Due to rapid population growth globally, digitally-enabled agricultural sectors are crucial for sustainable food production and making informed decisions about resource management for farmers and various stakeholders. The deployment of Internet of Things (IoT) technologies that collect real-time observations of various environmental (e.g., temperature, humidity, etc.) and operational factors (e.g., irrigation) influencing production is often seen as a critical step to enable additional novel downstream tasks, such as AI-based yield forecasting. However, since AI models require large amounts of data, this creates practical challenges in a real-world dynamic farm setting where IoT observations would need to be collected over a number of seasons. In this study, we deployed IoT sensors in strawberry production polytunnels for two growing seasons to collect environmental data, including water usage, external and internal temperature, external and internal humidity, soil moisture, soil temperature, and photosynthetically active radiation. The sensor observations were combined with manually provided yield records spanning a period of four seasons. To bridge the gap of missing IoT observations for two additional seasons, we propose an AI-based backcasting approach to generate synthetic sensor observations using historical weather data from a nearby weather station and the existing polytunnel observations. We built an AI-based yield forecasting model to evaluate our approach using the combination of real and synthetic observations. Our results demonstrated that incorporating synthetic data improved yield forecasting accuracy, with models incorporating synthetic data outperforming those trained only on historical yield, weather records, and real sensor data.

Internet of Things (IoT) technologies have enabled numerous data-driven mobile applications and have the potential to significantly improve environmental monitoring and hazard warnings through the deployment of a network of IoT sensors. However, these IoT devices are often power-constrained and utilize wireless communication schemes with limited bandwidth. Such power constraints limit the amount of information each device can share across the network, while bandwidth limitations hinder sensors' coordination of their transmissions. In this work, we formulate the communication planning problem of IoT sensors that track the state of the environment. We seek to optimize sensors' decisions in collecting environmental data under stringent resource constraints. We propose a multi-agent reinforcement learning (MARL) method to find the optimal communication policies for each sensor that maximize the tracking accuracy subject to the power and bandwidth limitations. MARL learns and exploits the spatial-temporal correlation of the environmental data at each sensor's location to reduce the redundant reports from the sensors. Experiments on wildfire spread with LoRA wireless network simulators show that our MARL method can learn to balance the need to collect enough data to predict wildfire spread with unknown bandwidth limitations.

Two is better than one, isn't it? This is undoubtedly true in the case of IoT and machine learning. These two most popular and trending technologies are offering a solid growth system for companies if implemented together correctly. When combined, they help you unlock the true power of data and boost business efficiency, sales, and customer relationships. Therefore, incorporation of IoT and machine learning in business is seen on a wide scale.

Internet of Things (IoT) applications are becoming more widespread. According to Statista, worldwide expenditure on IoT in 2022 is expected to be $1B. IoT enables a myriad of different business applications. Knowing those IoT examples and use cases can help businesses integrate IoT technologies into their future investment decisions. That is why we set out to create the most comprehensive list of IoT use cases in industries.

With the rise of Industry 4.0, businesses are increasingly turning to automation to streamline their operations and improve productivity. Hyperautomation, the process of automating as many tasks as possible using advanced technologies like artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA), is becoming a game-changer for businesses seeking to stay competitive. The integration of the Internet of Things (IoT) into hyperautomation is further enhancing this transformation by enabling businesses to connect and control a vast network of devices and sensors. Hyperautomation with IoT involves combining IoT-enabled devices with hyperautomation technologies to create a connected ecosystem that is capable of automating repetitive and mundane tasks. IoT devices collect and transmit data, while hyperautomation tools analyze this data to make intelligent decisions and automate processes.

Water utility operators around the world are under continuous pressure to operate efficiently, conserve water, reduce their environmental impact, and maintain a high level of supply and availability. The adoption of IoT sensors and artificial intelligence (AI) can help the water industry become more resilient and efficient. Many water utilities have started to implement technology such as IoT-connected sensors on pumps, valves, and meters, along with geographic information systems (GIS), supervisory control and data acquisition (SCADA), and advanced metering infrastructure (AMI). Each of these technologies helps to improve operations, and combined they produce a large volume of real-time data to which operators can apply artificial intelligence predictive modeling. Let's take a look at five benefits of IoT and AI.

With the rise of digitization, we're gathering more and more data that, if used to its full potential, will help businesses counter uncertainty and make business outcomes more predictable. Nowadays, companies face countless challenges -- inflation, supply chain delays, natural disasters, and global pandemics. The most valuable part of AI is its ability to take in huge amounts of data and calculate every possible outcome, then make recommendations based on a variety of parameters. It can also offer solutions to lessen these problems without the need for human interference. Combined with a fully integrated end-to-end ERP system, AI can be a critical factor in streamlining business processes.

One of the most widely used buzzwords in the logistics sector in 2022 is "digitalization." The word is a useful umbrella term for the evolution to computer-based processes from manual procedures that relied on pencils and clipboards in the warehouse or printed manifests at the loading dock. But references to the trend nearly always ignore the tactical steps needed to make digitalization happen. Your DC probably doesn't have a magic wand that transforms basic paper checklists into cloud-based software platforms. So how are practitioners driving toward the goal of pulling logistics processes into the 21st century?

In many parts of Asia, seasonal torrential rains bring with them floods that damage property and livelihood of citizens. Whereas in the past, city administrations, citizens and businesses can do almost nothing but ride out the unwelcoming waves of flood water and the potential diseases these carry, technologies like the Internet of Things (IoT), machine learning (ML) and artificial intelligence (AI) may provide respite for more forward-looking leaders. This is the case of the DKI Jakarta Provincial Government's Flood Control System in the Jakarta Smart City application. Developed by Jakarta Smart City in collaboration with the Jakarta Water Resource Service (DSDA) to optimise flood risk management in Jakarta, the project involved the use of IoT, AI and ML as part of an early warning system against the risk of floods in the city. As more organisations deploy IoT in commercial and industrial environments, the amount of data that is derived from these devices and sensors may prove important in improving quality, operational efficiency, and in the case of Jakarta – saving lives and property from natural disasters.

For Henkel, a chemical and consumer goods company, using analytics to gain insight into the supply chain has become hard-coded into its DNA over the past several years. With $20 billion in sales, the Düsseldorf, Germany-based company has three divisions: adhesive technologies, laundry and home care, and beauty care. Like other organizations, Henkel began a supply chain digitization journey several years ago, primarily to curb costs and become more efficient. But its initial foray into deploying Internet of Things (IoT) sensors to track various aspects of its supply chain – from manufacture and product supply to truck logistics and customer demand – gave way to a digital way of doing business that is automated, data-driven and agile in the face of rapid change. As moderator of a session on supply chain digitization at the Industrial AI Summit, Richard Self noted that Henkel's digitization brought the kind of ROI that companies seek when they introduce sensors, analytics dashboards and IoT applications into their environments.