In the GoGreen project an intelligent home that is able to identify inhabitants and events that take place is created. The location of sounds that are being produced is an important feature for the context awareness of this system. A a wireless solution that uses low-cost sensor nodes and microphones is described. Experiments show that solutions that only use the three sensor nodes that are closest to the origin of the sounds provide the best solutions, with an average accuracy of 40 cm or less.Paper published for the ICT Open 2013 proceedings (27-28 November 2013, Eindhoven).
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from the article: The demand for a wireless CO2 solution is ever increasing. One of the biggest problems with the majority of commercial available CO2 sensors is the high energy consumption which makes them unsuitable for battery operation. Possible candidates for CO2 sensing in a low power wireless application are very limited and show a problematic calibration process. This study focuses on one of those EMF candidates, which is a Ag4RbI5 based sensor. This EMF sensor is based on the potentiometric principle and consumes no energy. The EMF cell was studied in a chamber where humidity, temperature and CO2 level could be controlled. This study gives an detailed insight in the different drift properties of the potentiometric CO2 sensor and a method to amplify the sensors signal. Furthermore, a method to minimize the several types of drift is given. With this method the temperature drift can be decreased by a factor 10, making the sensor a possible candidate for a wireless CO2 sensor network.
Energy conservation is crucial in wireless ad hoc sensor network design to increase network lifetime. Since communication consumes a major part of the energy used by a sensor node, efficient communication is important. Topology control aims at achieving more efficient communication by dropping links and reducing interference among simultaneous transmissions by adjusting the nodes’ transmission power. Since dropping links make a network more susceptible to node failure, a fundamental problem in wireless sensor networks is to find a communication graph with minimum interference and minimum power assignment aiming at an induced topology that can satisfy fault-tolerant properties. In this paper, we examine and propose linear integer programming formulations and a hybrid meta-heuristic GRASP/VNS (Greedy Randomized Adaptive Search Procedure/Variable Neighborhood Search) to determine the transmission power of each node while maintaining a fault-tolerant network and simultaneously minimize the interference and the total power consumption. Optimal biconnected topologies for moderately sized networks with minimum interference and minimum power are obtained using a commercial solver. We report computational simulations comparing the integer programming formulations and the GRASP/VNS, and evaluate the effectiveness of three meta-heuristics in terms of the tradeoffs between computation time and solution quality. We show that the proposed meta-heuristics are able to find good solutions for sensor networks with up to 400 nodes and that the GRASP/VNS was able to systematically find the best lower bounds and optimal solutions.
Management policy for protected species is currently often based on literature reviews and expert judgement, even though it requires tailor-made species knowledge on a local level. While wildlife management should preferably be evidence based, tailor-made field data is seldom used in current practices, because it is hardly available, difficult to collect and expensive. Recent development of digital technology is changing the field of wildlife management with “more, better, faster and cheaper” ways of data collection. Especially automated collection of field data with different types of sensors is promising, whereas miniaturization and low cost mass-production increase availability and use of these sensors. For collection of field data about predator-prey interactions, there is a need to develop wireless sensor networks that automatically identify different species in a community, while they record their spatially explicit data and their behaviour. Therefore, we will put together a consortium of partners that will develop a EU LIFE programme proposal, with the focus to develop a sensor network necessary to automatically monitor multiple species (i.e., species communities) for species conservation management. The consortium will consist of Van Hall Larenstein, Sovon Dutch Centre for Field Ornithology, the Dutch Mammal Society, Sensing Clues and DIKW intelligence. It will bring together a strong mix of expert knowledge on applied species conservation and wildlife management, ecological field research, wildlife intelligence, and handling and analysis of big data. This project matches the Top sector High-tech Systems & Materials, and revolves around 4 distinct phases: selection of potential consortium partners, exploration of the problem, working towards a common action perspective and writing a EU LIFE programme proposal. We will use knowledge co-creation techniques to explore the first three project phases.
Brandweermensen lopen het meeste gevaar als ze onder tijdsdruk een gebouw moeten verkennen, of een brand moeten blussen terwijl de situatie nog niet goed kan worden overzien. Omvallende muren, instortende plafonds of gewoon gestruikeld over door de rook onzichtbare brokstukken leiden tot vermijdbare letsels of zelfs slachtoffers. Met name de inzet bij branden in stedelijke parkeergarages onder woontorens vormen een enorm risico. Het inzetten van onbemande, op afstand bestuurbare voertuigen voor verkenning en bluswerk is een oplossing die binnen de brandweer breed wordt gedragen. De brandweer moet deze innovatieve technologie echter zien te omarmen. Zij werken nu vanuit hun intuïtie en weten direct hoe te acteren op basis van wat zij waarnemen. Praktijkgericht onderzoek heeft echter uitgewezen dat scepsis over de inzet van blusplatforms bij incidenten plaats heeft gemaakt voor zeker vertrouwen. Een blusplatform, voorzien van juiste sensoren kan de Officier van Dienst (OVD) ondersteunen bij het nemen van een beslissing om al dan niet tot een ‘aanval’ over te gaan. Praktijktesten hebben echter laten zien dat de huidige blusplatforms nog niet optimaal functioneren om als volwaardig ‘teamlid’ te kunnen worden ingezet. Dit heeft enerzijds met technologische ontwikkelingen (sensoren en communicatieverbindingen) te maken, maar anderzijds moet de informatievoorziening (human-machine interfacing) naar de brandweer beter worden afgestemd. In dit project gaan Saxion, het instituut fysieke veiligheid, de universiteit Twente, het bedrijfsleven en vijf veiligheidsregio’s onderzoeken hoe en wanneer innovatieve blusplatforms op een intuïtieve manier kunnen worden ingezet door training én (kleine) productaanpassing zodat deze een volwaardig onderdeel kunnen zijn van het brandweerkorps. Een blusplatform kan letselschade en slachtoffers voorkomen, mits goed ingezet en vertrouwd door de mensen die daarvan afhankelijk zijn. Het vak van brandweer, als beroeps of vrijwilliger, is een van de gevaarlijkste die er is. Laten we er samen voor zorgen dat het iets veiliger kan worden.
Wildlife crime is an important driver of biodiversity loss and disrupts the social and economic activities of local communities. During the last decade, poaching of charismatic megafauna, such as elephant and rhino, has increased strongly, driving these species to the brink of extinction. Early detection of poachers will strengthen the necessary law enforcement of park rangers in their battle against poaching. Internationally, innovative, high tech solutions are sought after to prevent poaching, such as wireless sensor networks where animals function as sensors. Movement of individuals of widely abundant, non-threatened wildlife species, for example, can be remotely monitored ‘real time’ using GPS-sensors. Deviations in movement of these species can be used to indicate the presence of poachers and prevent poaching. However, the discriminative power of the present movement sensor networks is limited. Recent advancements in biosensors led to the development of instruments that can remotely measure animal behaviour and physiology. These biosensors contribute to the sensitivity and specificity of such early warning system. Moreover, miniaturization and low cost production of sensors have increased the possibilities to measure multiple animals in a herd at the same time. Incorporating data about within-herd spatial position, group size and group composition will improve the successful detection of poachers. Our objective is to develop a wireless network of multiple sensors for sensing alarm responses of ungulate herds to prevent poaching of rhinos and elephants.
