Mohamed Hasan Omar, Islam Taj-Eddin, Nagwa M. Omar, Hosny Ibrahim
Traffic road violations are increasing continuously in crowded and big cities, which requires an automatic system for monitoring and detecting. This system should be accurate and secure against poisoning attacks that intend to delete some or all traffic violations. Accordingly, this study proposes a secure road traffic management system using the Internet of Things (IoT) and blockchain technology. The system uses a network of sensors, traffic signals, and cameras to track violating vehicles and record their violations in the blockchain. In the proposed system, we used deep learning models to recognize the vehicle identification number and type of traffic violations, and we stored the traffic violation data on the Ethereum test network. The proposed system consists of three steps: (1) vehicle information detection. (2) Type of violation detection. (3) Violation storage in the blockchain. The proposed secure road traffic management system uses blockchain, IoT, encryption, and authentication to increase violation detection and recording, processing speed, and communication delay, ultimately improving service quality and customer experience. The experimental results show that the proposed system is more accurate and secure than the other systems that have been proposed in recent research.
Decentralized finance is a revolutionary change in the financial system, using blockchain technology to build a diverse and open network of financial services. By cutting out middlemen, DeFi promotes financial equality and reaches out to more people. Smart contracts can complete transactions without the need for traditional banks to lower costs and improve efficiency in lending, borrowing, trading, and yield farming activities. The proposed research compares four leading DeFi lending protocols: AAVE, MAKERDAO, COMPOUND, and VENUS Finance. We have used Long Short-Term Memory (LSTM) neural networks to analyze historical data and measure key parameters, such as lending and borrowing rates, Total Value Locked (TVL), Market Capitalization, and token price dynamics. We found that AAVE and COMPOUND exhibit similar mean rates but AAVE offers more precise predictions. MAKER provides potentially higher returns but with a higher degree of unpredictability. VENUS, despite its precise predictions, yields the lowest returns due to its lower mean lending rate. Overall, the approach enhances the understanding of the dynamics within the DeFi ecosystem, helping stakeholders to make informed decisions. Index Terms—Decentralized, Blockchain, LSTM, Time Series Analysis.
Elnaz Radmand, Jamshid Pirgazi, Mahmoud Odeh, Ali Ghanbari Sorki
Deep learning is now a top method for predicting the price of Bitcoin, providing an excellent and well thought out strategy. This technique makes use of a wealth of historical data from reliable sources covering the first ten years of Bitcoin. Technical data and complex patterns are deeply integrated into the dataset to enhance the model's comprehension of complex Bitcoin price patterns. These additions greatly deepen our understanding of the various factors affecting the price of Bitcoin. This method is based on a strategic procedure that involves the careful selection of certain high-performing characteristics. These traits go through a stringent screening process that uses advanced algorithms such as the Cuckoo algorithm. The chosen features are then smoothly incorporated into an encoder-decoder model of LSTM (Long Short-Term Memory), improving the model's ability to predict Bitcoin prices. In this case, it is critical to apply deep learning methods, especially the LSTM encoder-decoder model. Through the integration of technical indicators and sophisticated selection techniques with historical Bitcoin data, this approach leverages deep learning capabilities to identify intricate patterns in Bitcoin pricing movements. In the end, this gives the model the ability to forecast future changes in the price of Bitcoin with accuracy.
During rush hours in modern cities, drivers often encounter no available parking spaces and spend 5 to 30 minutes searching for parking spaces. During this time, not only is driver’s time wasted but air pollution and traffic jams are exacerbated. This is because people cannot immediately check whether there are empty spaces in other parking lots. However, parking lots are controlled by a centralized server, bringing the risk of a single point of failure and potential attacks from internal and external parties. This paper proposes a smart parking space certificate management system based on blockchain technology. this system mainly uses the Ethereum alliance chain to establish a parking space certificate management system. This allows data to be securely and decentralized stored on the chain. It then allows parking lot operators to interact with consumers through decentralized application (DApp) to save time finding parking spaces. The transaction process utilizes smart contract technology to automate delivery and save the digital signatures of parking space certificates. This realizes a decentralized and transparent system of management of parking space information.
