The increasing use of Internet-of-Things (IoT) devices for monitoring a wide spectrum of applications, along with the challenges of "big data" streaming support they often require for data analysis, is nowadays pushing for an increased attention to the emerging edge computing paradigm. In particular, smart approaches to manage and analyze data directly on the network edge, are more and more investigated, and Artificial Intelligence (AI) powered edge computing is envisaged to be a promising direction. In this paper, we focus on Data Centers (DCs) and Supercomputers (SCs), where a new generation of high-resolution monitoring systems is being deployed, opening new opportunities for analysis like anomaly detection and security, but introducing new challenges for handling the vast amount of data it produces. In detail, we report on a novel lightweight and scalable approach to increase the security of DCs/SCs, that involves AI-powered edge computing on high-resolution power consumption. The method -- called pAElla -- targets real-time Malware Detection (MD), it runs on an out-of-band IoT-based monitoring system for DCs/SCs, and involves Power Spectral Density of power measurements, along with AutoEncoders. Results are promising, with an F1-score close to 1, and a False Alarm and Malware Miss rate close to 0%. We compare our method with State-of-the-Art MD techniques and show that, in the context of DCs/SCs, pAElla can cover a wider range of malware, significantly outperforming SoA approaches in terms of accuracy. Moreover, we propose a methodology for online training suitable for DCs/SCs in production, and release open dataset and code.
NOTE: Controls wasn't the only area of interest at PACK EXPO. CONTROLS INNOVATIONS Two PACK EXPO Las Vegas exhibitors a few aisles apart in the Lower South Hall featured analytics platforms that provide better real-time visibility into the manufacturing process. From Oden Technologies comes The Oden Platform (1). It's a comprehensive industrial Internet of Things analytics platform that provides employees at each level of a manufacturing plant with clear visibility into multiple data sets pertaining to the manufacturing process. Oden helps manufacturers monitor their production process and improve operational efficiency in real time by diagnosing problems that otherwise would have been missed. Oden helps users track performance metrics of multiple assets and accurately predict downtime based on historical data. In addition, by utilizing the platform, manufacturers can reduce bottlenecks at each stage of production and can save costs by eliminating quality issues, waste, and downtime.Photo 1 This platform is designed to help manufacturing units attain the best performance out of their manufacturing assets and leverage artificial intelligence (AI) and machine learning (ML) algorithms to empower prescriptive analytics. This allows employees on the floor to diagnose and mitigate issues as soon as they arise or offer alerts to avoid issues. In a connected manufacturing environment, companies need real-time accurate insights to improve the productivity and efficiency of their production lines. While manufacturers are increasingly willing to adopt manufacturing analytics practices, legacy equipment and limited technical know-how among machine operators are holding them back. In addition, employees on the floor are unable to make the most of the analytics tools at their disposal and are simply not achieving the expected impact on their profitability.
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.
It wasn't so long ago that business analytics operated on a months-long cycle. For most of the twentieth century, the main interaction between a company and its data was a regular review of its most easily quantifiable measures, in the form of annual or quarterly financial assessments. Today, interacting with data this infrequently would be unimaginable in even a small business. As data availability and transfer speeds have grown at exponential rates, the time lag between intake and analysis of data has shortened to the point that, today, real-time data analytics is often part of an organisation's standard operating procedure. There are few industries which have not been lifted up by this rising tide of data.
We've written a lot about how new technologies are building a better supply chain. But technologies such as AI, IoT and advanced analytics can only achieve their true potential if all parties within the supply chain network are working together. Even the smallest, most well-intentioned decisions made by individual stakeholders can cause catastrophic failures in the manufacturing and delivery of goods. Modern collaborative business planning lets partners on a supply chain work off shared data unspoiled by human misconceptions and misestimations. Automated integrated planning removes bias from supply chain data and creates a single, transparent source that engenders collaboration.
