, and the organization of block information within Ganache, potentially creating a comprehensive and trustworthy ecosystem for data management and sharing.

[0073] FIG. 13 presents a comprehensive flow diagram depicting a method that may be employed to preserve IDS (Intrusion Detection System) data within a Blockchain. The diagram illustrates a series of coordinated steps that, when implemented, may establish a secure and transparent environment for the sharing and management of IDS data among authorized entities. The flow diagram (1300) provides an insightful overview of the entire process, potentially showcasing a systematic approach to safeguarding IDS data in the Blockchain. In the first step (1302), users may initiate the registration process by providing their identity ID (IDi) and corresponding password (PWi) to the administrators responsible for verifying their authenticity. In the subsequent step (1304), the administrators may create a mapping list {IDi, PWi}, which includes the unique identity ID (IDi) and corresponding password (PWi) for each user. This mapping list may be securely stored as a transaction in the Firebase database using IPFS technology.

[0074] At step (1306), following the registration, the user identities may be verified based on the provided registration details, ensuring that only authorized entities may access the system. At step (1308), authorized users may employ a distributed application (dApp) to efficiently upload their IDS data information, facilitating secure and streamlined data sharing. At step (1310), the dApp may seamlessly interact with the Ganache blockchain, potentially storing IDS data as transactions within a block, leveraging the inherent security and decentralization of the blockchain. At step (1312), the blockchain system may diligently maintain block information, keeping track of the total number of blocks and transactions per block within Ganache.

[0075] At step (1314), transaction details may be carefully recorded in Ganache, encompassing the hash value that uniquely represents the transaction between the seller and buyer addresses,thereby ensuring tamper-proof records. At step (1316), These measures may contribute to ensuring that only authorized entities are granted access to share and access IDS data, fostering trust and maintaining confidentiality. At step (1318), IDS data may be securely stored on the blockchain, guaranteeing immutability and data integrity, thus bolstering the overall security of the system. Finally at step (1320), the implementation of this method may empower authorized entities to access the recorded IDS data, potentially facilitating transparent and reliable information sharing, leading to improved collaboration and decision-making. The flow diagram depicted in FIG. 13 presents a systematic process, potentially utilizing various steps and technologies, to preserve IDS data securely within the Blockchain. The potential integration of the registration verification framework, Firebase data storage, IPFS technology, and the dApp for IDS data uploads contributes to the establishment of a robust and trustworthy ecosystem for data management and sharing.

[0076] According to a non-limiting exemplary embodiment, the proposed intruder detection system may utilize a variety of algorithms to enhance its capabilities. These algorithms, such as optimized IMPROVED-YOLOV7 with CSPNet, may be employed for real-time object detection and classification within the recorded video footage. By leveraging deep learning techniques, these algorithms can analyze the video frames and potentially identify intruders or suspicious objects with a high level of accuracy and efficiency. It should be noted that while optimized IMPROVED-YOLOV7 with CSPNet is mentioned as an exemplary algorithm, other similar algorithms may also be incorporated, depending on specific requirements and advancements in technology.

[0077] Algorithm 1: Ornithopter URCAV Simulator Step 1: Gathered the requirements- Double horse receiver board, carbon rods 30 cm x 0.15 cm, gear box, Coreless Motor, 3.7v Lithium-Ion battery, Device based Remote flight controller – 3 channel, ESP 32, OV3660 camera Step 2: DIY Ornithopter mainframe with double horse receiver board (OM) and articulated wingsStep (GM) for Flapping.receiver boardto ESP 32 with Camera OV3660

[0078] Algorithm 2: Intruder Detection Input: Images from the dataset(TR) Output: Vessel Detected Images (Vi) Step 1: Load Image Step 2: Generate low and high level Features using CSPNET and store in Li and Hi. Li, Hi ← CSPNET(TR) Step 3: Features are given to a max pool layer to reduce their spatial resolution. Mi ← MaxPoolLayer(Li, Hi) Step 4: The low and high level combined using feature fusion module(FFM) Fi ← FFM(Li, Hi) Step 5: Multiscale features are generated using Spatial Pyramid Pooling(SPP) Module MMi ← SPP(Fi) Step 6:Ground Truth ObjScore = 0 Step 7: Predicting Objectness score ObjScore ← 1 / (1 + exp(−MMi)) Step 8: If (ObjScorei) == 1 then object is present. Else object is not present Step 9: Finding Bounding Box Coordinate(bx, by, bw, bh) bx ← (sigmoid(tx) + cx) / S by ← (sigmoid(ty) + cy) / S bw ← (pw ∗ exp(tw)) / S bh ← (ph ∗ exp(th)) / Stx ← (x − xa) / wa ty ← (y − ya) / ha tw ← log(h / wa) th ← log(h / ha) cx ← (i + 0.5) ∗ stride cy ← (j + 0.5) ∗ stride S ← min(inputsize / featuremapsize) Step 10: Finding Class Probablities for j = 1 to S (pi ← exp(pi) / sum(exp(pj ))) Step 11: Loss Calculation based on anchor box assigned to an object, calculate its IoU with the corresponding ground truth box. Threshold = 0.5 Iouvalue ← IOU(anchor box, groundtruthbox) Step 12: If Iouvalue > Threshold then it is true positive and correctly predicted. Step 13: Non-maximum suppression(NMS) to remove redundant detections.

