Transit-time ultrasonic flowmeters present advantages for district heating applications, since they are accurate, non-intrusive, and cheap. However, such flowmeters are sensitive to velocity profile variations since the flow rate is measured in the volume area between two ultrasonic transducers. Ultrasonic flowmeters are therefore sensitive to installation effects. Installation effects could be either static or dynamic. A pulsating flow is a dynamic installation effect. In the field, the diagnostic can only be performed with the measured flow rate. Flow measurements with and without pulsating flow have been recorded in a flow meter calibration facility. The detection of a pulsating flow can be made by using Hinich's harmogram. It is possible to detect harmonics that emerge from the noise by using the harmogram.

Ultrasonic flow meters are accurate and non-intrusive. However, one of their drawbacks is their sensitivity to installation effects especially when elbows or pulsating flows are found in front of the meter's inlet. Depending on the flow rate, such installations can create perturbations on the flow measurement. These perturbations cause an increase of the noise level as well as a possible error on the flow estimation. A technique using the noise level as a criterion for making a diagnostic of the error is presented. The perturbations are examined and compared to gaussian distributed signals. A basic method for detecting gaussian perturbations with equal means is described. The likelihood ratio is first computed in the general case. It is also computed making the assumption of uncorrelated signals. Receiver Operating Characteristics (R.O.C.) are plotted. The assumption of gaussian perturbations is then investigated. Optimal thresholds are proposed for both methods and for a large interval of flow rates (from 0.4 ml/s to 0,75 1/s). Probabilities of detection and of false alarm are evaluated for both methods. It is concluded that correct detection can be performed. The optimal threshold is to be chosen by the operator.

Different types of errors are generated by pulsations in flows. Among these errors is the sampling error due to a unadapted time-averaging of the flow rate. An improved model for pulsations in flows including harmonics is derived. The localization of the harmonics is performed by a detector. The period of the pulsations is estimated. It is then possible to reduce the sampling error by performing a correct averaging. The reduction of the sampling error is confirmed by simulations.

Ultrasonic transit-time flow-meters estimate fluid or gas flows from the difference in times of flight of upstream and downstream acoustic pulses. However, any delay differences arising from sources other than the flow to be measured will cause a troublesome "zero flow" offset error.In theory, the transducers used in the measurement system should not influence the zero flow error, as electroacoustic systems based on piezoelectric transducers have been shown to be reciprocal (when the media is stationary). However, care is required when designing the electrical interfaces for the piezoelectric transducers, if reciprocity in the system is to be utilized.This work presents technique and measurements that apply reciprocity to an ultrasonic transit-time flow-meter. Specialized electrical transducer interfaces with options to drive the transducers from either low or high impedance sources were used. Combined with a high-impedance receive mode these options made it possible to change the conditions for reciprocity in the system.We show reduced delay difference in 9 cases out of 10 when trying to utilize the reciprocal property compared to when we disregard it in favor for larger excitation energy. The delay improvements were accompanied by reduced differences between the center frequencies of the signals from the two paths.

Vid EISLAB (Embedded Internet Systems Laboratory), Luleå tekniska universitet, pågår sedan några år tillbaka ett antal projekt kopplade till energimätning. Ett projekt behandlar noggrannhet, diagnostisering och optimering av energimätningar i fjärrvärmenät. Ett annat projekt syftar till att utveckla metoder baserade på ultraljud för mätning av energiflöde och sammansättning av bio- och naturgas. Föredraget ger en sammafattning av det arbete som hittills utförts inom dessa områden samt en framåtblick över de möjligheter och fördelar resultaten från dessa projekt ger i framtiden. Inom fjärrvärmeanvändningen finns ett behov av en förbättrad mätning av energianvändningen, dels för att kunna reglera processerna och dels för att kunder och leverantörer ska betala för rätt saker. Snabb och noggrann mätning är idag inte möjligt. Ur underhållssynpunkt är det även av intresse att på distans kunna diagnosticera systemen, både på leverantörs- och kundsidan. Samma teknik kommer även att kunna användas för optimering av processen. Bio- och naturgas har utpekats som möjliga alternativ till bensin och diesel som drivmedel. De mätmetoder som finns idag för att säkerställa kvaliteten och bestämma energivärdena lämpar sig inte för användning långt ut i distributionskedjan. Målet med forskningsprojektet vid EISLAB är att utveckla en ultraljudsmetod som kan mäta sammansättning, energivärde och flöde av dessa gaser. Fokus ligger på utveckling av statistiska metoder samt fysikaliska modeller för vågutbredning i gaser. Förutom tillämpningar inom bio- och naturgas, kan tekniken även användas inom petrokemisk- och processindustri.

