Muhammad Intizar Ali

• PhD
• Professor (Assistant) at Dublin City University

Industry 4.0 (I4.0) knowledge graphs are a common way to represent industrial information models. Conventional SPARQL querying systems require the users to be familiar with the data schema and SPARQL syntax. However, this is often very difficult for many users in industrial production, who have mostly an engineering background, instead of a semanti...

The integration of heterogeneous and unstructured data in Industry 4.0, poses a significant challenge, particularly with advanced manufacturing techniques. To address this issue, Knowledge Graphs (KGs) have emerged as a pivotal technology, yet their deployment often encounters the problem of incompletion due to data diversity and diverse storage fo...

Federated Learning (FL) has received tremendous attention as a decentralized machine learning (ML) framework that allows distributed data owners to collaboratively train a global model without sharing raw data. Since FL trains the model directly on edge devices, the heterogeneity of participating clients in terms of data distribution, hardware capa...

Machine Learning approaches are excellent but require a large amount of data which is not easy to get. Data augmentation approaches are used to generate data and improve models’ performance. This study investigates the efficacy of machine learning models in temperature prediction within the domain of climate research, addressing the challenge of li...

Estimating the remaining useful life (RUL) of critical industrial assets is of crucial importance for optimizing maintenance strategies, enabling proactive planning of repair tasks, enhanced reliability, and reduced downtime in prognostic health management (PHM). Deep learning-based data-driven approaches have made RUL prediction a lot better, but...

The emergence of Industry 4.0 has transformed modern-day factories into high-tech industrial sites through rapid automation and increased access to real-time data. Deep learning approaches possessing superior capabilities for intelligent, data-driven fault diagnosis have become critical in ensuring process safety and reliability in these industrial...

p>The field of the Internet of Things (IoT) is expanding rapidly and shows the potential to completely transform the healthcare sector. The integration of machine learning algorithms in Internet of Medical Things (IoMT) systems has shown the potential to revolutionize healthcare by enabling efficient, accurate, and privacy-preserving services. Howe...

p>The field of the Internet of Things (IoT) is expanding rapidly and shows the potential to completely transform the healthcare sector. The integration of machine learning algorithms in Internet of Medical Things (IoMT) systems has shown the potential to revolutionize healthcare by enabling efficient, accurate, and privacy-preserving services. Howe...

The soil water storage capacity is critical for soil management as it drives crop production, soil carbon sequestration, and soil quality and health. It depends on soil textural class, depth, land-use and soil management practices; therefore, the complexity strongly limits its estimation on a large scale with conventional-process-based approaches....

Industry 4.0 (I4.0) is a new era in the industrial revolution that emphasizes machine connectivity, automation, and data analytics. The I4.0 pillars such as autonomous robots, cloud computing, horizontal and vertical system integration, and the industrial internet of things have increased the performance and efficiency of production lines in the ma...

Meaningful feature extraction from multivariate time-series data is still challenging since it takes into account the correlation between pairs of sensors as well as the temporal information of each time-series. Meanwhile, the huge industrial system has evolved into a data-rich environment, resulting in the rapid development and deployment of deep...

Federated learning is one of the emerging areas of research in computer science. It has shown great potential in some application areas and we are witnessing evidence of new approaches where millions or even billions of IoT devices can contribute collectively to achieve a common goal of machine learning through federation. However, existing approac...

The ongoing industrial revolution termed Industry 4.0 (I4.0) has borne witness to a series of profound changes towards increasing smart automation, particularly in the industrial sectors of automotive, aerospace, manufacturing, etc. Automatic welding, a widely applied manufacturing process in these domains, is not an exception of these changes. One...

With the advent of Industry 4.0 (I4.0) leading to the proliferation of industrial process data, deep learning (DL) techniques have become instrumental in developing intelligent fault diagnosis (FD) applications. However, de- spite their potentially superior process monitoring capa- bilities, DL-based FD models are poorly calibrated and generate poi...

Motivation. Industry 4.0 [1, 2] comes with unprecedented amounts of heterogeneous industrial data [3,4,5].

Advancements in semiconductor technology have reduced dimensions and cost while improving the performance and capacity of chipsets. In addition, advancement in the AI frameworks and libraries brings possibilities to accommodate more AI at the resource-constrained edge of consumer IoT devices. Sensors are nowadays an integral part of our environment...

