The proposed approach uses blockchain-based technology to strengthen the data security of wireless sensor networks (WSNs). This paper integrates blockchain-based technology with data transfer to establish an extremely secure WSNs structure. The present wireless network is built on the architecture of the Internet of Things (IoT) and employs a blockchain-based method to make the reliability of data transmission strong. In this proposed research, many small-area wireless sensor networks establish the entire WSNs structure, and every small-area wireless sensor network has a primary data collection node called a “mobile database.” The “mobile database” node of this study uses embedded microcontrollers with an operating system, such as Raspberry Pi and Arduino Yun. This block contains the sensor data collected by itself and the hash value of the previous block. Then the hash value of its own block, which is also part of the hash calculation of the next block, was calculated through the mining calculation program. Any block in the proposed method includes the encrypted hash-value of the previous block, the current timestamp, and the transaction data. In our research content, the transaction data is represented as wireless network sensing data. Basically, the system employs the hash function for calculation using the Merkel-tree algorithm. Such programming makes the block with blockchain-based technology difficult to tamper with content. This study approach revises the blockchain-based transaction ledger to become a sensor data record. Therefore, the proposed system gathers and analyzes sensor data for more reliability in the wireless sensing network structure. Furthermore, the innovative system with blockchain-based technology can treat a private cloud-end. This paper also carries on to visualize the uploaded sensing data by the sensors and draws corresponding charts based on big data analysis. The wireless network architecture proposed in this paper is built on embedded devices, making it easy for the system to build a web server. Using Python or JavaScript programming language in the web environment is relatively more convenient for data visualization and data analysis. Finally, this study uses traditional methods and innovative methods to compare data transmission. When the system uses innovative methods with blockchain-based technology, it is almost impossible for any operator to tamper with the data transmitted by the sensor.

With rapidly changing technology, people have more and more requirements for thermal comforts regarding indoor temperature, humidity, and wind speed, and pay more attention to air quality. Indoor air quality has serious effects on the elderly, children, and those with respiratory allergies. Based on the architecture of the Internet of Things smart home, this study constructed an indoor air quality monitoring system to explore how people can live in an environment with good air quality. Among the numerous air quality indices (AQIs), the carbon dioxide index and AQI of the American Society of Heating, Refrigerating and Air-Conditioning Engineers are selected as the indices suitable for this study. The common points of the two indices are combined, and then, based on the data of the Environmental Protection Administration, indoor and outdoor environmental parameters are analyzed, and controllable environment variables are simulated to analyze their effects on air quality. This study designed effective load control using fuzzy control and developed a fuzzy rule base for simulation of the environment variables. Decision logic was used to replace the threshold control of indoor air quality in the past, and a comfortable air quality monitoring system was designed by combining the Arduino Uno development board and ESP8266 Wi-Fi wireless transmission modules.

With the development and progress of technology, people’s requirements for living quality are increasingly higher. This study builds an indoor thermal comfort environmental monitoring system through the Internet of Things (IoT) architecture to explore the thermal comfort of people in indoor environments. Then, the applicable indicators are selected from a series of thermal comfort pointers, and the controllable indoor environmental parameters are analyzed and simulated on MATLAB to obtain the impact on the thermal comfort indicators, which can serve as important data to set up the fuzzy rule base. Next, according to the ISO7730 comfort standard and energy saving, three ways to control thermal comfort are proposed. With Arduino UNO as the development substrate, the sensing nodes for the indoor environment are set up, and the wireless sensing network is configured with ESP8266 to transmit the sensing data to the terminal. Monitored by the C# human-machine interface, the controllable load is controlled by wireless remote mode. Finally, the data is stored in the database for follow-up experimentation and analysis. Through actual measurement experiments, the thermal comfort and energy saving effects, under comfort, general, and energy-saving modes, as proposed in this study, are verified to achieve a balance between thermal comfort and energy saving.

This paper proposes a blockchain-based supply chain information operation method to solve the current shortage of business information sharing in the supply chain. These approaches include the idea of blockchain application to realize the sharing of supply chain information, the supply chain architecture of production enterprises based on blockchain, and the hierarchical model of supply chain information flow based on blockchain. This paper proposes the information block recording method of internal and external data sources in the supply chain, including the composition structure and the analysis of the multisource data inside and outside the supply chain. In addition, the multisource data information block recording method is also proposed. Building a blockchain-based supply chain system can improve the integration and reconstruction capabilities of the supply chain. The information recording of the internal data of the supply chain and the external related multisource data in the block can improve the learnability of the supply chain. The internal and external information block connection of the supply chain system is the further integration and reconstruction of the supply chain information resources in the blockchain system. The information storage and access control of the blockchain-based supply chain is an information security guarantee system in the supply chain information sharing environment. Through the method of this study, the system can form a business architecture system based on blockchain-based supply chain information sharing.

According to data from the Earth's Volcano and Geological Disaster Reduction Center, a country like Indonesia has experienced many natural disasters, one of which is flooding. Floods are an annual natural disaster, especially on mountain slopes. Mountainous areas experience more dangerous than floods than the urban areas because they can cause other natural disasters, such as landslides and damage the hiking trails. The steep and winding roads minimize and limit the number of officers working in the mountains. Therefore, flood detection and monitoring equipment is needed. The proposed system based on AIoT technology provides real-time flood analysis so that the authorities can monitor residents around mountainous areas and provide early warning. This research focuses on the flood observation system as an early warning system to effectively monitor the flood-prone mountain slopes in real time while taking into account the cost, time efficiency, and safety measurement. The proposed system design includes the integration of sensors into the microcontroller, and the communication between the posts using LoRa and SIM900 sends data to the cloud server via the Internet. All sensor readings for each post are displayed on the app, and alerts are sent via SMS and the app.

The aquaculture production sector is one of the suppliers of global food consumption needs. Countries that have a large amount of water contribute to the needs of aquaculture production, especially the freshwater fisheries sector. Indonesia is a country that has a large number of large bodies of water and is the top-five producer of aquaculture production. Technology and engineering continue to be developed to improve the quality and quantity of aquaculture production. One aspect that can be observed is how the condition of fish pond water is healthy and supports fish growth. Various studies have been conducted related to the aquaculture monitoring system, but the problem is how effective it is in terms of accuracy of the resulting output, implementation, and costs. In this research, data fusion (DF) and deep reinforcement learning (DRL) were implemented in an aquaculture monitoring system with temperature, turbidity, and pH parameters to produce valid and accurate output. The stage begins with testing sensor accuracy as part of sensor quality validation, then integrating sensors with wireless sensor networks (WSNs) so they can be accessed in real time. The implemented DF is divided into three layers: first, the signal layer consists of WSNs and their components. Second, the feature layer consists of DRL combined with deep learning (DL). Third, the decision layer determines the output of the condition of the fish pond in “normal” or “not normal” conditions. The analysis and testing of this system look at several factors, i.e., (1) the accuracy of the performance of the sensors used; (2) the performance of the models implemented; (3) the comparison of DF-DRL-based systems with rule-based algorithm systems; and (4) the cost effectiveness compared to labor costs. Of these four factors, the DF-DRL-based aquaculture monitoring system has a higher percentage value and is a low-cost alternative for an accurate aquaculture monitoring system.