A Supermarket Anti-Theft RFID Scanner : digiSCAN

Supermarket shopping is an essential part of the livelihood of most people around the world. Consumers can acquire items essential for their daily activities. However, in an ever-evolving world with an increasing population and increasing crime rates, supermarket theft is becoming a prevalent problem with supermarket owners spending large sums of money on hiring security often with little effect. Organized Retail Crime (ORC) costs the retail industry approximately $30 billion each year, with 71.3% of retailers reporting an increase in ORC year-over-year. With the current global economic conditions, high labor costs are longer feasible. Consumers also face the issue of paying for items they did not select. Advanced camera systems, for some, may be an escape however, this approach is not feasible for all aspects of shopping and in underdeveloped countries that have technical constraints. The advent of digitization has helped improve the livelihood of consumers in Ghana. Currently, many large-scale retailers are oblivious to some of these advancements. The oblivion of the management of such retail services results in the loss of products, customer dissatisfaction and the mismanagement of untracked products by employees. To reduce theft and the mismanagement of products by employees, a smart antitheft system should be deployed in supermarkets; at the till and before the exit of supermarkets to ensure all products leaving the store are paid for and accounted for. The system consists of a deactivation and theft detection system. The product is deactivated by the store attendant at the till when the customer pays for the product. However, if a customer crosses the initial warning zone without paying, a warning sound is triggered and after the customer crosses the final warning zone, the alarm is triggered indicating an attempted theft by the customer. A log of products is also kept ensuring employees are not stealing products. This paper presents a smart way of detecting theft during supermarket shopping using Radio Frequency Identification (RFID) readers and tags, microcontroller-based control system, a database server and an Integroma

Automatic Multiple Choice Examination Questions Marking and Grade Generator Software

This paper discusses a feasible software solution that enables automatic marking andgrading of scripts. Technology keeps expanding, and more advanced innovations arebeing implemented with time. The marking and allocation of grades for examina-tion scripts through human efforts are gradually becoming a thing of the past. Hence,machines and software applications are introduced to make the entire marking andgrading of examination scripts more efficient, fast, and less tedious. Computer visionis an artificial intelligence (AI) knowledge domain that ensures devices obtain usefulinformation from digital images, videos, and other visual inputs. Image processingand recognition, a unique part of computer vision alongside the python program-ming language and the OpenCV library was employed for this project. These are themost used in developing most recent applications that utilize, to some extent, arti-ficial intelligence to attain specific desired results. The result of the project seeksto develop a maintainable android software application that uses image processingtechnology to scan patterns or images and grades results of multiple-choice questionscripts based on a set marking scheme. This ensures that desired results are obtainedwhile increasing efficiency and productivity

Adaptive Storage Optimization Scheme for Blockchain-IIoT Applications Using Deep Reinforcement Learning

Blockchain-IIoT integration into industrial processes promises greater security, transparency, and traceability. However, this advancement faces significant storage and scalability issues with existing blockchain technologies. Each peer in the blockchain network maintains a full copy of the ledger which is updated through consensus. This full replication approach places a burden on the storage space of the peers and would quickly outstrip the storage capacity of resource-constrained IIoT devices. Various solutions utilizing compression, summarization or different storage schemes have been proposed in literature. The use of cloud resources for blockchain storage has been extensively studied in recent years. Nonetheless, block selection remains a substantial challenge associated with cloud resources and blockchain integration. This paper proposes a deep reinforcement learning (DRL) approach as an alternative to solving the block selection problem, which involves identifying the blocks to be transferred to the cloud. We propose a DRL approach to solve our problem by converting the multi-objective optimization of block selection into a Markov decision process (MDP). We design a simulated blockchain environment for training and testing our proposed DRL approach. We utilize two DRL algorithms, Advantage Actor-Critic (A2C), and Proximal Policy Optimization (PPO) to solve the block selection problem and analyze their performance gains. PPO and A2C achieve 47.8% and 42.9% storage reduction on the blockchain peer compared to the full replication approach of conventional blockchain systems. The slowest DRL algorithm, A2C, achieves a run-time 7.2 times shorter than the benchmark evolutionary algorithms used in earlier works, which validates the gains introduced by the DRL algorithms. The simulation results further show that our DRL algorithms provide an adaptive and dynamic solution to the time-sensitive blockchain-IIoT environment

