Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Graph Neural Network for spatiotemporal data: methods and applications

In the era of big data, there has been a surge in the availability of data containing rich spatial and temporal information, offering valuable insights into dynamic systems and processes for applications such as weather forecasting, natural disaster management, intelligent transport systems, and precision agriculture. Graph neural networks (GNNs) have emerged as a powerful tool for modeling and understanding data with dependencies to each other such as spatial and temporal dependencies. There is a large amount of existing work that focuses on addressing the complex spatial and temporal dependencies in spatiotemporal data using GNNs. However, the strong interdisciplinary nature of spatiotemporal data has created numerous GNNs variants specifically designed for distinct application domains. Although the techniques are generally applicable across various domains, cross-referencing these methods remains essential yet challenging due to the absence of a comprehensive literature review on GNNs for spatiotemporal data. This article aims to provide a systematic and comprehensive overview of the technologies and applications of GNNs in the spatiotemporal domain. First, the ways of constructing graphs from spatiotemporal data are summarized to help domain experts understand how to generate graphs from various types of spatiotemporal data. Then, a systematic categorization and summary of existing spatiotemporal GNNs are presented to enable domain experts to identify suitable techniques and to support model developers in advancing their research. Moreover, a comprehensive overview of significant applications in the spatiotemporal domain is offered to introduce a broader range of applications to model developers and domain experts, assisting them in exploring potential research topics and enhancing the impact of their work. Finally, open challenges and future directions are discussed

Application of DMSP/OLS nighttime light images : a meta-analysis and a systematic literature review

© The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Remote Sensing 6 (2014): 6844-6866, doi:10.3390/rs6086844.Since the release of the digital archives of Defense Meteorological Satellite Program Operational Line Scanner (DMSP/OLS) nighttime light data in 1992, a variety of datasets based on this database have been produced and applied to monitor and analyze human activities and natural phenomena. However, differences among these datasets and how they have been applied may potentially confuse researchers working with these data. In this paper, we review the ways in which data from DMSP/OLS nighttime light images have been applied over the past two decades, focusing on differences in data processing, research trends, and the methods used among the different application areas. Five main datasets extracted from this database have led to many studies in various research areas over the last 20 years, and each dataset has its own strengths and limitations. The number of publications based on this database and the diversity of authors and institutions involved have shown promising growth. In addition, researchers have accumulated vast experience retrieving data on the spatial and temporal dynamics of settlement, demographics, and socioeconomic parameters, which are “hotspot” applications in this field. Researchers continue to develop novel ways to extract more information from the DMSP/OLS database and apply the data to interdisciplinary research topics. We believe that DMSP/OLS nighttime light data will play an important role in monitoring and analyzing human activities and natural phenomena from space in the future, particularly over the long term. A transparent platform that encourages data sharing, communication, and discussion of extraction methods and synthesis activities will benefit researchers as well as public and political stakeholders.This work is supported by the 111 project “Hazard and Risk Science Base at Beijing Normal University” under Grant B08008 (Ministry of Education and State Administration of Foreign Experts Affairs, PRC), the State Key Laboratory of Earth Surface Processes and Resource Ecology of Beijing Normal University (No. 2013-RC-03), and the Fundamental Research Funds for the Central Universities (Grant No. 201413037)

Landscape functional connectivity and animal movement: application of remote sensing for increasing efficiency of road mitigation measures

Roads are a major threat to wildlife due to induced mortality and restrictions to animal movement. A central issue in conservation biology is the accurate site identification for the implementation of multispecies mitigation measures, on roads. Those measures entail high costs and methodological challenges and their efficiency highly depend on the right location. The aim of this PhD is to inform, through remote sensing and connectivity modelling, how to increase the efficiency of planning mitigation measures to reduce roadkill and promote connectivity; and demonstrate the usefulness of remote sensing in defining suitable areas for the conservation of an endangered species that often occurs in the vicinity of roads. To do so, we first assessed whether occurrence-based strategies were able to infer functional connectivity, compared to those more complex and financially demanding based on telemetry, with respect to daily and dispersal movements. Secondly, we assessed whether remote sensing data were sufficiently informative to identify key habitats for a threatened species around road verges. Thirdly, we assessed the predictive and prioritisation ability of road mitigation units intercepting multispecies corridors to prevent vulnerability to roadkill. Findings revealed that simple models are suitable as complex ones for both daily and dispersal movements, allowing for costly-effective connectivity assessments. Results demonstrated the ability of free remote sensing data to identify microhabitat conditions in verges and surrounding landscape, for a threatened rodent, allowing for the delimitation of refugee areas and definition of monitoring strategies for the species. Undemanding data (occurrence and remote sensing) were able to describe species-specific ecological requirements for birds, bats and non-flying mammals as well as roadkill patterns, possibly due to similar overlapping corridors and habitats, despite some mismatches that occurred for highly mobile species. This framework ensured high efficiency in prioritisation of multispecies roadkill mitigation planning, resilient to long-term landscape dynamics; Conectividade funcional da paisagem e movimento animal: aplicação da detecção remota para aumentar a eficiência de medidas de mitigação em estradas. Resumo: As estradas constituem uma enorme ameaça para a vida selvagem devido à mortalidade. Uma questão central é a identificação dos locais para implementar medidas de mitigação multiespécies, em estradas. Essas medidas envolvem custos elevados e desafios metodológicos e sua eficiência depende muito da localização correcta. O objetivo deste doutoramento é informar, através de detecção remota e conectividade, como aumentar a eficiência do planeamento de medidas de mitigação para reduzir atropelamentos e promover a conectividade; e demonstrar a utilidade da detecção remota na definição de áreas adequadas para a conservação de espécies ameaçadas que podem ocorrer nas proximidades de estradas. Portanto, primeiro avaliamos se os dados resultantes de amostragens simples eram capazes de inferir conectividade funcional, em comparação com estratégias complexas, respeito aos movimentos diários e de dispersão. Segundo, avaliamos se os dados de detecção remota eram suficientemente informativos para identificar habitats-chave para uma espécie ameaçada em torno das margens das estradas. Terceiro, avaliamos a capacidade preditiva e de prioritização das unidades de mitigação de estradas que cruzam corredores multi-espécies para reduzir o risco de atropelamentos. Os resultados revelaram que os modelos simples são adequados quanto os complexos para os movimentos diários e de dispersão. Os resultados demonstraram a capacidade dos dados de detecção remota gratuitos em identificar condições de microhabitats nos habitats de berma e na paisagem circundante, para um roedor ameaçado, permitindo a delimitação de áreas de refúgio. Dados pouco exigentes (ocorrência e detecção remota) foram capazes de descrever os requisitos ecológicos específicos de aves, morcegos e mamíferos não voadores, bem como padrões de atropelamentos, possivelmente devido a corredores e habitats semelhantes, apesar de haver algumas incompatibilidades para espécies de maior mobilidade. Essa estrutura foi capaz de garantir uma elevada eficiência na prioritização de planeamento de mitigação de atropelamentos para multi-espécies, resiliente à dinâmica da paisagem de longo prazo