Andre B. Reis

Deploying Roadside Units in Sparse Vehicular Networks: What Really Works and What Does Not

The 802.11p/WAVE standard relies on the presence of onboard units (OBUs) and roadside units (RSUs) for communications in vehicular networks. In this paper, we study the benefits of deploying RSUs to improve communications in highway scenarios. We develop an analytical model to analyze communication delay in a highway scenario with bidirectional traffic, considering both connected and disconnected RSUs, and validate our model via simulations and experimental measurements with 802.11p equipment. Contrary to conventional wisdom, our results show that significant benefits of RSUs in terms of connectivity and message dissemination can only be achieved when the deployed RSUs are interconnected. Conversely, deploying a large number of disconnected RSUs will lead to little or no benefit in message dissemination delay.

On the Performance of Sparse Vehicular Networks with Road Side Units

The reliability of communication in vehicular networks depends mostly on the density of DSRC-enabled vehicles that form the network. In highway scenarios, and depending on the time of day, the probability of having a disconnected vehicular network can be very high, which hinders communication reliability. To improve communication in these scenarios, infrastructure points known as Road Side Units (RSU) may be used. RSUs, however, have an associated cost, and therefore the number of RSUs needs to be minimized while still providing a significant improvement on communications. In this paper we study the effect of including RSUs as relay nodes to improve communication in highway scenarios. We model the average time taken to propagate a packet to disconnected nodes (denoted as re-healing time) when considering both scenarios of connected and disconnected RSUs. We then compare the results of both these models and of a model with no RSUs. Results show significant improvements with RSU deployments, both connected and disconnected, particularly in multi-cluster communication scenarios.

Distortion Optimized Multi-Service Scheduling for Next-Generation Wireless Mesh Networks

Distributing multimedia content over wireless networks is challenging due to the limited resource availability and the unpredictability of wireless links. As more and more users demand wireless access to (real-time) multimedia services, the impact of constrained resources is different for different media types. Therefore, understanding this impact and developing mechanisms to optimize content delivery under resource constraints according to user perception will be key in improving user satisfaction. In this paper, we develop a novel scheduling algorithm for multi-hop wireless networks, which optimizes packet delivery for multiple audio, video and data flows according to user perceivable quality metrics. We formulate a multidimensional optimization problem to minimize the overall distortion while satisfying resource constraints for the wireless links. Our Quality-of-Experience (QoE)-optimized scheduler makes use of models to determine the users perception of quality that are specific to the type of service being provided. Our experimental results, obtained with the NS-2 IEEE 802.16 MESH-mode simulator, show that distortion-aware scheduling can significantly increase the perceived quality of multimedia streaming under bandwidth constraints. As the scheduler allows the modeling of fairness constraints among multiple competing flows, we also demonstrate an improvement in fairness across different flows.

Quality of experience optimized scheduling in multi-service wireless mesh networks

A growing trend has emerged in network architecture research to switch focus from Quality of Service (QoS) to Quality of Experience (QoE) optimization. In this paper, we first present QoE models that characterize user satisfaction of video, audio, and data services over wireless networks. We then develop a novel packet scheduling algorithm for multi-hop wireless networks that jointly optimizes the delivery of multiple video, audio, and data flows according to the QoE metrics. We formulate a multidimensional optimization problem that minimizes the overall distortion across all flows for the given network resources on wireless links. Fairness constraints over the flows are also considered as part of the optimization. Our experimental results, obtained with the NS-2 IEEE 802.16 MESH-mode simulator, show that distortion-aware scheduling can significantly increase the perceived quality of different wireless services under bandwidth constraints. Additionally, improved fairness across the competing flows is demonstrated relative to conventional scheduling techniques.

Parameters that affect safety message delay in sparse infrastructure-less vehicular networks

In sparse highway vehicular networks, the high probability for network disconnection at the initial stages of introducing the DSRC technology can be mitigated by the deployment of fixed infrastructure points known as Road Side Units (RSU). However, due to the cost associated with the deployment and maintenance of significant numbers of RSUs, it is highly unlikely that the majority of highways will be seeing RSU support in the near future. In this paper we study the impact of specific vehicular network parameters in the communication delays in infrastructure-less highway scenarios: first, the deceleration of vehicles, and consequently, a decrease in their separation from succeeding vehicles; and second, the transmission power of the IEEE 802.11p radio, which can be increased to achieve faster connectivity with the succeeding vehicle. Our results show that the connectivity of sparse vehicular networks can be improved substantially by varying these parameters.

Leveraging Parked Cars as Urban Self-Organizing Road-Side Units

In urban vehicular networks, Road-Side Units (RSUs) take a crucial role in improving the performance of the network, by working as content distribution points, relays for time-critical broadcasts, and points of central coordination. The high costs associated with the installation and maintenance of RSUs, however, keep these units from seeing widespread deployment. One approach to this problem is for cars to be used opportunistically as RSUs, and in urban areas, the presence of large numbers of parked cars make these entities promising candidates for establishing vehicular support networks. In this paper we introduce new methods for parked cars to self-organize and act as a support network to the existing urban vehicular network, alleviating the need for costly deployments of fixed road-side units. Our approach considers parked cars that can both complement existing fixed RSUs and take the role of RSUs themselves, improving the networks performance on multiple applications. We show that even a small number of parked cars can bring considerable improvements to the network, and that our proposed methods for self-organization create support networks of parked cars that can cover the urban area with an optimal numbers of vehicles.

Parked Cars are Excellent Roadside Units

A comprehensive implementation of the envisioned traffic safety and efficiency applications of the IEEE 802.11p and Wireless Access for Vehicular Environments (WAVE) standards assume the premise of the use of Dedicated Short-Range Communications (DSRC) technology both as on-board units and as roadside units (RSUs). The high cost associated with RSUs, however, has so far prevented massive deployment of RSUs. Finding alternative solutions to this longstanding problem is therefore very important. In this paper, we propose a self-organizing network approach to using parked cars in urban areas as RSUs. This self-organizing network approach enables parked cars to create coverage maps based on received signal strength and make important decisions, such as if and when a parked car should serve as an RSU. Our results show the feasibility and cost-effectiveness of the proposed approach, which is able to provide excellent coverage using only a small fraction of the cars parked in a city.

Statistics of parked cars for urban vehicular networks

The ability to predict the behavior of cars that are parked in an urban area can be very useful to the development of vehicular networks that leverage these parked cars. In this paper, we analyze the mobility patterns of people living in US cities who use cars as their primary means of transportation. We process and analyze survey data from the metropolitan areas of Atlanta, Chicago, and Knoxville, to extract statistics on the parking behaviors of individual cars.

Dynamic dual-reinforcement-learning routing strategies for quality of experience-aware wireless mesh networking

The impact of transmission impairments such as loss and latency on user perceived quality (QoE) depends on the service type. In a real network, multiple service types such as audio, video, and data coexist. This makes resource management inherently complex and difficult to orchestrate. In this paper, we propose an autonomous Quality of Experience management approach for multiservice wireless mesh networks, where individual mesh nodes apply reinforcement learning methods to dynamically adjust their routing strategies in order to maximize the user perceived QoE for each flow. Within the forwarding nodes, we develop a novel packet dropping strategy that takes into account the impact on QoE. Finally, a novel source rate adaptation mechanism is designed that takes into account the expected QoE in order to match the sending rate with the available network capacity. An evaluation of our mechanisms using simulations demonstrates that our approach is superior to the standard approaches, AODV and OLSR, and effectively balances the user perceived QoE between the service flows.