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
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.
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.
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.
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).
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.
Abstract With the exponential growth in the number of vital infrastructures such as nuclear plants and transport and distribution networks, these systems have become more susceptible to coordinated cyberattacks. One of the effective approaches used to strengthen the security of these infrastructures is the use of unmanned aerial vehicles (UAVs) for surveillance and data collection. However, UAVs themselves are prone to attacks on their collected sensor data. Recently, blockchain (BC) has been proposed as a revolutionary technology that can be integrated within Internet of things (IoT) to provide a desired level of security and privacy. However, the integration of BC within IoT networks, where UAV's sensors constitute a major component, is extremely challenging. The major contribution of this study is twofold:(1) survey the security issues for UAV's collected sensor data, define the security requirements for such systems, and identify ways to address them; and (2) propose a novel BC‐based solution to ensure the security of and the trust between the UAVs and their relevant ground control stations. Our implementation results and analysis show that using UAVs as means for protecting critical infrastructure is greatly enhanced through the utilization of trusted BC‐based unmanned aerial systems.
Information from surveillance video is essential for situational awareness (SAW). Nowadays, a prohibitively large amount of surveillance data is being generated continuously by ubiquitously distributed video sensors. It is very challenging to immediately identify the objects of interest or zoom in suspicious actions from thousands of video frames. Making the big data indexable is critical to tackle this problem. It is ideal to generate pattern indexes in a real-time, on-site manner on the video streaming instead of depending on the batch processing at the cloud centers. The modern edge-fog-cloud computing paradigm allows implementation of time sensitive tasks at the edge of the network. The on-site edge devices collect the information sensed in format of frames and extracts useful features. The near-site fog nodes conduct the contextualization and classification of the features. The remote cloud center is in charge of more data intensive and computing intensive tasks. However, exchanging the index information among devices in different layers raises security concerns where an adversary can capture or tamper with features to mislead the surveillance system. In this paper, a blockchain enabled scheme is proposed to protect the index data through an encrypted secure channel between the edge and fog nodes. It reduces the chance of attacks on the small edge and fog devices. The feasibility of the proposal is validated through intensive experimental analysis.