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Content prefetching at road side units (RSUs) in vehicular networks can improve users’ experience by reducing the average data access latency. Prefetching algorithms proposed in the literature usually rely on a fixed modulation scheme, that corresponds to the lowest possible data rate for RSU-to-vehicles transmission. As a consequence, the network bandwidth is often inefficiently used. In this article, we propose to study the problem of efficient content broadcasting in vehicular networks and express it as a joint data-prefetching and broadcast transmission scheduling optimization problem, subject to cache size constraints and transmission rate adaptation constraints. In our study we consider a highway scenario covered by several RSUs, and take into account the deadline requirement of interactive applications. We express the problem as an integer linear programming problem, and solve it using a heuristic algorithm that determines: the content prefetching schedule, the broadcast transmission schedule and the rate adaptation schedule sequentially. Simulation results show that, i) it is beneficial to use adaptive rates to broadcast data instead of relying on single-rate with unicast transmissions; ii) using vehicular trajectories and content popularity in conjunction yields better results than simply relying on content popularity for content prefetching and caching; and iii) this holds even with inaccurately predicted vehicle locations and with delay-sensitive contents. [ABSTRACT FROM AUTHOR]

In transportation-based cyberphysical systems (TCPS), also known as intelligent transportation systems (ITS), to increase traffic efficiency, a number of dynamic route guidance schemes have been designed to assist drivers in determining optimal routes for their travels. To determine optimal routes, it is critical to effectively predict the traffic condition of roads along the guided routes based on real-time traffic information collected by vehicular networks to mitigate traffic congestion and improve traffic efficiency. In this paper, we propose a Dynamic En-route Decision real-time Route guidance (DEDR) scheme to effectively mitigate road congestion caused by the sudden increase of vehicles and to reduce travel time and fuel consumption. DEDR considers real-time traffic information generation and transmission by vehicular networks. Based on the shared traffic information, DEDR introduces the trust probability to predict traffic conditions and to dynamically, en route, determine alternative optimal routes. DEDR also considers multiple metrics to comprehensively assess traffic conditions so that drivers can determine the optimal route with a preference to these metrics during travel. DEDR considers effects of external factors (bad weather, incidents, etc.) on traffic conditions as well. Through a combination of extensive theoretical analysis and simulation experiments, our data show that DEDR can greatly increase traffic efficiency in terms of time efficiency, balancing efficiency, and fuel efficiency, in comparison with existing schemes. [ABSTRACT FROM PUBLISHER]

The emerging vehicular ad hoc network (VANET) enables vehicles to access the Internet through roadside access points (APs). An important issue in system deployment is determining how many roadside APs shall be installed on a road. However, the existing works fail to provide rigorous and accurate analysis for VANETs. In this paper, we propose a general structure for Internet access in VANETs. It allows both real-time traffic and delay-tolerant traffic to be delivered to users in the most efficient ways. An analytical model is also proposed to analyze the system performance with random arrival of the vehicles. We finally develop an AP placement algorithm based on theoretical results derived from the model to deploy the minimal number of roadside APs with quality-of-service (QoS) guarantees. The simulation results have demonstrated the accuracy of the proposed analytical model and the efficiency of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

In multi-service wireless networks, asymmetric bandwidth allocation has been proposed to satisfy the requirements of asymmetric traffic load introduced by some data applications. However, it is difficult to promptly adjust bandwidth allocation on uplink and downlink according to the dynamics of traffic load. Inappropriate call admission control (CAC) policies in this environment may admit superfluous real-time (RT) calls or non real-time (NRT) calls and thus lead to low bandwidth utilization. In this paper, we propose and evaluate two new CAC schemes to address the problems caused by the mismatch of bandwidth allocation and traffic changing in multi-service wireless networks with bandwidth asymmetry between uplink and downlink. By determining the admissible regions for the RT calls and the NRT calls, the proposed schemes prevent the calls of a specific class from overusing the bandwidth resources. Mathematical analysis and simulation experiments are employed to study and compare the performance of the proposed schemes and the existing schemes. The numerical results show that the proposed schemes can achieve better performances in terms of call blocking probability and bandwidth utilization compared to some existing schemes, even those performing well in bandwidth asymmetry wireless networks. [ABSTRACT FROM AUTHOR]