Sensors play a crucial role in factory automation in making the system intellectual. Different types of sensors are available as per the suitability and applications; some of them are produced in mass and available in the market at affordable costs. The standard sensor types available are position sensors, pressure sensors, flow sensors, temperature sensors, and force sensors. They are used in many sectors, such as motorsport, medical, industry, aerospace, agriculture, and daily life. The objective of Industry 4.0 is to increase efficiency through automation. Sensors are vital components of Industry 4.0, allowing several transitions such as changes in positions, length, height, external and dislocations in industrial production facilities to be detected, measured, analysed, and processed. Smart factories will also enhance sustainability by tracking real-time output, and automated control systems will minimise potential factory maintenance costs. It can also be seen that digitalisation can improve production mobility, which gives advanced manufacturing firms a competitive advantage. This paper discusses sensors and their various types, along with significant capabilities for manufacturing. The step-by-step working Blocks and Quality Services of Sensors during implementation in Industry 4.0 are elaborated diagrammatically. Finally, we identified thirteen significant applications of sensors for Industry 4.0. Industry 4.0 provides an excellent opportunity for the development of the sensor market across the globe. In Industry 4.0, sensors will enjoy higher acceptance rates and benefit from a fully enabled connecting and data exchange and logistics integration. In the coming years, sensor installations may grow in process management, automated production lines, and digital supply chains.

Significance of sensors for industry 4.0: Roles, capabilities, and applications

The enhancement of energy performance of historic buildings is not a completely resolved theme because both the tools and European directives available to the designer are mainly designed to meet the needs of the new buildings. To improve the energy efficiency of this buildings built without technical installations and designed to exploit the climatic characteristics and local resources, it is necessary to investigate the key parameters that constitute the building envelope. This study presents a multidisciplinary approach allowing to compare the optimum insulation thickness determined from the transmittance values of historical walls measured in situ with the one calculated through literature data. In detail, the methodology includes a preliminary analysis of the historical envelope needed to carry out the in situ measurements of the thermal transmittance and an analysis of insulation material and its thickness to determine the optimal combination between the energy performance of the building and the investment cost for thermal insulation and environmental impact through the life-cycle cost analysis. After applying the methodology to a case study located in Italy, the results revealed that the exclusive use of literature data for the preparation of an energy recovery project for an existing historic building can lead to significant errors.

Environmental and economic benefits of optimal insulation thickness: A life-cycle cost analysis

Cyber-Physical Systems (CPS) are systems that are in feedback with their environment, possibly with humans in the loop. They are often distributed with sensors and actuators, smart, adaptive and predictive and react in real-time. Image- and video-processing pipelines are a prime source for environmental information improving the possibilities of active, relevant feedback. In such a context, FitOptiVis aims to provide end-to-end multi-objective optimization for imaging and video pipelines of CPS, with emphasis on energy and performance, leveraging on a reference architecture, supported by low-power, high-performance, smart devices, and by methods and tools for combined design-time and run-time multi-objective optimization within system and environment constraints.

The FitOptiVis ECSEL project: highly efficient distributed embedded image/video processing in cyber-physical systems

https://hal.archives-ouvertes.fr/hal-01810002/document

The MegaM@Rt2 ECSEL project: MegaModelling at Runtime – Scalable model-based framework for continuous development and runtime validation of complex systems

Industry 4.0 is the fourth industrial revolution for decentralized production through shared facilities to achieve on-demand manufacturing and resource efficiency. It evolves from Industry 3.0 which focuses on routine operation. Data analytics is the set of techniques focus on gain actionable insight to make smart decisions from a massive amount of data. As the performance of routine operation can be improved by smart decisions and smart decisions need the support from routine operation to collect relevant data, there is an increasing amount of research effort in the merge between Industry 4.0 and data analytics. To better understand current research efforts, hot topics, and tending topics on this critical intersection, the basic concepts in Industry 4.0 and data analytics are introduced first. Then the merge between them is decomposed into three components: industry sectors, cyber-physical systems, and analytic methods. Joint research efforts on different intersections with different components are studied and discussed. Finally, a systematic literature review on the interaction between Industry 4.0 and data analytics is conducted to understand the existing research focus and trend.

