Every day, innumerable items are lost and unclaimed in shopping malls, restaurants, airports, and other public places. While some lost and found systems exist, they are often non-automated, poorly structured, and vulnerable to data loss. We present a blockchain- and AI-based platform that integrates Internet of Things (IoT) for real-time tracking and zero-knowledge proofs (ZKPs) for privacy-preserving verification. In this platform, users can report lost or found items, for which information hashes are generated and then stored on the blockchain to ensure immutability, transparency, and trust. Artificial intelligence is used to compare lost items with potential found items to reduce the complexity of searching. To evaluate the AI component, we used a transfer learning technique with pre-trained CNN models, namely ResNet50, VGG16, and MobileNetV3, on the Caltech-256 dataset filtered to 10 relevant classes (1,219 images), attaining 95.46% ±1.09% accuracy in 5-fold cross-validation for ResNet50 without augmentation, 93.99% ±3.44% on holdout test, and 94.54% ±3.29% under Gaussian blur for robustness. Feature embeddings yielded top-1 matching accuracy of 89.01% and top-5 of 95.60%, outperforming recent image-matching baselines in noisy real-world conditions while maintaining sub-0.0003 s inference time. These results establish a scalable, trustworthy global ecosystem for lost-and-found management
Hari Suresh Babu Gummadi, Mohan Sankaran, R. D. Shelke, Venkata Siva Kumar Tankani · 6 authors
In the advancing domain of drone systems, cybersecurity is a critical issue owing to the rising threat of advanced cyberattacks. This study presents an innovative framework for drone cybersecurity that utilizes the integration of deep learning and blockchain technologies to efficiently detect and prevent malicious intrusions. The proposed architecture consists of four main stages: data normalization, feature selection utilizing the greylag goose optimization algorithm (GLGOA), long short-term memory (LSTM)-based cyberattack detection, and blockchain-based data validation. Initially, raw drone sensors and network data are standardized using normalization techniques to ensure consistency and minimize noise. GLGOA is utilized to extract the most pertinent features, thereby improving detection efficiency and reducing computational burden. The enhanced feature set is input into an LSTM model designed to capture temporal dependencies and classify potential cyber threats. Ultimately, blockchain integration guarantees the immutable recording of drone interactions and improves overall data security and reliability. Comprehensive experimental assessment illustrates the superiority of the proposed GLGOA-LSTM-BC model compared to traditional methods such as SVM, random forest, CNN, and GRU regarding -score. The proposed method demonstrates a 97.8% accuracy and a 97.5% f1-score, establishing it as a robust and reliable solution for real-time cyberattack detection in drone environments. The amalgamation of bio-inspired optimization, deep learning, and distributed ledger technologies facilitates the development of secure, intelligent, and autonomous drone systems within contemporary digital infrastructure.
Blockchain technology consists of distributed ledgers or database systems, regarded as immutable, secure, and innovative, characterized by unsupervised internal maintenance with a special security protocol used to prevent inference from malicious or third parties. The widespread use of this technology has led to deep research into the problems posed by this technology, which can be summarized in terms of computational cost and latency time. The crime detection process in video surveillance has made great progress with the use of technologies such as the Internet of Things and blockchain technologies. However, to reach high levels of security in the physical crime detection process in which data are sent to servers via a computer network, there must be a high degree of security for Internet of Things systems related to the crime detection process. There has been a significant increase in the number of problems associated with crime detection in video surveillance systems, including the modification of surveillance data during transfer to and from servers. For this reason, establishing a reliable and secure system for transferring video surveillance data to servers has become a high priority. This paper presents a lightweight security system to protect data generated in the crime detection process, both from video surveillance cameras and the servers that store these data. The challenges related to IoT-based video surveillance cameras and monitoring and control centers have been considered, turning the system primarily into a decentralized system. In this paper, a lightweight blockchain system based on a proof of secret share consensus algorithm technology is proposed, along with the encryption of surveillance data via modified Okamoto–Uchiyama homomorphic encryption technology. The proposed system is evaluated via standard blockchain and security evaluation metrics, demonstrating efficient utilization of computational costs and realization of security, with a high scalability rate. The VGG16 deep learning model is employed in the proposed system to detect and classify criminal activities in surveillance videos. Owing to its ability to identify patterns and anomalies, the model achieved an accuracy of 94%, demonstrating a high level of performance in crime detection and prevention. Overall, the use of VGG16 provides an efficient and reliable approach for improving the security of public spaces and reducing the risk of criminal activity.