Ultrasonic pulse-echo systems are widely used to estimate properties of liquids and gases. A common principle is to use a buffer material (buffer-rod) fixed to the ultrasound transducer. Assuming the acoustic properties of the buffer-rod are known, it is then possible to calculate the acoustic impedance of the unknown material. A problem occurs if the temperature of the buffer-rod changes during the measurements, since the properties of the buffer-rod, such as the acoustic attenuation depends on temperature. If, however, the temperature is recognized, it is possible to compensate for this. In this paper we present a method based on speed of sound changes in the buffer-rod to estimate the temperature. With the resulting model we are able to estimate temperatures in PMMA for the interval 5°C to 60°C with a 0.1 °C accuracy (at a 95% confidence level).

In ultrasonic pulse-echo systems, polymers like PMMA (polymethylmethacrylate) and PEEK (polyetheretherketone) are often used as buffer-rods, placed between the ultrasound transducer and the unknown material (liquid, gas, or solid material). Provided the acoustic properties of the buffer-rods are known, it is possible to calculate these also for the unknown material, based on reflections between the buffer-rod and the unknown medium. However, temperature changes also affect these properties. In this paper we present a method for measuring acoustic attenuation, speed of sound and density, for buffer-rod materials. We also give experimental values for PMMA and PEEK, for temperatures between 5/spl deg/C and 37/spl deg/C, and for 5 MHz and 10 MHz ultrasound frequency.

Various forms of cloud computing principles and technologies are becoming important recently. This paper ad- dresses cloud computing for automation and control applications. It’s argued that the open Internet cloud idea has such limitations that its not appropriate for automation.

Since automation is physically and geographically local, it is inevitable to introduce the concept of local automation clouds. It’s here proposed that local automation clouds should be self contained an be able to execute the intended automation func- tionalities without any external resources. Thus providing a fence at the rim of the local cloud preventing any inbound or outbound communication. Such a local cloud provides possibilities to address key requirements of both todays and future automation solutions. Adding mechanisms for secure inter-cloud administra- tion and data tranfere enables local automation cloud to meet IoT automation system requirements as: 1) Interoperability of a wide range of IoT and legacy devices 2) Automation requirement on latency guarantee/prediction for communication and control computations. 3) Scalability of automation systems enabling very large integrated automation systems 4) Security and related safety of automation systems 5) Ease of application engineering 6) Multi stakeholder integration and operations agility.

How these requirements can be met in such a local automation cloud is discussed with references to proposed solutions. The local automation cloud concept is further verified for a compartment climate control application. The control application included an IoT controller, four IoT sensors and actuators, and a physical layer communication gateway. The gateway acted as host for local cloud core functionalities. The climate control application has successfully been implemented using the open source Arrowhead Framework and its supports for design and implementation of self contained local automation clouds.

Heavy machinery in today's process industry is equipped with an increasing number of sensors for control and maintenance purposes. Each sensor is engineered and configured for a dedicated purpose; thus, the data or information provided by each sensor is confined to a specific purpose. Changing this individual approach to a holistic approach for control, operation and maintenance allows the data to be used in new ways, such as new control strategies, operation strategies and maintenance management. The holistic approach is demonstrated for a process of continuous energy generation and distribution: district heating. The purpose-based approach is replaced with the holistic approach, which focuses on system optimization and failure detection. The system was first set up in a Simulink simulation model to capture the thermodynamic behavior of the houses attached to the district heating system. In this model, each sensor is seen as a service that can be accessed for different purposes. Model analysis in this case led to new approaches for control and maintenance of critical parts in a district heating system.The new holistic approach is realized as a service-oriented architecture (SOA)-based wireless sensor network. It is currently installed in a small part of a district heating system in Piteå, Sweden. The system utilizes a wireless sensor network with service-oriented architecture using device profile web services (DPWS). Based on the data and information provided by the SOA wireless sensor network, the system can be identified. For the district heating example, the system identification provides system parameters that can be used to improve the thermodynamic Simulink model. Thus, comparisons can be made over time to determine system degradation. Furthermore, possible maintenance actions can be modeled, which allows prediction of system performance improvements related to specific maintenance actions.