Manjula K. Pawar, Harshitha S Hiregowdar, Sukruti Joshi
This research paper presents the design and implementation of a land registry system using multiple nodes with Geth, a command-line interface for running Ethereum nodes. The system is designed to address the issues of security and efficiency in the current land registration system. The system uses a distributed database with multiple nodes to store land records, providing redundancy and fault tolerance. The use of a distributed database also ensures that no single point of failure exists, improving the system's reliability. To ensure that the land records are impenetrable and cannot be changed without permission, the system makes use of the Ethereum blockchain. A consensus mechanism is used to implement the blockchain, ensuring that every node on the network concurs with the database's current state. The use of a consensus algorithm ensures that the system is secure and resilient to attacks. To automate the transfer of land ownership, the system also includes a smart contract framework. The smart contract system ensures that all parties involved in a land transaction agree on the terms of the transaction before it is executed. This reduces the possibility of land transaction conflicts. The system was developed using Geth, a commandline interface for managing Ethereum nodes. The system was tested using a simulated network of nodes and was found to be reliable and efficient. The system showed high throughput and low latency, which qualified it for usage in a production setting.
Many organizations use tendering including government to obtain goods and services from the providers of the service and manufacturing companies. There has been an advance evolution in the process from traditional hardcopy based to modern electronic based tendering. Since in the present scenario internet is being used there are many security implications that can be associated with it. Blockchain having decentralization of information and other features like immutability can be used as a step to mitigate the implications. In this paper, a distributed e-tendering system based on IoT, smart contracts and hybrid cryptography is explored. The design consists of sections based on the way the tendering and bidding organization participates like creation and publication of tender by the tendering organization, bidding process by the bidder, evaluations of the proposed bid, selection of the best one to declare the winner and finally to track the development of the procurement process and give stakeholders real-time updates IoT devices are used. Maintaining transparency, traceability, fairness, and security is kept in mind throughout the process which is the main motive of this paper.
Abstract With the addition of multiple blockchain platforms in the ecosystem, the Dapp owners need to migrate their smart contracts from one platform to another to remain competitive , cost-effective, and secure. A smart contract is a piece of code that contain logic and data. To migrate a smart contract, whether it’s on the same blockchain platform or a different one, we need both its source code that represents the logic and data which indicate the state of the contract. The source code can be easily set up, but to complete the migration, we have to extract the current state of the contract. In this paper, we have developed an advanced state extraction technique that uses static analysis to analyze the smart contract’s call graph and events, and extracts the entire storage state from the storage trie, along with the proper associations across function calls, enabling users to visualize, manage, and transform the state as desired for migration. The soundness of the extracted state was confirmed using the method of abstract interpretation. Further, the migration adapter is designed that transform extracted state into slot-value pair and migrated it to the target blockchain. Our new approach has allowed us to analyze 14% more smart contracts with the extraction of 15% more data from 67,993 contracts, and migrate some of them to the Polygon test-net.
Nowadays with the rising number of personal vehicles, finding a parking space has become an issue for many residents.Advancements in technologies like Internet of Things (IoT) have helped to maximize the productivity and reliability of urban infrastructure.In this paper, we propose an online parking rent system to reserve a parking space for the residents and other users, via a blockchain on the Ethereum platform which uses a non-fungible token as currency and a barcode for ease of use.A barcode will be provided while reserving the parking space, and is used when unlocking the bollards.This will help residents to save time by knowing their parking spot.
