Vehicular cloud computing is envisioned to deliver services that provide traffic safety and efficiency to vehicles. Vehicular cloud computing has great potential to change the contemporary vehicula...

Characterizing the role of vehicular cloud computing in road traffic management

The past eight years have seen the emergence of vehicular clouds as a topic of research in its own right. Vehicular clouds were inspired by the insight that present-day vehicles feature an impressive array of on-board compute, storage and sensing capabilities. These on-board capabilities are a vast untapped resource that, at the moment, is wasted. One of the defining ways in which vehicular clouds differ from conventional clouds is resource volatility. As vehicles enter and leave the cloud, new compute resources become available while others depart, creating a volatile environment where the tasks of enhancing reliability and availability become very challenging. It is intuitively clear that the longer and more predictable the vehicle residency times in the cloud are, the easier it is to ensure reliability and system availability. In this work we look at vehicular clouds with short and unpredictable vehicular residency times. We propose to enhance the reliability and availability of these types of vehicular clouds through a family of redundancy-based job assignment strategies that attempt to mitigate the effect of resource volatility. We offer a theoretical prediction of the Mean Time To Failure (MTTF) of these strategies. We also show how to fine-tune the granularity of the redundancy in order to meet QoS requirements specified in terms of a minimum MTTF for a given user job. Extensive simulations, using vehicle residency data derived from shopping mall statistics, have confirmed the accuracy of our analytical predictions.

Enhancing Reliability and Availability through Redundancy in Vehicular Clouds

Nowadays, inter‐vehicle and intra‐vehicle communication triggers the development of safety and non‐safety applications in Vehicular Adhoc NETworks (VANET). These applications need to be evaluated before the actual implementation. The performance of these applications is generally evaluated through indispensable tools such as simulators, emulators, and testbeds. Although simulators, emulators, and testbeds are available as open‐source or proprietary for VANET applications, finding the correct tool for the experiment is intriguing and challenging from the researcher's perspective. In this paper, our focus is to provide a comprehensive survey on the existing simulators, emulators, and real time testbeds based on technical specifications, capabilities, etc. To the best of our knowledge, there is no such an exhaustive survey that exists in the literature. Further, we developed a real time low‐cost VANET testbed on our institute campus to implement the VANET related projects.

A comprehensive survey on simulators, emulators, and testbeds for VANETs

The vehicular ad-hoc networks integrates with long-term evolution (LTE) forming a heterogeneous network, capable of providing seamless connectivity, which meets the communication requirements of intelligent transportation systems. However, heterogeneous network-based applications involve LTE resource (data and spectrum) usage cost and must be taken care while developing such a solution. One of the scenarios is the access of the information to/from remote server over the internet via LTE for road traffic management applications. Although clustering of the vehicle is significant to minimize the data and LTE network usage, however, the problem of non-cooperation of the vehicles in clustering process and within a cluster are major issues in sharing costly data acquired from the internet. Because, who and why one (vehicle) should pay the cost is the big question, proliferating the non-cooperative behavior among the cluster members. To solve these issues, strategic game-theoretic based clustering mechanism named as cooperative interest-aware clustering (CIAC) is developed. The proposed CIAC not only balance the cost of usage by controlling non-cooperative behavior among the vehicles within the cluster but at the same time motivate vehicles to participate in the clustering process to share the data and cost as well. It consists of a cluster head selection process based on the strategic game-theoretic approach and a fair-use policy. The implementation results show superiority in performance of our protocol over the existing approaches.

A Cooperative Heterogeneous Vehicular Clustering Mechanism for Road Traffic Management

Smart vehicles equipped with computers and wireless communication devices are emerging on the road. These vehicles can drive themselves, communicate to other vehicles, connect to the Internet, and provide value-added services to the drivers and passengers. With the advent of such technology, it is possible to form a "platoon" of autonomous vehicles on the road, where they follow a common leader vehicle in the same lane on the highway and maintain close proximity to save fuel, improve road capacity and passenger comfort. However, realizing such vision faces difficulties on both software architecture and vehicle control. In this paper, we propose a service-oriented perspective for the software modules on the autonomous vehicles, where platooning is designed as an independent service interacting with other components of the vehicle. We also built a prototype system with low cost vehicle-like mobile robots and ran experiments to demonstrate the effectiveness of our service framework and our platooning control algorithm. Our hope is that the platooning as a service approach can help in the construction of more efficient, interoperable, and secure autonomous vehicles in the future.

Platooning as a service of autonomous vehicles

Smart vehicles with computing, sensing, and communication capabilities are gaining popularity. With various vehicular applications equipped, these smart vehicles not only improve driving safety, but also facilitate data collection and information sharing for traffic optimization, insurance estimation, and infotainment. However, developing and testing such cloud based vehicular application is challenging due to the high cost of running the application on actual cars in various traffic scenarios. For the same reason it is also difficult to understand and model the network protocol behavior among multiple vehicles. In this paper we proposed VC-bots, a vehicular cloud testbed using mobile robot vehicles, which can emulate different types of vehicles for testing vehicular network protocols and vehicular cloud applications in various scenarios, which can be easily reconfigured without any infrastructure assistance. To facilitate software integration, we also developed a message based service framework for applications running on the robot vehicle and in the cloud.

VC-bots: a vehicular cloud computing testbed with mobile robots

Toward the ambitious long-term goal of a fleet of cooperating Flexible Autonomous Machines operating in an uncertain Environment (FAME), this two part paper addresses several critical modeling, design and control objectives for ground vehicles. Within Part I of this paper, a low-cost differential drive robotic vehicle was introduced for FAME research. Suitable nonlinear/linear-models were used to develop inner/outer-loop control laws for a single vehicle; e.g. wheel (and vehicle translational/rotational) velocity inner-loop control, etc. Part II of this paper focusses on the coordination of multiple vehicles. The (faster) inner-loop control law discussed within Part I is used for all (slower) outer-loop control modes demonstrated within Part II. We specifically demonstrate (via simulations and hardware) the following specific outer-loop control laws: (1) Δx − θ separation-direction control, (2) collision avoidance, (3) separation control for a longitudinal platoon of vehicles. Empirically collected data is shown to agree well with simulation results. Reasons for observed differences are provided. The simple separation-direction control structure is adequate because of the (higher bandwidth) inner-loop control law. We observed (and expected) that (1) collision avoidance works well as long as the controlled vehicle is not traveling too fast with respect to obstacles. (2) with respect to platoon control, we demonstrated that feedforward of leader speed information significantly and uniformly improves separation performance as we move rearward in the platoon. In short, many capabilities that are critical for reaching the longer-term FAME goal are demonstrated within this two part paper.

Modeling, design and control of low-cost differential-drive robotic ground vehicles: Part II — Multiple vehicle study

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VC-bots: a vehicular cloud computing testbed with mobile robots

Modeling, design and control of low-cost differential-drive robotic ground vehicles: Part II — Multiple vehicle study