Unmanned aerial vehicles (UAVs) are one of the most popular and effective systems in various industrial applications such as surveillance, security, and infrastructure inspection. It is gradually becoming an essential part of navigation as a consequence of high progress in military and civilian missions. Path planning of UAVs in military and civilian missions or in unknown and restricted environments is one of the biggest problems facing the operation of UAVs. This problem is not only searching for a path from an initial point to the final but also linked to find an optimal among all possible paths and provides collision avoidance. By examining the best path for UAVs, there is a need for the consideration of various other issues such as security and privacy, turning angle, overtake speed of obstacle, etc. The fundamental problem of UAVs is finding an optimal and secure route in a challenging environment. To overcome these challenges, many researchers have used optimization techniques such as ant colony, particle swarm, artificial bee colony, etc. with planning and coordination. In this paper, a blockchain-based solution is used to secure and authenticate UAVs. Hence, we propose a blockchain-based method that uses a genetic algorithm, which solves both constrained and unconstrained optimization problems. The purpose of this technique is to locate the best possible flight path for the UAVs in a three-dimensional setting. In a genetic algorithm, each iteration is designed to surpass the previous one in terms of improvement. To achieve an ideal route, solving the travelling salesman problem is a crucial step in the proposed approach. Consequently, the blockchain technology offers a reliable wireless communication and a dependable network for UAVs path planning, guaranteeing efficient service. Simulation results demonstrate the impact of the proposed scheme. They show that a genetic algorithm is suitable for optimal path planning for UAVs.
Integrating the unmanned aerial vehicles (UAVs) assisted mobile edge computing (MEC) network with the blockchain technology emerges its superiority in the network utilization, differentiated service, and security, which has been regarded as a promising technique for time-critical applications. In this paper, we propose a UAV-assisted MEC network architecture and a comprehensive data processing flow, where the UAVs cooperate with the base station in computation as edge servers and act as blockchain nodes. We formulate an optimization problem that jointly considers UAVs’ position, data offloading, and resource allocation for minimizing the total time consumption of data processing. To address this problem, we decouple it as three tractable subproblems and propose a Block Coordinate Descent (BCD)-based iterative algorithm. In addition, we analyze the task migration and resource allocation problem in computation, and obtain analytical solutions by the Karush-Kuhn-Tucker (KKT) conditions. The simulated results indicate that the proposed algorithm leads to substantial performance gains.
Mohamed El Amine Kheraifia, Abdelatif Sahraoui, Makhlouf Derdour
The video surveillance system is a key component of the technologies deployed in smart cities. It serves a variety of applications, including public safety, crime prevention, traffic management, and environmental monitoring. The data captured by these systems includes sensitive information related to privacy, crime and national security, requiring robust protection against data breaches to ensure confidentiality. In this paper, we introduce a video fingerprinting-based method that uses a timestamp and device number, intended to prevent and detect image manipulation or replacement of original images with copies during transmission and of receiving the monitored data. Additionally, we propose the use of a blockchain system with an immutable distributed ledger for traceability and auditing of authentication procedures.
Advanced Steganography and Watermarking Techniques
Abstract This paper presents a novel method for decentralized storage in deep-learning-based face recognition systems using the Hierarchical Navigable Small World (HNSW) algorithm. The proposed solution utilizes Ethereum smart contracts, which acts as highly available data storage systems for storing identifiable data for authorized personnel. In addition, the solution is integrated with a centralized vector database that is in charge of vector indexing, searching and associating face embeddings to an identity on the Ethereum blockchain with anonymous hashes. Vector indexing and search processes involve different machine learning algorithms that enable computations to be carried out in a reasonable time with good matching accuracy. Specifically, we compared different approaches and selected the HNSW algorithm. Accordingly, we successfully implemented a prototype of a reliable and privacy-focused decentralized face identification system for areas under government surveillance, such as customs inspection sites. In our measurements, the system could handle 20,000 face vectors easily with high matching accuracy, and the performance could be further improved using more powerful hardware. Finally, we also propose additional methods to further scale up the system to handle millions of face vectors.