The present toll collection system has a number of problems, including varying toll rates on different roadways, heavy traffic, etc. Long wait times, theft from toll plazas, and other issues are problems that the toll collection system, particularly in India, encounters. Only 300 automobiles may be serviced by these systems each hour, and if more cars arrive at that area than that, severe traffic Jams could happen. For toll collection, the present toll methods, including Radio Frequency Identification (RFID), still require a lot of time. To address the above mentioned issues, this study discusses a GPS-based toll collection system. Every time a vehicle switches from a highway to a service road, this system would track the coordinates of the moving object. As a result, the user can pay the toll without being pulled over anywhere along the freeway. This system will leverage blockchain technology to construct a decentralized and intelligent mechanism for toll collection and tracking records because it has recently become one of the most well-liked technologies and has the potential to change the field of intelligent transportation systems. The technology uses blockchain smart contracts for the maintenance of security and integrity of records. Thus, presenting a strategy for putting into place a seamless toll collection system with the most transparency possible.
License Plate Recognition (LPR) is essential for the Internet of Vehicles (IoV) since license plates are a necessary characteristic for distinguishing vehicles for traffic management. As the number of vehicles on the road continues to grow, managing and controlling traffic has become increasingly complex. Large cities in particular face significant challenges, including concerns around privacy and the consumption of resources. To address these issues, the development of automatic LPR technology within the IoV has emerged as a critical area of research. By detecting and recognizing license plates on roadways, LPR can significantly enhance management and control of the transportation system. However, implementing LPR within automated transportation systems requires careful consideration of privacy and trust issues, particularly in relation to the collection and use of sensitive data. This study recommends a blockchain-based approach for IoV privacy security that makes use of LPR. A system handles the registration of a user's license plate directly on the blockchain, avoiding the gateway. The database controller may crash as the number of vehicles in the system rises. This paper proposes a privacy protection system for the IoV using license plate recognition based on blockchain. When a license plate is captured by the LPR system, the captured image is sent to the gateway responsible for managing all communications. When the user requires the license plate, the registration is done by a system connected directly to the blockchain, without going through the gateway. Moreover, in the traditional IoV system, the central authority has full authority to manage the binding of vehicle identity and public key. As the number of vehicles increases in the system, it may cause the central server to crash. Key revocation is the process in which the blockchain system analyses the behaviour of vehicles to judge malicious users and revoke their public keys.
The current Layer 1 blockchain, such as Ethereum and Bitcoin, has several problems related to scalability and cost.Layer 1 blockchain networks have always been a struggle for Ethereum over scalability.Many of these factors can be attributed to the network's nature of linear processing of transactions.During high traffic, these networks get crowded, slow the transaction time, and charge high fees.For many industries, these have made blockchains unusable, even though previously, I described them as getting from bad to worse in terms of functionality.To tackle these issues, developers have a singular aim in mindto craft blockchain scaling solutions that improve efficiency, expand capacity, and bring down costs of transactions so that blockchains can deliver the high performance demanded of them to match today's digital world.Rollups have become one of the most promising innovations in satisfying the stated need.Rollups can be described as a particular class of blockchains whose primary function is to increase the transactional capacity.Rollups, in the case of blockchain, are 'layer 2' scaling solutions that improve the transaction rate of the blockchain network.Thus, "Rollups" accurately name what they dothey combine several transactions into batches outside the mainstream network before performing these operations as one transaction in the mainnet.
