Estimating Attendance From Cellular Network Data

We present a methodology to estimate the number of attendees to events happening in the city from cellular network data. In this work we used anonymized Call Detail Records (CDRs) comprising data on where and when users access the cellular network. Our approach is based on two key ideas: (1) we identify the network cells associated to the event location. (2) We verify the attendance of each user, as a measure of whether (s)he generates CDRs during the event, but not during other times. We evaluate our approach to estimate the number of attendees to a number of events ranging from football matches in stadiums to concerts and festivals in open squares. Comparing our results with the best groundtruth data available, our estimates provide a median error of less than 15% of the actual number of attendees

Classification and prediction of whereabouts patterns from the Reality Mining dataset

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Predict Cellular network traffic with markov logic

Forecasting spatio-temporal data is a challenging task in transportation scenarios involving agents. In this paper, we propose a statistical relational learning approach to cellular network traffic forecasting, that exploits spatial relationships between close cells in the network grid. The approach is based on Markov logic networks, a powerful framework that combines first-order logic and graphical models into a hybrid model capable of handling both uncertainty in data, and background knowledge of the problem. Experimental results conducted on a real-world data set show the potential of using such information. The proposed methodology can have a strong impact in mobility demand forecasting and in transportation applications

Automatic identification of relevant places from cellular network data

We present a methodology to automatically identify users\u2019 relevant places from cellular network data.1 In this work we used anonymized Call Detail Record (CDR) comprising information on where and when users access the cellular network. The key idea is to effectively cluster CDRs together and to weigh clusters to determine those associated to frequented places. The approach can identify users\u2019 home and work locations as well as other places (e.g., associated to leisure and night life). We evaluated our approach threefold: (i) on the basis of groundtruth information coming from a fraction of users whose relevant places were known, (ii) by comparing the resulting number of inhabitants of a given city with the number of inhabitants as extracted by the national census. (iii) Via stability analysis to verify the consistency of the extracted results across multiple time periods. Results show the effectiveness of our approach with an average 90% precision and recall

Engineering Pervasive Service Ecosystems: The SAPERE approach

Emerging pervasive computing services will typically involve a large number of devices and service components cooperating together in an open and dynamic environment. This calls for suitable models and infrastructures promoting spontaneous, situated, and self-adaptive interactions between components. SAPERE (Self-Aware Pervasive Service Ecosystems) is a general coordination framework aimed at facilitating the decentralized and situated execution of self-organizing and self-adaptive pervasive computing services. SAPERE adopts a nature-inspired approach, in which pervasive services are modeled and deployed as autonomous individuals in an ecosystem of other services and devices, all of which interact in accord to a limited set of coordination laws, or eco-laws. In this article, we present the overall rationale underlying SAPERE and its reference architecture. We introduce the eco-laws--based coordination model and show how it can be used to express and easily enforce general-purpose self-organizing coordination patterns. The middleware infrastructure supporting the SAPERE model is presented and evaluated, and the overall advantages of SAPERE are discussed in the context of exemplary use cases

Location-dependent services for mobile users

Abstract—One of the main issues in mobile services ’ research (M-service) is supporting M-service availability, regardless of the user’s context (physical location, device employed, etc.). However, most scenarios also require the enforcement of context-awareness, to dynamically adapt M-services depending on the context in which they are requested. In this paper, we focus on the problem of adapting M-services depending on the users ’ location, whether physical (in space) or logical (within a specific distributed group/application). To this end, we propose a framework to model users ’ location via a multiplicity of local and active service contexts. First, service contexts represent the mean to access to M-services available within a physical locality. This leads to an intrinsic dependency of M-service on the users’ physical location. Second, the execution of service contexts can be tuned depending on who is requesting what M-service. This enables adapting M-services to the logical location of users (e.g., a request can lead to different executions for users belonging to different groups/applications). The paper firstly describes the framework in general terms, showing how it can facilitate the design of distributed applications involving mobile users as well as mobile agents. Then, it shows how the MARS coordination middleware, implementing service contexts in terms of programmable tuple spaces, can be used to develop and deploy applications and M-services coherently with the above framework. A case study is introduced and discussed through the paper to clarify our approach and to show its effectiveness. Index Terms—Context-awareness, coordination infrastructures, M-services, mobility, multiagent systems. I

An Argumentation-based Perspective over the Social IoT

The crucial role played by social interactions between smart objects in the Internet of Things is being rapidly recognized by the Social Internet of Things (SIoT) vision. In this paper, we build upon the recently introduced vision of Speaking Objects – “things” interacting through argumentation – to show how different forms of human dialogue naturally fit cooperation and coordination requirements of the SIoT. In particular, we show how speaking objects can exchange arguments in order to seek for information, negotiate over an issue, persuade others, deliberate actions, and so on, namely, striving to reach consensus about the state of affairs and their goals. In this context, we illustrate how argumentation naturally enables such a form of conversational coordination through practical examples and a case study scenario

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

Analisis Perubahan Penggunaan Lahan Berdasarkan Hasil Interpretasi Visual Citra Satelit Untuk Penerimaan Pbb (Studi Kasus : Kecamatan Semarang Utara)

Pesatnya pembangunan menyebabkan tingginya Perubahan pola penggunaan lahan. Lahan yang dulunya merupakan lahan kosong atau lahan tidak terbangun, banyak mengalami Perubahan fungsi menjadi lahan terbangun. Perubahan penggunaan lahan dapat di monitoring menggunakan data spasial remot sensing. Akusisi data remote sensing secara berseri dari waktu ke waktu memungkinkan untuk melakukan analisis Perubahan lahan. Citra yang dipakai dalam penelitian adalah Citra Ikonos tahun 2007, sedangkan pembandingnya merupakan peta penggunaan lahan kecamatan Semarang Utara tahun 2009. Software yang digunakan adalah E.R. Mapper 7.0 dan Arc.GIS 10. Proses rektifikasi menggunakan metode Map to Image dimana titik GCP diperoleh berdasarkan data sekunder dari peta yang mempunyai liputan yang sama dengan citra yang akan dikoreksi. Berdasarkan pengolahan citra Ikonos tahun 2007 dan peta penggunaan lahan tahun 2009 didapatkan Perubahan luas penggunaan lahan sebesar 62,656 Ha. Dengan adanya Perubahan luas tersebut dapat mempengaruhi Perubahan harga NJOP, Perubahan harga NJOP yang terjadi sebesar 21,6 %

Comparing deep learning and statistical methods in forecasting crowd distribution from aggregated mobile phone data

Accurately forecasting how crowds of people are distributed in urban areas during daily activities is of key importance for the smart city vision and related applications. In this work we forecast the crowd density and distribution in an urban area by analyzing an aggregated mobile phone dataset. By comparing the forecasting performance of statistical and deep learning methods on the aggregated mobile data we show that each class of methods has its advantages and disadvantages depending on the forecasting scenario. However, for our time-series forecasting problem, deep learning methods are preferable when it comes to simplicity and immediacy of use, since they do not require a time-consuming model selection for each different cell. Deep learning approaches are also appropriate when aiming to reduce the maximum forecasting error. Statistical methods instead show their superiority in providing more precise forecasting results, but they require data domain knowledge and computationally expensive techniques in order to select the best parameters