With the introduction of ultra-low-power machine learning (TinyML), IoT devices are becoming smarter as they are driven by ML models. However, any loss of communication at the device level can lead to a failure of the entire IoT system or misleading information transmission. Since there exist numerous heterogeneous devices within an IoT system, it...

Invariable of the agriculture type (precision, smart, or digital), the monitoring process of factors that increase the crop yield and growth is mostly non-ML, manually structured approaches with practical pain points. In this scenario, to reduce monitoring costs and maintenance efforts, there is a requirement for low-cost semi-autonomous distribute...

Meeting design characteristics are specified by meeting organizers and participants. The different design characteristics have a direct impact on the quality of the meeting. In this paper, different meeting characteristics are assessed to determine the optimum conditions for effective meetings as perceived by the meeting members. A number of meetin...

This article presents a novel over-the-air (OTA) technique to remotely deploy tiny ML models over Internet of Things (IoT) devices and perform tasks, such as machine learning (ML) model updates, firmware reflashing, reconfiguration, or repurposing. We discuss relevant challenges for OTA ML deployment over IoT both at the scientific and engineering...

In training distributed machine learning, communicating model updates among workers has always been a bottleneck. The magnitude of impact on the quality of resultant models is higher when distributed training on low hardware specification devices and in uncertain real-world IoT networks where congestion, latency, band-width issues are common. In th...

Federated Learning (FL) is a state-of-the-art technique used to build machine learning (ML) models based on distributed data sets. It enables In-Edge AI, preserves data locality, protects user data, and allows ownership. These characteristics of FL make it a suitable choice for IoT networks due to its intrinsic distributed infrastructure. However,...

The proliferation of sensing technologies such as sensors has resulted in vast amounts of time-series data being produced by machines in industrial plants and factories. There is much information available that can be used to predict machine breakdown and degradation in a given factory. The downtime of industrial equipment accounts for heavy losses...

The majority of IoT devices like smartwatches, smart plugs, HVAC controllers, etc., are powered by hardware with a constrained specification (low memory, clock speed and processor) which is insufficient to accommodate and execute large, high-quality models. On such resource-constrained devices, manufacturers still manage to provide attractive funct...

Soil moisture is a key parameter of the climate system as it relates to plant transpiration and photosynthesis and impacts land–atmosphere interactions. Recent developments have seen an increasing number of electromagnetic sensors available commercially (EM) for soil volumetric water content (θ). Their use is constantly expanding, and they are beco...

Typically a Neural Networks (NN) is trained on data centers using historic datasets, then a C source file (model as a char array) of the trained model is generated and flashed on IoT devices. This standard process impedes the flexibility of billions of deployed ML-powered devices as they cannot learn unseen/fresh data patterns (static intelligence)...

Transmitting updates of high-dimensional neural network (NN) models between client IoT devices and the central aggregating server has always been a bottleneck in collaborative learning - especially in uncertain real-world IoT networks where congestion, latency, bandwidth issues are common. In this scenario, gradient quantization is an effective way...

Over the recent years, Industry 4.0 (I4.0) technologies such as the Industrial Internet of Things (IIoT), Artificial Intelligence (AI), and the presence of Industrial Big Data (IBD) have helped achieve intelligent Fault Detection (FD) in manufacturing. Notably, data-driven approaches in FD apply Deep Learning (DL) techniques to help generate insigh...

The majority of Internet of Things (IoT) devices are tiny embedded systems with a micro-controller unit (MCU) as its brain. The memory footprint (SRAM, Flash, and EEPROM) of such MCU-based devices is often very limited, restricting onboard Machine Learning (ML) model training for large trainsets with high feature dimensions. To cope with memory iss...

Training a problem-solving Machine Learning (ML) model using large datasets is computationally expensive and requires a scalable distributed training platform to complete training within a reasonable time frame. In this paper, we propose a novel concept where, instead of distributed training within a GPU cluster, we train one ML model by utilizing...

Edge analytics refers to the application of data analytics and Machine Learning (ML) algorithms on IoT devices. The concept of edge analytics is gaining popularity due to its ability to perform AI-based analytics at the device level, enabling autonomous decision-making, without depending on the cloud. However, the majority of Internet of Things (Io...