Todays district heating and gas substations for domestic heating uses a since long time established heat metering and control technology. The measurement and control system architecture is based on a paradigm of non communicating sensors and regulators. The now evolving paradigm of sensors and actuators connected in sensor networks open for new measurements and control concepts to be used in energgy measurement and control. The sensor network approach enables the use of sensor fusion theory. Thus enabling improved measurement accuracy, improved control possibilites and estimation of interesting more parameters like tap water energy consumption etc. Further it leads to a capability of customer communication using live energy data. Using the Embedded Internet System (EIS) architecture every sensor, actuator and control box in a substation will be individually connected to the Internet. This opens for flow of data from the heat meter to the control system opening new possibilites for control and data estimation. It also opens for data from the control system to support improved heat measurement. Similar solutions can be found in the gas application. In district heating we expect improvements like, improved heat metering accuracy, maximized differanse temperature, minimized forward flow temperature, hot water energy measurement each of them having economical impact.

This paper presents the technology concepts for a “thumb”-sized self-contained ultrasonic IoT measurement sys- tem. An overall architecture is proposed, and key elements are discussed with solutions using existing technology, thus arguing that realization is possible with the current technology.

Such an ultrasonic IoT measurement system is constrained by its size and available energy, although it requires at least decent computational and communication resources. Because streaming data from such a device is not advisable from an energy viewpoint, there is a need for resource efficient (energy, memory and computational power) data analysis.

An architecture with the following parts as well as some implementation details and performance data are proposed here:

• Energy supply, battery and super capacitor

• Transducer excitation achieving almost zero electrical losses

• Event detection sensor interface

• Data aggregation using sparse approximation and learned

  feature dictionaries, adapted to resource constrained em-

  bedded systems

• IoT communication protocols and implementations enabling

  event -based communication and System of Systems integra- tion capabilities

  The optimization of system level performance requires each subsystem to be optimized for the specific measurement situation taking into account the subsystem interdependencies. This can be performed using a combined electrical and acoustical model of the system. Here, the model allowing electronic and acoustic co-simulation using SPICE is an important tool bridging the electronic and acoustic domains. 

This paper presents a novel approach to automationof flexible manufacturing systems with mechatronic intelligenceand distributed control. The mechatronic intelligence layer isimplemented using a combination of wireless sensor/actuatornetworks with service-oriented architecture, where services arelocated at the device level, as well as in local and global Cloudsfollowing the Arrowhead framework.The machine/floor level coordination is implemented using thedistributed automation architecture of IEC 61499, which is alsoused as a graphical tool for orchestration of services.The paper discusses the enablers developed in-order to combineIEC 61499 and Arrowhead and the use is illustrated ona laboratory scale flexible factory example. By integration ofIndustrial IoT with IEC 61499, we envision that large gains interms of engineering effort and system operation performancecan be made.

To achieve flexible manufacturing, increasingly large amounts of data are being generated, stored, analyzed, archived and eventually fed back into the product life cycle. But where is this data stored and how is it transported? Current methods rely on centralized or federated databases to manage the data storage. This approach has several challenges, such as collection bottlenecks, secure retrieval, single point of failure and data-scheme fragility as data heterogeneity increases. Additionally, manufacturers are finding the need to open their networks for service based equipment suppliers. This means previous security assumptions regarding network encryption and information access-control must be re-evaluated. Proposed here is a method of in-network processing that gathers information only where and when it is needed. Systems build context at runtime by creating dynamic queries which make service composition. The service composition processes raw data and presents it as information to the calling system. This reduces the movement of data/information and removes single point collection bottlenecks. Furthermore, fine grained access control and shared trust can be granted between untrusted systems. The proposed methods are demonstrated on a lab setup of an industrial use case.