Following consumer adoption and changing attitudes, purpose-built smart assistants like Alexa for Business are paving the way for AI at work. Smart assistants naturally complement intelligent smartphone apps, such as Edison, formerly EasilyDo. Using predictive analytics and deep learning, these smart apps extract meaningful, actionable data in real-time. For example, Edison proactively notifies you when it's time to leave based on traffic patterns and meeting start times. While these apps classify as consumer offerings, it's easy to understand why employees want these tools at work.
Twenty IT leaders look into their crystal balls to predict the technologies and trends that will drive the sector in 2020. CIO Australia asked Australian technology bosses about their top line predictions for 2020, the technologies that will have the greatest impact next year, and what top trends will impact the IT and business landscape. Here are the predictions from IT leaders across vendor land to CIOs and CTOs across a host of industries. Intelligent systems (machine learning, artificial intelligence and automation) are the top trends in 2020. Intelligent systems will have a significant impact on increasing situational awareness (insights) and using these insights to enhance decision making – to deliver optimal outcomes for customers. One large impact on the business landscape will be the expanding role of digital twins – extending beyond the optimisation of individual assets/systems to driving improvements at the organisational level. We are introducing a reference to'Digital Twin of Operations (DTO)' – having recently built some proof of concepts. The DTO brings together inputs from a range of different systems and assets onto a common data & analytics platform; is able to process large-scale and real-time data sets to simulate millions of'what if' scenarios through cloud technologies.
Research and Markets has published a new report titled: "Digital Transformation Asia Pacific: 5G, Artificial Intelligence, Internet of Things, and Smart Cities in APAC 2019 – 2024." This report identifies market opportunities for deployment and operations of key technologies within the Asia-Pacific region. The report cites examples such as H3C Technologies plans to offer a comprehensive digital transformation platform within Thailand. The platform includes core cloud and edge computing, big data, interconnectivity, information security, IoT, AI, and 5G solutions. The report predicts what will happen with 5G technology, to identifying how 5G will digitally transform business in Asia Pacific.
--Global physical event detection has traditionally relied on dense coverage of physical sensors around the world; while this is an expensive undertaking, there have not been alternatives until recently. The ubiquity of social networks and human sensors in the field provides a tremendous amount of real-time, live data about true physical events from around the world. However, while such human sensor data have been exploited for retrospective large-scale event detection, such as hurricanes or earthquakes, they has been limited to no success in exploiting this rich resource for general physical event detection. Prior implementation approaches have suffered from the concept drift phenomenon, where real-world data exhibits constant, unknown, unbounded changes in its data distribution, making static machine learning models ineffective in the long term. We propose and implement an end-to-end collaborative drift adaptive system that integrates corroborative and probabilistic sources to deliver real-time predictions. Furthermore, out system is adaptive to concept drift and performs automated continuous learning to maintain high performance. We demonstrate our approach in a real-time demo available online for landslide disaster detection, with extensibility to other real-world physical events such as flooding, wildfires, hurricanes, and earthquakes. Physical event detection, such as extreme weather events or traffic accidents have long been the domain of static event processors operating on numeric sensor data or human actors manually identifying event types. However, the emergence of big data and associated data processing and analytics tools and systems have led to several applications in large-scale event and trend detection in the streaming domain -. However, it is important to note that many of these works are a form of retrospective analysis, as opposed to true real-time event detection, since they perform analyses on cleaned and processed data within a short-time frame in the past, with the assumption that their approaches are sustainable and will continue to function over time.
Artificial Intelligence and the Internet of Things are both one of a kind innovations all alone, however, what makes them all the more intriguing is the place they converge. As the applications of IoT and AI are independently interesting, their joined use cases hold even more dazzling potential, as indicated by scientists and industry specialists. The Internet of Things is getting more brilliant. Organizations are fusing artificial intelligence--specifically, machine learning--into their IoT applications. With an influx of investment, a pile of new products, and a rising tide of big business organizations, artificial intelligence is making a splash in the Internet of Things (IoT).