Low-cost magnetic resonance (MR) sensors have in recent years been used to investigate a number of systems by measuring the relaxation times T₁ and T₂^eff. These values vary in line with changes in many systems giving the investigator a useful non-invasive probe. While the use of MR for in-line or on-line process monitoring in the food industry is not a novel concept, much of the work conducted previously has involved acquiring spatially resolved data which requires a magnetic resonance imaging system. These are both expensive to purchase and maintain, occupy large amounts of space and present problems with safety. In this work we show the value that a very inexpensive (<£100) MR sensor can bring to process monitoring. An MR sensor utilising an eight-element Halbach cylinder with internal diameter of 10mm has been constructed giving a highly uniform magnetic field yielding a strong signal-to-noise ratio. It is shown to be useful for assessing the relaxation times of a range of relevant samples.

Pressure control management of a water distribution network is considered as an effective approach for the reduction of leakage in the network and for optimized savings in pumping energy. A successful pressure management scheme usually requires single- or multi-feed regulation through Pressure Reduction Valves, the network division in District Metered Areas and dense monitoring of pressure and flow throughout the network. A structural component of this approach would be a hydraulic model which would relate pressure and flow in the network, in accordance to the monitored values. In the ISS-EWATUS project (issewatus.eu) and specifically for the Skiathos, Greece case study, we solve the water distribution network hydraulically with the EPANET software, so as to estimate the pressure-map of the island and later link pressure to leakage. The need to zoom-in spatially and temporarily makes the spatial and temporal disaggregation of aggregated water demand, as provided by the water utility, a necessity. We use the daily aggregated water supply time series and consumer quarterly billing data for each water meter in the DMA, in order to produce approximate, daily water consumption datasets of each household and daily leakage per household. In this article, we present our disaggregation methodology for the production of these data sets.

As a concept, material-integrated intelligent systems represent the vision of embedding not only sensors, but full sensor networks with smart sensors in technical materials, irrespective of their application being dominated by functional or structural properties. In this sense, the term full sensor networks encompasses the sensors, the associated signal and data processing, the data evaluation and information retrieval, provisions for communication within the network and beyond it, and an energy supply system. The concept as such can be applied to any type or class of host material, ranging from organic materials to composites, metals and ceramics. The result are materials that are, in a manner of speaking, able to “feel” in the broader sense associated with this term. Applications which would profit from such materials are diverse and range from structural health monitoring and control to fly by feel, robotics, human machine interaction to new types of user interfaces. Similarly, the fields of use differ widely from aerospace and other transport applications via advanced manufacturing systems to consumer products. Of specific interest for economic reasons are semi-finished materials that can be processed into several different products.

Needless to say, systems integrated into rather than externally attached to a host material or structure face several unique challenges, among them the need for

• reliability and fault tolerance
• autonomy in terms of energy supply and information processing
• mechanical and thermal stability sufficient to survive production
• mechanical and thermal stability sufficient to survive service life
• compatibility of mechanical and thermal properties with the host material
• scalability regarding network size on hard- and software level
• adaptability w.r.t. changes in environment and inner state

The present works discusses current approaches towards realizing material-integrated intelligent systems. In doing so, addressing the conceptual level is just one side of our work; besides, we attempt to provide a matrix that matches the challenges listed above with technological approaches that show the necessary potential for providing solutions. In this, the focus is clearly not limited to a hardware perspective, but includes software-based methods, too, e. g. in terms of guaranteeing reliability and fault tolerance in data evaluation and communication, or in making best use of available energetic resources, to name but a few examples: Material-embedded sensor networks must operate under low-resource, low-energy, and technical failure constraints, requiring new concepts in information processing.

Based on this analysis of fundamental technologies and already-realised concepts, we identify knowledge gaps that currently still hamper the broader implementation of material-integrated intelligent systems and derive suggestions for future research paths from it.

A special section is dedicated to the advent of additive manufacturing techniques adapted to facilitate sensor integration: The present growth in this field is expected to extend into the field of sensor-integrated materials and structures. First approaches in this direction will be discussed in the present work.