Abstract—Industrial Internet of Things covers all aspects ofnetworked intelligent manufacturing systems. This means coveringa wide array of application domains and user requirements.In such scenarios it is not feasible to define a single protocol forall situations. Hence, a multi-protocol approach is required. OPCUA has strong backing from Industry 4.0 as the protocol for theIndustrial Internet of Things. Interoperability of OPC UA hasbeen investigated in the context of migration from legacy andwith protocols such as DPWS. Additionally HTTP and CoAPhave been investigated as possible transport mediums.However, OPC UA interoperability has not been investigatedwithin a multi-protocol settings and no generic protocol translationexists. This paper proposes an OPC UA translator followingthe service translator model proposed in the Arrowhead project.Utilizing a mapping to intermediate format, it can be used alongside CoAP, HTTP and MQTT protocols.

In this paper, the feasibility of using a sensor network for automotive testing is investigated. Testing is becoming ever more important for the car industry, where the demands for quicker time-to-market and shortened development cycles are increasing. Car testing is time consuming, and is often performed in remote rural areas. Traditional methods include wiring up a vehicle with sensors connected to a data logging device. We envision that the use of wireless sensors can drastically decrease the time required to perform a set of test cases. A sensor network based on Bluetooth was used to validate our design approach. The network supports real-time monitoring of sensor data, and precludes the need of manually configuring each sensor node. Preliminary tests indicates that the proposed design is well suited for vehicle testing, due to its inherent support for ad-hoc networking and auto configuration of services.

In this paper a distributed system for engine management is presented. The system is in use on the 2006 and 2005 Formula SAE cars from Luleå University of Technology. The purpose of building such a system from scratch is to have a comprehensive, predictable and easily extendable platform, giving the possibility to add extra features even at the racetrack. This allows the system to serve as a research platform for embedded real-time systems and vehicle dynamics. Another motivation is to get low weight on the complete system, and to integrate the electronics in such a way that the total cabling required will be minimal. The initial requirements are that the system should implement launch control, traction control, electric gear shift and clutch control. To control the engine the system must implement sequential fuel injection, direct fire ignition and closed loop lambda control. Moreover to remotely tune and monitor the system parameters in real-time - even on the racetrack, the system should facilitate wireless communication. To achieve these goals a system consisting of five units communicating over a standard automotive bus (CAN1) was developed. In this paper we will describe the systems functionality and the units developed.

When testing modern active safety systems on cars, it is essential to test vehicle response to steering inputs. This paper highlights the use of a path-following steering robot for repetitive car testing in winter conditions. Experiments have been made with commonly used test sequences, e.g., lane-change, double lane-change, constant radius circle, and handling circuit. The steering input from the human driver is replaced by a steering robot and a path-following algorithm. The main focus for this paper is to describe what happens when one pushes the path-following system to, and beyond, the physical limitations of road grip when cornering at high vehicle speed. It shows that, with an appropriate tuning of the path-following parameters, the system performs predictably and satisfactorily. The overall conclusion is that a path-following steering robot is indeed useful for repetitive test in winter conditions with poor road grip.

In this paper, we evaluate whether the primary supply temperature in district heating networks can be used to control radiator systems in buildings connected to district heating; with the purpose of increasing the ΔT. The primary supply temperature in district heating systems can mostly be described as a function of outdoor temperature; similarly, the radiator supply temperature in houses, offices and industries can also be described as a function of outdoor temperature. To calibrate the radiator control system to produce an ideally optimal radiator supply temperature that produces a maximized ΔT across the substation, the relationship between the primary supply temperature and outdoor temperature must be known. However, even if the relation is known there is always a deviation between the expected primary supply temperature and the actual temperature of the received distribution media. This deviation makes the radiator control system incapable of controlling the radiator supply temperature to a point that would generate a maximized ΔT. Published simulation results show that it is possible and advantageous to utilize the primary supply temperature for radiator system control. In this paper, the simulation results are experimentally verified through implementation of the control method in a real district heating substation. The primary supply temperature is measured by the heat-meter and is shared with the radiator control system; thus no additional temperature sensors were needed to perform the experiments. However additional meters were installed for surveillance purposes. To maintain a stable indoor temperature at times when the primary supply and outdoor temperatures deviates from their assumed relation, the radiator system flow must be controlled by an additional control-loop. The results confirms that it is possible to control the radiator system based on the primary supply temperature while maintaining comfort; however, conclusions regarding improvements in ΔT were hard to distinguish.

