Context-Aware Telco Outdoor Localization

Recent years have witnessed the fast growth in telecommunication (Telco) techniques from 2G to upcoming 5G. Precise outdoor localization is important for Telco operators to manage, operate and optimize Telco networks. Differing from GPS, Telco localization is a technique employed by Telco operators to localize outdoor mobile devices by using measurement report (MR) data. When given MR samples containing noisy signals (e.g., caused by Telco signal interference and attenuation), Telco localization often suffers from high errors. To this end, the main focus of this paper is how to improve Telco localization accuracy via the algorithms to detect and repair outlier positions with high errors. Specifically, we propose a context-aware Telco localization technique, namely RLoc, which consists of three main components: a machine-learning-based localization algorithm, a detection algorithm to find flawed samples, and a repair algorithm to replace outlier localization results by better ones (ideally ground truth positions). Unlike most existing works to detect and repair every flawed MR sample independently, we instead take into account spatio-temporal locality of MR locations and exploit trajectory context to detect and repair flawed positions. Our experiments on the real MR data sets from 2G GSM and 4G LTE Telco networks verify that our work RLoc can greatly improve Telco location accuracy. For example, RLoc on a large 4G MR data set can achieve 32.2 meters of median errors, around 17.4 percent better than state-of-the-art.Peer reviewe

An Experimental Platform for large-scale research facing FI-IoT scenarios

Providing experimental facilities for the Internet of Things (IoT) world is of paramount importance to materialise the Future Internet (FI) vision. The level of maturity achieved at the networking level in Sensor and Actuator networks (SAN) justifies the increasing demand on the research community to shift IoT testbed facilities from the network to the service and information management areas. In this paper we present an Experimental Platform fulfilling these needs by: integrating heterogeneous SAN infrastructures in a homogeneous way; providing mechanisms to handle information, and facilitating the development of experimental services. It has already been used to deploy applications in three different field trials: smart metering, smart places and environmental monitoring and it will be one of the components over which the SmartSantander project, that targets a large-scale IoT experimental facility, will rely o

5g and Iot digital era: the transformation of mobile network operators into end-to-end solution providers

The forthcoming 5G and IoT large-scale implementation reveals new business opportunities in completely new sectors that mobile network operators should seize. This survey paper wants to identify the necessary transformations such operators must undergo to build a sustainable competitive advantage in the future industry. A qualitative research composed of semi-structured interviews incumbents’stronger intent of diversification and creates the base for strategic recommendations.A sample of recent actions carried out by mobile network operators to improve their position in the 5G and IoT environments is shown at the end of the work

Visualization of Crowd Trajectory, Geospatial Sets, and Audience Prediction at Roskilde Festival 2018

We present a large-scale study on Geospatial Big Data Analytics in a festival management and crowd safety scenario based on our volunteer work at the largest music festival in Northern Europe, the 2017 and 2018 Roskilde music festival. As large crowds move between concerts, campsites, private parties and public viewing of the FIFA world cup soccer matches across the vast festival area, previously available visualization solutions for the crowd safety staff at Roskilde Festival lack a real-time visualization of crowd trajectory for monitoring of previously known chokepoints and the discovery of potential future chokepoints. We present a real-time visualization of crowd trajectory based on mobile device GPS data that is collected through the festival’s smartphone app from a significant subset of all festivalgoers. Hence, we present a recent case for the applications of real-time visualization of geospatial data for large-scale open-air events outside the confinements of urban architecture. Furthermore, we investigate and quantify the phenomenon of festivalgoers staying at the camping areas and sometimes not once visiting the musical performances in the inner perimeters of the festival, thereby finding that up to 3,000 festivalgoers both in 2017 and 2018 chose to not attend musical performances at all. Subsequently, we evaluate a Facebook-based approach on concert audience prediction based on social media audience overlaps between artist and festival Facebook pages, and benchmark its predictive power with a GPS-based measurement of audience sizes based on mobile device GPS data

Towards the Internet of Behaviors in Smart Cities through a Fog-To-Cloud Approach

Recent advances in the Internet of Things (IoT) and the rise of the Internet of Behavior (IoB) have made it possible to develop real-time improved traveler assistance tools for mobile phones, assisted by cloud-based machine learning and using fog computing in between the IoT and the Cloud. Within the Horizon2020-funded mF2C project, an Android app has been developed exploiting the proximity marketing concept and covers the essential path through the airport onto the flight, from the least busy security queue through to the time to walk to the gate, gate changes, and other obstacles that airports tend to entertain travelers with. It gives travelers a chance to discover the facilities of the airport, aided by a recommender system using machine learning that can make recommendations and offer vouchers based on the traveler’s preferences or on similarities to other travelers. The system provides obvious benefits to airport planners, not only people tracking in the shops area, but also aggregated and anonymized view, like heat maps that can highlight bottlenecks in the infrastructure, or suggest situations that require intervention, such as emergencies. With the emergence of the COVID-19 pandemic, the tool could be adapted to help in social distancing to guarantee safety. The use of the fog-to-cloud platform and the fulfillment of all centricity and privacy requirements of the IoB give evidence of the impact of the solution. Doi: 10.28991/HIJ-2021-02-04-01 Full Text: PD

Evaluation of home detection algorithms on mobile phone data using individual-level ground truth

Inferring mobile phone users’ home location, i.e., assigning a location in space to a user based on data generated by the mobile phone network, is a central task in leveraging mobile phone data to study social and urban phenomena. Despite its widespread use, home detection relies on assumptions that are difficult to check without ground truth, i.e., where the individual who owns the device resides. In this paper, we present a dataset that comprises the mobile phone activity of sixty-five participants for whom the geographical coordinates of their residence location are known. The mobile phone activity refers to Call Detail Records (CDRs), eXtended Detail Records (XDRs), and Control Plane Records (CPRs), which vary in their temporal granularity and differ in the data generation mechanism. We provide an unprecedented evaluation of the accuracy of home detection algorithms and quantify the amount of data needed for each stream to carry out successful home detection for each stream. Our work is useful for researchers and practitioners to minimize data requests and maximize the accuracy of the home antenna location. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1140/epjds/s13688-021-00284-9

From grids to clouds: recap on challenges and solutions

Grid Computing is a set of resources; the separate computational power of these resources has combination to execute a huge task. Usually, in a Computational Grid environment, the main resource is the Central Processing Unit (CPU), mostly used in research fields that demand high computational power to perform massive and complicated calculations. Cloud Computing is a promising computing pattern which offers facilities and common resources on demand over the Web. The implementation of cloud computing applications has high priority, especially in the modern world, for example in providing adequate funding for social services and purchasing programs. In this paper, we discuss the implementation of cloud computing over a Smart Grid: reliable, guaranteed and efficient with low cost, it is expected to offer Long Term Evolution (LTE). This allows larger pieces of the spectrum, or bands, to be used, with greater coverage and less latency. The third technology is the Vehicular Network, an important research area because of its unique features and potential applications. In this survey, we present an overview of the smart grid, LTE and vehicular network integrated with cloud computing. We also highlight the open issues and research directions in implementing these technologies with cloud computing in terms of energy and information management for smart grids; applying cloud computing platforms for 4G networks to achieve specific criteria; and finally architectural formation, privacy and security for vehicular cloud computing