Crowdsourcing as a tool for urban emergency management: lessons from the literature and typology

Recently, citizen involvement has been increasingly used in urban disaster prevention and management, taking advantage of new ubiquitous and collaborative technologies. This scenario has created a unique opportunity to leverage the work of crowds of volunteers. As a result, crowdsourcing approaches for disaster prevention and management have been proposed and evaluated. However, the articulation of citizens, tasks, and outcomes as a continuous flow of knowledge generation reveals a complex ecosystem that requires coordination efforts to manage interdependencies in crowd work. To tackle this challenging problem, this paper extends to the context of urban emergency management the results of a previous study that investigates how crowd work is managed in crowdsourcing platforms applied to urban planning. The goal is to understand how crowdsourcing techniques and quality control dimensions used in urban planning could be used to support urban emergency management, especially in the context of mining-related dam outages. Through a systematic literature review, our study makes a comparison between crowdsourcing tools designed for urban planning and urban emergency management and proposes a five-dimension typology of quality in crowdsourcing, which can be leveraged for optimizing urban planning and emergency management processes

Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation

The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for mass-market localization applications. However, there are various error sources that have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -- localization data sources -- localization algorithms -- localization error sources and mitigation -- localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors

Piggyback on Idle Ride-Sourcing Drivers for Intracity Parcel Delivery

This paper investigates the operational strategies for an integrated platform that provides both ride-sourcing services and intracity parcel delivery services over a transportation network utilizing the idle time of ride-sourcing drivers. Specifically, the integrated platform simultaneously offers on-demand ride-sourcing services for passengers and multiple modes of parcel delivery services for customers, including: (1) on-demand delivery, where drivers immediately pick up and deliver parcels upon receiving a delivery request; and (2) flexible delivery, where drivers can pick up (or drop off) parcels only when they are idle and waiting for the next ride-sourcing request. A continuous-time Markov Chain (CTMC) model is proposed to characterize the status change of drivers under joint movement of passengers and parcels over the transportation network with limited vehicle capacity, where the service quality of ride-sourcing services, on-demand delivery services, and flexible delivery services are rigorously quantified. Building on the CTMC model, incentives for ride-sourcing passengers, delivery customers, drivers, and the platform are captured through an economic equilibrium model, and the optimal operational decisions of the platform are derived by solving a non-convex profit-maximizing problem. We prove the well-posedness of the model and develop a tailored algorithm to compute the optimal decisions of the platform at an accelerated speed. Furthermore, we validate the proposed model in a comprehensive case study for San Francisco, demonstrating that joint management of ride-sourcing services and intracity package delivery services can lead to a Pareto improvement that benefits all stakeholders in the integrated ride-sourcing and parcel delivery market

Advanced Location-Based Technologies and Services

Since the publication of the first edition in 2004, advances in mobile devices, positioning sensors, WiFi fingerprinting, and wireless communications, among others, have paved the way for developing new and advanced location-based services (LBSs). This second edition provides up-to-date information on LBSs, including WiFi fingerprinting, mobile computing, geospatial clouds, geospatial data mining, location privacy, and location-based social networking. It also includes new chapters on application areas such as LBSs for public health, indoor navigation, and advertising. In addition, the chapter on remote sensing has been revised to address advancements

Flexible online task assignment in real-time spatial data

The popularity of Online To Offline (O2O) service platforms has spurred the need for online task assignment in real-time spatial data, where streams of spatially distributed tasks and workers are matched in real time such that the total number of assigned pairs is maximized. Existing online task assignment models assume that each worker is either assigned a task immediately or waits for a subsequent task at a fixed location once she/he appears on the platform. Yet in practice a worker may actively move around rather than passively wait in place if no task is assigned. In this paper, we define a new problem Flexible Two-sided Online task Assignment (FTOA). FTOA aims to guide idle workers based on the prediction of tasks and workers so as to increase the total number of assigned worker-task pairs. To address the FTOA problem, we face two challenges: (i) How to generate guidance for idle workers based on the prediction of the spatiotemporal distribution of tasks and workers? (ii) How to leverage the guidance of workers’ movements to optimize the online task assignment? To this end, we propose a novel two-step framework, which integrates offline prediction and online task assignment. Specifically, we estimate the distributions of tasks and workers per time slot and per unit area, and design an online task assignment algorithm, Prediction-oriented Online task Assignment in Real-time spatial data (POLAR-OP). It yields a 0.47-competitive ratio, which is nearly twice better than that of the state-of-the-art. POLAR-OP also reduces the time complexity to process each newly-arrived task/worker to O(1). We validate the effectiveness and efficiency of our methods via extensive experiments on both synthetic datasets and real-world datasets from a large-scale taxi-calling platform.ISSN:2150-809