In this paper, we describe a new alternative control approach for indirectly connected district heating substations. Simulations results showed that the new approach results in an increased ΔT across the substation. Results were obtained for both ideal and non-ideal operation of the system, meaning that less water must be pumped through the district heating network, and a higher overall fuel efficiency can be obtained in the district heating power plants. When a higher fuel efficiency is achieved, the usage of primary fuel sources can be reduced. Improved efficiency also increases the effective heat transfer capacity of a district heating network, allowing more customers to be connected to an existing network without increasing the heating plant or network capacity.Also, if combined heat and power plants are used to produce the heat, the increased ΔT will result in a further improved overall fuel efficiency, as more electricity can be produced with colder cooling water.The idea behind the new control method is to consider the temperature of the water supplying the district heating substation with heat, often referred to as the primary supply temperature. This represents a logical next step, as currently, the only parameter generally taken into account or measured when controlling the temperature level of the radiator circuit is the local outdoor temperature. In this paper we show how the primary supply temperature together with thermodynamic knowledge of the building can be used to maximize the ΔT across the district heating substation.

A physical thermodynamic model of a detached house connected to a low-tempered district heating network is presented. The model is created in Mathworks Simulink\textregistered\space with a pedagogic approach in mind, e.g. masked subsystems divided in to physical components. The house model is easily modified to any detached house. Provision is also made to make it scalable to multi-family houses. The district heating substation modeled is a parallel coupled plate heat exchanger, which is the most common substation in smaller buildings such as villas. The purpose of creating the model was to provide a platform for test and evaluation of new control methods for district heating system based on wireless sensor networks. Initial validation of the model is presented.

A thermodynamic model of a detached house using district heating has been created in Mathworks Simulink to form a realistic tool to test new control methods to optimize district heating systems. We here present the experimental validation process of the model. Detailed measurements were made using high performance ultrasonic flow-meters with embedded temp sensors. The flow-meters measures e.g. total tap water consumption, total primary energy consumption and radiator energy consumption separately. We show satisfying thermodynamic results of the model versus the real house. We also show that wind and sun exposure play a role in this validation.

In this study, the implementation of a wireless, lowpower, sensor network with IP capabilities in a district heating substation was evaluated. The aim of the study was to show that an open standard solution is technically feasible. Low-power wireless communication was established using IPv6/6LoWPAN on an IEEE 802.15.4 wireless network. An experimental district heating substation was equipped with sensor platforms in vital devices located within or near a district heating substation. As a result, all connected devices could obtain a direct internet connection.A system with open standards facilitates the introduction of new energy services such as individual measurements and improved space heating control. In this study, we found that resource-limited batterypowered devices possess a life expectancy of over 10 years, using small batteries while participating in IPv6 compatible communication.

This paper proposes a passive Barkhausen noise sensor design suitable for low power applications. The sensor uses a permanent magnet and the relative motion between itself and a measured specimen instead of the conventional method that uses a fixed sensor and an alternating magnetic field. Since this novel design is passive, the sensor is well suited for low power applications and could potentially be used in e.g. A condition monitoring system integrated into a rolling element bearing. Proof of concept testing has been performed showing that the proposed sensor produces similar results as conventional Barkhausen noise sensors when applied to specimens being cyclically loaded until failure in a rotating bending rig. The results imply that material fatigue detection using the Barkhausen noise can be performed with the proposed sensor at a fraction of the energy cost compared to a conventional sensor. This warrants future research into the development of the proposed sensor, its advantages, disadvantages, and functionality

The white cane is used by many visually impaired individuals as the primary aid for avoiding obstacles. In this it is unparalleled, but it cannot provide a large-scale view of the surroundings the way vision does. This makes navigating independently a challenge for the visually impaired. We are developing the Virtual White Cane (VWC), a device that uses sensors and haptic technologies to complement the limited view of the cane. Sensors makes it possible to probe obstacles far beyond the range of the white cane, and haptic feedback is familiar to users of the regular cane. The purpose of this device is to act as a complement to the standard cane, providing information about the surroundings that are beyond the cane's reach. This kind of extended view not only helps in anticipating obstacles, but also to navigate. The presentation will focus on the hardware of the currently developed prototype, in addition to some initial user experiences.

