Sensor data fusion for the industrial artificial intelligence of things

The emergence of smart sensors, artificial intelligence, and deep learning technologies yield artificial intelligence of things, also known as the AIoT. Sophisticated cooperation of these technologies is vital for the effective processing of industrial sensor data. This paper introduces a new framework for addressing the different challenges of the AIoT applications. The proposed framework is an intelligent combination of multi-agent systems, knowledge graphs and deep learning. Deep learning architectures are used to create models from different sensor-based data. Multi-agent systems can be used for simulating the collective behaviours of the smart sensors using IoT settings. The communication among different agents is realized by integrating knowledge graphs. Different optimizations based on constraint satisfaction as well as evolutionary computation are also investigated. Experimental analysis is undertaken to compare the methodology presented to state-of-the-art AIoT technologies. We show through experimentation that our designed framework achieves good performance compared to baseline solutions.publishedVersio

Enhanced non-parametric sequence learning scheme for internet of things sensory data in cloud infrastructure

The Internet of Things (IoT) Cloud is an emerging technology that enables machine-to-machine, human-to-machine and human-to-human interaction through the Internet. IoT sensor devices tend to generate sensory data known for their dynamic and heterogeneous nature. Hence, it makes it elusive to be managed by the sensor devices due to their limited computation power and storage space. However, the Cloud Infrastructure as a Service (IaaS) leverages the limitations of the IoT devices by making its computation power and storage resources available to execute IoT sensory data. In IoT-Cloud IaaS, resource allocation is the process of distributing optimal resources to execute data request tasks that comprise data filtering operations. Recently, machine learning, non-heuristics, multi-objective and hybrid algorithms have been applied for efficient resource allocation to execute IoT sensory data filtering request tasks in IoT-enabled Cloud IaaS. However, the filtering task is still prone to some challenges. These challenges include global search entrapment of event and error outlier detection as the dimension of the dataset increases in size, the inability of missing data recovery for effective redundant data elimination and local search entrapment that leads to unbalanced workloads on available resources required for task execution. In this thesis, the enhancement of Non-Parametric Sequence Learning (NPSL), Perceptually Important Point (PIP) and Efficient Energy Resource Ranking- Virtual Machine Selection (ERVS) algorithms were proposed. The Non-Parametric Sequence-based Agglomerative Gaussian Mixture Model (NPSAGMM) technique was initially utilized to improve the detection of event and error outliers in the global space as the dimension of the dataset increases in size. Then, Perceptually Important Points K-means-enabled Cosine and Manhattan (PIP-KCM) technique was employed to recover missing data to improve the elimination of duplicate sensed data records. Finally, an Efficient Resource Balance Ranking- based Glow-warm Swarm Optimization (ERBV-GSO) technique was used to resolve the local search entrapment for near-optimal solutions and to reduce workload imbalance on available resources for task execution in the IoT-Cloud IaaS platform. Experiments were carried out using the NetworkX simulator and the results of N-PSAGMM, PIP-KCM and ERBV-GSO techniques with N-PSL, PIP, ERVS and Resource Fragmentation Aware (RF-Aware) algorithms were compared. The experimental results showed that the proposed NPSAGMM, PIP-KCM, and ERBV-GSO techniques produced a tremendous performance improvement rate based on 3.602%/6.74% Precision, 9.724%/8.77% Recall, 5.350%/4.42% Area under Curve for the detection of event and error outliers. Furthermore, the results indicated an improvement rate of 94.273% F1-score, 0.143 Reduction Ratio, and with minimum 0.149% Root Mean Squared Error for redundant data elimination as well as the minimum number of 608 Virtual Machine migrations, 47.62% Resource Utilization and 41.13% load balancing degree for the allocation of desired resources deployed to execute sensory data filtering tasks respectively. Therefore, the proposed techniques have proven to be effective for improving the load balancing of allocating the desired resources to execute efficient outlier (Event and Error) detection and eliminate redundant data records in the IoT-based Cloud IaaS Infrastructure

Analyzing the Impacts of Emerging Technologies on Workforce Skills: A Case Study of Industrial Engineering in the Context of the Industrial Internet of Things

New technologies can result in major disruptions and change paradigms that were once well established. Methods have been developed to forecast new technologies and to analyze the impacts of them in terms of processes, products, and services. However, the current literature does not provide answers on how to forecast changes in terms of skills and knowledge, given an emerging technology. This thesis aims to fill this literature gap by developing a structured method to forecast the required set of skills for emerging technologies and to compare it with the current skills of the workforce. The method relies on the breakdown of the emerging technology into smaller components, so then skills can be identified for each component. A case study was conducted to implement and test the proposed method. In this case study, the impacts of the Industrial Internet of Things (IIoT) on engineering skills and knowledge were assessed. Text data analytics validated IIoT as an emerging technology, thus justifying the case study based on engineering and manufacturing discussions. The set of skills required for IIoT was compared to the current skills developed by Industrial Engineering students at the University of Windsor. Text data analytics was also used to evaluate the importance of each IIoT component by measuring how associated individual components are to IIoT. Therefore, existing skill gaps between the current Industrial Engineering program and IIoT requirements were not only mapped, but they were also given weights

O Impacto da Adoção da I4.0 na Satisfação dos Colaboradores: Um Caso de Estudo

Ao longo do tempo, as Revoluções Industriais foram um marco importante para as novas transformações que daí advinham. Cada revolução subsequente à anterior é sinónimo de mudança e transição para um novo mundo. A nova revolução, apelidada de Quarta Revolução Industrial – I4.0, envolve importantes tecnologias de ponta, abrangendo o campo da tecnologia e da respetiva informação, controlo, área de automação e dos novos sistemas computacionais. O objetivo da presente Dissertação recai na análise do Impacto da Adoção da I4.0 na Satisfação dos Colaboradores, tendo sido aplicado especificamente um estudo a uma empresa no concelho da Batalha. Pretendeu-se observar quais os Impactos da I4.0 na Satisfação dos Colaboradores, mas também nas Práticas de Gestão de Recursos Humanos. De forma a avaliar quais os impactos inerentes ao Departamento de Recursos Humanos, realizou-se uma entrevista à responsável de RH e à Diretora Financeira da respetiva entidade interveniente no estudo. Para analisar o Impacto da Adoção da I4.0 na Satisfação dos Colaboradores procedeu-se à aplicação de questionários por inquérito a 70 colaboradores, de modo a conseguir-se analisar a maneira como o colaborador perceciona a inserção de normas relativas à I4.0, assim como as mudanças que daí advém. Trata-se de um estudo de carácter exploratório, com recurso a dados primários. O tratamento estatístico utilizado permitiu concluir que o Impacto da Indústria 4.0 é positivo na Satisfação dos Colaboradores, no entanto, se a amostra fosse maior, seria possível observar as diferenças significativas. Sendo, igualmente, possível verificar que a I4.0 também terá um impacto significativo nas Práticas de Gestão de Recursos Humanos. Os Recursos Humanos vão ter de adaptar novas estratégias e novos métodos para enfrentar a nova revolução, no entanto, os colaboradores também vão ter de ajustar-se às novas tecnologias e às novas medidas impostas pela entidade