Distributed Data Management in Vehicular Networks Using Mobile Agents

En los últimos años, las tecnologías de la información y las comunicaciones se han incorporado al mundo de la automoción gracias a sus avances, y han permitido la creación de dispositivos cada vez más pequeños y potentes. De esta forma, los vehículos pueden ahora incorporar por un precio asequible equipos informáticos y de comunicaciones.En este escenario, los vehículos que circulan por una determinada zona (como una ciudad o una autopista) pueden comunicarse entre ellos usando dispositivos inalámbricos que les permiten intercambiar información con otros vehículos cercanos, formando así una red vehicular ad hoc, o VANET (Vehicular Ad hoc Network). En este tipo de redes, las comunicaciones se establecen con conexiones punto a punto por medio de dispositivos tipo Wi-Fi, que permiten la comunicación con otros del mismo tipo dentro de su alcance, sin que sea necesaria la existencia previa de una infraestructura de comunicaciones como ocurre con las tecnologías de telefonía móvil (como 3G/4G), que además requieren de una suscripción y el pago de una tarifa para poder usarlas.Cada vehículo puede enviar información y recibirla de diversos orígenes, como el propio vehículo (por medio de los sensores que lleva incorporados), otros vehículos que se encuentran cerca, así como de la infraestructura de tráfico presente en las carreteras (como semáforos, señales, paneles electrónicos de información, cámaras de vigilancia, etc.). Todos estas fuentes pueden transmitir datos de diversa índole, como información de interés para los conductores (por ejemplo, atascos de tráfico o accidentes en la vía), o de cualquier otro tipo, mientras sea posible digitalizarla y enviarla a través de una red.Todos esos datos pueden ser almacenados localmente en los ordenadores que llevan los vehículos a medida que son recibidos, y sería muy interesante poder sacarles partido por medio de alguna aplicación que los explotara. Por ejemplo, podrían utilizarse los vehículos como plataformas móviles de sensores que obtengan datos de los lugares por los que viajan. Otro ejemplo de aplicación sería la de ayudar a encontrar plazas de aparcamiento libres en una zona de una ciudad, usando la información que suministrarían los vehículos que dejan una plaza libre.Con este fin, en esta tesis se ha desarrollado una propuesta de la gestión de datos basada en el uso de agentes móviles para poder hacer uso de la información presente en una VANET de forma eficiente y flexible. Esta no es una tarea trivial, ya que los datos se encuentran dispersos entre los vehículos que forman la red, y dichos vehículos están constantemente moviéndose y cambiando de posición. Esto hace que las conexiones de red establecidas entre ellos sean inestables y de corta duración, ya que están constantemente creándose y destruyéndose a medida que los vehículos entran y salen del alcance de sus comunicaciones debido a sus movimientos.En un escenario tan complicado, la aproximación que proponemos permite que los datos sean localizados, y que se puedan hacer consultas sobre ellos y transmitirlos de un sitio cualquiera de la VANET a otro, usando estrategias multi-salto que se adaptan a las siempre cambiantes posiciones de los vehículos. Esto es posible gracias a la utilización de agentes móviles para el procesamiento de datos, ya que cuentan con una serie de propiedades (como su movilidad, autonomía, adaptabilidad, o inteligencia), que hace que sean una elección muy apropiada para este tipo de entorno móvil y con un elevado grado de incertidumbre.La solución propuesta ha sido extensamente evaluada y probada por medio de simulaciones, que demuestran su buen rendimiento y fiabilidad en redes vehiculares con diferentes condiciones y en diversos escenarios.<br /

A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants or a crowd. Smartphones, tablets, and wearable devices are deployed widely and already equipped with a rich set of sensors, making them an excellent source of information. Mobility and intelligence of humans guarantee higher coverage and better context awareness if compared to traditional sensor networks. At the same time, individuals may be reluctant to share data for privacy concerns. For this reason, MCS frameworks are specifically designed to include incentive mechanisms and address privacy concerns. Despite the growing interest in the research community, MCS solutions need a deeper investigation and categorization on many aspects that span from sensing and communication to system management and data storage. In this paper, we take the research on MCS a step further by presenting a survey on existing works in the domain and propose a detailed taxonomy to shed light on the current landscape and classify applications, methodologies, and architectures. Our objective is not only to analyze and consolidate past research but also to outline potential future research directions and synergies with other research areas

Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications

Nowadays, the availability of the location information becomes a key factor in today’s communications systems for allowing location based services. In outdoor scenarios, the Mobile Terminal (MT) position is obtained with high accuracy thanks to the Global Positioning System (GPS) or to the standalone cellular systems. However, the main problem of GPS or cellular systems resides in the indoor environment and in scenarios with deep shadowing effect where the satellite or cellular signals are broken. In this paper, we will present a review over different technologies and concepts used to improve indoor localization. Additionally, we will discuss different applications based on different localization approaches. Finally, comprehensive challenges in terms of accuracy, cost, complexity, security, scalability, etc. are presente

A Survey of Smart Parking Solutions

International audienceConsidering the increase of urban population and traffic congestion, smart parking is always a strategic issue to work on, not only in the research field but also from economic interests. Thanks to information and communication technology evolution, drivers can more efficiently find satisfying parking spaces with smart parking services. The existing and ongoing works on smart parking are complicated and transdisciplinary. While deploying a smart parking system, cities, as well as urban engineers, need to spend a very long time to survey and inspect all the possibilities. Moreover, many varied works involve multiple disciplines, which are closely linked and inseparable. To give a clear overview, we introduce a smart parking ecosystem and propose a comprehensive and thoughtful classification by identifying their functionalities and problematic focuses. We go through the literature over the period of 2000-2016 on parking solutions as they were applied to smart parking development and evolution, and propose three macro-themes: information collection, system deployment, and service dissemination. In each macro-theme, we explain and synthesize the main methodologies used in the existing works and summarize their common goals and visions to solve current parking difficulties. Lastly, we give our engineering insights and show some challenges and open issues. Our survey gives an exhaustive study and a prospect in a multidisciplinary approach. Besides, the main findings of the current state-of-the-art throw out recommendations for future research on smart cities and the Internet architecture