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The need for wireless condition monitoring and control of hydraulic systems in an autonomous and battery‐free manner is attracting increasing attention in an effort to provide improved sensing functionality, monitoring of system health, and to avoid catastrophic failures. The potential to harvest energy from hydraulic pressure ripples and noise is particularly attractive since they inherently have a high energy intensity, which is associated with the hydraulic mean pressure and flow rate. This paper presents a comprehensive overview of the state of the art in hydraulic pressure energy harvesting, which includes the fundamentals of pressure ripples in hydraulic systems, the choice of electroactive materials and device structures, and the influence of the fluid–mechanical interface. In addition, novel approaches for improving the harvested energy and potential applications for the technology are discussed, and future research directions are proposed and outlined. [ABSTRACT FROM AUTHOR]

Condition monitoring devices in hydraulic systems that use batteries or require wired infrastructure have drawbacks that affect their installation, maintenance costs, and deployment flexibility. Energy harvesting technologies can serve as an alternative power supply for system loads, eliminating batteries and wiring requirements. Despite the interest in pressure fluctuation energy harvesters, few studies consider end-to-end implementations, especially for cases with lowamplitude pressure fluctuations. This generates a research gap regarding the practical amount of energy available to the load under these conditions, as well as interface circuit requirements and techniques for efficient energy conversion. In this paper, we present a self-powered sensor that integrates an energy harvester and a wireless sensing system. The energy harvester converts pressure fluctuations in hydraulic systems into electrical energy using an acoustic resonator, a piezoelectric stack, and an interface circuit. The prototype wireless sensor consists of an industrial pressure sensor and a low-power Bluetooth System-on-chip that samples and wirelessly transmits pressure data. We present a subsystem analysis and a full system implementation that considers hydraulic systems with pressure fluctuation amplitudes of less than 1 bar and frequencies of less than 300 Hz. The study examines the frequency response of the energy harvester, the performance of the interface circuit, and the advantages of using an active power improvement unit adapted for piezoelectric stacks. We show that the interface circuit used improves the performance of the energy harvester compared to previous similar studies, showing more power generation compared to the standard interface. Experimental measurements show that the self-powered sensor system can start up by harvesting energy from pressure fluctuations with amplitudes starting at 0.2 bar at 200 Hz. It can also sample and transmit sensor data at a rate of 100 Hz at 0.7 bar at 200 Hz. The system is implemented with off-the-shelf circuits. [ABSTRACT FROM AUTHOR]

The growing interest in indoor light energy harvesting for wireless sensor systems and low-power electronics has created a demand for systematic design methods that optimize the system implementation and component choices for different lighting scenarios. Although the spectrum of light is known to influence the efficiency and output power of a photovoltaic (PV) cell, existing power estimation methods neglect the spectrum. By contrast, the power estimation method proposed in this article takes spectral and intensity information into account. It uses low-cost sensors to measure spectral information and select an appropriate device model based on the classification of the light source. The method is evaluated under different light conditions, including individual light sources, mixed artificial light sources, and mixtures of artificial light and sunlight. The results demonstrate that the proposed implementation selects a reasonable model in most cases, including mixed light source conditions. Using light source specific models for PV panels, the resulting estimation error is low and has clear advantages over methods neglecting spectral information. [ABSTRACT FROM AUTHOR]

Introduction: Sensor‐based assessment of challenging behaviors in dementia may be useful to support caregivers. Here, we investigated accelerometry as tool for identification and prediction of challenging behaviors. Methods: We set up a complex data recording study in two nursing homes with 17 persons in advanced stages of dementia. Study included four‐week observation of behaviors. In parallel, subjects wore sensors 24 h/7 d. Participants underwent neuropsychological assessment including MiniMental State Examination and Cohen‐Mansfield Agitation Inventory. Results: We calculated the accelerometric motion score (AMS) from accelerometers. The AMS was associated with several types of agitated behaviors and could predict subject's Cohen‐Mansfield Agitation Inventory values. Beyond the mechanistic association between AMS and behavior on the group level, the AMS provided an added value for prediction of behaviors on an individual level. Discussion: We confirm that accelerometry can provide relevant information about challenging behaviors. We extended previous studies by differentiating various types of agitated behaviors and applying long‐term measurements in a real‐world setting. [ABSTRACT FROM AUTHOR]

