Forecasting Parking Lots Availability: Analysis from a Real-World Deployment

Smart parking technologies are rapidly being deployed in cities and public/private places around the world for the sake of enabling users to know in real time the occupancy of parking lots and offer applications and services on top of that information. In this work, we detail a real-world deployment of a full-stack smart parking system based on industrial-grade components. We also propose innovative forecasting models (based on CNN-LSTM) to analyze and predict parking occupancy ahead of time. Experimental results show that our model can predict the number of available parking lots in a ±3% range with about 80% accuracy over the next 1-8 hours. Finally, we describe novel applications and services that can be developed given such forecasts and associated analysis

Evaluating origin–destination matrices obtained from CDR data

Understanding and correctly modeling urban mobility is a crucial issue for the development of smart cities. The estimation of individual trips from mobile phone positioning data (i.e., call detail records (CDR)) can naturally support urban and transport studies as well as marketing applications. Individual trips are often aggregated in an origin–destination (OD) matrix counting the number of trips from a given origin to a given destination. In the literature dealing with CDR data there are two main approaches to extract OD matrices from such data: (a) in time-based matrices, the analysis focuses on estimating mobility directly from a sequence of CDRs; (b) in routine-based matrices (OD by purpose) the analysis focuses on routine kind of movements, like home-work commute, derived from a trip generation model. In both cases, the OD matrix measured by CDR counts is scaled to match the actual number of people moving in the area, and projected to the road network to estimate actual flows on the streets. In this paper, we describe prototypical approaches to estimate OD matrices, describe an actual implementation, and present a number of experiments to evaluate the results from multiple perspectives

CAMeL: A Self-Adaptive Framework for Enriching Context-Aware Middlewares with Machine Learning Capabilities

Context-aware middlewares support applications with context management. Current middlewares support both hardware and software sensors providing data in structured forms (e.g., temperature, wind, and smoke sensors). Nevertheless, recent advances in machine learning paved the way for acquiring context from information-rich, loosely structured data such as audio or video signals. This paper describes a framework (CAMeL) enriching context-aware middlewares with machine learning capabilities. The framework is focused on acquiring contextual information from sensors providing loosely structured data without the need for developers of implementing dedicated application code or making use of external libraries. Nevertheless the general goal of context-aware middlewares is to make applications more dynamic and adaptive, and the proposed framework itself can be programmed for dynamically selecting sensors and machine learning algorithms on a contextual basis. We show with experiments and case studies how the CAMeL framework can (i) promote code reuse and reduce the complexity of context-aware applications by natively supporting machine learning capabilities and (ii) self-adapt using the acquired context allowing improvements in classification accuracy while reducing energy consumption on mobile platforms

Geotechnical and hydrological characterization of hillslope deposits for regional landslide prediction modeling

We attempt a characterization of the geotechnical and hydrological properties of hillslope deposits, with the final aim of providing reliable data to distributed catchment-scale numerical models for shallow landslide initiation. The analysis is based on a dataset built up by means of both field tests and laboratory experiments over 100 sites across Tuscany (Italy). The first specific goal is to determine the ranges of variation of the geotechnical and hydrological parameters that control shallow landslide-triggering mechanisms for the main soil classes. The parameters determined in the deposits are: grain size distribution, Atterberg limits, porosity, unit weight, in situ saturated hydraulic conductivity and shear strength parameters. In addition, mineral phases recognition via X-ray powder diffraction has been performed on the different soil types. The deposits mainly consist of well-sorted silty sands with low plastic behavior and extremely variable gravel and clay contents. Statistical analyses carried on these geotechnical and hydrological parameters highlighted that it is not possible to define a typical range of values only with relation to the main mapped lithologies, because soil characteristics are not simply dependent on the bedrock type from which the deposits originated. A second goal is to explore the relationship between soil type (in terms of grain size distribution) and selected morphometric parameters (slope angle, profile curvature, planar curvature and peak distance). The results show that the highest correlation between soil grain size classes and morphometric attributes is with slope curvature, both profile and planar

Resilient distributed collection through information speed thresholds

Part 6: Large-Scale Decentralised SystemsInternational audienceOne of the key coordination problems in physically-deployed distributed systems, such as mobile robots, wireless sensor networks, and IoT systems in general, is to provide notions of “distributed sensing” achieved by the strict, continuous cooperation and interaction among individual devices. An archetypal operation of distributed sensing is data summarisation over a region of space, by which several higher-level problems can be addressed: counting items, measuring space, averaging environmental values, and so on. A typical coordination strategy to perform data summarisation in a peer-to-peer scenario, where devices can communicate only with a neighbourhood, is to progressively accumulate information towards one or more collector devices, though this typically exhibits problems of reactivity and fragility, especially in scenarios featuring high mobility. In this paper, we propose coordination strategies for data summarisation involving both idempotent and arithmetic aggregation operators, with the idea of controlling the minimum information propagation speed, so as to improve the reactivity to input changes. Given suitable assumptions on the network model, and under the restriction of no data loss, these algorithms achieve optimal reactivity. By empirical evaluation via simulation, accounting for various sources of volatility, and comparing to other existing implementations of data summarisation algorithms, we show that our algorithms are able to retain adequate accuracy even in high-variability scenarios where all other algorithms are significantly diverging from correct estimations

