Opportunistic Data Collection and Routing in Segmented Wireless Sensor Networks

International audienceIn this paper we address routing in the context of segmented wireless sensor networks in which a mobile entity, known as MULE, may collect data from the different subnetworks and forward it to a sink for processing. The chosen settings are inspired by the potential application of wireless sensor networks for airport surface monitoring. In such an environment, the subnetworks could take advantage of airport service vehicles, buses or even taxiing aircraft to transfer information to the sink (e.g., control tower), without interfering with the regular functioning of the airport. Generally, this kind of communication problem is addressed in the literature considering a single subsink in each subnetwork. We consider in this paper the multiple subsinks case and propose two strategies to decide when and where (to which subsink) sensor nodes should transmit their sensing data. Through a dedicated simulation model we have developed, we assess and compare the performance of both strategies in terms of packet delivery ratio, power consumption and workload balance among subsinks. This paper is an intermediate step in the research of this problem, which evidences the benefit of storing the information on the subsinks and distributing it among them before the arrival of the MULE. Based on results, we provide some information on further works

RefineDetLite: A Lightweight One-stage Object Detection Framework for CPU-only Devices

Previous state-of-the-art real-time object detectors have been reported on GPUs which are extremely expensive for processing massive data and in resource-restricted scenarios. Therefore, high efficiency object detectors on CPU-only devices are urgently-needed in industry. The floating-point operations (FLOPs) of networks are not strictly proportional to the running speed on CPU devices, which inspires the design of an exactly "fast" and "accurate" object detector. After investigating the concern gaps between classification networks and detection backbones, and following the design principles of efficient networks, we propose a lightweight residual-like backbone with large receptive fields and wide dimensions for low-level features, which are crucial for detection tasks. Correspondingly, we also design a light-head detection part to match the backbone capability. Furthermore, by analyzing the drawbacks of current one-stage detector training strategies, we also propose three orthogonal training strategies---IOU-guided loss, classes-aware weighting method and balanced multi-task training approach. Without bells and whistles, our proposed RefineDetLite achieves 26.8 mAP on the MSCOCO benchmark at a speed of 130 ms/pic on a single-thread CPU. The detection accuracy can be further increased to 29.6 mAP by integrating all the proposed training strategies, without apparent speed drop.Comment: 16 pages, 8 figure

Mobiiliverkkodatan käytön validointi lähtö-määränpää -matriisien luomisessa

The rapid development in telecommunication networks during last years has made it possible to study human travel behaviour effectively from mobile network data. The combination of passive and active signalling events gathered by mobile network operators allow analysing movements of people with full longitudinal and spatial coverage. Therefore, recent years have seen an increasing interest in utilizing mobile network data in transportation studies, as an alternative or a complementary data source for conventional transport data. This study validates the capability of mobile network data to produce long-distance origin-destination matrices in Finland. Features that are being validated include trip counts, seasonal trip count changes and modal split. As reference data sources of the study, the National Travel Survey 2016, HELMET-transport demand model (Transport model by HSL) and LAM-data (automated traffic census) are used. Validation is done by analysing correlations between mobile network data and the reference data sources. By being able to demonstrate the validity and reliability of mobile network data usage in producing origin-destination matrices, cost-effectiveness and more accurate methods to gather information from long-distance transportation can be provided for the field in general. The overall results of the study are in line with the few similar related studies that have been conducted. The thesis work suggests that mobile network data is capable of producing more reliable trip counts from sparsely populated areas than the National Travel Survey. In addition, it seems to be more capable of capturing the high summer peak in longdistance travelling in Finland. The results regarding modal split are promising, but more studies regarding the modal detection will be needed.Matkapuhelinverkkojen viime vuosien nopea kehitys on mahdollistanut yhä tarkemman matkapuhelinten solupaikannuksen. Teleoperaattoreiden keräämä passiivisten ja aktiivisten matkapuhelinverkon signaalihavaintojen yhdistelmä mahdollistaa ihmisten liikkumiskäyttäytymisen tutkimisen kattavasti sekä ajallisesti että alueellisesti. Viime aikoina matkapuhelinverkkodatan hyödyntäminen liikennetutkimuksissa on tästä syystä herättänyt kasvavaa kiinnostusta perinteisten tiedonkeruumenetelmien korvaajana ja täydentäjänä. Tämä tutkimus validoi mobiiliverkkodatan käyttöä lähtö-määränpää -matriisien luomisessa Suomen pitkän matkan liikenteessä. Validoitavia ominaisuuksia ovat matkamäärät, matkamäärien vuodenajoittainen vaihtelu ja matkojen kulkumuotojakauma. Referenssiaineistona työssä käytetään Suomen Henkilöliikennetutkimusta, HELMET-liikennemallia ja LAM-dataa. Validointi suoritetaan analysoimalla mobiiliverkkodatan ja referenssiaineistojen välisiä korrelaatioita. Osoittamalla mobiiliverkkodatan käytettävyys lähtö-määränpää matriisien luomisessa, liikennesuunnittelun kustannustehokkuutta ja keinoja tarkemman tiedon keräämiseen pitkämatkaisesta liikkumisesta voidaan edistää. Työn tulokset ovat linjassa aiemman tutkimuksen kanssa. Tulokset näyttävät mobiiliverkkodatan olevan kykenevä tuottamaan lähtö-määränpää -tietoa hajaasutusalueilta luotettavammin kuin Henkilöliikennetutkimus. Lisäksi, mobiiliverkkodata näyttää pystyvän observoimaan kesän lomakauden matkapiikin tarkemmin kuin Henkilöliikennetutkimus. Tulokset mobiiliverkkodatan kulkumuototunnistukseen ovat lupaavia, mutta lisää tutkimusta tarvitaan näiden havaintojen vahvistamiseen