Edge analytics refers to the application of data analytics and Machine Learning (ML) algorithms on IoT devices. The concept of edge analytics is gaining popularity due to its ability to perform AI-based ana-lytics at the device level, enabling autonomous decision-making, without depending on the cloud. However, the majority of Internet of Things (I...

With the introduction of ultra-low-power machine learning (TinyML), IoT devices are becoming smarter as they are driven by Machine Learning (ML) models. However, any increase in the training data results in a linear increase in the space complexity of the ML models. It is highly challenging to deploy such ML models on IoT devices with limited memor...

The majority of IoT edge devices are embedded systems with a tiny microcontroller unit (MCU), which acts as its brain. When users want their edge devices to continuously improve for better edge-analytics results, there is a need to equip their devices with algorithms that can learn/train from the continuously evolving real-world data. Currently, su...

In recent years, due to technological advancements, the concept of Industry 4.0 (I4.0) is gaining popularity, while presenting several technical challenges being tackled by both the industrial and academic research communities. Semantic Web including Knowledge Graphs is a promising technology that can play a significant role in realizing I4.0 imple...

With the introduction of edge analytics, IoT devices are becoming smarter and ready for AI applications. However, any increase in the training data results in a linear increase in the space complexity of the trained Machine Learning (ML) models, which means they cannot be deployed on IoT devices that have limited memory. To alleviate such memory is...

In the Industry 4.0 era, Microcontrollers (MCUs) based tiny embedded sensor systems have become the sensing paradigm to interact with the physical world. In 2020, 25.6 billion MCUs were shipped, and over 250 billion MCUs are already operating in the wild. Such low-power, low-cost MCUs are being used as the brain to control diverse applications and...

In today's IoT smart environments, dozens of MCU-based connected device types exist such as HVAC controllers, smart meters, smoke detectors, etc. The security conditions for these essential IoT devices remain unsatisfactory since: (i) many of them are built with cost as the driving design tenet, resulting in poor configurations and open design; (ii...

Every modern household owns at least a dozen of IoT devices like smart speakers, video doorbells, smartwatches, where most of them are equipped with a Keyword spotting (KWS) system-based digital voice assistant like Alexa. The state-of-the-art KWS systems require a large number of operations, higher computation, memory resources to show top perform...

Recent advancements in the field of ultra-low-power machine learning (TinyML) promises to unlock an entirely new class of edge applications. However, continued progress is restrained by the lack of benchmarking Machine Learning (ML) models on TinyML hardware, which is fundamental to this field reaching maturity. In this paper, we designed 3 types o...

Deep learning (DL) using large scale, high-quality IoT datasets can be computationally expensive. Utilizing such datasets to produce a problem-solving model within a reasonable time frame requires a scalable distributed training platform/system. We present a novel approach where to train one DL model on the hardware of thousands of mid-sized IoT de...

Digital twin (DT) plays a pivotal role in the vision of Industry 4.0. The idea is that the real product and its virtual counterpart are twins that travel a parallel journey from design and development to production and service life. The intelligence that comes from DTs’ operational data supports the interactions between the DTs to pave the way for...

With the introduction of edge analytics, IoT devices are becoming smart and ready for AI applications. A few modern ML frameworks are focusing on the generation of small-size ML models (often in kBs) that can directly be flashed and executed on tiny IoT devices, particularly the embedded systems. Edge analytics eliminates expensive device-to-cloud...

Smart manufacturing or Industry 4.0, a trend initiated a decade ago, aims to revolutionize traditional manufacturing using technology driven approaches. Modern digital technologies such as the Industrial Internet of Things (IIoT), Big Data analytics, augmented/virtual reality, and artificial intelligence (AI) are the key enablers of new smart manuf...

Forestry 4.0 is inspired by the Industry 4.0 concept, which plays a vital role in the next industrial generation revolution. It is ushering in a new era for efficient and sustainable forest management. Environmental sustainability and climate change are related challenges to promote sustainable forest management of natural resources. Internet of Fo...

Welcome to this special issue of the Semantic Web (SWJ) journal. The special issue compiles four technical contributions that significantly advance the state-of-the-art in Semantic Web of Things for Industry 4.0 including the use of Semantic Web technologies and techniques in Industry 4.0 solutions.

