61 research outputs found
CDAR : contour detection aggregation and routing in sensor networks
Wireless sensor networks offer the advantages of low cost, flexible measurement of phenomenon in a wide variety of applications, and easy deployment. Since sensor nodes are typically battery powered, energy efficiency is an important objective in designing sensor network algorithms. These algorithms are often application-specific, owing to the need to carefully optimize energy usage, and since deployments usually support a single or very few applications.
This thesis concerns applications in which the sensors monitor a continuous scalar field, such as temperature, and addresses the problem of determining the location of a contour line in this scalar field, in response to a query, and communicating this information to a designated sink node. An energy-efficient solution to this problem is proposed and evaluated. This solution includes new contour detection and query propagation algorithms, in-network-processing algorithms, and routing algorithms. Only a small fraction of network nodes may be adjacent to the desired contour line, and the contour detection and query propagation algorithms attempt to minimize processing and communication by the other network nodes. The in-network processing algorithms reduce communication volume through suppression, compression and aggregation techniques. Finally, the routing algorithms attempt to route the contour information to the sink as efficiently as possible, while meshing with the other algorithms. Simulation results show that the proposed algorithms yield significant improvements in data and message volumes compared to baseline models, while maintaining the integrity of the contour representation
Generating Contour Maps for Dynamic Fields Monitored by Sensor Networks
Wireless sensor networks provide a new tool that enables researchers and scientists to efficiently monitor dynamic fields. In order to extend the lifetime of the network, it is important for us to minimize network data transmission as much as possible. Previous work proposed many useful aggregation techniques to answer max, min and average questions, and some of them have been employed in real applications. But we cannot get spatial information from these aggregation techniques. This thesis presents an efficient aggregation technique for continuous generation of contour maps for a dynamic field monitored by a wireless sensor network. A contour map is a useful data representation schema that provides an efficient way to visualize an approximation to the monitored field. In this thesis, we discuss an energy-efficient technique, which we call Isovector Aggregation, for generating such contours using an in-network approach. Our technique achieves energy efficiency in two principal ways. Firstly, only a selection of nodes close to contours are chosen to report, and each reported message contains information about a part or all of the contours, rather than any single nodeās ID and value pair. Secondly, contours are progressively merged and simplified along the data routing tree, which eliminates many unnecessary contour points from contour vectors before they are transmitted back to the base station. Using Isovector Aggregation, the base station receives a complete representation of the contours that requires no further processing. Analysis shows that for region-related monitoring, Isovector Aggregation is the only technique that has O( n) traffic generation and that considers in-network traffic reduction at the same time. These two factors make Isovector Aggregation highly scalable. Experimental results using simulations also show that Isovector Aggregation involves considerably less data transmission compared to other approaches, such as the no-aggregation approach and the Isolines Aggregation technique, without compromising the accuracy of representations of the baseline maps
Level based sampling techniques for energy conservation in large scale wireless sensor networks
As the size and node density of wireless sensor networks (WSN) increase,the energy conservation problem becomes more critical and the conventional methods become inadequate. This dissertation addresses two different problems in large scale WSNs where all sensors are involved in monitoring,but the traditional practice of periodic transmissions of observations from all sensors would drain excessive amount of energy.
In the first problem,monitoring of the spatial distribution of a two dimensional correlated signal is considered using a large scale WSN. It is assumed that sensor observations are heavily affected by noise. We present an approach that is based on detecting contour lines of the signal distribution to estimate the spatial distribution of the signal without involving all sensors in the network. Energy efficient algorithms are proposed for detecting and tracking the temporal variation of the contours. Optimal contour levels that minimize the estimation error and a practical approach for selection of contour levels are explored. Performance of the proposed algorithm is explored with different types of contour levels and detection parameters.