[0079] According to another exemplary embodiment, the proposed solution may involve the integration of the Intruder Detection System with an ornithopter, which requires careful engineering and design to achieve stable and controlled flight. The ornithopter itself may feature a wingspan of approximately 1 meter, with lightweight symmetrical wings designed to mimic the curvature of a bird's wing. To enable the flapping motion necessary for flight, the ornithopter may incorporate a complex system of pulleys, gears, and motors.

[0080] The integration of the Intruder Detection System with the ornithopter involves utilizing the specified components shown in figure 1 listed in Table 1 to construct the proposed architecture. Key components of the system include the ESP32 microcontroller with OV3660 camera, which work together to facilitate face recognition and verification against stored facial data in the cloud. By combining these elements, the system aims to detect and identify unauthorized individuals. A detailed breakdown of the system's parts can be found in the accompanying table 1.

[0081] TABLE 1: COMPONETS AND THEIR FUNCTIONALITY Component Configuration Functionality d e, ut or g at a g e d er ol g ts e 2 at yRemote 3-channel Device It gives the user improved control over the he e d ’s - le d o

[0082] According to the exemplary embodiment, the proposed invention offers a range of distinct advantages over alternative devices or processes with similar objectives. First and foremost, the system combines mobility and versatility by integrating the intruder detection capabilities into an Ornithopter URCAV. This enables the system to effortlessly navigate and surveil remote areas that are typically challenging to access using conventional security measures.

[0083] One key advantage is its ability to significantly reduce the detection rate. The Ornithopter URCAV's design, resembling a bird's flight mechanism, minimizes the chances of potential intruders noticing the surveillance device. This inconspicuous appearance allows for discreet monitoring, enhancing the system's effectiveness in detecting unauthorized individuals or activities. Real-time video streaming and analysis form another critical advantage. By leveraging advanced technologies, the captured live video footage is instantly streamed and analyzed in real time. This enables prompt detection and response to potential security threats, ensuring a proactive approach to intrusion prevention. The incorporation of deep learning algorithms furtherenhances the system's capabilities. These algorithms analyze the video footage, identify patterns, and accurately detect intruders or suspicious activities. Over time, the system continues to improve its detection performance through the continuous learning and refinement of these algorithms.

[0084] To ensure secure and tamper-proof data management, the system integrates blockchain technology. This feature guarantees the integrity and transparency of captured data, including video footage, detection results, and alerts. Storing this information on a blockchain network provides an immutable record, reinforcing the reliability and trustworthiness of the system's surveillance data. In summary, the intruder detection system excels by combining mobility and versatility, reducing the detection rate, facilitating real-time video streaming and analysis, utilizing advanced deep learning algorithms, and implementing secure blockchain-based data management. These advantages position the invention as a superior solution for remote surveillance and intruder detection, surpassing conventional devices or processes in terms of performance, functionality, and reliability.

[0085] According to the exemplary embodiment, the invention opens up numerous applications across various industries. In the field of security and surveillance, the system finds immediate use in critical infrastructure protection, industrial sites, military installations, and remote areas where conventional security measures face limitations. Its real-time detection and alert capabilities enhance security measures and enable rapid responses to potential threats. Law enforcement agencies can benefit from the system's capabilities in monitoring public spaces, border control, and crime prevention. The ability to detect intruders and suspicious activities empowers law enforcement personnel with improved situational awareness and early intervention. Wildlife protection efforts can leverage the invention to monitor and safeguard wildlife habitats. By detecting intrusions and potential threats, it aids in preventing illegal activities such as poaching or habitat destruction, contributing to conservation initiatives.

