ne of the major goals of vehicular ad hoc networks (VANETs) is to support traffic safety. Cooperative awareness applications require frequent and low-delay information exchange among vehicles, including data such as current position, movement, and acceleration. This is realized by broadcasting socalled (single-hop) beacon messages. As a result, every vehicle is aware of other vehicles within a certain range. Beaconing is also the basic supporting process that enables geographic routing and message dissemination. However, this also requires a significant amount of bandwidth. The higher the frequency and thus the accuracy, the higher the bandwidth consumption.The first phase of research on VANETs has set the boundary conditions in terms of basic communication protocols and routing paradigms. Communication in VANETs will be based on IEEE 802.11p. Message dissemination and routing are based on geocast principles. Beaconing takes place on a single communication channel (commonly referred to as the control channel) that is shared by all nodes.However, it has also been shown in [1] that the limited bandwidth of the wireless channel has a severe impact on the efficiency of the communication. This means that if the beacon rate is fixed, channel load may increase too much in scenarios with high vehicle density. High channel utilization increases the information loss as packets are received erroneously, which is especially observed at large distances between sender and receiver.Simply reducing the beacon rate is not a suitable solution because it reduces the information quality at the same time. The error between the real position of a vehicle and the last known position retrieved from a beacon increases as the beacon rate is reduced. This results in position inaccuracies, which may disturb correct operation of active safety applications, which rely on accurate and up-to-date information.Currently, there is no final recommendation for a particular static beacon rate. No upper boundary in terms of maximum channel load has been specified. Furthermore, no requirements from the different applications have yet been clearly defined. As a consequence, even minimum and maximum beacon rate are hard to derive as these two boundaries have to satisfy all imaginable road traffic situations.In this article we pave the way for further enhancements of beaconing algorithms for the second phase of research on VANETs. After a short assessment of the current state of the art, we present a detailed analysis of the problem space and how different beacon rates influence 1) the offered load to the channel and 2) the resulting average and maximum accuracy of information Based on this problem evaluation, we motivate a flexible approach to control both appropriately. For this purpose, we propose a situation adaptive beaconing process that adapts the beacon rate continuously. The design space and different candidates for such an algorithm are introduced in this article. AbstractIn the future intervehicle communication will make driving safer, easier...
BackgroundElective patient admission and assignment planning is an important task of the strategic and operational management of a hospital and early on became a central topic of clinical operations research. The management of hospital beds is an important subtask. Various approaches have been proposed, involving the computation of efficient assignments with regard to the patients’ condition, the necessity of the treatment, and the patients’ preferences. However, these approaches are mostly based on static, unadaptable estimates of the length of stay and, thus, do not take into account the uncertainty of the patient’s recovery. Furthermore, the effect of aggregated bed capacities have not been investigated in this context. Computer supported bed management, combining an adaptable length of stay estimation with the treatment of shared resources (aggregated bed capacities) has not yet been sufficiently investigated. The aim of our work is: 1) to define a cost function for patient admission taking into account adaptable length of stay estimations and aggregated resources, 2) to define a mathematical program formally modeling the assignment problem and an architecture for decision support, 3) to investigate four algorithmic methodologies addressing the assignment problem and one base-line approach, and 4) to evaluate these methodologies w.r.t. cost outcome, performance, and dismissal ratio.MethodsThe expected free ward capacity is calculated based on individual length of stay estimates, introducing Bernoulli distributed random variables for the ward occupation states and approximating the probability densities. The assignment problem is represented as a binary integer program. Four strategies for solving the problem are applied and compared: an exact approach, using the mixed integer programming solver SCIP; and three heuristic strategies, namely the longest expected processing time, the shortest expected processing time, and random choice. A baseline approach serves to compare these optimization strategies with a simple model of the status quo. All the approaches are evaluated by a realistic discrete event simulation: the outcomes are the ratio of successful assignments and dismissals, the computation time, and the model’s cost factors.ResultsA discrete event simulation of 226,000 cases shows a reduction of the dismissal rate compared to the baseline by more than 30 percentage points (from a mean dismissal ratio of 74.7% to 40.06% comparing the status quo with the optimization strategies). Each of the optimization strategies leads to an improved assignment. The exact approach has only a marginal advantage over the heuristic strategies in the model’s cost factors (≤3%). Moreover,this marginal advantage was only achieved at the price of a computational time fifty times that of the heuristic models (an average computing time of 141 s using the exact method, vs. 2.6 s for the heuristic strategy).ConclusionsIn terms of its performance and the quality of its solution, the heuristic strategy RAND is the preferred method fo...
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