The proper handling of waste is one of the biggest challenges of modern society. Municipal Solid Waste (MSW) requires categorization into a number of types, including bio, plastic, glass, metal, paper, etc. The most efficient techniques proposed by researchers so far include neural networks. In this paper, a detailed summarization was made of the existing deep learning techniques that have been proposed to classify waste. This paper proposes an architecture for the classification of litter into the categories specified in the benchmark approaches. The architecture used for classification was EfficientNet-B0. These are compound-scaling based models proposed by Google that are pretrained on ImageNet and have an accuracy of 74% to 84% in top-1 over ImageNet. This research proposes EfficientNet-B0 model tuning for images specific to particular demographic regions for efficient classification. This type of model tuning over transfer learning provides a customized model for classification, highly optimized for a particular region. It was shown that such a model had comparable accuracy to that of EfficientNet-B3, however, with a significantly smaller number of parameters required by the B3 model. Thus, the proposed technique achieved efficiency on the order of 4X in terms of FLOPS. Moreover, it resulted in improvised classifications as a result of fine-tuning over region-wise specific litter images.

In recent years, the concept of the Internet of Things has been introduced. Information, communication, and network technology can be integrated, so that the unmanned aerial vehicle (UAV) from consumer leisure and entertainment toys can be utilized in high value commercial, agricultural, and defense field applications, and become a killer product. In this paper, a traceable and privacy-preserving authentication is proposed to integrate the elliptic curve cryptography (ECC), digital signature, hash function, and other cryptography mechanisms for UAV application. For sensitive areas, players must obtain flight approval from the ground control station before they can control the UAV in these areas. The traditional cryptography services such as integrity, confidentiality, anonymity, availability, privacy, non-repudiation, defense against DoS (Denial-of-Service) attack, and spoofing attack can be ensured. The feasibility of mutual authentication was proved by BAN logic. In addition, the computation cost and the communication cost of the proposed scheme were analyzed. The proposed scheme provides a novel application field.

Internet of Things (IoT) technology is now widely used in energy, healthcare, services, transportation, and other fields. With the increase in industrial equipment (e.g., smart mobile terminals, sensors, and other embedded devices) in the Internet of Things and the advent of Industry 4.0, there has been an explosion of data generated that is characterized by a high volume but small size. How to manage and protect sensitive private data in data sharing has become an urgent issue for enterprises. Traditional data sharing and storage relies on trusted third-party platforms or distributed cloud storage, but these approaches run the risk of single-node failure, and third parties and cloud storage providers can be vulnerable to attacks that can lead to data theft. To solve these problems, this paper proposes a Hyperledger Fabric blockchain-based secure data transfer scheme for enterprises in the Industrial Internet of Things (IIOT). We store raw data in the IIoT in the InterPlanetary File System (IPFS) network after encryption and store the Keyword-index table we designed in Hyperledger Fabric blockchain, and enterprises share the data by querying the Keyword-index table. We use Fabric’s channel mechanism combined with our designed Chaincode to achieve privacy protection and efficient data transmission while using the Elliptic Curve Digital Signature Algorithm (ECDSA) to ensure data integrity. Finally, we performed security analysis and experiments on the proposed scheme, and the results show that overall the data transfer performance in the IPFS network is generally better than the traditional network, In the case of transferring 5 MB file size data, the transmission speed and latency of IPFS are 19.23 mb/s and 0.26 s, respectively, and the IPFS network is almost 4 times faster than the TCP/IP network while taking only a quarter of the time, which is more advantageous when transferring small files, such as data in the IIOT. In addition, our scheme outperforms the blockchain systems mainly used today in terms of both throughput, latency, and system overhead. The average throughput of our solution can reach 110 tps (transactions are executed per second), and the minimum throughput in experimental tests can reach 101 tps.

In the current medical insurance claims process, there are problems of low efficiency and complex services. When a patient applies for medical insurance claims, he/she must go to the hospital to apply for a diagnosis certificate and receipt and then send the relevant application documents to the insurance company. The patient will not receive compensation until the company completes the verification with the patient’s hospital. However, we can improve the current dilemma through blockchain technology. Blockchain technology can effectively open up the information channels of the insurance industry and medical institutions, promote industry integration, and enhance the ability of insurance companies to obtain information. In this research, we used blockchain and smart contract technology to make the following contributions to the development of Internet insurance. First, blockchain and smart contract technology can effectively solve the problem of online underwriting. Second, it is conducive to improving supervision. Third, it is conducive to solving risk control problems. Fourth, it is conducive to effective anti-money laundering. The proposed scheme fulfills the following security requirements: mutual authentication of identities, non-repudiation between each of two roles, and other major blockchain-based security requirements. In the event of a dispute, we also proposed an arbitration mechanism to divide responsibilities.

Wireless Underground Sensor Networks (WUSNs) have been showing prospective supervising application domains in the underground region of the earth through sensing, computation, and communication. This paper presents a novel Deep Learning (DL)-based Cooperative communication channel model for Wireless Underground Sensor Networks for accurate and reliable monitoring in hostile underground locations. Furthermore, the proposed communication model aims at the effective utilization of cluster-based Cooperative models through the relay nodes. However, by keeping the cost effectiveness, reliability, and user-friendliness of wireless underground sensor networks through inter-cluster Cooperative transmission between two cluster heads, the determination of the overall energy performance is also measured. The energy co-operative channel allocation routing (ECCAR), Energy Hierarchical Optimistic Routing (EHOR), Non-Cooperative, and Dynamic Energy Routing (DER) methods were used to figure out how well the proposed WUSN works. The Quality of Service (QoS) parameters such as transmission time, throughput, packet loss, and efficiency were used in order to evaluate the performance of the proposed WUSNs. From the simulation results, it is apparently seen that the proposed system demonstrates some superiority over other methods in terms of its better energy utilization of 89.71%, Packet Delivery ratio of 78.2%, Average Packet Delay of 82.3%, Average Network overhead of 77.4%, data packet throughput of 83.5% and an average system packet loss of 91%.

Counterfeit products are internationally regarded as “the world’s second greatest public health hazards after drugs”. Counterfeiters produce counterfeit brand clothing and then sell them to consumers through unofficial channels; thus, consumers spend a lot of money without getting the value they deserve. With the rise of e-shopping, the safety and security of branded clothing supply chains are also under threat. Counterfeit branded apparel manufacturers generate profits while genuine manufacturers suffer, which ultimately violates the interests of the public. This study proposes a traceable anti-counterfeit management system for branded clothing based on Hyperledger Fabric technology. This system can achieve full traceability of the production information of branded clothing. It uses the unique characteristics of blockchain, such as being unforgeable, traceable, open, and transparent, and collectively ‘maintaining’, to record the specific production processes of the brand clothing, and ensure the authenticity and legitimacy of the production information of brand clothing. The end-user can self-verify the product’s authenticity by sharing the product’s details on the immutable framework. It solves problems surrounding information asymmetry, opaque supply chain data, and easy falsification in the production process of branded clothing in traditional branded clothing supply chains.