In crowded cities, searching for the availability of parking lots is a herculean task as it results in the wastage of drivers’ time, increases air pollution, and traffic congestion. Smart parking systems facilitate the drivers to determine the information about the parking lot in real time and book them depending on the requirement. But the existing smart parking systems necessitate the drivers to reveal their sensitive information that includes their mobile number, personal identity, and desired destination. This disclosure of sensitive information makes the existing centralized smart parking systems more vulnerable to service providers’ security breaches, single points of failure, and bottlenecks. In this paper, an Improved Asymmetric Consortium Blockchain and Homomorphically Computing Univariate Polynomial-based private information retrieval (IACB-HCUPPIR) scheme is proposed to ensure parking lots’ availability with transparency security in a privacy-preserving smart parking system. In specific, an improved Asymmetric Consortium Blockchain is used for achieving secure transactions between different parties interacting in the smart parking environment. It further adopted the method of Homomorphically Computing Univariate Polynomial-based private information retrieval (HCUPPIR) scheme for preserving the location privacy of drivers. The results of IACB-HCUPPIR confirmed better results in terms of minimized computation and communication overload with throughput, latency, and response time with maximized drivers’ privacy preservation. Moreover, the proposed fully homomorphic algorithm (FHE) was compared against partial-homomorphic encryption (PHE) and technique without encryption and found that the proposed model has quick communication in allocating the parking slots starting with 24.3 s, whereas PHE starts allocating from 24.7 s and the technique without encryption starts at 27.4 s. Thus, we ensure the proposed model performs well in allocating parking slots with less time and high security with privacy preservation.
Parin Somani, Sunil Kumar Vohra, S. Chowdhury, Shashi Kant Gupta
On holidays and weekends, retail malls in metro areas have heavy foot traffic. Billing in the conventional manner, which entails employees standing in a line and scanning each customer’s purchase with a barcode scanner, is time-consuming. When there is a mistake in barcode scanners, the cashier may have to manually input the barcode number. As a consequence of this technique, clients are inconvenienced by lengthy lines at the billing counters. To overcome this issue, this chapter implemented a blockchain based smart shopping system. Here, smart carts are employed which performs the automated bill generation using the information from the Radio Frequency Identification (RFID) reader. The data from the smart carts are validated using the smart contracts and stored in the blockchain network for efficient bill generation. To avoid the data from being tampered, we have proposed Synchronous Discrete Twofish Encryption Algorithm (SDTEA), optimized using Genetically Modified Firefly Optimization Algorithm (GM-FOA). The bill is generated using the encrypted data and payment can be done after decryption of data. The simulation findings reveal that, by using this approach, a lot of time and man power can be saved. The proposed system is compared with conventional systems to prove its efficiency.
The parking lot is one of the important components of the intelligent transportation system (ITS). The current parking lots mainly use instant parking, which has low parking efficiency, during peak hours, which leads to traffic congestion. To guarantee the stable operation of parking lots, we propose a blockchain-enabled parking reservation framework, called BPR. Traditional parking reservation systems may exist the condition of malicious reservations, and resulting in wasted parking spaces. Therefore, we design a reputation mechanism to manage the parking reservation behavior of vehicles and reduce the number of malicious nodes. In addition, to balance the performance of the blockchain at different times (especially during peak hours), we use deep learning (DL) to dynamically adjust the block size to make the blockchain run more efficiently and stably. We deploy the system in Hyperledger Fabric and conduct effectiveness experiments. The comprehensive evaluation results and analysis show that the proposed reputation mechanism can effectively curb malicious nodes from reserving parking spaces and reduce the waste of parking resources. And the block size will be dynamically adjusted to balance the performance of the blockchain at different periods, this method is also applicable to other blockchain performance-sensitive scenes. Finally, this paper is compared with related work to demonstrate the innovation and feasibility of this work from various aspects.
The popularity of vehicles brings parking issues, especially in the downtown area. To tackle these issues, the concept of smart parking is presented, which utilizes industrial Internet of Things (IIoT) devices and vehicular sensor networks (VSNs) to monitor the available parking spaces and nearby traffic conditions. Unfortunately, the centralized architecture of existing solutions cannot guarantee data reliability. Besides, some privacy issues still violate the VSN participants' sensitive information. We propose a novelBlockchain-based smart parking scheme in digital-twin empowered VSNs with privacy protection, named BSDP. In BSDP, the digital twin network is introduced to monitor and predict traffic conditions nearby a parking lot. The blockchain and smart contract are utilized to achieve reliable data storage and correct parking response, respectively. Besides, the privacy of both driver and VSN participants can be protected. Experimental result shows that the proposed BSDP scheme achieves acceptable efficiency in resource-constrained vehicular networks.
