Collaborating Authors


AI in IoT for a Better Future - NASSCOM Community


Introducing IoT & AI Artificial intelligence helps machines to behave like humans such as face recognition, decision making, learning and solving problems. Artificial Intelligence are used for learning and making self-decisions by using the process of complex organized or unorganized data. This technology has given a new horizon to the digital world like the way smartphones made a change in our lives. Every day, we get to hear about new upgrades and new technologies bringing rapid change in the globe. With every change, the tech world is also growing resulting in advanced technology which is bringing us closer. Such an example is the development & advancement of IoT or internet of things. Here artificial experience plays the role to speed up user experience. Before getting into the technical skills of IoT, lets understand what is it and where is it required? (Picture Reference: Actually IoT cannot work without AI. Why? Internet of things (IoT) is a network of technologies or sensors that contains some advance technology embedded into it. It helps in communicating and interacting with their data. The process involves receiving and transferring data through the network without human interactions or human to computer involvement. These data from devices or sensors can be stored in the cloud and it can be made available for real-time analytics. IoT collects these vast amount of data from different environment. With the help of data science and applying analytics, AI converts these collective data into applications. So the whole process involves collecting and processing data. AI and IoT: Why Do We Need It? According to a report, companies like Deloitte have already using AI and IOT for establishing themselves in the market in 2017.  So why is it so important? Actually artificial intelligence has become a perfect solution to manage multiple connected IoT elements, its unlimited processing and learning abilities. These are considered to be quite useful for making sense of millions of data transmitted by IoT devices. (Reference: ) How Does The Steps Takes Place? We can call IoT as the data “supplier” while machine learning can be considered as data “miner.” The process takes place as follows: IoT sensors supplies millions of data points. The “miner” or machine learning identifies the relations between them Extract meaningful insight from these variables. Transport it to the storage for further analysis. (Picture Reference: Earlier the traditional analytical approach was used which was as follow: The system gathers past data. Data processing. Generate reports. Thus we can conclude that IoT and machine learning works more on prediction. It starts with the desired outcome and searches interactions between input variables to produce results. As more data are being received and aggregated, the system returns even more accurate predictions due to its smart thinking. In this way, businesses can conclude to a perfect decision without actual “thinking” or human interaction. How IoT Benefits From AI? Soon, IoT would produce vast amount of data due to the rapid growing of devices and sensors. According to a research, 50 billion devices will be connected to the internet by 2020, ranging from smartphones, gadgets, smart watches, various computer systems and vehicles. These data would be a lot helpful for various things such as predicting natural calamities, accidents and crimes, helps doctors getting real-time information from medical equipment, optimized productivity across industries, predictive maintenance on equipment and machinery, create smart homes with connected appliances and provide critical communication between self-driving cars. The possibilities are endless. (Picture Reference: These big data are important only when it is transformed into valuable and actionable information within a given time period. Obviously it is not possible for human hands to do it. This is where artificial intelligence comes into play. AI collects the data and extracts the meaning from it by applying analytics. When we feed data from IoT devices into an AI system, it reviews and analyzes the data, produces decisions made either by machines or humans. (Reference: Examples showing implementing AI in IoT applications. Smart decisions. When a device detects unusual conditions due to any error, it needs to know how to and when to react or whether it need human assistance. Obviously intelligent learning and decision-making capabilities are required to make such wise decisions. Google uses this approach in the Rank Brain algorithm. Once the solution is made, it responds in real-time without any human intervention. (Reference: Smart Meters. Smart meters use specially designed sensors, incorporated into smart grids to record and upload electrical and background data. Here Artificial Intelligence techniques are applied to the grid to integrate privacy. They are used in every electricity consumption unit. Not only does they have the bidirectional flow of both electricity, they are equipped with real-time sensors which collects data on relevant factors that includes frequencies used by different equipment and appliances. (Reference: Boosting efficiency. Machine learning with AI can decipher trends and make predictions about future events, by applying predictive analytics. This shows the real benefits of IoT in a variety of manufacturing industries. Healthcare. In the healthcare sector, AI with IoT can improve patient care. Sensors from medical devices such as healthcare mobile apps, fitness trackers and digital medical records have been producing and storing patient’s data. The AI and IoT approach can help predict diseases, suggest preventive maintenance, track physical activity, heart rate, body mass, temperature and provide drug administration by reviewing the medical history and identifying the health problem. When it is regarding health protection or disease control, patients and doctors would accept the benefits that come with the AI and IoT approach. (Reference: Forecasting. Accurate forecasts help farmers to plan farming or harvesting. Train or plane schedules fully depends on weather forecasting to modify for expected weather interruptions. Businesses that are weather dependent, such as landscaping or utility companies can accurately employ labor and resources according to expected weather events. AI can help make more accurate forecasting. Artificial intelligence (AI) techniques apply its method on past predictions and actual outcomes. By comparing predictions with outcomes, it produces results for the future, with greater accuracy. AI feed both old and currently available data into algorithms that effective at past occurrences with future predictions. (Reference: Scalability. IoT can scale data. It means: AI extracts information from one device. Analyses and summarizes the data. Transfer it to the other. Thus it reduces the enormous amount of data to a lesser amount and enables a larger number of IoT devices to be connected to the network. This is called scalability. (Picture Reference: ) Smart Devices: Today we have basic things fitted with technology like smart TV, smart watch, smart security system. Even we have “intelligent” vacuum cleaners, doorbells and lightning systems which have already come to the market. All this is due to artificial intelligence and it do makes life easier. AI can make life in smart homes even more comfortable. It can detect your mood and analyze your interaction with home objects such as Adjusting temperature for both heating and cooling. Adjusting lighting. Put on music of your choice. Close or open windows depending on the weather. Conclusion The IoT and Artificial Intelligence (AI) will play a vital role in the future as it has become a growing need for technologies in both private and government sectors. Engineers, scientists and technologists have already started to implement it in various levels. The potential opportunities and benefits of both AI and IoT can be gained once they are combined, both at the devices end as well as at the server. (References :, , ) Written by: Ayanti Goswami

6G White Paper on Edge Intelligence Artificial Intelligence

In this white paper we provide a vision for 6G Edge Intelligence. Moving towards 5G and beyond the future 6G networks, intelligent solutions utilizing data-driven machine learning and artificial intelligence become crucial for several real-world applications including but not limited to, more efficient manufacturing, novel personal smart device environments and experiences, urban computing and autonomous traffic settings. We present edge computing along with other 6G enablers as a key component to establish the future 2030 intelligent Internet technologies as shown in this series of 6G White Papers. In this white paper, we focus in the domains of edge computing infrastructure and platforms, data and edge network management, software development for edge, and real-time and distributed training of ML/AI algorithms, along with security, privacy, pricing, and end-user aspects. We discuss the key enablers and challenges and identify the key research questions for the development of the Intelligent Edge services. As a main outcome of this white paper, we envision a transition from Internet of Things to Intelligent Internet of Intelligent Things and provide a roadmap for development of 6G Intelligent Edge.

pAElla: Edge-AI based Real-Time Malware Detection in Data Centers Machine Learning

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.

Report: Innovative New Controls at PACK EXPO Las Vegas


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.

A Survey on Edge Intelligence Artificial Intelligence

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.

How bigger data is activating analytics


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.

Collaborative business planning harnesses the power of the global supply chain - IBM Services


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.

The Closing Bulletin Joe Baguley - VMware's Joe Baguley on AI in the workplace


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.

20 on 2020 - IT leaders dish out predictions


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.

Report highlights Asia Pacific digital transformation market -


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.