Ruilin Liu

Bio: Ruilin Liu is an academic researcher from Rutgers University. The author has contributed to research in topics: Vehicular ad hoc network & Android (operating system). The author has an hindex of 8, co-authored 14 publications receiving 523 citations. Previous affiliations of Ruilin Liu include Tsinghua University.

Real-time air quality monitoring through mobile sensing in metropolitan areas

Abstract: Traditionally, pollution measurements are performed using expensive equipment at fixed locations or dedicated mobile equipment laboratories. This is a coarse-grained and expensive approach where the pollution measurements are few and far in-between. In this paper, we present a vehicular-based mobile approach for measuring fine-grained air quality in real-time. We propose two cost effective data farming models -- one that can be deployed on public transportation and the second a personal sensing device. We present preliminary prototypes and discuss implementation challenges and early experiments.

GeoSVR: A map-based stateless VANET routing

Abstract: Compared with traditional routing techniques, geographic routing has been proven to be more suitable for highly mobile environments like Vehicular Ad-Hoc Networks (VANETs) because of enhanced scalability and feasibility. These routings use greedy modes or forwarding paths to forward packets. However, the dynamic nature of vehicular network such as frequently changed topology, vehicles density and radio obstacles, could create local maximum, sparse connectivity and network partitions. We propose GeoSVR, a geographic stateless routing combined with node location and digital map. The proposed GeoSVR scheme enhances forwarding path to solve local maximum and sparse connectivity problem, and the proposed restricted forwarding algorithm overcomes unreliable wireless channel issues. In our study, simulations and real world experiments were conducted to evaluate the efficacy and efficiency of the proposed solution. Our results show GeoSVR can provide higher packet delivery ratio with comparable latency to other geographic routing schemes.

Investigating Remote Driving over the LTE Network

Abstract: Remote driving brings human operators with sophisticated perceptual and cognitive skills into an over-the-network control loop, with the hope of addressing the challenging aspects of vehicular autonomy based exclusively on artificial intelligence (AI). This paper studies the human behavior in a remote driving setup, i.e., how human remote drivers perform and assess their workload under the state-of-the-art network conditions. To explore this, we build a scaled remote driving prototype and conduct a controlled human study with varying network delays based on current commercial LTE network technology. The study demonstrates that remote driving over LTE is not immediately feasible, primarily caused by network delay variability rather than delay magnitude. In addition, our findings indicate that the negative effects of remote driving over LTE can be mitigated by a video frame arrangement strategy that regulates delay magnitude to achieve a smoother display.

Seeing Is Believing: Sharing Real-Time Visual Traffic Information via Vehicular Clouds

Abstract: From today’s conventional cars to tomorrow’s self-driving cars, advances in technology will enable vehicles to be equipped with more and more-sophisticated sensing devices, such as cameras. As vehicles gain the ability to act as mobile sensors that carry useful traffic information, people and vehicles are sharing sensing data to enhance the driving experience. This paper describes a vehicular cloud service for route planning, where users collaborate to share traffic images by using their vehicles’ on-board cameras. We present the architecture of a collaborative traffic image-sharing system called social vehicle navigation, which allows drivers in the vehicular cloud to report and share visual traffic information called NaviTweets. A set of NaviTweets is then filtered, refined, and condensed into a concise, user-friendly snapshot summary of the route of interest, called a traffic digest. These digests can provide more pertinent and reliable information about the road situation and can complement predictions, such as estimated time of arrival, thereby supporting users’ route decision making. As proof of concept, this paper presents the system design and a prototype implementation running on the Android smartphone platform, along with its evaluation.

Balanced traffic routing

Abstract: Navigators based on real-time traffic conditions achieve suboptimal results since, in face of congestion, they greedily shift drivers to currently light-traffic roads and cause new traffic jams. This article presents Themis, a participatory system navigating drivers in a balanced way. By analyzing time-stamped position reports and route decisions collected from the Themis mobile app, the Themis server estimates both the current traffic rhythm and the future traffic distribution. According to the estimated travel time and a popularity score computed for each route, Themis coordinates the traffic between alternative routes and proactively alleviates congestion. Themis has been implemented and its performance has been evaluated in both a synthetic experiment using real data from over 26,000 taxis and a field study. Results from both experiments demonstrate that Themis reduces traffic congestion and average travel time at various traffic densities and system penetration rates.

Urban Computing: Concepts, Methodologies, and Applications

Abstract: Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people's lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety and security, presenting representative scenarios in each category. Third, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we give an outlook on the future of urban computing, suggesting a few research topics that are somehow missing in the community.

Internet of Vehicles: Motivation, Layered Architecture, Network Model, Challenges, and Future Aspects

Abstract: Internet of Things is smartly changing various existing research areas into new themes, including smart health, smart home, smart industry, and smart transport. Relying on the basis of “smart transport,” Internet of Vehicles (IoV) is evolving as a new theme of research and development from vehicular ad hoc networks (VANETs). This paper presents a comprehensive framework of IoV with emphasis on layered architecture, protocol stack, network model, challenges, and future aspects. Specifically, following the background on the evolution of VANETs and motivation on IoV an overview of IoV is presented as the heterogeneous vehicular networks. The IoV includes five types of vehicular communications, namely, vehicle-to-vehicle, vehicle-to-roadside, vehicle-to-infrastructure of cellular networks, vehicle-to-personal devices, and vehicle-to-sensors. A five layered architecture of IoV is proposed considering functionalities and representations of each layer. A protocol stack for the layered architecture is structured considering management, operational, and security planes. A network model of IoV is proposed based on the three network elements, including cloud, connection, and client. The benefits of the design and development of IoV are highlighted by performing a qualitative comparison between IoV and VANETs. Finally, the challenges ahead for realizing IoV are discussed and future aspects of IoV are envisioned.

Routing in Internet of Vehicles: A Review

Abstract: This work aims to provide a review of the routing protocols in the Internet of Vehicles (IoV) from routing algorithms to their evaluation approaches. We provide five different taxonomies of routing protocols. First, we classify them based on their transmission strategy into three categories: unicast, geocast, and broadcast ones. Second, we classify them into four categories based on information required to perform routing: topology-, position-, map-, and path-based ones. Third, we identify them in delay-sensitive and delay-tolerant ones. Fourth, we discuss them according to their applicability in different dimensions, i.e., 1-D, 2-D, and 3-D. Finally, we discuss their target networks, i.e., homogeneous and heterogeneous ones. As the evaluation is also a vital part in IoV routing protocol studies, we examine the evaluation approaches, i.e., simulation and real-world experiments. IoV includes not only the traditional vehicular ad hoc networks, which usually involve a small-scale and homogeneous network, but also a much larger scale and heterogeneous one. The composition of classical routing protocols and latest heterogeneous network approaches is a promising topic in the future. This work should motivate IoV researchers, practitioners, and new comers to develop IoV routing protocols and technologies.