Vehicle re-identification (ReID) is a hot topic in intelligent city surveillance. With the development of smart cameras and vehicular edge computing (VEC), numerous media data has opened up new possibilities for enhancing the applications of vehicle ReID. However, traditional vehicle re-identification systems face the following challenges: 1) it is difficult to recognize the identities of the vehicles in various views and similar appearance, 2) the current system is hard to be extended to large-scale of cameras in a low-trust VEC environment. To solve these problems, we propose a Blockchain-based Collaborative Vehicle ReID (BCV-ReID) system in this paper. It contains two core parts including Viewpoint-identity Query Net (VQNet) and VehicleChain (VChain). By utilizing the viewpoint information and local details simultaneously, VQNet can distinguish the vehicle identities in various cross-camera scenes. It employs viewpoint queries and spatial self-attention to learn the inherent correlation of the vehicle parts, enhancing the ability to distinguish vehicles among various viewpoints. Then, we integrate VChain with VQNet to realize a collaborative vehicle ReID system. The ReID task is illustrated from the perspective of blockchain transactions. All transactions are validated by a deeply integrated ReID consensus to counter potential malicious attacks. Experiments show that the proposed method achieves comparable results in three famous ReID datasets, as well as outstanding performance in real applications.
Video Surveillance and Tracking Methods
Advanced Neural Network Applications
Advanced Steganography and Watermarking Techniques
With the development of smart cities, video surveillance has become more prevalent in urban areas. The rapid growth of data brings challenges to video processing and analysis. Multi-object tracking (MOT), one of the most fundamental tasks in computer vision, has a wide range of applications and development prospects. MOT aims to locate multiple objects and maintain their unique identities by analyzing the video frame by frame. Most existing MOT frameworks are deployed in centralized systems, which are convenient for management but have problems such as weak algorithm adaptability, limited system scalability, and poor data security. In this paper, we propose a distributed MOT algorithm based on multi-agent reinforcement learning (DMARL-Tracker), which formulates MOT as a Markov decision process (MDP). Each object adjusts its tracking strategy during interactions with the environment. The benchmark results on MOT17 and MOT20 prove that our proposed algorithm achieves state-of-the-art (SOTA) performance. Based on this, we further integrate DMARL-Tracker into the blockchain and propose a blockchain-based collaborative MOT framework. All nodes collaborate and share information through the blockchain, achieving adaptation in different complex scenarios while ensuring data security. The simulation results show that our framework achieves good performance in terms of tracking and resource consumption.
The modern warfare scenario has immense challenges that can risk personnel's lives, highlighting the need for data acquisition to win a military operation successfully. In this context, unmanned aerial vehicles (UAVs) play a significant role by covertly acquiring reconnaissance data from an enemy location to make the friendly troops aware. The acquired data is mission-critical and needs to be secured from the intruders, which can implicitly manipulate it for their benefit. Moreover, UAVs collect a large amount of data, including high-definition images and surveillance videos; handling such a massive amount of data is a bottleneck on traditional communication networks. To mitigate these issues, this article proposes a blockchain and machine learning (ML)-based secure and intelligent UAV communication underlying sixth-generation (6G) networks, that is, Block-USB. The proposed system refrain the disclosure of highly-sensitive military operations from intruders (either a rogue UAV or a malicious controller). The proposed system uses off-chain storage, that is, Interplanetary file system (IPFS), to improve the blockchain storage capacity. We also present a case study on securing UAV-based military operations by considering multiple scenarios considering controller/UAV malicious. The performance of the proposed system outperforms the traditional baseline 4G/5G and non IPFS-based systems in terms of classification accuracy, communication latency, and data scalability.
