Contextual sensing : integrating contextual information with human and technical geo-sensor information for smart cities

In this article we critically discuss the challenge of integrating contextual information, in particular spatiotemporal contextual information, with human and technical sensor information, which we approach from a geospatial perspective. We start by highlighting the significance of context in general and spatiotemporal context in particular and introduce a smart city model of interactions between humans, the environment, and technology, with context at the common interface. We then focus on both the intentional and the unintentional sensing capabilities of todays technologies and discuss current technological trends that we consider have the ability to enrich human and technical geo-sensor information with contextual detail. The different types of sensors used to collect contextual information are analyzed and sorted into three groups on the basis of names considering frequently used related terms, and characteristic contextual parameters. These three groups, namely technical in situ sensors, technical remote sensors, and human sensors are analyzed and linked to three dimensions involved in sensing (data generation, geographic phenomena, and type of sensing). In contrast to other scientific publications, we found a large number of technologies and applications using in situ and mobile technical sensors within the context of smart cities, and surprisingly limited use of remote sensing approaches. In this article we further provide a critical discussion of possible impacts and influences of both technical and human sensing approaches on society, pointing out that a larger number of sensors, increased fusion of information, and the use of standardized data formats and interfaces will not necessarily result in any improvement in the quality of life of the citizens of a smart city. This article seeks to improve our understanding of technical and human geo-sensing capabilities, and to demonstrate that the use of such sensors can facilitate the integration of different types of contextual information, thus providing an additional, namely the geo-spatial perspective on the future development of smart cities.(VLID)165464

Characterization of behavioral patterns exploiting description of geographical areas

Abstract The enormous amount of recently available mobile phone data is providing unprecedented direct measurements of human behavior. Early recognition and prediction of behavioral patterns are of great importance in many societal applications like urban planning, transportation optimization, and health-care. Understanding the relationships between human behaviors and location's context is an emerging interest for understanding human-environmental dynamics. Growing availability of Web 2.0, i.e. the increasing amount of websites with mainly user created content and social platforms opens up an opportunity to study such location's contexts. This paper investigates relationships existing between human behavior and location context, by analyzing log mobile phone data records. First an advanced approach to categorize areas in a city based on the presence and distribution of categories of human activity (e.g., eating, working, and shopping) found across the areas, is proposed. The proposed classification is then evaluated through its comparison with the patterns of temporal variation of mobile phone activity and applying machine learning techniques to predict a timeline type of communication activity in a given location based on the knowledge of the obtained category vs. land-use type of the locations areas. The proposed classification turns out to 1 arXiv:1510.02995v1 [cs.SI] 11 Oct 2015 be more consistent with the temporal variation of human communication activity, being a better predictor for those compared to the official land use classification

Cell Towers as Urban Sensors: Understanding the Strengths and Limitations of Mobile Phone Location Data

Understanding urban dynamics and human mobility patterns not only benefits a wide range of real-world applications (e.g., business site selection, public transit planning), but also helps address many urgent issues caused by the rapid urbanization processes (e.g., population explosion, congestion, pollution). In the past few years, given the pervasive usage of mobile devices, call detail records collected by mobile network operators has been widely used in urban dynamics and human mobility studies. However, the derived knowledge might be strongly biased due to the uneven distribution of people’s phone communication activities in space and time. This dissertation research applies different analytical methods to better understand human activity and urban environment, as well as their interactions, mainly based on a new type of data source: actively tracked mobile phone location data. In particular, this dissertation research achieves three main research objectives. First, this research develops visualization and analysis approaches to uncover hidden urban dynamics patterns from actively tracked mobile phone location data. Second, this research designs quantitative methods to evaluate the representativeness issue of call detail record data. Third, this research develops an appropriate approach to evaluate the performance of different types of tracking data in urban dynamics research. The major contributions of this dissertation research include: 1) uncovering the dynamics of stay/move activities and distance decay effects, and the changing human mobility patterns based on several mobility indicators derived from actively tracked mobile phone location data; 2) taking the first step to evaluate the representativeness and effectiveness of call detail record and revealing its bias in human mobility research; and 3) extracting and comparing urban-level population movement patterns derived from three different types of tracking data as well as their pros and cons in urban population movement analysis

The Spatio-Temporal Analysis of the Use and Usability Problems of EV Workplace Charging Facilities

With the worldwide calls to meet greenhouse gas targets and policy objectives by 2030, finding an electric vehicle (EV) on the way to work every day has become less surprising. Adapting to owning an EV is challenging to all potential users. Current users tend to rely on domestic charging for a more certain and less hassle charging opportunity. The demand is shifting towards workplace charging (WPC) as a cheap and convenient solution due to the relatively long time the car is parked there. WPC fills a critical gap in EV charging infrastructure needs by extending electric miles and building range confidence. This chapter reports on the social practice of using one of the WPC facilities in the UK. It investigates the use and usability problems that are faced (n = 12) by EV users at workplace environment in one of the UK public sector employer

