No-code IT/OT convergence with an Industrial IoT hub


It's one thing to have sensors, it's another to extract value from them. As businesses across all sectors take their first steps toward Industrial IoT and edge computing solutions, extracting machine/sensor data and making it available for things like predictive maintenance and condition-based monitoring can prove challenging. The gap between OT and IT is widest at the edge – factories, oil rigs, wind farms, water treatment plants and more. In this webinar, we'll explain how an IIoT edge hub bridges the gap by collecting, parsing and storing machine/sensor data, and making it accessible via standard protocols such as MQTT and SQL – all without the need to write a single line of code. If you have already registered, click here to access.

From health care to infrastructure, how AI is changing the world for the better

MIT Technology Review

Over the past several years, our world has been confronted with a range of unprecedented and, at times, deadly challenges--from the covid-19 pandemic to severe weather conditions, and a concurrent rise of societal issues including aging population, urban congestion, and unequal access to health care. But as the development of artificial intelligence (AI) and its applications grow, AI technologies are playing an instrumental role in addressing socio-economic and environmental challenges faced by the modern world, ultimately helping us to reach a better standard of living. One of the most promising applications of AI in recent years has been in augmenting human workers in key sectors that are chronically understaffed, contributing to major advances in solving challenging social issues. In China, for instance, the medical system has long grappled with a shortage of health-care professionals, with an average of just 17.9 doctors for 10,000 people. The situation is even more imbalanced in small towns and rural areas, forcing many patients to travel long distances to cities to access quality medical care and specialist treatments.

A Texas town approved an AI border security camera


The city council of Presidio, Texas, voted on June 7, 2021 to approve locating a new camera system for Customs and Border Patrol on city property. The Sentry camera is a re-deployable 30-foot-tall tower bristling with sensors and powered by solar panels. It's made by Anduril, a security technology startup. As the city council agenda notes, Presidio approved locating one such Sentry "on city property near the City of Presidio Waste Water Treatment Plant." Presidio, population 4,000, sits on the US side of the confluence of the Rio Grande and Rio Conchos rivers, across from Ojinaga in Mexico, in the broader Big Bend region of the state.

Graph Neural Network-Based Anomaly Detection in Multivariate Time Series Artificial Intelligence

Given high-dimensional time series data (e.g., sensor data), how can we detect anomalous events, such as system faults and attacks? More challengingly, how can we do this in a way that captures complex inter-sensor relationships, and detects and explains anomalies which deviate from these relationships? Recently, deep learning approaches have enabled improvements in anomaly detection in high-dimensional datasets; however, existing methods do not explicitly learn the structure of existing relationships between variables, or use them to predict the expected behavior of time series. Our approach combines a structure learning approach with graph neural networks, additionally using attention weights to provide explainability for the detected anomalies. Experiments on two real-world sensor datasets with ground truth anomalies show that our method detects anomalies more accurately than baseline approaches, accurately captures correlations between sensors, and allows users to deduce the root cause of a detected anomaly.

COMSovereign to Acquire RVision, Inc., Expanding Smart City Capabilities


COMSovereign Holding Corp. (NASDAQ: COMS) ("COMSovereign" or "Company"), a U.S.-based developer of 4G LTE Advanced and 5G Communication Systems and Solutions, today announced that it has executed an agreement to acquire RVision, Inc. ("RVision"), a developer of technologically advanced, environmentally hardened video and communications products and physical security solutions designed for government and private sector commercial industries. Terms of the transaction include total consideration of approximately $5.58 million consisting exclusively of shares of restricted common stock. The transaction is expected to close within approximately 15 days subject to traditional closing conditions. Smart Cities and Smart Campuses (educational and industrial) are urban areas designed to integrate advanced technologies including IoT ("Internet of Things"), AI ("Artificial Intelligence"), machine learning, Big Data, and sustainable or "green" energy systems to benefit and secure the daily lives of its residents. Around the world today, these technologies are being deployed to efficiently improve public services and safety through enhancements to everything from mass transportation and waste management to the real-time monitoring of environmental conditions including air and water quality.

Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems Artificial Intelligence

The threats faced by cyber-physical systems (CPSs) in critical infrastructure have motivated research into a multitude of attack detection mechanisms, including anomaly detectors based on neural network models. The effectiveness of anomaly detectors can be assessed by subjecting them to test suites of attacks, but less consideration has been given to adversarial attackers that craft noise specifically designed to deceive them. While successfully applied in domains such as images and audio, adversarial attacks are much harder to implement in CPSs due to the presence of other built-in defence mechanisms such as rule checkers(or invariant checkers). In this work, we present an adversarial attack that simultaneously evades the anomaly detectors and rule checkers of a CPS. Inspired by existing gradient-based approaches, our adversarial attack crafts noise over the sensor and actuator values, then uses a genetic algorithm to optimise the latter, ensuring that the neural network and the rule checking system are both deceived.We implemented our approach for two real-world critical infrastructure testbeds, successfully reducing the classification accuracy of their detectors by over 50% on average, while simultaneously avoiding detection by rule checkers. Finally, we explore whether these attacks can be mitigated by training the detectors on adversarial samples.

These robots aren't crappy. But they do handle your crap.


As I look out over the Port of Los Angeles with its shipping cranes and waterways, I think about the 800,000 gallons of water similar in quality to drinking water lying in tanks under my feet. Less than 24 hours earlier, it had been raw sewage entering the Terminal Island Water Reclamation plant, where environmental engineer Lance Thibodeaux is showing me around so I can see high-tech filtration in action. What made the water's transformation possible? Advanced purification systems, constantly and automatically run by a centralized computer program housed in a small office a few hundred yards away. Between heat, fluid dynamics, bacteria, and gravity, nature has its own tools for safely reabsorbing human waste back into the environment.

Sensor Technology and AI for Smart Water Management - ELE Times


Sensor Technology and Artificial Intelligence for Water Management seem quite fascinating. Likewise, water conservation for sustainable development has been and still the most apprehended process. A better conservation and management process would necessarily help the economy grow secure and stable for the future. From the elementary levels of education, we learn about the usage of water efficiently for better health and stability. Conservation and management have always been a moral trait and governing this with maximum advocacy was always the challenge.

How AI Is Transforming The Water Sector


Human settlement has always been dependent on a stable supply of clean water nearby. With the increase in global population and a decline in the quality of our freshwater resources, we are constantly looking for technologies that will ensure a reliable supply of clean water. The Union Budget 2021-22 announced Jal Jeevan Mission (Urban) to bring safe water to 2.86 Cr households through tap connection. This in line with the Centre's rural water supply project launched in 2019. Finance minister Nirmala Sitharaman announced an outlay of INR 50,011 Cr for this scheme.

Optimization of operation parameters towards sustainable WWTP based on deep reinforcement learning Artificial Intelligence

A large amount of wastewater has been produced nowadays. Wastewater treatment plants (WWTPs) are designed to eliminate pollutants and alleviate environmental pollution resulting from human activities. However, the construction and operation of WWTPs still have negative impacts. WWTPs are complex to control and optimize because of high nonlinearity and variation. This study used a novel technique, multi-agent deep reinforcement learning (DRL), to optimize dissolved oxygen (DO) and dosage in a hypothetical WWTP. The reward function is specially designed as LCA-based form to achieve sustainability optimization. Four scenarios: baseline, LCA-oriented, cost-oriented and effluent-oriented are considered. The result shows that optimization based on LCA has lowest environmental impacts. The comparison of different SRT indicates that a proper SRT can reduce negative impacts greatly. It is worth mentioning that the retrofitting of WWTPs should be implemented with the consideration of other environmental impacts except cost. Moreover, the comparison between DRL and genetic algorithm (GA) indicates that DRL can solve optimization problems effectively and has great extendibility. In a nutshell, there are still limits and shortcomings of this work, future studies are required.