Shuai Wang, Hao Sheng, Yang Zhang, Da Yang · 6 authors
The rapid increase in the volume of video data generated from edges in the Industrial Internet of Things, opens up new possibilities for enhancing the application of video service. Multicamera multiobject tracking (MCMT) has always been a fundamental task in video surveillance or traffic control. However, the traditional MCMT methods are limited by the communication bottleneck and computation resources of the centralized curator, and suffer from security and privacy issues. In this article, we first design multicamera multihypothesis tracking (MC-MHT) framework to achieve real-time tracking performance among edge cameras. The complex association of objects is described by multiskip trees. The tracking task is well distributed to each camera. Then, we integrate multicamera tracking chain into MC-MHT to ensure security and trust. The state transition of targets in multicamera is illustrated from the perspective of blockchain transactions. The transactions are validated by an integrated tracking consensus to counter Byzantine behavior. Numerical results derived from real-world scenarios and CAMPUS dataset show that the proposed method achieves real-time performance (24–36 FPs) and 79.0–82.4 MOTA indicator, as well as reduces identity switch errors about 71% under Byzantine attack.
Abdullah Aljumah, Tariq Ahamed Ahanger, Imdad Ullah
Unmanned aerial vehicles, drones, and internet of things (IoT) based devices have acquired significant traction due to their enhanced usefulness. The primary use is aerial surveying of restricted or inaccessible locations. Based on the aforementioned aspects, the current study provides a method based on blockchain technology for ensuring the safety and confidentiality of data collected by virtual circuit-based devices. To test the efficacy of the suggested technique, an IoT-based application is integrated with a simulated vehicle monitoring system. Pentatope-based elliptic curve encryption and secure hash algorithm (SHA) are employed to provide anonymity in data storage. The cloud platform stores technical information, authentication, integrity, and vehicular responses. Additionally, the Ethbalance MetaMask wallet is used for BCN-based transactions. Conspicuously, the suggested technique aids in the prevention of several attacks, including plaintext attacks and ciphertext attacks, on sensitive information. When compared to the state-of-the-art techniques, the outcomes demonstrate the effectiveness and safety of the suggested method in terms of operational cost (2.95 units), scalability (14.98 units), reliability (96.07%), and stability (0.82).
T.S. Arulananth, Bittu Kumar, Kiran Dasari, S. V. S. Prasad · 6 authors
Other material endeavors hit during these challenging intervals, as the overwhelming majority of things moved to virtual mode and challengers attempted to advance beyond one. Every small material shop can assist their customers with an internet-based trial room with a virtual trial room without danger of contracting the illness. The advantage of using this method is that it saves time and effort to evaluate the clothing accurately. Everything, from food to apparel, is now available online. Instead of travelling to shopping malls and textile stores, where they have to try on items in a trial room to see if they fit correctly and how they appear on themselves, they now go to the internet. Furthermore, most malls have a limited number of trial rooms that are overcrowded. As a result, people have to wait a long time, which is inconvenient. Trial rooms are not so secure that no one can be certain that hidden cameras are not present. As a result, using these online buying websites is extremely safe. People can simply choose from a variety of outfits. On the other hand, people do not know how a piece of clothing will appear on them until it is brought to them. People can try on their items by just selecting the goods and seeing how it appears on them. As a result, this article uses python to create a “Smart Trial Room” that allows users to try on garments virtually. This “Smart Trial Room” project comprises a homepage with several clothing-related products comparable to an e-commerce website. HTML and CSS were used to create the website. Users can choose a product they like, and it will launch a webcam that will show them how the thing looks on them. Multiple people can also try the product at the same time. The major goal of this experiment is to show that greater interaction features in clothes websites can increase online sales.
On the Internet of Battlefield Things (IoBT), unmanned aerial vehicles (UAVs) provide significant operational advantages. However, the exploitation of the UAV by an untrustworthy entity might lead to security violations or possibly the destruction of crucial IoBT network functionality. The IoBT system has substantial issues related to data tampering and fabrication through illegal access. This paper proposes the use of an intelligent architecture called IoBT-Net, which is built on a convolution neural network (CNN) and connected with blockchain technology, to identify and trace illicit UAV in the IoBT system. Data storage on the blockchain ledger is protected from unauthorized access, data tampering, and invasions. Conveniently, this paper presents a low complexity and robustly performed CNN called LRCANet to estimate AOA for object localization. The proposed LRCANet is efficiently designed with two core modules, called GFPU and stacks, which are cleverly organized with regular and point convolution layers, a max pool layer, and a ReLU layer associated with residual connectivity. Furthermore, the effectiveness of LRCANET is evaluated by various network and array configurations, RMSE, and compared with the accuracy and complexity of the existing state-of-the-art. Additionally, the implementation of tailored drone-based consensus is evaluated in terms of three major classes and compared with the other existing consensus.