Mobile Data Traffic Modeling: Revealing Temporal Facets

Using a large-scale dataset collected from a major 3G network in a dense metropolitan area, this paper presents the first detailed measurement-driven model of mobile data traffic usage of smartphone subscribers. Our main contribution is a synthetic, measurement-based, mobile data traffic generator capable of simulating traffic-related activity patterns for different categories of subscribers and time periods for a typical day in their lives. We first characterize individual subscribers routinary behaviour, followed by a detailed investigation of subscribers' temporal usage patterns (i.e., "when" and "how much" traffic is generated). We then classify the subscribers into six distinct profiles according to their usage patterns and model these profiles according to two daily time periods: peak and non-peak hours. We show that the synthetic trace generated by our data traffic model consistently replicates a subscriber's profiles for these two time periods when compared to the original dataset. Broadly, our observations bring important insights into network resource usage. We also discuss relevant issues in traffic demands and describe implications in network planning and privacy.Comprendre la demande de trafic de données mobiles est essentielle pour l'évaluation des stratégies portant sur le problème de l'utilisation de bande passante élevée et l'évolutivité des ressources du réseau, apporté par l'ère "pervasive". Dans cet article, nous effectuons la première modélisation détaillée de l'utilisation du trafic mobile des smartphones dans un scénario métropolitain. Nous utilisons un ensemble de données à grande échelle recueillis au coeur d'un des majeurs réseaux 3G de la capitale du Mexique. Nous analysons d'abord le comportement individuel routinier et nous avons observé des modèle d'utilisation identiques pour les différents jours. Cela nous motive à choisir un jour pour étudier le mode d'utilisation des abonnés (c'est à dire, "quand" et "combien" de trafic est généré) en détail. Nous classons ensuite les abonnés en quatre profils distincts en fonction de leur mode d'utilisation. Nous modélisons enfin le mode d'utilisation de ces quatre profils d'abonnés selon deux périodes différents: de pointe et les heures creuses. Nous montrons que la trace synthétique produite par le modèle de trafic de données imite fidèlement les différents profils d'abonnés en deux périodes, par rapport à l'ensemble de données d'origine

Design and Implementation of System Components for Radio Frequency Based Asset Tracking Devices to Enhance Location Based Services. Study of angle of arrival techniques, effects of mutual coupling, design of an angle of arrival algorithm, design of a novel miniature reconfigurable antenna optimised for wireless communication systems

The angle of arrival estimation of multiple sources plays a vital role in the field of array signal processing as MIMO systems can be employed at both the transmitter and the receiver end and the system capacity, reliability and throughput can be significantly increased by using array signal processing. Almost all applications require accurate direction of arrival (DOA) estimation to localize the sources of the signals. Another important parameter of localization systems is the array geometry and sensor design which can be application specific and is used to estimate the DOA. In this work, various array geometries and arrival estimation algorithms are studied and then a new scheme for multiple source estimation is proposed and evaluated based on the performance of subspace and non-subspace decomposition methods. The proposed scheme has shown to outperform the conventional Multiple Signal Classification (MUSIC) estimation and Bartlett estimation techniques. The new scheme has a better performance advantage at low and high signal to noise ratio values (SNRs). The research work also studies different array geometries for both single and multiple incident sources and proposes a geometry which is cost effective and efficient for 3, 4, and 5 antenna array elements. This research also considers the shape of the ground plane and its effects on the angle of arrival estimation and in addition it shows how the mutual couplings between the elements effect the overall estimation and how this error can be minimised by using a decoupling matrix. At the end, a novel miniaturised multi element reconfigurable antenna to represent the receiver base station is designed and tested. The antenna radiation patterns in the azimuth angle are almost omni-directional with linear polarisation. The antenna geometry is uniplanar printed logspiral with striplines feeding network and biased components to improve the impedance bandwidth. The antenna provides the benefit of small size, and re-configurability and is very well suited for the asset tracking applications

Large-scale Mobile Traffic Analysis: a Survey

International audienceThis article surveys the literature on analyses of mobile traffic collected by operators within their network infrastructure. This is a recently emerged research field, and, apart from a few outliers, relevant works cover the period from 2005 to date, with a sensible densification over the last three years. We provide a thorough review of the multidisciplinary activities that rely on mobile traffic datasets, identifying major categories and sub-categories in the literature, so as to outline a hierarchical classification of research lines. When detailing the works pertaining to each class, we balance a comprehensive view of state-of-the-art results with punctual focuses on the methodological aspects. Our approach provides a complete introductory guide to the research based on mobile traffic analysis. It allows summarizing the main findings of the current state-of-the-art, as well as pinpointing important open research directions

Knowledge and Attitudes toward Persons with Schizophrenia among Hispanic University Students Enrolled in Mental Health and Non-mental Health Disciplines

The present study was designed to examine university students’ knowledge regarding schizophrenia and factors that can influence university students’ attitudes toward persons with schizophrenia. An attribution questionnaire ([AQ-27], Corrigan, Markowitz, Watson, Rowan, & Kubiak, 2003), the Error Choice Test ([ECT], Michaels & Corrigan, 2013), and the Social Desirability Scale ([SDS-13], Reynolds, 1982) were utilized to collect data from undergraduate students attending The University of Texas Rio Grande Valley (UTRGV), the largest U.S. Hispanic serving university located on the Texas-Mexico border. Descriptive and inferential statistics were utilized to analyze the survey data. Results indicated a non-significant relationship between students’ knowledge regarding schizophrenia and their self-reported stigma toward persons with schizophrenia. The discussion includes implications of the present study and recommendations for research to enhance knowledge about schizophrenia and mitigate stigma among university students toward persons with schizophrenia