Hydraulic pressure fluctuation energy harvesters are promising alternatives to power up wireless sensor nodes in hydraulic systems. The characterization of these harvesters under dynamic and band-limited pressure signals is imperative for the research and development of novel concepts. To generate and control these signals in a hydraulic medium, a versatile apparatus capable of reproducing pressure signals is proposed. In this paper, a comprehensive discussion of the design considerations for this apparatus and its performance is given. The suggested setup enables the investigation of devices tailored for the harvesting of energy in conventional hydraulic systems. To mimic these systems, static pressures can be tuned up to 300 bar, and the pressure amplitudes with a maximum of 28 Bar at 40 Hz and 0.5 bar at 1000 Hz can be generated. In addition, pressure signals found in commercial hydraulic systems can be reproduced with good accuracy. This apparatus proves to be an accessible, robust, and versatile experimental setup to create environments for the complete performance estimation of pressure fluctuation energy harvesters. [ABSTRACT FROM AUTHOR]

Energy harvesting converts ambient energy to electrical energy that can be used to power, for example, sensors and sensor systems. Variable reluctance (VR) energy harvesting is a suitable candidate for the conversion of rotary kinetic motion, an energy form commonly found in industrial applications. The implementation of a VR energy harvester (VREH), however, has a significant effect on its performance and is not well studied. In this paper, we therefore conduct a survey on different structures of VREHs. Six existing structures, previously used in VR sensors, are presented and analyzed according to their approaches for magnetic flux change improvement. Together with a newly proposed structure, these structures are evaluated based on a finite element analysis, and their results are compared. It is demonstrated that the choice of structure considerably affects the power output of the harvester and is dependent on the improvement approaches the structure utilizes. The newly proposed structure outperforms all existing structures with respect to power output and power density, which comes at a cost of higher parasitic torque generation. A 53-fold power improvement over the reference and an 1.2-fold power improvement over the next best structure is observed. As a result, applications of VR energy harvesting become viable even at low angular velocities. [ABSTRACT FROM PUBLISHER]

The characterization of light conditions plays an important role in the estimation of available energy levels to ambient light energy harvesting systems. Indoor light conditions are commonly described by illuminance levels. The same illuminance levels, however, can be generated by different light source types, which radiate different spectral components. This means that based on their spectral response, solar panels can produce different output powers even though identical illuminance levels are observed. We propose a method to distinguish these conditions based on limited spectral information. Using low-cost sensors, spectral characteristics of the light condition can be acquired and used to classify the underlying light source type, which allows for a more accurate estimation of the solar panel response. The method was evaluated experimentally for a number of common indoor light sources and under different conditions. Evaluation results have shown that the method can be used to distinguish the light sources under test with very high classification accuracy. Moreover, the method can be used accurately in situations with limited interference. This makes it a low-cost alternative to the characterization of light conditions using spectrometers, the use of which is infeasible in spatially distributed characterization applications. [ABSTRACT FROM PUBLISHER]

Solar energy harvesting allows for wireless sensor networks to be operated over extended periods of time. In order to select an appropriate harvesting architecture and dimension for its components, an effective method for the comparison of system implementations is required. System simulations have the capability to accomplish this in an accurate and efficient manner. In this paper, we evaluate the existing work on solar energy harvesting architectures and common methods for their modeling. An analysis of the existing approaches demonstrates a mismatch between the requirement of the task to be both accurate and efficient and the proposed modeling methods, which are either accurate or efficient. As a result, we propose a data-driven modeling method based on artificial neural networks for further evaluation by the research community. Preliminary results of an initial investigation demonstrate the capability of this method to accurately capture the behavior of a solar energy harvesting architecture, while providing a time-efficient model generation procedure based on system-level data. [ABSTRACT FROM AUTHOR]

With the widespread of mobile Internet-enabled devices we can consider nearly every user everywhere as a potential consumer of cloud services. Especially campus environments provide a multitude of heterogeneous scientific information and services to the users. Here different problems arise concerning the integration and dissemination of cloud services and resources. While location is usually considered as the core context for mobile service access, its value is very limited in evolving large-scale smart environments with a multitude of user roles, tasks, devices and services. This is even more challenging if multiple users work collaboratively in remote locations on the same project. Therefore, we propose with CASA, Context- Aware Service Access, a concept which can use different kinds of context as source and provides support for reactive and proactive actions based on the current situation. The evolving and changing aspects are handled with an extensive usage of crowdsourcing to encourage a decentralized and user-driven development. This shall lead to a novel open and extendable recommender system which also supports the adaption and integration of cloud-based services according to the situation. Additionally, as proof of concept, we present two use cases for smart campus and smart lecture rooms. [ABSTRACT FROM AUTHOR]