Intelligent Transportation System (ITS) is an enhanced application which mainly aims towards the curation of smart and interconnectivity services to ease the existing transportation systems. It is a field with a plethora of future possibilities so as to provide efficient and advanced techniques by solving and tackling the existing challenges and problems. Especially in cities and countries which aim towards developing smart cities, ITS plays an essential role in advancing the transportation sector with regards to the city. Using a number of AI and ML techniques, a variety of approaches, algorithms and techniques can be put to effective utilization to boost the ITS ecosystem. However, with every boon compliment few banes and plausible bottlenecks. One of the major concerns while using V2V networks is the privacy and security issue. The data being transferred amidst vehicles mainly caters to the status updates on the vehicles adjacent to it, the speed, and driver's information in case of any accidents or wherever required. In such cases, the privacy of the drivers details and personal information are at stake and often poses a major security challenge. Furthermore, there are chances of data breaches and similar attacks by malicious users which can lead to huge data loss and tampering of data. A possible solution to this problem is to approach it through the perspective of Blockchain. Blockchain is a concept of sharing information/ data in a distributed and decentralized manner. The numerous blocks present in a blockchain which are responsible for storing information do so by encrypting and securing the data and hence can be integrated with a V2V system for overcoming the privacy and security challenges.
T. Shanthi, M. Ramprasath, A. Kavitha, T. Muruganantham
The latest 6G improvements secured autonomous driving's realism in Intelligent Autonomous Transport Systems (IATS). Despite the IATS's benefits, security remains a significant challenge. Blockchain technology has grown in popularity as a means of implementing safe, dependable, and decentralised independent IATS systems, allowing for more utilisation of legacy IATS infrastructures and resources, which is especially advantageous for crowdsourcing technologies. Blockchain technology can be used to address security concerns in the IATS and to aid in logistics development. In light of the inadequacy of reliance and inattention to rights created by centralised and conventional logistics systems, this paper discusses the creation of a blockchain-based IATS powered by deep learning for secure cargo and vehicle matching (BDL-IATS). The BDL-IATS approach utilises Ethereum as the primary blockchain for storing private data such as order and shipment details. Additionally, the deep belief network (DBN) model is used to select suitable vehicles and goods for transportation. Additionally, the chaotic krill herd technique is used to tune the DBN model’s hyperparameters. The performance of the BDL-IATS technique is validated, and the findings are inspected under a variety of conditions. The simulation findings indicated that the BDL-IATS strategy outperformed recent state-of-the-art approaches.
The images with non-fungible token (NFT) are employed as the digital artistic works in metaverse for creation, transaction, sharing, and collection.Being different from natural images, the content of NFT images is defined by user and distributed in the digital space widely.It is convenient for the hidden of secret data.In this case, covert communication with NFT images is a new branch of image steganography.Then, a steganographic method for NFT images was proposed accordingly.Given a NFT image, the regions of its profile and the components with high frequency were enhanced firstly to enrich the details which were beneficial to hide the modification trace of steganography.In this way, the enhanced image was used as cover since it is more suitable for steganography.Then, the tendency modification direction of each pixel was determined by the differences between the enhanced image and the given image.The differences were also used to determine the cost value of modification amplitude.Thus, the undetectability of steganography can be increased further.Secret data was embedded into the cover image using the popular steganographic coding schemes.Experimental results showed that the proposed method had imporoved undetectability on NFT images compared with existing digital steganographic schemes.Compared with HILL, MiPOD, and DEFI, the proposed method can increase the detection error P<sub>E</sub> of steganalysis by 8.7%, 9.2% and 6.2%, respectively (the average value for the cases of different payload and steganalytic features).Therefore, the proposed method is suitable for NFT images and it provides targeted steganographic method for the third kind of images, i.e., NFT images, except of natural images and generated images.For further study, the deep learning-based steganographic method can be designed for NFT images using the strong fitting and learning ability of neural networks.
