--In this work, we explore the application of recent data imputation techniques to enhance monitoring and management of water distribution networks using smart water meters, based on data derived from a real-world IoT water grid monitoring deployment. Despite the detailed data produced by such meters, data gaps due to technical issues can significantly impact operational decisions and efficiency. Our results, by comparing various imputation methods, such as k-Nearest Neighbors, MissForest, Transformers, and Recurrent Neural Networks, indicate that effective data imputation can substantially enhance the quality of the insights derived from water consumption data as we study their effect on accuracy and reliability of water metering data to provide solutions in applications like leak detection and predictive maintenance scheduling. In the era of smart cities and advanced utility management, the monitoring of water grids has become increasingly pivotal to ensuring efficient distribution, sustainability, and infrastructure reliability. However, despite their sophistication, the occurrence of missing data due to various factors--ranging from technical malfunctions to data transmission errors-- remains an open challenge that undermines the integrity and actionable insights that can be derived from the datasets produced by such infrastructure. Moreover, the significance of addressing missing data extends beyond mere data completeness. In the context of water grid monitoring, it impacts decision-making processes related to water management, leak detection, and predictive maintenance, all of which have profound implications for operational efficiency and environmental sustainability.

Rosário said the chatbot processed a 250-character command and took some 15 seconds to employ its algorithmic magic and spit out a policy -- a process that would normally take him about three days. The result, he said, showcased how artificial intelligence can be a useful tool for optimizing and improving public service. Yet Brazil's first ChatGPT-crafted law has launched the South American nation into a debate ringing across the globe: As artificial intelligence takes the world by storm, is society gearing toward a future where automation replaces humans?

Technology has enabled humans to work smarter and more efficiently. In theory, this allows them to be more productive. It's an even taller order for developers in smart city environments: to harness technologies like artificial intelligence (AI) to enable urban environments to operate more efficiently, utilize resources more intelligently, reduce crime and pollution, improve mobility, rid cities of traffic backlogs, enhance community safety, encourage social inclusivity, attract and support business, provide more infrastructural services, support the vulnerable, make city information available to citizens at the click of a button, and offer ordinary people a sustainable, eco-friendly lifestyle. Smart cities are made possible by the intelligent gathering and utilization of data from numerous sources. But development is something of a moving target as technology matures.

This image shows a traditional water meter that's been converted into a web API, using a cheap ESP32 camera and machine learning to understand the dials and numbers. I expect there are going to be billions of devices like this deployed over the next decade, not only for water meters but for any older device that has a dial, counter, or display. I've already heard from multiple teams who have legacy hardware that they need to monitor, in environments as varied as oil refineries, crop fields, office buildings, cars, and homes. Some of the devices are decades old, so until now the only option to enable remote monitoring and data gathering was to replace the system entirely with a more modern version. This is often too expensive, time-consuming, or disruptive to contemplate.

Around 163 million people in India have no access to clean water. Further, water wastage in India is appallingly high at about 125 million litres a day. The numbers are worrying and show no sign of abating. Meanwhile, developed nations are leveraging artificial intelligence to move closer towards sustainability goals. And India is catching up in a small way.

A Texas company is using edge computing and IoT sensors to help cities modernize crumbling water infrastructure and inaccurate water meters. The American Society of Civil Engineers has given the country's drinking water system a D- for the last 10 years. Many components of city water systems date back to the Civil War era. Olea Edge Analytics is using 21st century technology to spot needed repairs and make sure water bills are accurate. Dave Mackie, Olea Edge Analytics' CEO, said the company combines edge computing with artificial intelligence and machine learning to help cities make more informed decisions.

The goal of the project is to read the exact consumption of water in cubic meters. As shown on the picture below, the cubic meters are written in white on a black background. As YOLO is one of the best Convolutional Neural Network (CNN) algorithms for object detection, we decided to implement our model with a Pytorch version of YOLOv4. We defined 12 classes of objects that we want to detect on the pictures: the 10 digits from 0 to 9, the part of the meter corresponding to the liters, and the whole counter. We labelled each picture by drawing the bounding boxes of each object found.

Amazon's Echo devices and its virtual assistant are meant to help find answers by listening for your voice commands. However, police in Arkansas want to know if one of the gadgets overheard something that can help with a murder case. According to The Information, authorities in Bentonville issued a warrant for Amazon to hand over any audio or records from an Echo belonging to James Andrew Bates. Bates is set to go to trial for first-degree murder for the death of Victor Collins next year. Amazon declined to give police any of the information that the Echo logged on its servers, but it did hand over Bates' account details and purchases.