In the second problem,a WSN is considered that performs health monitoring of equipment from a power substation. The monitoring applications require transmissions of sensor observations from all sensor nodes on a regular basis to the base station,which is very costly in terms of communication cost. To address this problem,an efficient sampling technique using level-crossings (LCS) is proposed. This technique saves communication cost by suppressing transmissions of data samples that do not convey much information. The performance and cost of LCS for several different level-selection schemes are investigated. The number of required levels and the maximum sampling period for practical implementation of LCS are studied. Finally,in an experimental implementation of LCS with MICAzmote,the performance and cost of LCS for temperature sensing with uniform,logarithmic and a combined version of uniform and logarithmically spaced levels are compared with that using periodic sampling
INSPIRE PROJECT: INTEGRATED TECHNOLOGIES FOR SMART BUILDINGS AND PREDICTIVE MAINTENANCE
Abstract. Applying integrated digital technologies for the management and maintenance of the existing built heritage appears to be one of the main current challenges for the definition and application of digitisation protocols for the construction supply chain. Key enabling technologies, collaborative platforms, Big Data management and information integration in a BIM environment are areas of increasing experimentation. In the field of intervention on the built heritage, it is the boundaries and opportunities offered by the integration of many different information sources that constitutes the main challenge. Furthermore, the study of the accessibility and usability of data and information from sources such as the three-dimensional terrestrial survey, existing databases, sensor networks, and satellite technologies make it possible to investigate both different ways of data modelling, even with a view to the development of predictive algorithms, and of visualisation and information management. The study illustrates part of the results of the InSPiRE project, an industrial research project financed with European structural funds and carried out in a public-private partnership by four universities and public research bodies, an innovation centre and six companies, SMEs, large enterprises, and start-ups. Specifically, the project highlights the growing importance of BIM-based modelling as a tool to lead users, both experts and non-experts, through the multiple information paths resulting from the relation between data and metadata
Quantifying the costs of transport networksā components
In dieser Arbeit werden die Kosten von StraĆentransportnetzwerken mit Hilfe von verschiedenen Datenquellen und Methodiken quantifiziert
A distributed data extraction and visualisation service for wireless sensor networks
With the increase in applications of wireless sensor networks, data extraction and visualisation have become a key issue to develop and operate these networks. Wireless sensor networks typically gather data at a discrete number of locations. By bestowing the ability to predict inter-node values upon the network, it is proposed that it will become possible to build applications that are unaware of the concrete reality of sparse data. The aim of this thesis is to develop a service for maximising information return from large scale wireless sensor networks. This aim will be achieved through the development of a distributed information extraction and visualisation service called the mapping service. In the distributed mapping service, groups of network nodes cooperate to produce local maps which are cached and merged at a sink node, producing a map of the global network. Such a service would greatly simplify the production of higher-level information-rich representations suitable for informing other network services and the delivery of field information visualisations. The proposed distributed mapping service utilises a blend of both inductive and deductive models to successfully map sense data and the universal physical principles. It utilises the special characteristics of the application domain to render visualisations in a map format that are a precise reflection of the concrete reality. This service is suitable for visualising an arbitrary number of sense modalities. It is capable of visualising from multiple independent types of the sense data to overcome the limitations of generating visualisations from a single type of a sense modality. Furthermore, the proposed mapping service responds to changes in the environmental conditions that may impact the visualisation performance by continuously updating the application domain model in a distributed manner. Finally, a newdistributed self-adaptation algorithm, Virtual Congress Algorithm,which is based on the concept of virtual congress is proposed, with the goal of saving more power and generating more accurate data visualisation.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
AgrƩgation de donnƩes dans les rƩseaux de capteurs sans fil