On Blockchain and IoT Integration Platforms: Current Implementation Challenges and Future Perspectives

Digitization and automation have engulfed every scope and sphere of life. Internet of Things (IoT) has been the main enabler of the revolution. There still exist challenges in IoT that need to be addressed such as the limited address space for the increasing number of devices when using IPv4 and IPv6 as well as key security issues such as vulnerable access control mechanisms. Blockchain is a distributed ledger technology that has immense benefits such as enhanced security and traceability. Thus, blockchain can serve as a good foundation for applications based on transaction and interactions. IoT implementations and applications are by definition distributed. This means blockchain can help to solve most of the security vulnerabilities and traceability concerns of IoTs by using blockchain as a ledger that can keep track of how devices interact, in which state they are and how they transact with other IoT devices. IoT applications have been mainly implemented with technologies such as cloud and fog computing, and AI to help address some of its key challenges. The key implementation challenges and technical choices to consider in making a successful blockchain IoT (BIoT) project are clearly outlined in this paper. The security and privacy aspect of BIoT applications are also analyzed, and several relevant solutions to improve the scalability and throughput of such applications are proposed. The paper also reviews integration schemes and monitoring frameworks for BIoT applications. A hybrid blockchain IoT integration architecture that makes use of containerization is proposed

An Overview of Technologies for Improving Storage Efficiency in Blockchain-Based IIoT Applications

Since the inception of blockchain-based cryptocurrencies, researchers have been fascinated with the idea of integrating blockchain technology into other fields, such as health and manufacturing. Despite the benefits of blockchain, which include immutability, transparency, and traceability, certain issues that limit its integration with IIoT still linger. One of these prominent problems is the storage inefficiency of the blockchain. Due to the append-only nature of the blockchain, the growth of the blockchain ledger inevitably leads to high storage requirements for blockchain peers. This poses a challenge for its integration with the IIoT, where high volumes of data are generated at a relatively faster rate than in applications such as financial systems. Therefore, there is a need for blockchain architectures that deal effectively with the rapid growth of the blockchain ledger. This paper discusses the problem of storage inefficiency in existing blockchain systems, how this affects their scalability, and the challenges that this poses to their integration with IIoT. This paper explores existing solutions for improving the storage efficiency of blockchain–IIoT systems, classifying these proposed solutions according to their approaches and providing insight into their effectiveness through a detailed comparative analysis and examination of their long-term sustainability. Potential directions for future research on the enhancement of storage efficiency in blockchain–IIoT systems are also discussed

A Survey on Network Optimization Techniques for Blockchain Systems

The increase of the Internet of Things (IoT) calls for secure solutions for industrial applications. The security of IoT can be potentially improved by blockchain. However, blockchain technology suffers scalability issues which hinders integration with IoT. Solutions to blockchain’s scalability issues, such as minimizing the computational complexity of consensus algorithms or blockchain storage requirements, have received attention. However, to realize the full potential of blockchain in IoT, the inefficiencies of its inter-peer communication must also be addressed. For example, blockchain uses a flooding technique to share blocks, resulting in duplicates and inefficient bandwidth usage. Moreover, blockchain peers use a random neighbor selection (RNS) technique to decide on other peers with whom to exchange blockchain data. As a result, the peer-to-peer (P2P) topology formation limits the effective achievable throughput. This paper provides a survey on the state-of-the-art network structures and communication mechanisms used in blockchain and establishes the need for network-based optimization. Additionally, it discusses the blockchain architecture and its layers categorizes existing literature into the layers and provides a survey on the state-of-the-art optimization frameworks, analyzing their effectiveness and ability to scale. Finally, this paper presents recommendations for future work