The design of user-customized water demand management strategies (WDMS) does require detailed information on the users’ water consumption habits and demand patterns. While low-resolution consumption data, as traditionally collected for billing purposes, do not allow for detailed investigations on consumers’ behavior, the recent advent of smart water sensors allows metering residential water consumption with sub-daily frequency. High-frequency water data are advancing our ability in characterizing and modeling consumers’ behavior, thus supporting the design of user-oriented residential water demand management strategies. Furthermore, they allow to close the information and communication loop between water utilities and their customers, as they provide instantaneous information on the state of the network and allow to continuously inform the users on their consumption and savings.

SmartH2O is a EU funded project which develops an ICT platform for raising users’ awareness about water consumption and pursuing water savings in the residential sector thanks to the integrated use of smart meters, advanced behavioral modeling, social computation, and personalized demand management strategies such as customized feedbacks and price schemes.

In this work, we present the main components of the SmartH2O integrated platform, with a particular focus on (i) high-resolution water consumption data collection through smart meters, (ii) users’ consumption modeling at the house level and end-use characterization through automatic disaggregation algorithms, and (iii) water consumption data visualization.

  Body Impedance Spectroscopy (BIS) is a noninvasive fast method for assessing the composition of body districts. For these reasons BIS could provide important physiological or clinical information in sports medicine studies or in rehabilitation protocols. However, commercial  instruments are generally nonportable, limiting their use in sport applications. Wearable prototypes proposed in recent literature do not simultaneously satisfy the demanding needs of recording BIS in different segments at the same time, over a broad frequency range, for long periods and with high measurements rate, as sports medicine applications or exercise tests often requires.

 Therefore aim of this work is to present a new wearable prototype for monitoring multi-segment, multi-frequency BIS, unobtrusively over long periods, to be used in studies of rehabilitation- or sports- medicine.

  The system is designed to guarantee low weight, low power consumption and small size, consisting in an analog board with 2 current injecting electrodes and 4 electrodes sensing the voltage drops across 3 body segments; and in a digital board with a digital signal processor that generates square-wave current stimuli, digitalizes the sensed voltages and computes the electrical impedance at 10 frequencies from 1 to 796 kHz.

  The system performance is exemplified monitoring BIS in 3 body segments before, during and after physical exercise and a postural shift in a volunteer. The system was able to describe  exercise-induced changes in active and inactive muscular districts, their persistence during recovery and the effect of a sit-to-stand maneuver, demonstrating the information derivable from such class of BIS devices.

There are a lot of historic buildings whose construction is based on timber frame walls. Most of the constructed buildings during the nineteenth and early twentieth century were based on walls of timber frame with vertical support element.

These timber frame elements are affected both by the moisture content, such as the time variation of it. The maintenance of these buildings could be significantly improved if was known the interaction of the wall timber framing with hygrothermal climate variations.

To determine the moisture content of the wood there are two types of meters on the market: one hand capacitance meters which consist of two side ends and the moisture content is measured locally between two peaks. On the other hand, there are meters based on the variation of electromagnetic transmittance (on the range of the microwave) of timber depending on the moisture of it. The first one is cheaper, but measurement mades are local and not very precise and the second one is very expensive and difficult to handle.

This work presents a new non-intrusive capacitive sensor that measures the global moisture content in the section of the wall timber framing and therefore it accuracy is similar to those obtained by electromagnetic transmittance meters, but due to it is a capacitive sensor, it has a low cost and an easy operation.

Wireless Sensor Networks (WSN) as emerging technology are becoming even more important through the technisation of industry, our mobility driven lives and our “smart homes”. In a world where a plethora of our devices interact to serve our purposes, it's clear that competing groups may want to use these devices for their own objectives.

Therefore we focus on honeypots for WSNs as an opportunity to become acquainted with the techniques used to misuse or even to take-over WSNs. This knowledge has the potential to improve the WSN designs and even those already deployed in the field.

Honeypots in information security can be found in computer networks where a client-server model is applied and client-honeypots are very different from server-honeypots. In WSNs the client-server model is not applicable. This was the motivation behind determining whether it would be a honeypot at all if brought into this domain. What if we have to call it something else? The uncertainty of a missing naming convention leading to ambiguous conversations about this new topic needs discussion. In this paper we show the classic definition, discuss the categorisation and give recommendations for further use as well as the wording that will be used further on in the following research.