Wireless Sensor Networks (WSNs) have been regarded as an emerging and promis- ing field in both academia and industry. Currently, such networks are deployed due to their unique properties, such as self-organization and ease of deployment. How- ever, there are still some technical challenges needed to be addressed, such as energy and network capacity constraints. Data aggregation, as a fundamental solution, pro- cesses information at sensor level as a useful digest, and only transmits the digest to the sink. The energy and capacity consumptions are reduced due to less data packets transmission. As a key category of data aggregation, aggregation function, solving how to aggregate information at sensor level, is investigated in this thesis.We make four main contributions: firstly, we propose two new networking-oriented metrics to evaluate the performance of aggregation function: aggregation ratio and packet size coefficient. Aggregation ratio is used to measure the energy saving by data aggregation, and packet size coefficient allows to evaluate the network capac- ity change due to data aggregation. Using these metrics, we confirm that data ag- gregation saves energy and capacity whatever the routing or MAC protocol is used. Secondly, to reduce the impact of sensitive raw data, we propose a data-independent aggregation method which benefits from similar data evolution and achieves better re- covered fidelity. Thirdly, a property-independent aggregation function is proposed to adapt the dynamic data variations. Comparing to other functions, our proposal can fit the latest raw data better and achieve real adaptability without assumption about the application and the network topology. Finally, considering a given application, a tar- get accuracy, we classify the forecasting aggregation functions by their performances. The networking-oriented metrics are used to measure the function performance, and a Markov Decision Process is used to compute them. Dataset characterization and classification framework are also presented to guide researcher and engineer to select an appropriate functions under specific requirements.Depuis plusieurs anneĢes, les reĢseaux de capteurs sans fil sont consideĢreĢs comme un domaine eĢmergent et prometteur tant dans le milieu universitaire que dans lāindustrie. De tels reĢseaux ont deĢjaĢ eĢteĢ largement deĢployeĢs en raison de leurs proprieĢteĢs cleĢs, telles que lāauto-organisation et leur autonomie en eĢnergie. Cependant, il reste de nombreux deĢfis scientifiques telles que la reĢduction de la consommation dāeĢnergie sur des capteurs de plus en plus petits et la capaciteĢ du reĢseau tenant compte de liens aĢ bande passante reĢduite. Selon nous, lāagreĢgation de donneĢes apparaiĢt comme une so- lution pour ces deux deĢfis, car au lieu dāenvoyer une donneĢe, lāagreĢgation va traiter les informations collecteĢes au niveau du capteur et produire une donneĢe agreĢgeĢe qui sera effectivement transmise au puits. LāeĢnergie et la capaciteĢ du reĢseau seront donc eĢconomiseĢes car il y aura moins de transmissions de donneĢes. Le travail de cette theĢse sāinteĢresse principalement aux fonctions dāagreĢgationNous faisons quatre contributions principales. Tout dāabord, nous proposons deux nouvelles meĢtriques pour eĢvaluer les performances des fonctions dāagreĢgations vue au niveau reĢseau : le taux dāagreĢgation et le facteur dāaccroissement de la taille des paquets. Le taux dāagreĢgation est utiliseĢ pour mesurer le gain de paquets non trans- mis graĢce aĢ lāagreĢgation tandis que le facteur dāaccroissement de la taille des pa- quets permet dāeĢvaluer la variation de la taille des paquets en fonction des politiques dāagreĢgation. Ces meĢtriques permettent de quantifier lāapport de lāagreĢgation dans lāeĢconomie dāeĢnergie et de la capaciteĢ utiliseĢe en fonction du protocole de routage con- sideĢreĢ et de la couche MAC retenue. DeuxieĢmement, pour reĢduire lāimpact des don- neĢes brutes collecteĢes par les capteurs, nous proposons une meĢthode dāagreĢgation de donneĢes indeĢpendante de la mesure physique et baseĢe sur les tendances dāeĢvolution des donneĢes. Nous montrons que cette meĢthode permet de faire une agreĢgation spa- tiale efficace tout en ameĢliorant la fideĢliteĢ des donneĢes agreĢgeĢes. En troisieĢme lieu, et parce que dans la plupart des travaux de la litteĢrature, une hypotheĢse sur le com- portement de lāapplication et/ou la topologie du reĢseau est toujours sous-entendue, nous proposons une nouvelle fonction dāagreĢgation agnostique de lāapplication et des donneĢes devant eĢtre collecteĢes. Cette fonction est capable de sāadapter aux donneĢes mesureĢes et aĢ leurs eĢvolutions dynamiques. Enfin, nous nous inteĢressons aux outilspour proposer une classification des fonctions dāagreĢgation. Autrement dit, consid- eĢrant une application donneĢe et une preĢcision cible, comment choisir les meilleures fonctions dāagreĢgations en termes de performances. Les meĢtriques, que nous avons proposeĢ, sont utiliseĢes pour mesurer la performance de la fonction, et un processus de deĢcision markovien est utiliseĢ pour les mesurer. Comment caracteĢriser un ensem- ble de donneĢes est eĢgalement discuteĢ. Une classification est proposeĢe dans un cadre preĢcis
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