Hyperledger is a well-known blockchain platform that has made substantial advancements in the field of logistics and supply chain management. The blockchain too Hyperledger is a well-known blockchain platform that has made substantial advancements in the field of logistics and supply chain management. The blockchain too

This study was performed to assess the properties of combustion of the briquette formed using coconut husk bound with the cow dung in the ratio of 3:2 to higher This study was performed to assess the properties of combustion of the briquette formed using coconut husk bound with the cow dung in the ratio of 3:2 to higher

Decentralized and promising, blockchain technology brings enhanced security for data along with privacy issues particularly in health and supply chains. Here, we aim to show the usage of blockchain towards the secure data of devices that come under Internet of Medical Things or IoMT by providing data integrity and further sensitive medical information protection. A secure, transparent, and tamper-proof system in health care management can be supported by an IoT framework that uses blockchain. Four data security and privacy algorithms based on blockchain have been considered and tested with excellent results to show a clear indication of how much prevention in un-authorized access can be done in ensuring authenticity, tracking products in the medical supply chain. Some experiments were demonstrated where the blockchain-enabled system had reduced the data breach by 95% from traditional systems. Supply chain traceability had improved by 92%. The combination of machine learning and blockchain increased real-time threat detection by 88%. The outcome shows that blockchain can deal with problems like tampering, privacy violation, and counterfeiting in medical products. However, scalability of applications and adoption by regulators are two major challenges towards full integration. This paper aids in the development of a complete holistic framework that may make use of the usage of blockchain to secure health care and other supply chain systems to allow further avenues for digitizing data safety.

Decentralized and promising, blockchain technology brings enhanced security for data along with privacy issues particularly in health and supply chains. Here, we aim to show the usage of blockchain towards the secure data of devices that come under Internet of Medical Things or IoMT by providing data integrity and further sensitive medical information protection. A secure, transparent, and tamper-proof system in health care management can be supported by an IoT framework that uses blockchain. Four data security and privacy algorithms based on blockchain have been considered and tested with excellent results to show a clear indication of how much prevention in un-authorized access can be done in ensuring authenticity, tracking products in the medical supply chain. Some experiments were demonstrated where the blockchain-enabled system had reduced the data breach by 95% from traditional systems. Supply chain traceability had improved by 92%. The combination of machine learning and blockchain increased real-time threat detection by 88%. The outcome shows that blockchain can deal with problems like tampering, privacy violation, and counterfeiting in medical products. However, scalability of applications and adoption by regulators are two major challenges towards full integration. This paper aids in the development of a complete holistic framework that may make use of the usage of blockchain to secure health care and other supply chain systems to allow further avenues for digitizing data safety.

The Intelligent Mobile Data Collection (IMDC) framework is presented in this study for use in IoT-based sensor networks. Clusters of nearby Internet of Things (IoT) devices and sensors are organised using this approach. A gateway node acts as the coordinator of each cluster and is responsible for gathering information from its members, processing it, and then sending it on to the main data collector (MDC). In order to examine trends in data creation, the framework uses a labelling method called FRL (Very Rare Labels). Time intervals, packet numbers, and types are some of the parameters that can be used to classify clusters using these labels. Local models that take into account states, actions, and rewards are developed by individual Internet of Things (IoT) sensors or devices within the FRL framework using reinforcement learning (RL) approaches. After collecting data from various regional models, the gateway compiles it into a global model and returns it to the IoT-Internet of Things sensors. In addition, the framework uses cluster categories to find the MDC's sleep length, visitation time, and Time Division Multiple Access (TDMA) slots, among other factors. This framework's efficacy in lowering latency and energy usage during data collecting and enhancing accuracy is demonstrated by its implementation using NS2.