Background: Wearable sensors to monitor activity are easily available nowadays. Intelligent assistive technologies bear potential to offer innovative solutions that increase safety and facilitate independent activities in persons with dementia (PwD). It is important to incorporate knowledge about user's current state (e.g. cognitive and physical abilities) and situation to generate suitable interventions and provide meaningful assistance for PwD. Method: Within the "SAMi"‐project, we set up an (ongoing) field study with PwD living in a nursing home. Participants wear smartwatches to measure activity (accelerometry) and enable indoor positioning via Bluetooth. In parallel, subjects' behavior is annotated in real time by trained observers using an elaborated scheme that contains activity, posture and place of the person, as well as disorientation, falls or request for help. The annotation scheme and possible intervention domains were derived from interviews with different groups of stakeholders. With the field study, we aim to identify in real time if a patient is at need of support and to select the most promising intervention in a given situation. Therefore, we plan to train algorithms on the synchronized sensor‐data and examine the accuracy of situation‐classification. Result: To date, we included nine PwD in the age of 76‐96 years in moderate to severe stages of dementia (MMSE 5‐18). We gained >183 hours of observational data, accompanied by sensor data. We observed 134 events of disorientation, 95 events of caregiver support but only 3 requests for help. Ca. 79% of the annotated time patients wore the smartwatch. The majority of patients accepted the device (despite advanced cognitive decline). Setting up the technical infrastructure for the indoor positioning was challenging, for example due to restricted access to power points. Conclusion: Analysis of data from wearable and ambient sensors bears potential to build a situation‐aware assistive device and is feasible to acquire in the setting of dementia patients living in nursing homes. A large number of situations where need for help became obvious was matched only by very few instances of active help seeking by the patients. This emphasizes the necessity for automated sensor‐based recognition of such situations. [ABSTRACT FROM AUTHOR]

Graphs of the single-step operator for first-order logic programs—displayed in the real plane—exhibit self-similar structures known from topological dynamics, i.e., they appear to be fractals, or more precisely, attractors of iterated function systems. We show that this observation can be made mathematically precise. In particular, we give conditions which ensure that those graphs coincide with attractors of suitably chosen iterated function systems, and conditions which allow the approximation of such graphs by iterated function systems or by fractal interpolation. Since iterated function systems can easily be encoded using recurrent radial basis function networks, we eventually obtain connectionist systems which approximate logic programs in the presence of function symbols. [Copyright &y& Elsevier]

Investigating the coil–magnet structure plays a significant role in the design process of the electromagnetic energy harvester due to the effect on the harvester's performance. In this paper, the performance of four different electromagnetic vibration energy harvesters with cylindrical shapes constrained in the same volume were under investigation. The utilized structures are (i) two opposite polarized magnets spaced by a mild steel; (ii) a Halbach array with three magnets and one coil; (iii) a Halbach array with five magnets and one coil; and (iv) a Halbach array with five magnets and three coils. We utilized a completely automatic optimization procedure with the help of an optimization algorithm implemented in Python, supported by simulations in ANSYS Maxwell and MATLAB Simulink to obtain the maximum output power for each configuration. The simulation results show that the Halbach array with three magnets and one coil is the best for configurations with the Halbach array. Additionally, among all configurations, the harvester with two opposing magnets provides the highest output power and volume power density, while the Halbach array with three magnets and one coil provides the highest mass power density. The paper also demonstrates limitations of using the electromagnetic coupling coefficient as a metric for harvester optimization, if the ultimate goal is maximization of output power. [ABSTRACT FROM AUTHOR]