Modeling Industrial Internet of Things (IIoT) architectures for the automation of wind turbines and farms(WT/F), as well as their condition monitoring (CM) is a growing concept among researchers. Several end-to-end automated cloud-based solutions that digitize CM operations intelligently to reduce manual efforts and costs are being developed. However, establishing robust and secure communication across WT/F is still difficult for the wind energy industry. We propose a fully automated cloud-based collaborative learning (CCL) architecture using the Eclipse Arrowhead Framework and an unsupervised dictionary learning (USDL) CM approach. The scalability of the framework enabled digitization and collaboration across the WT/Fs. Collaborative learning is a novel approach that allows all WT/Fs to learn from each other in real-time. Each turbine has CCL based CM using USDL as micro-services that autonomously perform feature selection and failure prediction to optimize cost, computation, and resources. The fundamental essence of the USDA approach is to enhance the WT/F’s learning and accuracy. We use dictionary distances as a metric for analyzing the CM of WT in our proposed USDL approach. A dictionary indicates an anomaly if its distances increased from the dictionary computed at a healthy state of that WT. Using CCL, a WT/F learns all types of failures that could occur in a similar WT/F, predicts any machinery failure, and sends alerts to the technicians to ensure guaranteed proactive maintenance. The results of our research support the notion that when testing a turbine with dictionaries of all the other turbines, every dictionary converges to similar behavior and captures the fault that occurs in that turbine.

In the modern manufacturing industry, collaborative architectures are growing in popularity. We propose an Industry 5.0 value-driven manufacturing process automation ecosystem in which each edge automation system is based on a local cloud and has a service-oriented architecture. Additionally, we integrate cloud-based collaborative learning (CCL) across building energy management, logistic robot management, production line management, and human worker Aide local clouds to facilitate shared learning and collaborate in generating manufacturing workflows. Consequently, the workflow management system generates the most effective and Industry 5.0-driven workflow recipes. In addition to managing energy for a sustainable climate and executing a cost-effective, optimized, and resilient manufacturing process, this work ensures the well-being of human workers. This work has significant implications for future work, as the ecosystem can be deployed and tested for any industrial use case.

The rapid transformation of the manufacturing industry under Industry 4.0 demands systems that can quickly adapt to dynamic market conditions and customer needs. Agile manufacturing emphasizes flexibility, adaptability, and real-time responsiveness, posing challenges in run-time value chain analysis (VCA), including cost flows and production times. This paper presents a novel two-stage VCA approach using an activity-based costing mechanism via microservices to address these challenges. The VCA system enables real-time cost accounting and decision-making, supporting both pre- and post-production VCA, contrasting with traditional methods that rely on historical data. The first stage involves top-down cost calculations from resources to microservices, while the second focuses on constructing efficient manufacturing activities based on product requirements, allowing for a granular analysis of costs and production times across microservices, activities, broader business processes, and finally, cost objects (e.g., customized products, batches of products, or customer invoices). The approach is validated through a proof-of-concept implementation of the VCA system integrated with the Eclipse Arrowhead framework and simulating Fischertechnik indexed line milling, drilling, and conveying operations. The results demonstrate the effectiveness of the proposed method in providing detailed insights into costs and production times, enhancing the efficiency and competitiveness of agile manufacturers.

Integrating smart manufacturing ecosystems with industrial-grade smart energy and building automation systems enables real-time adaptation to changes in demands and factory conditions, the supply chain, and the needs of customers and society. However, integrating, managing, and controlling data exchange usually incurs high overheads in such a collaborative industrial environment. Smart home IoT technologies are a cost-effective solution for smart energy and building automation systems; they are not fully interoperable with industrial IoT technologies. This paper presents a mechanism to solve this interoperability problem using the Eclipse Arrowhead framework. The proposed solution provides a microservice-oriented architecture to develop protocol-specific smart adapter systems for the Arrowhead framework. These smart adapter systems provide seamless and highly scalable integrations between smart home and industrial IoT technologies. Our solution enables smart manufacturing ecosystems to meet Industry 5.0’s core values and reduce their carbon footprint to save the planet. We present the performance of our solution using an example from a real-world use case of a smart heating system scenario in a smart factory.

