Future of the traditional beekeeping is to implement smart apiary management and start to use automatic and remote tools for bee colony monitoring together with beehive control mechanisms to improve bee colony productivity. Within the ERA-NET ICT-Agri project “ITAPIC” different bee colony monitoring and control systems together with its combinations were introduced and analysed. This paper presents authors vision for implementation of Precision Beekeeping together with the smart apiary concept. Different parameters of the bee colony can be monitored: temperature, humidity, gas content, sound, vibration etc. Continuous monitoring of some bee colony parameters is very challenging and not user friendly, allowing using it only for research purposes, not for practical implementation by the beekeepers. Precision beekeeping idea is to introduce tools that can be easily implemented into beekeeping practice. This paper describes developed systems and its combinations for successful smart apiary management. Developed systems are based on temperature, sound and video monitoring. Both data transmission types: wired and wireless are applied and compared. As well discussion of automatic beehive heating or/and cooling system implementation into practice is opened. As apiaries usually are placed outside in rural areas, important part of the smart beekeeping is usage of alternative energy for powering all the devices. Most suitable alternative power supply to this moment is usage of solar power with solar panels, which can be mounted on the hive. Together with hardware part it is needed to develop software part for data observation. Software part should be developed as a web system or/and mobile application. Cloud system with decision support functionality and with additional option for informing the beekeepers about changes in the state of the bee colonies could be considered as well.

This study explores the use of edge computing technologies to enhance the quality control processes in the dairy industry. Traditional milk quality control methods can be time-consuming and sometimes inadequate, whereas this new approach offers real-time data processing and rapid decision-making capabilities. The objective of the study is to assess the effectiveness of evaluating various spectral characteristics of milk in predicting critical parameters such as protein and fat content. In this research, a multi-channel sensor capable of collecting spectral data at various wavelengths was utilized. The collected data were processed using advanced machine learning models, where XGBoost and other regression models were assessed for their accuracy in predicting protein and fat content. The findings demonstrate the suitability of this technology for quality control in the dairy industry. The results reveal that edge computing-based systems can determine milk quality more quickly and accurately. This technology holds significant potential for overcoming the challenges faced in milk quality control, particularly in developing countries. This study provides valuable insights into how the use of edge computing can enhance operational efficiency and ensure product quality in the dairy industry. This research represents an important step towards developing more effective quality control mechanisms in the dairy industry and aims to establish a robust foundation for future studies. Recommendations focus on the adaptation of this technology to other food safety applications and its diversification for widespread industrial use.

This paper describes the design and implementation of a wireless home automation system that can be used for general purposes in our life. The proposed system is designed for easy installation, so it could be use at any location to gather indoor conditions. The proposed system comprises of a wireless sensor and actuator network which is low cost and low power. Each wireless sensor node senses and transmits the variations in the local temperature, humidity and luminosity to the cloud database. The cloud server receives the data and stores it in the table and plotting the variations simultaneously. The user web interface provides graphical data analysis. The Web of low-power wireless home energy management system allows the temperature, humidity and luminosity variations to be viewed and controlled whenever user want from anywhere in the world. Also, when the large variations in temperature, humidity and luminosity value occur, system will run actuators like heater, cooler, ventilation fan and lighting equipments. It is expected that the proposed system will be more useful in our existing home because of not necessary wire connection and massive energy saving potential.

Recently, new devices have been developed for the Internet of Things concept. The devices commonly use RF (Radio Frequency) based wireless communication. With the increase in the number of devices, the space allocated for the radio frequency band in wireless communication fills rapidly. Visible Light Communication (VLC) is an alternative, secure and economical communication technology that uses light instead of radio frequencies. While Web of Things (WoT) is the adaptation of the experience and knowledge acquired from the web into the internet of things ecosystems. By combining these two technologies, the development of the Visible Light Communication for Web of Things (VLC4WoT) system, which can use VLC and WoT technologies, has been our motivation. In our study, microcontroller control circuit was created for VLC4WoT system. Control of the circuits over the internet was performed. VLC based receiver and transmitter units have been developed for wireless communication. Web based interface was created for control. The test apparatus consisting of four objects with four outputs and a transfer unit was carried out. In this test, communication was achieved successfully. It was presented in the study that VLC can be used in the web of things architecture. In the future, it is envisaged to use this system as a safe and economical system in indoor environments.

This study presents the development of a portable, low-cost, and edge computing-based system for real-time milk adulteration detection. Utilizing an AS7265x multispectral sensor and Arduino Nano 33 BLE Sense microcontroller, this system employs an optimized logistic regression model to identify starch adulteration in milk samples with near-perfect accuracy. Unlike complex neural network models, the logistic regression model offers simplicity, low power consumption, and efficient operation on microcontrollers. The collected spectral data is processed in real-time, and results are transmitted via Bluetooth for immediate analysis. The system demonstrates high accuracy, portability, and cost-effectiveness, making it suitable for use in various stages of the milk supply chain, including farms, processing facilities, and retail points. Future work will explore the detection of other adulterants and the integration of cloud-based analytics to enhance monitoring capabilities. This study provides an innovative approach to ensuring milk quality and consumer safety.

Urban air quality has significant and far-reaching impacts on both human health and the broader environment. Pollutants like particulate matter (PM2.5 and PM10), for instance, are associated with a range of health issues including respiratory conditions, asthma, heart diseases, and even contribute to low birth weight in newborns. These health implications extend to larger environmental concerns such as air pollution, greenhouse gas emissions, and global climate change. Recognizing the urgent need for effective and dynamic air quality monitoring solutions, this paper explores the use of smart environmental drones as a promising approach. Our drone is equipped with a state-of-the-art, low-cost particulate matter sensor that can accurately measure PM2.5 and PM10 concentrations. Operating at a flight speed of 10 m/s and capable of covering a range of 5 km, the drone executes a pre-programmed flight plan to autonomously map pollution levels across urban areas. With a 95% accuracy rate in sensor readings, our model significantly minimizes potential errors commonly associated with traditional air quality monitoring methods. Furthermore, it simplifies maintenance procedures, reducing both time and financial costs. By employing drone technology in this innovative manner, our model offers a cost-effective, reliable, and dynamic solution for monitoring urban air quality. It provides real-time, actionable pollution indices that can inform public health decisions, regulatory policies, and community awareness, thereby contributing to the broader goal of improving air quality and public health.