Low-power energy harvesting has been demonstrated as a feasible alternative for the power supply of next-generation smart sensors and IoT end devices. In many cases, the output of kinetic energy harvesters is an alternating current (AC) requiring rectification in order to supply the electronic load. The rectifier design and selection can have a considerable influence on the energy harvesting system performance in terms of extracted output power and conversion losses. This paper presents a quantitative comparison of three passive rectifiers in a low-power, low-voltage electromagnetic energy harvesting sub-system, namely the full-wave bridge rectifier (FWR), the voltage doubler (VD), and the negative voltage converter rectifier (NVC). Based on a variable reluctance energy harvesting system, we investigate each of the rectifiers with respect to their performance and their effect on the overall energy extraction. We conduct experiments under the conditions of a low-speed rotational energy harvesting application with rotational speeds of 5 rpm to 20 rpm, and verify the experiments in an end-to-end energy harvesting evaluation. Two performance metrics—power conversion efficiency (PCE) and power extraction efficiency (PEE)—are obtained from the measurements to evaluate the performance of the system implementation adopting each of the rectifiers. The results show that the FWR with PEEs of 20% at 5 rpm to 40% at 20 rpm has a low performance in comparison to the VD (40–60%) and NVC (20–70%) rectifiers. The VD-based interface circuit demonstrates the best performance under low rotational speeds, whereas the NVC outperforms the VD at higher speeds (>18 rpm). Finally, the end-to-end system evaluation is conducted with a self-powered rpm sensing system, which demonstrates an improved performance with the VD rectifier implementation reaching the system's maximum sampling rate (40 Hz) at a rotational speed of approximately 15.5 rpm. [ABSTRACT FROM AUTHOR]

With the aim of increasing the efficiency of maintenance and fuel usage in airplanes, structural health monitoring (SHM) of critical composite structures is increasingly expected and required. The optimized usage of this concept is subject of intensive work in the framework of the EU COST Action CA18203 "Optimising Design for Inspection" (ODIN). In this context, a thorough review of a broad range of energy harvesting (EH) technologies to be potentially used as power sources for the acoustic emission and guided wave propagation sensors of the considered SHM systems, as well as for the respective data elaboration and wireless communication modules, is provided in this work. EH devices based on the usage of kinetic energy, thermal gradients, solar radiation, airflow, and other viable energy sources, proposed so far in the literature, are thus described with a critical review of the respective specific power levels, of their potential placement on airplanes, as well as the consequently necessary power management architectures. The guidelines provided for the selection of the most appropriate EH and power management technologies create the preconditions to develop a new class of autonomous sensor nodes for the in-process, non-destructive SHM of airplane components. [ABSTRACT FROM AUTHOR]

The digital revolution affects every aspect of society and economy. In particular, the manufacturing industry faces a new age of production processes and connected collaboration. The underlying ideas and concepts, often also framed as a new "Internet of Things", transfer IT technologies to the shop floor, entailing major challenges regarding the heterogeneity of the domain. On the other hand, web technologies have already proven their value in distributed settings. SOLID (derived from "social linked data") is a recent approach to decentralize data control and standardize interactions for social applications in the web. Extending this approach towards industrial applications has the potential to bridge the gap between the World Wide Web and local manufacturing environments. This paper proposes SOLIOT—a combination of lightweight industrial protocols with the integration and data control provided by SOLID. An in-depth requirement analysis examines the potential but also current limitations of the approach. The conceptual capabilities are outlined, compared and extended for the IoT protocols CoAP and MQTT. The feasibility of the approach is illustrated through an open-source implementation, which is evaluated in a virtual test bed and a detailed analysis of the proposed components. [ABSTRACT FROM AUTHOR]

The ongoing digital transformation has the potential to revolutionize nearly all industrial manufacturing processes. However, its concrete requirements and implications are still not sufficiently investigated. In order to establish a common understanding, a multitude of initiatives have published guidelines, reference frameworks and specifications, all intending to promote their particular interpretation of the Industrial Internet of Things (IIoT). As a result of the inconsistent use of terminology, heterogeneous structures and proposed processes, an opaque landscape has been created. The consequence is that both new users and experienced experts can hardly manage to get an overview of the amount of information and publications, and make decisions on what is best to use and to adopt. This work contributes to the state of the art by providing a structured analysis of existing reference frameworks, their classifications and the concerns they target. We supply alignments of shared concepts, identify gaps and give a structured mapping of regarded concerns at each part of the respective reference architectures. Furthermore, the linking of relevant industry standards and technologies to the architectures allows a more effective search for specifications and guidelines and supports the direct technology adoption. [ABSTRACT FROM AUTHOR]