Smart doorbells have been playing an important role in protecting our modern homes. Existing approaches of sending video streams to a centralized server (or Cloud) for video analytics have been facing many challenges such as latency, bandwidth cost and more importantly users' privacy concerns. To address these challenges, this paper showcases the a...

World Health Organisation (WHO) advises that humans must try to avoid touching their eye, nose and mouth, which is an effective way to stop the spread of viral diseases. This has become even more prominent with the widespread coronavirus (COVID-19), resulting in a global pandemic. However, we humans on average touch our face (eye, nose and mouth) 1...

Microcontroller Units (MCUs) in edge devices are resource constrained due to their limited memory footprint, fewer computation cores, and low clock speeds. These limitations constrain one from deploying and executing machine learning models on MCUs. To fit, deploy and execute Convolutional Neural Networks (CNNs) for any IoT use-case on small MCUs,...

In recent years, ML (Machine Learning) models that have been trained in data centers can often be deployed for use on edge devices. When the model deployed on these devices encounters unseen data patterns, it will either not know how to react to that specific scenario or result in a degradation of accuracy. To tackle this, in current scenarios, mos...

The design of products and services such as a Smart doorbell, demonstrating video analytics software/algorithm functionality, is expected to address a new kind of requirements such as designing a scalable solution while considering the trade-off between cost and accuracy; a flexible architecture to deploy new AI-based models or update existing mode...

The concept of the Internet of Things (IoT) is a reality now. This paradigm shift has caught everyones attention in a large class of applications, including IoT-based video analytics using smart doorbells. Due to its growing application segments, various efforts exist in scientific literature and many video-based doorbell solutions are commercially...

Industry 4.0 is considered to be the fourth industrial revolution introducing a new paradigm of digital, autonomous, and decentralized control for manufacturing systems. Two key objectives for Industry 4.0 applications are to guarantee maximum uptime throughout the production chain and to increase productivity while reducing production cost. As the...

The rapid evolution of digital technology and designed intelligence, such as the Internet of Things (IoT), Big data analytics, Artificial Intelligence (AI), Cyber-Physical Systems (CPS), has been a catalyst for the 4th industrial revolution (known as industry 4.0). Among others, the two key state-of-the-art concepts in Industry 4.0, are Industrial...

In an IoT system, the response time of edge devices is calculated during the design time. These edge devices continuously provide data streams to ensure the smooth execution of a real-time IoT system. However, edge devices are prone to errors, and very often suffer issues when trying to maintain a certain level of communication quality in the prese...

Industry 4.0 is considered to be the fourth industrial revolution introducing a new paradigm of digital, autonomous, and decentralized control for manufacturing systems. Two key objectives for Industry 4.0 applications are to guarantee maximum uptime throughout the production chain and to increase productivity while reducing production cost. As the...

Industry 4.0 refers to the 4th Industrial Revolution—the recent trend of automation and data exchange in manufacturing technologies. Traditionally, Manufacturing Executing System (MES) collects data, and it is only used for periodic reports giving insight about past events. It does not incorporate real-time data for up-to-date reports. Production t...

Paper: http://aics2019.datascienceinstitute.ie/papers/aics_29.pdf What's is this Smart Speaker capable of? Identify and recognize faces during the human gaze, wake up Alexa only when a known face is recognized A microphone array with an on-board chip hosting DSP based speech algorithms is used to capture, process and provide a noise suppressed v...

Advancements in semiconductor technology have reduced dimensions and cost while improving the performance and capacity of chipsets. In addition, advancement in the AI frameworks and libraries brings possibilities to accommodate more AI at the resource-constrained edge of consumer IoT devices. Sensors are nowadays an integral part of our environment...

In this paper, we propose a domain agnostic and query driven approach to monitor, assess, and analyze quality of the linked data hosted by public SPARQL endpoints. We identified various quality related met-rics for linked datasets and used linked data vocabulary to represent quality information. We provide a Linked Data Quality (LDQ) dataset, which...