Metamodeling approach to preference management in the semantic Web

2008 AAAI Workshop; Chicago, IL; United States; 13 July 2008 through 14 July 2008Preference is a superiority state to determine the preferable or the superior of one entity, property or constraint to another from a specified selection set. Preference issue is heavily studied in Semantic Web research area. The existing preference management approaches only consider the importance of concepts for capturing users' interests. This paper presents a metamodeling approach to preference management. Preference meta model consists of concepts and semantic relations to represent users' interests. Users may have the same type preferences in different domains. Thus, metamodeling must be used to define similar preferences for interoperability in different domains. In this paper, preference meta model defines a general storage structure to manage different types of preferences for personalized applications. Copyright © 2008, Association for the Advancement of Artificial Intelligence

How to become the Leader of the Mobile Telecom Industry

Five main observations have been made. First, the integration is likely to continue within the mobile telecom industry. Second, the value creation process will change significantly during the next few years. Third, the operators must reinvent their position in the value chain to maintain high profitability. Fourth, ecosystem keystones will capture most of the value. Fifth, flexibility will become even more important in the future. Both horizontal and vertical integration makes the companies larger and less flexible, which in turn makes it more difficult for them to adapt to the market and the rapidly changing consumer needs. However, it is through size, integration and cooperations that a company can take a keystone advantage position. To become a so called keystone, and be able to capture most of the value created within the industry, it is important to have the customer in focus and apply co-creation and the customers-as-innovators approach. By taking in the consumer early in a product development process, the risk of losing flexibility to changing consumer needs can be reduced. Currently, it is the operators and the mobile phone brands that are competing for the position as keystone within the mobile telecom industry

Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens

The monitoring and forecasting of environmental conditions is a task to which much effort and resources are devoted by the scientific community and relevant authorities. Representative examples arise in meteorology, oceanography, and environmental engineering. As a consequence, high volumes of data are generated, which include data generated by earth observation systems and different kinds of models. Specific data models, formats, vocabularies and data access infrastructures have been developed and are currently being used by the scientific community. Due to this, discovering, accessing and analyzing environmental datasets requires very specific skills, which is an important barrier for their reuse in many other application domains. This paper reviews earth science data representation and access standards and technologies, and identifies the main challenges to overcome in order to enable their integration in semantic open data infrastructures. This would allow non-scientific information technology practitioners to devise new end-user solutions for citizen problems in new application domainsThis research was co-funded by (i) the TRAFAIR project (2017-EU-IA-0167), co-financed by the Connecting Europe Facility of the European Union, (ii) the RADAR-ON-RAIA project (0461_RADAR_ON_RAIA_1_E) co-financed by the European Regional Development Fund (ERDF) through the Iterreg V-A Spain-Portugal program (POCTEP) 2014-2020, and (iii) the Consellería de Educación, Universidade e Formación Profesional of the regional government of Galicia (Spain), through the support for research groups with growth potential (ED431B 2018/28)S

Modeling Software Product Lines Using Feature Diagrams

The leading strategies for systematic software reuse focus on reuse of domain knowledge. One such strategy is software product line engineering. This strategy selects a set of reusable software components that form the core around which software products in a domain are built. Feature modeling is a process that enables engineers to identify these core assets, in particular the com(e.g., shared) and variable features of products. The focus of this thesis is to give an overview of the feature modeling process by introducing feature diagrams. Feature diagrams capture and represent comand variable properties (features) of the software products in a domain, focusing on properties that may vary, which are further used to produce different software products. We present practical examples that show how feature models are used to represent a set of valid composition of features (configurations), in which each configuration can be considered as a specification of a software system instantiated from a software product line