Detailed analysis of X chromosome inactivation in a 49,XXXXX pentasomy

<p>Abstract</p> <p>Background</p> <p>Pentasomy X (49,XXXXX) has been associated with a severe clinical condition, presumably resulting from failure or disruption of X chromosome inactivation. Here we report that some human X chromosomes from a patient with 49,XXXXX pentasomy were functionally active following isolation in inter-specific (human-rodent) cell hybrids. A comparison with cytogenetic and molecular findings provided evidence that more than one active X chromosome was likely to be present in the cells of this patient, accounting for her abnormal phenotype.</p> <p>Results</p> <p>5-bromodeoxyuridine (BrdU)-pulsed cultures showed different patterns among late replicating X chromosomes suggesting that their replication was asynchronic and likely to result in irregular inactivation. Genotyping of the proband and her mother identified four maternal and one paternal X chromosomes in the proband. It also identified the paternal X chromosome haplotype (P), indicating that origin of this X pentasomy resulted from two maternal, meiotic non-disjunctions. Analysis of the <it>HUMANDREC </it>region of the androgen receptor (<it>AR</it>) gene in the patient's mother showed a skewed inactivation pattern, while a similar analysis in the proband showed an active paternal X chromosome and preferentially inactivated X chromosomes carrying the 173 <it>AR </it>allele. Analyses of 33 cell hybrid cell lines selected in medium containing hypoxanthine, aminopterin and thymidine (HAT) allowed for the identification of three maternal X haplotypes (M1, M2 and MR) and showed that X chromosomes with the M1, M2 and P haplotypes were functionally active. In 27 cell hybrids in which more than one X haplotype were detected, analysis of X inactivation patterns provided evidence of preferential inactivation.</p> <p>Conclusion</p> <p>Our findings indicated that 12% of X chromosomes with the M1 haplotype, 43.5% of X chromosomes with the M2 haplotype, and 100% of the paternal X chromosome (with the P haplotype) were likely to be functionally active in the proband's cells, a finding indicating that disruption of X inactivation was associated to her severe phenotype.</p

Binding the Smart City Human-Digital System with Communicative Processes

This chapter will explore the dynamics of power underpinning ethical issues within smart cities via a new paradigm derived from Systems Theory. The smart city is an expression of technology as a socio-technical system. The vision of the smart city contains a deep fusion of many different technical systems into a single integrated “ambient intelligence”. ETICA Project, 2010, p. 102). Citizens of the smart city will not experience a succession of different technologies, but a single intelligent and responsive environment through which they move. Analysis of such an environment requires a framework which transcends traditional ontologically-based models in order to accommodate this deep fusion. This chapter will outline a framework based on Latour’s Actor-Network Theory and Luhmann’s treatment of society as an autopoetic system. We shall use this framework to map the influence of relevant factors on ethical issues, irrespective of their composition or type. For example, under this treatment, both human praxis and technical design can be viewed as comparable tools of domination. This chapter will provide a framework for the analysis of relations between any elements of the smart city, ranging from top-level urban management processes down to individual device operations. While we will illustrate the use of this schema through examination of ethical issues arising from power dynamics within the smart city, it is intended that this example will demonstrate the wider utility of the model in general

Autonomic communication learns from nature

Autonomic communication focuses on distributed systems and management of network resources at both the infrastructure level and the user level. It is distinct from autonomic computing, which is more oriented toward application software and management of computing resources, although both share the same goals. Autonomic communication research probes into fundamental rethinking of communication, networking, and distributed computing paradigms, to deal with the complexities and dynamics of modern networks. Many researchers, including the authors, are looking to self-organization in nature, such as in colonies of insects for lessons they can apply to self-organizing autonomic communication networks

User-aware comfort in retail environments

A retail environment can be thought as an environment where customers can buy products, goods or services. The user-experience in physical retail environments is important not only for facilitating the selling of goods and services, but also for providing satisfaction and appealing to retain customers over the long term. The user-experience can be enhanced by adapting aspects of the physical environment such as music, colour, fragrance to the tastes of the customers. In this paper we propose a user-aware approach to adapt physical aspects of a retail environment in order to improve the perceived comfort level. We introduce a model of the user context, which can be used both for representing information about the customers and for driving the adaptation of the environment. The proposed decision system is based on a microservice architecture providing both modularity and flexibility. Real-world examples are also used to show applications of the approach