The Internet of Thing (IoT) is generating an unprecedented volume of data, facilitating the rise of the Data Economy. Under this ecosystem, the IoT data marketplace (IDM) provides an online platform for IoT data trading. Most current IDM solutions are centralized, serving as an intermediary between the data provider and consumer. They support selli...

With the ever increasing number of IoT devices getting connected, an enormous amount of streaming data is being produced with very high velocity. In order to process these large number of data streams, a variety of stream processing platforms and query engines are emerging. In the stream query processing, an infinite data stream is divided into sma...

An increasing number of cities are confronted with challenges resulting from the rapid urbanisation and new demands that a rapidly growing digital economy imposes on current applications and information systems. Smart city applications enable city authorities to monitor, manage and provide plans for public resources and infrastructures in city envi...

Stream reasoning is an emerging research area focused on providing continuous reasoning solutions for data streams. The exponential growth in the availability of streaming data on the Web has seriously hindered the applicability of state-of-the-art expressive reasoners, limiting their applicability to process streaming information in a scalable way...

The nature of Web data is changing. The popularity of news feeds and social media, the rise of the Web of Things, and the adoption of sensor technologies are examples of streaming data that reached the Web scale. The different nature of streaming data calls for specific solutions to problems like data integration and analytics. There is a need for...

AI techniques combined with recent advancements in the Internet of Things, Web of Things, and Semantic Web-jointly referred to as the Semantic Web-promise to play an important role in Industry 4.0. As part of this vision, the authors present a Semantic Web of Things for Industry 4.0 (SWeTI) platform. Through realistic use case scenarios, they showc...

We propose a sequence-to-sequence based method to predict vessels' destination port and estimated arrival time. We consider this problem as an extension of trajectory prediction problem, that takes a sequence of historical locations as input and returns a sequence of future locations, which is used to determine arrival port and estimated arrival ti...

In this paper, we propose a neural network based system to predict vessels' trajectories including the destination port and estimated arrival time. The system is designed to address DEBS Grand Challenge 2018, which provides a set of data streams containing vessel information and coordinates ordered by time. Our goal is to design a system which can...

With the growing adoption of IoT and sensor technologies, an enormous amount of data is being produced at a very rapid pace and in different application domains. This sensor data consists mostly of live data streams containing sensor observations, generated in a distributed fashion by multiple heterogeneous infrastructures with minimal or no intero...

Nowadays a handful applications are designed to consume dynamic real-time continuous stream data from IoT, Social network, Smart sensors and more. Several RDF Stream Processing (RSP) engines are available to query those data streams. Application designers have the freedom to select the best available RSP engine based on their application requiremen...

This article presents a flexible architecture for Internet of Things (IoT) data analytics using the concept of fog computing. The authors identify different actors and their roles in order to design adaptive IoT data analytics solutions. The presented approach can be used to effectively design robust IoT applications that require a tradeoff between...

With the growing popularity of Internet of Things (IoT) and sensing technologies, a large number of data streams are being generated at a very rapid pace. To explore the potentials of the integration of IoT and semantic technologies, a few RDF Stream Processing (RSP) query engines are made available which are capable of processing, analyzing and re...

Environmental awareness and knowledge may help people to take more informed decisions in their everyday lives, ensuring their health and safety. The Web of Things enables embedded sensors to become easily deployed in urban areas for environmental monitoring such as air quality, electromagnetism, radiation, etc. In this paper, we propose an ecosyste...

An ever growing interest and wide adoption of Internet of Things (IoT) and Web technologies are unleashing a true potential of designing a broad range of high-quality consumer applications. Smart cities, smart buildings, and e-health are among various application domains which are currently benefiting and will continue to benefit from IoT and Web t...

In this demonstration, we present our Semantic Web of Things~(SWoT) prototyping toolkit called SWoTSuite. It is a set of tools supporting an easy and fast prototyping of end-to-end SWoT applications. SWoTSuite facilitates - (i) automation of application development life-cycle, (ii) reducing the amount of time and effort required for developing WoT...

This paper introduces an automated rule discovery approach for IoT device data (S-LOR: Sensor-based Linked Open Rules) and its use in smart cities. S-LOR is built following Linked OpenData(LOD)Standardsandprovidessupportforsemantics- based mechanisms to share, reuse and execute logical rules for interpreting data produced by IoT systems. S-LOR fol-...