The fourth and fifth industrial revolutions (Industry 4.0 and Industry 5.0) have driven significant advances in digitalization and integration of advanced technologies, emphasizing the need for sustainable solutions. Smart Energy Systems (SESs) have emerged as crucial tools for addressing climate change, integrating smart grids and smart homes/buildings to improve energy infrastructure. To achieve a robust and sustainable SES, stakeholders must collaborate efficiently through an energy management framework based on the Internet of Things (IoT). Demand Response (DR) is key to balancing energy demands and costs. This research proposes an edge-based automation cloud solution, utilizing Eclipse Arrowhead local clouds, which are based on Service-Oriented Architecture that promotes the integration of stakeholders. This novel solution guarantees secure, low-latency communication among various smart home and industrial IoT technologies. The study also introduces a theoretical framework that employs AI at the edge to create environment profiles for smart buildings, optimizing DR and ensuring human comfort. By focusing on room-level optimization, the research aims to improve the overall efficiency of SESs and foster sustainable energy practices.

Industry 4.0 has revolutionized industrial automation, with models like RAMI 4.0 providing a structured framework for optimizing value chains and processes. However, the complexity and abstract nature of RAMI 4.0 have limited its practical application, especially due to the lack of clear visualization methods to understand industrial ecosystems. Effective visualization is essential to translate this framework into actionable insights, enabling stakeholders to grasp system interactions, dependencies, and value-creation processes. This paper proposes a multidimensional visualization approach, illustrated through a smart heat pump example, to map information and operational technologies, their interactions, and value chains. Combining 3D visualizations for integrated system overviews with 2D visualizations for task-specific analysis, the approach provides a comprehensive understanding of RAMI 4.0 value chains, enabling stakeholders to address their analytical needs with clarity. It facilitates run-time value chain analysis, offering real-time insights for decision-making during operations. The approach maps industrial systems across RAMI 4.0 axes and aligns them with engineering processes and lifecycle phases, enabling the exploration of system interactions, dependencies, and stakeholder contributions. This supports the analysis of engineering and business processes, optimizes infrastructure, and facilitates smooth technological transitions. It enhances RAMI 4.0’s utility for real-time decision-making and operational efficiency, boosting competitiveness in industrial ecosystems.

An architecture for the energy measurement and control and monitoring at a district heating substation is propsoed. A district heating substation is comprised of a set of sensors and actuators crutial for the well being of the substation. We want to make the data from the sensors available over the internet and provide access to the control system of the district heating substation over the internet. This technology will be implemented in a district heating laboratory being constructed at Lulea ̊ University of Technology.The architecture is based on wireless sensor and actuator nodes each talking the TCP/IP Internet suite of protocols. The implementation is made with the wireless Embedded Internet System plattform MULLE that supports the TCP/IP suite of protocols. The basic architecture and means for its implementation is given. Other applications where this vision can be readily applied are for example, sports and metrology.

Heat measurement errors cause large revenue discrepancies in the district heating industry. Some of these errors are static and can be estimated using standard error analysis, but the largest error cause is the dynamic load such systems are subject to, as in the case of warm water tapping. The frequency at which heat meters estimate and update the energy is either constant or depends on the flow rate. They are often battery operated and their power consumption is proportional to their estimation frequency. Heat meters with a flow rate dependent estimation frequency are usually based on volume-flow meters. They are widely used in district heating due to their lower estimation frequency, which prolongs their battery life. Such devices are inaccurate especially at low flow rates. A Feed-forward method that measures the heat energy only when changes occur is presented in this paper.This method reduces the heat measurement error due to the dynamics of the system while minimizing the battery power consumption. The Feed-Forward method has been implemented and tested at cross-purposes with flow rate dependent heat meters in a Simulink model of a district heating substation.

Industry 4.0 is advancing the use of Internet of Things (IoT) devices in industrial applications, which enablesefficient device-to-device (D2D) communication. However, these devices are often heterogeneous in nature, i.e. from different manufacturers, use different protocols, etc. and adds requirements such as security, interoperability, etc.To address these requirements, the Service-Oriented Architecture-Based (SOA) Arrowhead Framework was previously proposed using the concept of local clouds. These local clouds provide a set of mandatory and support core systems to enable industrial automation applications. One of these mandatory core systems is an Authentication, Authorisationand Accounting (AAA) system, which is used to authenticate and provide access control to the devices in a local cloud. In an industrial context, with multiple stakeholders, the AAA mustsupport fine-grain access control. For example, in a distributed control loop, a controller should only have read access to its sensor such as a flow meter and write access to its actuator, such as a valve. The controller should not have access to anyother information besides what is needed to implement the desired functionality. In this work, an NGAC-based AAA solution to achieve fine-grain service level access control between IoT devices has been proposed and implemented. The solution is presented using a district heating use case.