Trustworthy and real-time video surveillance aims to analyze the live camera streams in a privacy-preserving manner for the decision-making of various advanced services, such as pedestrian reidentification and traffic monitoring. In recent years, edge computing has been identified as a promising technology for trustworthy and real-time video surveillance because it keeps confidential video data locally and reduces the latency caused by massive data transmission. Generally, a single edge device can hardly afford the computation-intensive video analytics tasks. Most existing solutions incorporate cloud servers to handle the overloaded tasks. However, such an edge-cloud collaboration approach still suffers from unpredictable latency and privacy concerns because the remote cloud is centralized and distant from the cameras. In this work, we designed a blockchain-based collaborative edge intelligence (BCEI) approach for trustworthy and real-time video surveillance. In BCEI, geo-distributed edge devices form a peer-to-peer network to maintain a permissioned blockchain and share data and computation resources to perform computation-intensive video analytics tasks. The video analytics results are written on the blockchain in an immutable manner to guarantee trustworthiness. To reduce task execution time, we formulate and solve a joint stream mapping and task scheduling problem to schedule video streams and machine learning models among edge devices. A pedestrian reidentification prototype is implemented and deployed based on BCEI with the extensive performance evaluation, indicating the superiority of BCEI in latency reduction and system throughput improvement by leveraging collaboration among edge devices.
Unmanned Aerial Vehicle (UAV) communications have recently entered a new period of interest, motivated by technological advances and the gradual emergence of the Space-Air-Ground Integrated Network (SAGIN). The current survey aims to capture the use of UAVs in the SAGIN while highlighting the most promising open research topics. The traditional UAV network architecture is not adequate to meet the challenges presented by the SAGIN, and an effective and secure space-air-ground integrated UAV network needs to be constructed. Given its well-distributed management and consensus mechanism, blockchain technology can make up for the deficiency of the traditional UAV network. In this work, we review the role of UAVs in the SAGIN. Then, three applications of the blockchain-envisioned UAV network are introduced through several classifications. Future challenges and the corresponding open research topics are also described.
In this research work and unmanned aerial vehicle (UAV) that uses blockchain methodology to collect health data from the users and saves it on a server nearby is introduced. In this paper the UAV communicates with the body sensor hives (BSH) through a low-power secure manner. This process is established using a token with which the UAV establishes relationship with the BSH. The UAV decrypts the retrieved HD with the help of of the shared key, creating a two-phase authentication mechanism. When verified, the HT is transmitted to a server nearby in a safe manner using blockchain. The proposed healthcare methodology is analysed to determine its feasibility. Simulation and implementation is executed and a performance of the work is observed. Analysis indicates that the proposed work provides good assistance in a secure environment.
The COVID-19 pandemic situation has proved to be disastrous for humanity throughout the world. However, during this period, people must take precautions for safety purposes. One of the essential steps towards eliminating or reducing the effect of COVID-19 is maintaining social distancing while in public places. Some people are neglecting the social distancing norms while on the move. Still, no surveillance system exists, which monitors the people’s movement for social distancing and securely & efficiently shares the information with the concerned administration department. There also exists no penalty system which forces the people to ensure social distancing. Motivated from the aforementioned facts, in this paper, we present a blockchain and artificial intelligence (AI)-envisioned scheme for monitoring social distancing to combat COVID-19 situations. The proposed scheme uses fast region-based convolutional neural networks (RCNN) and you only look once (YOLO) models for the object (i.e., human) detection through the live video feed captured from the static CCTV cameras as well as lens-equipped drones. Further, the efficient euclidean distance calculation is embedded for calculating the distance between two humans. Blockchain technology ensures the secure and trusted exchange of information between the entities at the physical layer and the administration departments. Blockchain wallets are also used to pay the fine when people do not follow social distance norms. The performance of the proposed scheme is evaluated based on three broad parameters such as (i) human detection and violation identification, (ii) blockchain simulation and analysis, and (iii) network performance comparison. The parameters considered for (i) is confidence score, for (ii) are scalability, hash rate, and simulation interface, and for (iii) are network bandwidth, throughput, packet loss rate, and network latency. By analyzing all the parameters mentioned above, we observe the proposed scheme outperforms the traditional approaches.