Dennis Schieferdecker

Combining hierarchical and goal-directed speed-up techniques for dijkstra's algorithm

In recent years, highly effective hierarchical and goal-directed speed-up techniques for routing in large road networks have been developed. This article makes a systematic study of combinations of such techniques. These combinations turn out to give the best results in many scenarios, including graphs for unit disk graphs, grid networks, and time-expanded timetables. Besides these quantitative results, we obtain general insights for successful combinations.

Combining hierarchical and goal-directed speed-up techniques for Dijkstra's algorithm

In [1], basic speed-up techniques for Dijkstras algorithm have been combined. The key observation in their work was that it is most promising to combine hierarchical and goal-directed speed-up techniques. However, since its publication, impressive progress has been made in the field of speed-up techniques for Dijkstras algorithm and huge data sets have been made available. Hence, we revisit the systematic combination of speed-up techniques in this work, which leads to the fastest known algorithms for various scenarios. Even for road networks, which have been worked on heavily during the last years, we are able to present an improvement in performance. Moreover, we gain interesting insights into the behavior of speed-up techniques when combining them.

Candidate Sets for Alternative Routes in Road Networks

We study the computation of good alternatives to the shortest path in road networks. Our approach is based on single via-node routing on top of contraction hierarchies and achieves superior quality and efficiency compared to previous methods. We present a fast preprocessing method for computing multiple good alternatives and apply this result in an online setting. This setting makes our result applicable in legacy systems with negligible memory overhead. An extensive experimental analysis on a continental-sized real- world road network proves the performance of our algorithm and supports the general systematic algorithm engineering approach. We also show how to combine our results with the competing concept of alternative graphs that encode many alternative paths at once.

Candidate sets for alternative routes in road networks

We present a fast algorithm with preprocessing for computing multiple good alternative routes in road networks. Our approach is based on single via node routing on top of Contraction Hierarchies and achieves superior quality and efficiency compared to previous methods. The algorithm has neglectable memory overhead.

Evolution and Evaluation of the Penalty Method for Alternative Graphs

Computing meaningful alternative routes in a road network is a complex problem ‐ already giving a clear definition of a best alternative seems to be impossible. Still, multiple methods [1, 2, 4, 17, 18] describe how to compute reasonable alternative routes, each according to their own quality criteria. Among these methods, the penalty method has received much less attention than the via-node or plateaux based approaches. A mayor cause for the lack of interest might be the unavailability of an ecient implementation. In this paper, we take a closer look at the penalty method and extend upon its ideas. We provide the first viable implementation ‐suitable for interactive use‐ using dynamic runtime adjustments to perform up to multiple orders of magnitude faster queries than previous implementations. Using our new implementation, we thoroughly evaluate the penalty method for its flaws and benefits. 1998 ACM Subject Classification G.2.2 Graph Theory

Efficient algorithms for distributed detection of holes and boundaries in wireless networks

We propose two novel algorithms for distributed and locationfree boundary recognition in wireless sensor networks. Both approaches enable a node to decide autonomously whether it is a boundary node, based solely on connectivity information of a small neighborhood. This makes our algorithms highly applicable for dynamic networks where nodes can move or become inoperative. We compare our algorithms qualitatively and quantitatively with several previous approaches. In extensive simulations, we consider various models and scenarios. Although our algorithms use less information than most other approaches, they produce significantly better results. They are very robust against variations in node degree and do not rely on simplified assumptions of the communication model. Moreover, they are much easier to implement on real sensor nodes than most existing approaches.

Lifetime maximization of monitoring sensor networks

We study the problem of maximizing the lifetime of a sensor network assigned to monitor a given area. Our main result is a linear time dual approximation algorithm that comes arbitrarily close to the optimal solution, if we additionally allow the sensing ranges to increase by a small factor. The best previous result is superlinear and has a logarithmic approximation ratio. We also provide the first proof of NP-completeness of this specific problem.

Parallel track reconstruction in CMS using the cellular automaton approach

The Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) is a general-purpose particle detector and comprises the largest silicon-based tracking system built to date with 75 million individual readout channels. The precise reconstruction of particle tracks from this tremendous amount of input channels is a compute-intensive task. The foreseen LHC beam parameters for the next data taking period, starting in 2015, will result in an increase in the number of simultaneous proton-proton interactions and hence the number of particle tracks per event. Due to the stagnating clock frequencies of individual CPU cores, new approaches to particle track reconstruction need to be evaluated in order to cope with this computational challenge. Track finding methods that are based on cellular automata (CA) offer a fast and parallelizable alternative to the well-established Kalman filter-based algorithms. We present a new cellular automaton based track reconstruction, which copes with the complex detector geometry of CMS. We detail the specific design choices made to allow for a high-performance computation on GPU and CPU devices using the OpenCL framework. We conclude by evaluating the physics performance, as well as the computational properties of our implementation on various hardware platforms and show that a significant speedup can be attained by using GPU architectures while achieving a reasonable physics performance at the same time.

Location-Free Detection of Network Boundaries

A novel algorithm is proposed for the distributed and location-free detection of boundaries in sensor networks. The approach allows a node to decide autonomously, based solely on connectivity information of a small 2-hop neighborhood, whether it is in the interior of the network or on its fringes. This makes the presented algorithm well suited for scenarios that include mobility or dynamic changes to the network topology. The algorithm is compared qualitatively and quantitatively to multiple previous approaches. Various models and network settings are considered in extensive simulations. Even though the algorithm uses less information than most other approaches, it yields significantly better results. It is very robust against variations in node degree and does not rely on simplified assumptions of the communication model. Moreover, the approach is easy to implement on real sensor nodes, as it requires little computational power.