Combining edge and cloud computing for mobility analytics

Mobility analytics using data generated from the Internet of Mobile Things (IoMT) is facing many challenges which range from the ingestion of data streams coming from a vast number of fog nodes and IoMT devices to avoiding overflowing the cloud with useless massive data streams that can trigger bottlenecks [1]. Managing data flow is becoming an important part of the IoMT because it will dictate in which platform analytical tasks should run in the future. Data flows are usually a sequence of out-of-order tuples with a high data input rate, and mobility analytics requires a real-time flow of data in both directions, from the edge to the cloud, and vice-versa. Before pulling the data streams to the cloud, edge data stream processing is needed for detecting missing, broken, and duplicated tuples in addition to recognize tuples whose arrival time is out of order. Analytical tasks such as data filtering, data cleaning and low-level data contextualization can be executed at the edge of a network. In contrast, more complex analytical tasks such as graph processing can be deployed in the cloud, and the results of ad-hoc queries and streaming graph analytics can be pushed to the edge as needed by a user application. Graphs are efficient representations used in mobility analytics because they unify knowledge about connectivity, proximity and interaction among moving things. This poster describes the preliminary results from our experimental prototype developed for supporting transit systems, in which edge and cloud computing are combined to process transit data streams forwarded from fog nodes into a cloud. The motivation of this research is to understand how to perform meaningfulness mobility analytics on transit feeds by combining cloud and fog computing architectures in order to improve fleet management, mass transit and remote asset monitoringComment: Edge Computing, Cloud Computing, Mobility Analytics, Internet of Mobile Things, Edge Fog Fabri

The design of a Bayesian Network for mobility management in Wireless Sensor Networks

Mobility in Wireless Sensor Networks (WSNs) is achieved by attaching sensors to mobile objects such as animals (Juang et al. 2002), people (Campbell et al. 2008), and robots (Dantu et al. 2005). Currently, the research about WSN management is mainly focused on energy management functions to control how sensors should use their power; fault management functions to solve sensor problems; quality of services (QoS) management functions to quantify and control the performance; and mobility management functions to detect the sensor movement so that the network wireless connectivity is always maintained (Wang et al. 2010; Ruiz et al. 2003). However, the sensor mobility has not only an impact on the network connectivity, but also on the network spatial coverage. In mobile WSNs, the extension of the spatial coverage is often changing, and as a result, the region of interest might be inaccurately sensed by the mobile sensors. Therefore, the representation of a movement context is important to avoid making interpretations and decisions outside of the situation in which the WSN is capturing information; and make possible to decide where, when and how the sensing is performed in order to obtain the most suitable spatial coverage of a region of interest. This paper proposes a Bayesian network (BN) approach for making explicit the structural and parametric components of a movement context using WSN metadata. The aim is to infer mobility management requirements when a spatial coverage is incorrectly covering a Region of Interest (ROI), regardless the network connectivity. The BN approach provides several advantages regarding to the probabilistic representation of a movement context, the inference of mobility management requirements based on such a context, and the dynamic updating of the movement context every time new metadata are retrieved from the WSN. Previous research works in WSNs have used a similar approach focusing on energy management (Elnahrawy and Nath 2004) and prediction of sensor movement directions (Coles et al. 2009). The main contribution of our work is the analysis of how well a ROI is being covered by mobile sensors, and what are the requirements to improve that coverage given a movement context. A controlled experiment was carried out and the results show that, when the ROI is not being sufficiently covered by a WSN, the BN can probabilistically infer different mobility management requirements, based on a given movement context. Two movement contexts have been used to illustrate this approach. They are related to whether the sensing is being carried out in an emergency situation or not

A conceptual representation for modelling the synchronization process of complex road networks

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014

The Interoperability of Wireless Sensor Networks

The interoperability of heterogeneous sensor networks is needed for the achievement of a world integrated sensing system. The aim of this paper is to describe the results of an exploratory study which has been carried out to determine the role of metadata in an interoperability model for Wireless Sensor Networks. This model includes a description of the observations, processes, functionalities, status and configuration of a network to help improving the knowledge of a network itself, as well as to ensure the integration with other sensor networks. The results demonstrate the use of metadata to support different interoperability levels of Wireless Sensor Networks as a first step towards defining an interoperability model of Wireless Sensor Network

Propuesta de un núcleo estándar de metadatos para los recursos del Patrimonio Histórico Español

La amplitud con la que se define legalmente el Patrimonio Histórico Español según la Ley 16/1985 de 25 de junio es uno de los factores claves en la identificación y caracterización del mismo: “... inmuebles y objetos muebles de interés artístico, históricos, paleontológicos, arqueológicos, etnográficos, científico o técnico. Asimismo el patrimonio documental o bibliográfico, los yacimientos o zonas arqueológicas así como sitios naturales, jardines y parques que tengan valor artístico, histórico y antropológico”. Los recursos patrimoniales están catalogados y descritos mediante un conjunto de metadatos, que permiten su búsqueda en la red, así como la consulta de las propiedades básicas de los mismos. INSPIRE plantea la directiva donde quedan enmarcados los recursos digitales georreferenciados. En este sentido el patrimonio es un recurso con componente espacial y la propia directiva lo considera dentro de los datos de referencia (Sistemas de coordenadas de referencia, Sistema de cuadrículas geográficas, Nombres geográficos, Unidades administrativas, Direcciones, Parcelas catastrales, Redes de transporte, Hidrografía, Lugares protegidos) como se puede ver en el anexo I. Según INSPIRE: “Lugares protegidos: zonas designadas o gestionadas dentro de un marco legislativo internacional, comunitario o propio de los estados miembros, para la consecución de unos objetivos de conservación específicos”