/pdf/data-analytics-in-industry-4-0-a-survey-305z7cq04n.pdf

Data Analytics in Industry 4.0: A Survey.

Today on-chip monitoring solutions should be characterized by a reduced software and hardware overheads. So, this work deals with techniques to profile computational behavior and communication patterns of hardware/software components belonging to systems with multiple processing elements, i.e. a more general representation of on-chip embedded systems. In particular, the paper focuses on profiling techniques based on ad-hoc hardware mechanisms in order to avoid possible distortion of system behavior due to the monitoring action itself, thus satisfying unobtrusive profiling requirement. The final goal is the definition of a set of metrics to profile memory accesses and communication between processing elements, and the development of proper hardware mechanisms to support their evaluation. The profiling system architecture is described and its possible advantages over alternative software-based and hardware-based solutions are analyzed. Tests on proposed system are also done by means of a prototype implementation specifically targeting Xilinx MicroBlaze based multi-processor system.

Hardware performance sniffers for embedded systems profiling

In recent years, embedded applications have been characterized by increasingly stringent requirements, both from functional and non-functional point of view. This led to the adoption of complex hardware platforms (multi-core and many-core architectures), able to guarantee high computational power with low energy consumption and reduced footprint. An efficient characterization of these platforms can be problematic, given the large number of hardware resources and the complexity of software applications. For this reason, the need for a runtime analysis support should be taken in account during the design phase (a design "for monitorability"). This paper introduces "Adaptive Profiling Hardware Sub-system" (AIPHS), a hardware profiling tool supporting the development of runtime monitorable systems. Two different multicore platforms are considered in order to evaluate AIPHS functionalities: an asymmetric dual-MicroBlaze system and a quad-Leon3 system supporting symmetric multiprocessing.

A Flexible Profiling Sub-System for Reconfigurable Logic Architectures

Modern field-programmable gate arrays offer dynamic partial reconfiguration (DPR) capabilities, a characteristic that opens new scheduling opportunities for real-time applications running on heterogeneous platforms. To evaluate when it is really useful to exploit a DPR, in this letter, we present the characterization of its reconfiguration cost in terms of time and a definition of the “DPR Profitability” concept targeting real-time systems. To obtain such results, the components involved in a DPR process have been identified and an innovative approach to calculate the DPR time and its worst-case bound is provided. We validate our approach on a real DPR-compliant platform, showing that our proposal is general enough to be applied to modern DPR-compliant platforms.

Dynamic Partial Reconfiguration Profitability for Real-Time Systems

This work focuses on the definition of a methodology for handling embedded real-time applications, starting from an existing HW/SW co-design methodology able to support the design of dedicated heterogeneous parallel systems. The state-of-the-art related to similar tools and methodologies is presented and the reference framework with the proposed extension to the realtime world is introduced. A case study is then described to show a design space exploration able to consider such an extension.

HEPSYCODE-RT: a Real-Time Extension for an ESL HW/SW Co-Design Methodology

We here present a new PLC-POWERLINK industrial solution for Industry 4.0 applications. The proposed solution provides the capability to separate the sensing functionality from the PLC-side, in demand for the reconfigurable FPGA implementation. In particular, we here provide a framework that supports the interfacing between the POWERLINK protocol and commonly used standards, such as I2C, SPI, and UART. This has been obtained by using a framework built around a soft IP-core Application Processor, which manages the interfacing with several POWERLINK slaves, able to support the data exchange with the POWERLINK Communication Processor. A practical application example and related implementation details are presented in the paper.

https://www.mdpi.com/1996-1073/12/9/1633/pdf

Giacomo Valente

Papers

Hardware performance sniffers for embedded systems profiling

A Flexible Profiling Sub-System for Reconfigurable Logic Architectures

Dynamic Partial Reconfiguration Profitability for Real-Time Systems

HEPSYCODE-RT: a Real-Time Extension for an ESL HW/SW Co-Design Methodology

SPOF—Slave Powerlink on FPGA for Smart Sensors and Actuators Interfacing for Industry 4.0 Applications