A large number of potential applications for Wireless Sensor and Actuator Networks (WSAN) have yet to be embraced by industry despite high interest amongst academic researchers. This is due to various factors such as unpredictable costs related to development, deployment and maintenance of WSAN, especially when integration with existing IT infrastructure and legacy systems is needed. Service-Oriented Architecture (SOA) is seen as a promising technique to bridge the gap between sensor nodes and enterprise applications such as factory monitoring, control and tracking systems where sensor data is used. To date, research efforts have focused on middleware software systems located in gateway devices that implement standard service technology, such as Devices Profile for Web Services (DPWS), for interacting with the sensor network. This paper takes a different approach - deploying interoperable Simple Object Access Protocol (SOAP)-based web services directly on the nodes and not using gateways. This strategy provides for easy integration with legacy IT systems and supports heterogeneity at the lowest level. Two-fold analysis of the related overhead, which is the main challenge of this solution, is performed; Quantification of resource consumption as well as techniques to mitigate it are presented, along with latency measurements showing the impact of different parts of the system on system performance. A proof-of-concept application using Mulle - a resource-constrained sensor platform - is also presented.

The continuously rising costs and the environmental impact of energy generation, transmission and consumption are a major concern for governments, industry and society alike. Among research in renewable energy sources as well as in energy efficiency of buildings, electrical appliances, vehicles etc., a considerable amount of attention has been devoted to effective energy management. In this paper, we present a survey on emerging energy management standards with focus on enabling application layer Information and Communications Technologies (ICT) that are a central part of these standards. The presented work includes an analysis on the challenges, future trends, security and application prospects of energy management standards. As part of the survey, the emerging Open Automated Demand Response (OpenADR) version 2.0 and Smart Energy Profile (SEP) version 2.0 were identified as the most promising and complete solutions. The presented survey provides an important insight on the future developments in the area of energy management protocols and highlights a number of key ICT solutions and challenges.

Through the Centre for Automotive Systems Technologies and Testing, Luleå University of Technology aims to first of all support automotive winter testing in Northern Sweden. This means to support the local automotive test entrepreneurs and through them their customers: the car manufacturers and their suppliers. To succeed in this task, the center relies on the university's areas of leading research and most importantly on the cooperation between those areas.

Demand response (DR) has received significant attention in recent years and several DR programs are being deployed and evaluated worldwide. DR systems provide a wide range of economic and operational benefits to different stakeholders of the electrical power system including consumers, generators and distributors. DR can be achieved through a number of different mechanisms such as direct-load-control, incentives, pricing signals, or a combination of these schemes. Due to the remarkable variation in demand response systems, it becomes a challenge to evaluate and compare the effectiveness of different DR programs holistically. In this work, we define a number of different performance metrics that could be used to evaluate DR programs based on peak reduction, demand variation and reshaping, and economic benefits

A digital twin (DT), the digital counterpart of a physical entity, process, or system, is a pivotal innovation driving the manufacturing industry's digital transformation. DT plays a significant role in product lifecycle management (PLM) and product condition monitoring. However, the diversity of systems and processes involved poses challenges in DT and data management within PLM, particularly regarding efficiency, standardized data mapping, and latency.The paper presents a solution architecture to address these challenges and contribute towards an efficient and cost-effective product lifecycle management system. The architecture focuses on DT's data management and communication aspects, utilizing the edge-based, decentralized Eclipse Arrowhead Framework and EDMtruePLM (Enterprise Data Management True Product Lifecycle Management) for standardized data management and condition monitoring of products.Integrating the ISO 10303 STEP standard for data modeling and the Open Platform Communications Unified Architecture (OPC UA) standard for communication is emphasized, improving the contextual significance of the data and the system's interoperability. A use case implementation is presented, where a fischertechnik assembly line is monitored, capturing sensor data through the PLC's OPC UA server. The sensor data is then aligned with the STEP standard and stored in the EDMTruePLM database for monitoring.