IEEE Transactions on Big Data

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From the July-September 2017 issue

STaRS: Simulating Taxi Ride Sharing at Scale

By Masayo Ota, Huy Vo, Cláudio Silva, and Juliana Freire

As urban populations grow, cities face many challenges related to transportation, resource consumption, and the environment. Ride sharing has been proposed as an effective approach to reduce traffic congestion, gasoline consumption, and pollution. However, despite great promise, researchers and policy makers lack adequate tools to assess the tradeoffs and benefits of various ride-sharing strategies. In this paper, we propose a real-time, data-driven simulation framework that supports the efficient analysis of taxi ride sharing. By modeling taxis and trips as distinct entities, our framework is able to simulate a rich set of realistic scenarios. At the same time, by providing a comprehensive set of parameters, we are able to study the taxi ride-sharing problem from different angles, considering different stakeholders’ interests and constraints. To address the computational complexity of the model, we describe a new optimization algorithm that is linear in the number of trips and makes use of an efficient indexing scheme, which combined with parallelization, makes our approach scalable. We evaluate our framework through a study that uses data about 360 million trips taken by 13,000 taxis in New York City during 2011 and 2012. We describe the findings of the study which demonstrate that our framework can provide insights into strategies for implementing city-wide ride-sharing solutions. We also carry out a detailed performance analysis which shows the efficiency of our approach.

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Guest Editorials

Call for Papers

Special Issue on Wireless Big Data

Extended submission deadline: September 30, 2017. View PDF.

Big data, which has been ushered from the exponential growth in different commercial areas, has profoundly changed the way we live and received considerable attentions in various applications, such as distribute computing, e-health, intelligent transportation system, wireless sensor network, etc. In the meantime, the emerging 5G and Internet-of-Things (IoT) have been expected to include a bunch of new requirements, applications and scenarios. These new directions bring a dramatic increase in the amount and type of data. To some extent, wireless communications are heading into a new era of big data. Therefore, understanding the vast amount of data residing in wireless systems can greatly facilitate better network design and optimization, which benefits equipment vendors and operators alike.

In this special issue, we solicit high-quality research articles addressing key challenges and state-of-the-art solutions on how wireless big data analytics could improve the wireless technologies in various scenarios.

Special Issue on Big Data from Space

Submission deadline: January 31, 2018. View PDF.

Big Data from Space refers to the massive spatio-temporal Earth and Space observation data collected by space-borne sensors, and their use in synergy with data coming from other aerial or ground based sensors or sources. This domain is currently facing sharp development with numerous new initiatives and breakthroughs ranging from computational sensors to space sensor web, covering almost the entire electromagnetic spectrum from Gamma-rays to radiowaves, or from gravitational to quantum principles. The analysis of these data largely contributes to the broad scientific effort to understand the Universe and to enhance life on Earth. The recent multiplication of open access initiatives to Big Data from Space is giving momentum to the field by widening substantially the spectrum of scientific communities and users as well as awareness among the public while offering new benefits at all levels from individual citizens to the whole society.

In this Special Issue, we solicit high-quality scientific research articles, in areas such as, but not limited to, Earth Observation, planetary sciences, Space and Security, deep space exploration, astrophysics, satellite telecommunication, navigation and positioning systems, addressing key challenges and innovative solutions on how Big Data paradigms can improve the space sciences, technologies, and applications.

Special Issue on Edge Analytics in the Internet of Things

Submission deadline: February 1, 2018. View PDF.

The cloud-based Internet of Things (IoT) that connects a wide variety of things including sensors, mobile devices, vehicles, manufacturing machines, and industrial equipments, etc. is changing the way we live. IDC forecasts that the IoT will grow to 50 billion connected devices by 2020, and will generate an unprecedented volume and variety of data. However, moving this big volume of data from the network edge to a central data center for processing and analysis not only adds latency but also consumes network bandwidth. Therefore, the cloud-based IoT with a centralized data center may not be able to enable smart environments, such as cities, homes, schools, etc., or smart systems, such as automated vehicles, traffic controls, factories, etc., whose data need to be analyzed and acted on quickly. This is especially true in scenarios such as health monitoring or autopilot, where milliseconds can have fatal consequences. Such demand indicates that data processing and analysis has to be performed where the data are collected or generated instead of waiting for the data to be sent back to the centralized data center. Also, often these smart environments or systems need to be capable of self-monitoring, self-diagnosing, self-healing, and self-directing, and thus the task of edge-based data analytics may need to incorporate the technology of machine learning. Thus, there is a need to find a way to push intelligence from the central data center to the edge of the network. Indeed, IDC also predicts that up to 40% of IoT data will need edge-based analytics for applications that need real-time action. To solve this issue, fog computing, in which a set of interconnected micro data centers, called fog nodes, are deployed in between the things and the cloud data center, has been adopted as a bridge linking IoT devices and their remote data center. Since a fog node can run IoT-enabled applications for real-time data analytics with millisecond response time, fog computing enables application services of the IoT to be performed close to their consumers, and has created an emerging technology { edge analytics. Meanwhile, some IoT things are getting more capable and more powerful, making edge-based analytics possible. On the other hand, for the moment, most of the IoT things still do not have the computing and storage resources to perform intelligent analytics directly. For such IoT things, a nearby fog node or cloudlet may perform the tasks on their behalf. Furthermore, since data sources are widely distributed, some analytics tasks may need to be collaboratively performed by a set of fog nodes working together with some IoT things. As such, orchestrating fog nodes by means of topology control and network function virtualization may leverage the edge analytics performance.

Though edge analytics is in its nascent stage, it is getting more and more popular. The goal of this special issue is to provide a forum for researchers working on IoT and fog computing to present their recent research results in edge analytics

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TBD is financially cosponsored by:

IEEE Computer SocietyIEEE Communications SocietyIEEE Computational Intelligence SocietyIEEE Sensors CouncilIEEE Consumer Electronics Society


IEEE Signal Processing SocietyIEEE Systems, Man, & Cybernetics SocietyIEEE Systems CouncilIEEE Vehicular Technology Society


TBD is technically cosponsored by:

IEEE Control Systems SocietyIEEE Photonics SocietyIEEE Engineering in Medicine & Biology SocietyIEEE Power & Energy SocietyIEEE Biometrics Council