Open access
Advanced Steganography and Watermarking Techniques
Industrial Internet of Things (IoT) depends on a vast array of sensors, processing elements, and actuators to automate data collection, processing, and response. One of the major challenges in the stage of data collection is the accuracy of electromechanical sensors. The concept of Proof-of-Stake (PoS), which is popular in Ethereum blockchain platform, can be used to improve the robustness of data collection. PoS works by selecting a subset of nodes based on their holdings called “stakes”, to perform a specific task. This algorithm is incorporated into IIoT by assigning a stake value to each node internally. The stake is assigned a predefined value during installation and changes periodically based on accuracy. A stake threshold is set so that only those nodes with stake above that value are considered contributors and others act as validators. During the initial setup, everyone validates every other node. Beyond that, the selection of contributors is repeated periodically. At every time interval, validators take the global average of contributors and find the deviation from the observed value. This is compared with a deviation limit. If none are within the limit, it indicates outliers, and the system invalidates itself. Otherwise, the node with the least deviation will emerge victoriously and send its value across to the processing unit. Those nodes with high deviation will have a part of their stake transferred to those with less deviation. Hence, the total stake remains the same, whereas the distribution keeps changing. The advantage is that this process is entirely distributed once all nodes multicast their readings to the validator group. This also ensures that sensors with persistent errors are singled out because they cannot recover their stake.
Riya Kakkar, Jafar A. Alzubi, Amit Dua, Smita Agrawal · 9 authors
In most countries, traffic congestion has reached a level where managing traffic is tedious for regulatory bodies. The traffic management faced many issues such as route routing based on congestion, delivery of messages/emails to end-users, and real-time allocation of parking slots. There have been many works on predicting parking prices for traffic management, but most favor users or owners and are not secure. To address these issues, a blockchain and Interplanetary File System (IPFS)-based parking price prediction scheme (PADaaV) is proposed to facilitate the users to reserve a parking slot securely and efficiently. It mainly focuses on ensuring security, privacy, and transparency for parking slot owners and users. Furthermore, we employ a second price auction model to optimize the parking price for users, and parking slot owners can also get benefit from it. The performance of the PADaaV has been simulated for 100 users with 40 parking slots based on different auction models. The various performance parameters considered are profit for users, profit for parking slot owners, overall revenue of the system, scalability, computation time, and data storage cost. The performance results show that the PADaaV is secure and beneficial for users and parking slot owners.
Henri Bouma, Armin Reuter, Patrick Brouwer, Martin George · 19 authors
Authentication of travel documents (e.g., passports) and breeder documents (e.g., birth certificates) is important to facilitate legal movement of passengers and to prevent cross-border crime, such as terrorism, smuggling, illegal migration and human trafficking. However, it is time consuming and difficult to verify all security features, the border guards differ in experience and expertise, and it is hard to stay alert every minute of a working day. New (artificial-intelligence based) technologies can assist in the automated fraud detection in travel and breeder documents, which may lead to faster and more consistent checks. This paper presents five categories of new technologies in automated document authentication to overcome the limitations of current document analysis systems in automated and non-automated border control scenarios. The first category consists of techniques related to the verification of visual security features on the holder page of travel documents. This category includes the verification of KINEGRAMs and other Optically Variable features under different light sources and lighting angles, and the analysis of printing techniques. The second category consists of techniques related to the analysis of breeder documents. This analysis can be at detail level (e.g., investigation of stamps) and at tactical level (e.g., verification of a check digit in a document number). The third category concerns the analysis of travel patterns, using information from the visa pages in passports. The stamps on these pages can be used to extract a travel pattern to support risk assessments and to detect anomalies. The fourth category is an analysis of the border-guard inspection history based upon a distributed ledger and blockchain technology that enables secure storage and prevents undesired manipulations. The last category analyzes the electronic chip of a passport. The software analyses document signer and country signer certificates on the chip to detect vulnerable cryptographic keys and tactical anomalies.