The proposed IoT-based energy monitoring system is a powerful tool for managing and monitoring energy consumption in homes and small businesses. It is designed to measure the voltage and power consumed by appliances connected to it, providing users with real-time energy consumption data and a historical record of energy usage over time. Additionally, the system is equipped with features that enable remote access via a software application, allowing users to monitor their energy consumption and track their daily usage patterns to identify opportunities for energy savings. One of the most significant advantages of this system is its ability to turn off the appliances connected to it. Users can set up the system to turn off appliances when they are not in use, reducing energy consumption and lowering their electricity bills. The software application provides users with a comprehensive view of their energy consumption, allowing them to identify the appliances that consume the most energy and develop strategies to conserve energy. The application's data storage capabilities enable the safe and secure storage of energy consumption data, which can be accessed at any time. The appliance is a sophisticated energy monitoring system that operates by measuring the power and voltage consumption of appliances. It can be easily integrated into a home or small business network, and its data can be accessed remotely through the software application. With its powerful features, including real-time energy consumption monitoring, historical data tracking, and the ability to turn off connected appliances, this IoT-based energy monitoring system is an ideal choice for those looking to conserve energy and lower their energy bills.
In the pharmaceutical industry, accurate demand forecasting is crucial for the efficient management of manufacturing, acquiring, and distribution activities. This paper concentrates on utilising data mining techniques, namely the Decision Tree and Random Forest algorithms, to forecast the demand for pharmaceuticals associated with seasonal ailments that are anticipated to emerge in the upcoming months of 2024. The findings of the analysis demonstrate that the Random Forest algorithm exhibits superior performance in comparison to the Decision Tree algorithm. This is evidenced by the observation of lower mean Root Mean Square Error (RMSE) values of 80.53 and 97.10, respectively, which indicates that Random Forest outperforms Decision Tree. The present work's results offer significant contributions to the pharmaceutical sector by providing valuable insights that facilitate improved planning and resource allocation to address the variable demand patterns that are influenced by seasonal diseases.
In real-time embedded control (RTEC) systems, sensors collect data which is processed and sent to different control nodes. RTEC deployments have numerous applications in diverse verticals like industrial control, healthcare, and vehicular networks. In such cases, a trusted and verifiable control is required, particularly when the data is kept in a distributed manner, and is exchanged over open wireless channels. Thus, blockchain (BC) is a viable option to store the sensor data between RTEC systems, which maintains a trusted ledger of associated operations. Existing works have not focused on the integration of BC in RTEC systems. Motivated by the gap, the paper presents a systematic approach to integrating BC in RTEC ecosystems. We present a reference architecture and discuss the device registration, the hyperledger fabric set up, and the task offloading strategy between edge gateways and cloud nodes, and present the performance analysis of the architecture. The discussion of open issues and challenges also highlights the practical implications of the approach, emphasizing its importance for future deployments of real-time embedded control systems.
The fast growth of information and communication technologies (ICTs) has had a big impact and changed a lot of businesses. Wireless sensor networks (WSN) are used in many industries for mobility, scalability, reliability, smart monitoring, and management. Utilization of modern equipment in the agriculture industry is essentially required to boost people’s income and create a positive impact on their social lives. WSNs comprise various self-designed devices that gather extensive data from the environment. Numerous techniques have been developed recently to enhance WSN productivity in various industries. WSN has become one of the emerging innovations. In wireless sensor networks, the biggest problems are data loss, node failure, and the need to use more energy to make the sensor nodes last longer. To overcome these limitations, in this research, a novel Tri-Head Fuzzy C Multipath Routing (THFCMR) protocol is proposed for WSNs. The proposed THFCMR approach is designed by a tri-head static fuzzy C means clustering with hybrid energy-efficient distributed clustering (HEED) integrated with a hybrid energy-efficient multipath routing protocol (HEEMP) approach. This paper focused on reviewing the cluster base protocol’s usability and weaknesses, along with a proposal for a solution to enhance network consistency and data availability in WSNs.
Thеrе аrе numеrοus tіtlеs fοr сrурtο сurrеnсіеs. Mοst lіkеlу, уοu must hаvе hеаrd аbοut thе mοst wеll-knοwn сrурtο сurrеnсіеs, іnсludіng Bіtсοіn, Tеthеr, Еthеrеum. Сrурtο сurrеnсіеs аrе іnсrеаsіnglу рοрulаr аltеrnаtіvе fοr οnlіnе рауmеnts. А dіgіtаl сurrеnсу, οftеn knοwn аs а сrурtο сurrеnсу, іs а dіffеrеnt tуре οf рауmеnt sуstеm сrеаtеd utilising еnсrурtіοn mеthοds. Bу utіlіsіng еnсrурtіοn аlgοrіthms, сrурtο сurrеnсіеs mау sеrvе аs а vіrtuаl ассοuntіng sуstеm аnd аn еxсhаngе mеdіum. Tурісаllу, nο gοvеrnmеnt οr аnу οthеr central bοdу іssuеs οr сοntrοls сrурtο сurrеnсіеs. Thеу аrе mаnаgеd vіа рееr-tο-рееr nеtwοrks οfiсοmрutеrs runnіng οреn-sοurсе, frее sοftwаrе. Gеnеrаllу, аnуοnе whο wаnts tο jοіn thеm іn wеlсοmе tο dο sο.