[0086] In disaster management scenarios, the system plays a crucial role by providing real-time monitoring of affected areas. It assists in search and rescue operations by identifying intruders or individuals in distress, enabling faster and more effective response. Environmental monitoringinitiatives can utilize the system to detect and combat illegal activities in remote and environmentally sensitive areas. Timely alerts enable intervention against logging, mining, pollution, and other detrimental actions, helping to preserve natural resources. Looking ahead, as technology advances, the invention's applications may expand further. It holds potential for smart city surveillance, integration with emerging technologies like artificial intelligence and augmented reality, and collaborative surveillance operations involving drones and other devices. In summary, the invention's applications span multiple industries, including security, law enforcement, wildlife protection, disaster management, environmental monitoring, and future advancements. Its integration of advanced technologies makes it a versatile and powerful tool for enhancing safety, protection, and situational awareness in various contexts.

[0087] Reference throughout this specification to “one embodiment”, “an embodiment”, or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrases “in one embodiment”, “in an embodiment” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0088] Although the present disclosure has been described in terms of certain preferred embodiments and illustrations thereof, other embodiments and modifications to preferred embodiments may be possible that are within the principles and spirit of the invention. The above descriptions and figures are therefore to be regarded as illustrative and not restrictive.

[0089] Thus the scope of the present disclosure is defined by the appended claims and includes both combinations and sub-combinations of the various features described hereinabove as well as variations and modifications thereof, which would occur to persons skilled in the art upon reading the foregoing description.

Claims

CLAIMS 1. An ornithopter unmanned remote control aerial vehicle (URCAV) for intruder detection and data preservation comprising: a right wing and a left wing enabling flapping motion for ornithopter URCAV; a central control unit and camera module incorporated within the URCAV for real-time video streaming and image processing; an intruder detection system comprising a receiver board housing electronics configured for analyzing real-time video footage captured by the camera module for intruder or suspicious object detection; a blockchain server configured to secure and tamper-proof storage of data related to detected intrusions or suspicious activities.

2. The ornithopter URCAV as claimed in claim 1, further comprising a Lithium-Ion Battery to supply power to the various components of the URCAV, ensuring uninterrupted operation during flight and data capture.

3. The ornithopter URCAV as claimed in claim 1, wherein the intruder detection system employs deep learning algorithms to analyze the real-time video footage captured by the camera module.

4. The ornithopter URCAV as claimed in claim 1, wherein the blockchain server utilizes a decentralized and distributed network to securely store and maintain the data related to detected intrusions or suspicious activities.

5. The ornithopter URCAV as claimed in claim 1, wherein the receiver board further comprises flight control electronics and circuitry for ensuring proper flight dynamics and maneuverability of the URCAV.

6. The ornithopter URCAV as claimed in claim 1, wherein the central control unit is an ESP32 PSRAM timer camera (OV3660) capable of capturing high-resolution video footage for accurate intruder detection.

7. The ornithopter URCAV as claimed in claim 1, wherein the intruder detection system 1utilizes the optimized IMPROVED-YOLOV7 algorithm with CSPNet for real-time object detection and classification within the captured video footage.

8. The ornithopter URCAV as claimed in claim 1, further comprising a Coreless Motor to control the movement and stability of the URCAV's tail, enabling precise maneuvering and flight control.

9. The ornithopter URCAV as claimed in claim 1, wherein the intruder detection system generates alerts upon detecting intrusions or suspicious activities, facilitating immediate action or communication to relevant personnel.

10. A method for real-time intruder detection, identification and data preservation using an ornithopter unmanned remote control aerial vehicle (URCAV), comprising the steps of: a. Designing the Virtual Circuit-based Ornithopter URCAV Simulator by incorporating various functional components; b. Integrating an Ornithopter URCAV with an ESP32 microcontroller and OV3660 camera module for real-time video streaming and image capture; c. Implementing deep learning algorithms to analyze live video footage and detect potential intruders or suspicious objects; d. Configuring the system to generate alerts upon detecting intrusions or suspicious activities, enabling immediate action or communication through different communication platforms; e. Establishing a blockchain network for secure and tamper-proof storage of data related to detected intrusions or suspicious activities; f. Storing relevant data, such as images or video footage, within the blockchain network to ensure integrity, transparency, and immutability; g. Managing and maintaining the blockchain network with a dedicated Blockchain Server, providing authorized access for investigations or post-analysis purposes. 2

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