Current Issue : July-September Volume : 2023 Issue Number : 3 Articles : 5 Articles
The increasing challenges of agricultural processes and the growing demand for food globally are driving the industrial agriculture sector to adopt the concept of ‘smart farming’. Smart farming systems, with their real-time management and high level of automation, can greatly improve productivity, food safety, and efficiency in the agri-food supply chain. This paper presents a customized smart farming system that uses a low-cost, low-power, and wide-range wireless sensor network based on Internet of Things (IoT) and Long Range (LoRa) technologies. In this system, LoRa connectivity is integrated with existing Programmable Logic Controllers (PLCs), which are commonly used in industry and farming to control multiple processes, devices, and machinery through the Simatic IOT2040. The system also includes a newly developed web-based monitoring application hosted on a cloud server, which processes data collected from the farm environment and allows for remote visualization and control of all connected devices. A Telegram bot is included for automated communication with users through this mobile messaging app. The proposed network structure has been tested, and the path loss in the wireless LoRa is evaluated....
Intelligent sensing systems based on the edge-computing paradigm are essential for the implementation of Internet of Things (IoT) and Agriculture 4.0 applications. The development of edge-computing wireless sensing systems is required to improve the sensor’s accuracy in soil and data interpretation. Therefore, measuring and processing data at the edge, rather than sending it back to a data center or the cloud, is still an important issue in wireless sensor networks (WSNs). The challenge under this paradigm is to achieve a sustainable operation of the wireless sensing system powered with alternative renewable energy sources, such as plant microbial fuel cells (PMFCs). Consequently, the motivation of this study is to develop a sustainable forage-grass-power fuel cell solution to power an IoT Long-Range (LoRa) network for soil monitoring. The stenotaphrum secundatum grass plant is used as a microbial fuel cell proof of concept, implemented in a 0.015 m3-chamber with carbon plates as electrodes. The BQ25570 integrated circuit is employed to harvest the energy in a 4 F supercapacitor, which achieves a maximum generation capacity of 1.8 mW. The low-cost pH SEN0169 and the SHT10 temperature and humidity sensors are deployed to analyze the soil parameters. Following the edgecomputing paradigm, the inverse problem methodology fused with a system identification solution is conducted, correcting the sensor errors due to non-linear hysteresis responses. An energy power management strategy is also programmed in the MSP430FR5994 microcontroller unit, achieving average power consumption of 1.51 mW, ∼19% less than the energy generated by the forage-grasspower fuel cell. Experimental results also demonstrate the energy sustainability capacity achieving a total of 18 consecutive transmissions with the LoRa network without the system’s shutting down....
Coronavirus had some healthcare organizations embrace cloud hosting without proper investigation or review. This raises challenges concerning security and privacy aspects and could lead to lasting risks and vulnerabilities in the system. Our project’s main objectives include separating medical information from other data, such as billing and accounting information, and integrating e-health cards into the system. The TVD infrastructure isolates and protects the TVD from adversaries from the outside. In this paper, we examined security and privacy issues in E-health systems, and then we proposed a secure e-health infrastructure based on Trusted Virtual Domains (TVDs) to ensure fundamental security and privacy properties....
The financial big data intelligent service system belongs to the technical field of financial data management. It is an innovation of the client and server of the financial service system, which promotes the electronic office of the financial system. This paper aimed to analyze the cloud computing means of the Internet of things (IoT), select a more suitable specific algorithm, and conduct an indepth study of the financial big data intelligent service system so that it can better serve the current financial situation. This paper gave a general introduction to the cloud computing of the Internet of things, researched and analyzed the financial big data intelligent service system machine, and applied the cloud computing of the Internet of things to the research of the financial big data intelligent service system. Based on the experiments in this paper, it can be seen that among the students in the three colleges and universities in place A, 567 people thought that they can adapt to the intelligent financial system better than the already employed salesmen, and 245 people held a negative attitude. It showed that the intelligent development of financial systems is a trend, but at the same time, it is also a development trend to strengthen the business training capabilities of professionals. The experimental results of this paper showed that the process of studying the financial big data intelligent service system based on the cloud computing of the IoT is more scientific and effective than using other means to analyze the experimental data, and it has greater reference significance for the intelligent development of the financial system....
Aiming at the problems of low detection rate and high false detection rate of intrusion detection algorithms in the traditional cloud computing environment, an intrusion detection-data security protection scheme based on particle swarm-BP network algorithm in a cloud computing environment is proposed. First, based on the four modules of data collection, data preprocessing, feature selection, and intrusion detection, the overall framework of the intrusion detection model is constructed by designing corresponding functions. Then, by introducing the decision tree algorithm, the overfitting is reduced and the data processing speed of the model is improved, and on this basis, the feature selection is carried out through the “gain rate” optimization method, which reduces the redundant information of the feature vector. Finally, by introducing the Particle Swarm Optimization (PSO) algorithm into the optimization of the initial weights and thresholds of the BP neural network, the BP neural network is improved based on the momentum factor and adaptive learning rate, and the high detection rate and low false detection rate are realized. Through simulation experiments, the proposed intrusion detection method and the other three methods are compared and analyzed under the same conditions. The results show that the detection rate and false detection rate of the method proposed in this paper are the best under five different types of sample data, the highest detection rate reaches 95.72%, and the lowest false detection rate drops to 2.03%. The performance of the proposed algorithm is better than that of the other two comparison algorithms....
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