In Europe, as in the whole world, because of its construction, life cycle and demolition processes, the housing have turned into one of the big energy consumers, due to the ever rising demand of comfort conditions, technological features and innovative materials. It ecological fingerprint is noticeable, too. In a world increasingly compromised with sustainability, the technological development oriented to the improvement of living conditions coexists with that one oriented to decrease the energetic requirements of the houses.

Half way between both approaches, the home automation proposes the implementation of intelligent, connected and governed devices which, commanded by a common programming and the multiple interfaces for user interactions, allow the management of the house facilities. The objective is a housing which obtains a high auto-management degree, which become more comfortable and safe and, at the same time, with an improved energetic efficiency.

This document develops the study of an implementation project of a home automation system in a housing placed in the town of Galapagar, in Madrid. This house, which is going to be occupied by a four members family, consist of 67 constructed square meters distributed in lounge, kitchen, three bedrooms, bath, bathroom and terrace, being this a very common arrangement in Spain. So, this study will allow to extract conclusions about the adequacy of the home automation in a wide percentage of housings in Spain.

Along this document three house automation proposal where develop based on the requirements of the client and the different home automation levels that the Spanish House an Building Automation Association has established, besides two parallel proposals relating to the safety and the technical alarms. The above mentioned proposed systems are described by means of product datasheets and descriptions, distribution plans, measurements, budgets and flow charts that describe the functioning of the system in every case. An evaluation of each system is included, based on other studies conclusions on this matter, where it is evaluated the energetic saving that is expected from each one and, depending on the nowadays cost of light, water and gas, the expected economic amortization.

With the growing demand of Intelligent Transportation Systems (ITS) for safer and more efficient transportation, the research and development of such vehicular communication systems have increased considerably in the last years. The use of wireless networks has been extended in an exponential growing in vehicular environments. However, it is highly important to conduct previous radio propagation analysis when deploying a wireless sensor network in such complex scenarios. In this work, the radio wave characterization for ISM 2.4GHz Wireless Sensor Networks (WSN) for a traffic light control system has been assessed. By means of an in-house developed 3D Ray Launching algorithm, the impact of topology as well as morphology of the environment has been analyzed, emulating the realistic operation in the framework of the scenario. The complexity of the scenario, which is an intersection city area with traffic lights, vehicles, people, buildings, vegetation and urban environment, makes necessary the channel characterization with accurate models before the deployment of wireless networks. A measurements campaign has been conducted by deploying a ZigBee Wireless Sensor Network in such specific environment emulating the traffic light control system. Experimental measurements and simulations results are analyzed and compared in this paper. Results show that the use of deterministic tools in WSN deployment can aid in providing optimal layouts in terms of coverage, capacity and energy efficiency of the network, making the use of these systems essential before the deployment of WSN for traffic light control applications in urban areas.

The production processes in the packaging materials industry has to be very efficient and cost-effective. These processes usually take place under extreme conditions and high speeds that requires a high level of reliability and efficiency. Rollers including their supporting bearings and motors are the most common components of production machines in the packaging materials industry. Bearing faults, which often occur gradually, represent one of the foremost causes of failures in the industry. Therefore it is very important to take care of bearings during maintenance and detect their faults in an early stage in order to assure safe and efficient operation. We present a new automated technique for early fault detection and diagnosis in rolling-element bearings based on vibration signal analysis. After normalization and the wavelet transform of vibration signals, the standard deviation as a measure of average energy level and the logarithmic energy entropy as a measure of the degree of order/disorder are extracted in a few sub-bands of interest as representative features. Then the feature space dimension is optimally reduced to two using scatter matrices. In the reduced two-dimensional feature space the fault detection is performed by a quadratic classifier and the fault diagnosis by another two quadratic classifiers. Accuracy of the new technique was tested on the ball bearing data recorded at the Case Western Reserve University Bearing Data Center. In total four classes of the vibrations signals were studied, i.e. normal, with the fault of inner race, outer race and balls operation. An overall accuracy of 100% was achieved. The new technique can be used to increase reliability and efficiency by preventing unexpected faulty operation of machinery bearings.