Greedy algorithm

About: Greedy algorithm is a research topic. Over the lifetime, 15347 publications have been published within this topic receiving 393945 citations.

On greedy geographic routing algorithms in sensing-covered networks

Abstract: Greedy geographic routing is attractive in wireless sensor networks due to its efficiency and scalability. However, greedy geographic routing may incur long routing paths or even fail due to routing voids on random network topologies. We study greedy geographic routing in an important class of wireless sensor networks that provide sensing coverage over a geographic area (e.g., surveillance or object tracking systems). Our geometric analysis and simulation results demonstrate that existing greedy geographic routing algorithms can successfully find short routing paths based on local states in sensing-covered networks. In particular, we derive theoretical upper bounds on the network dilation of sensing-covered networks under greedy geographic routing algorithms. Furthermore, we propose a new greedy geographic routing algorithm called Bounded Voronoi Greedy Forwarding (BVGF) that allows sensing-covered networks to achieve an asymptotic network dilation lower than 4:62 as long as the communication range is at least twice the sensing range. Our results show that simple greedy geographic routing is an effective routing scheme in many sensing-covered networks.

An efficient approximate allocation algorithm for combinatorial auctions

Abstract: We propose a heuristic for allocation in combinatorial auctions. We first run an approximation algorithm on the linear programming relaxation of the combinatorial auction. We then run a sequence of greedy algorithms, starting with the order on the bids determined by the approximate linear program and continuing in a hill-climbing fashion using local improvements in the order of bids. We have implemented the algorithm and have tested it on the complete corpus of instances provided by Vohra and de Vries as well as on instances drawn from the distributions of Leyton-Brown, Pearson, and Shoham. Our algorithm typically runs two to three orders of magnitude faster than the reported running times of Vohra and de Vries, while achieving an average approximation error of less than 1%. This algorithm can provide, in less than a minute of CPU time, excellent solutions for problems with over 1000 items and 10,000 bids. We thus believe that combinatorial auctions for most purposes face no practical computational hurdles.

Tracking multiple targets using binary proximity sensors

Abstract: Recent work has shown that, despite the minimal information provided by a binary proximity sensor, a network of such sensors can provide remarkably good target tracking performance. In this paper, we examine the performance of such a sensor network for tracking multiple targets. We begin with geometric arguments that address the problem of counting the number of distinct targets, given a snapshot of the sensor readings. We provide necessary and sufficient criteria for an accurate target count in a one-dimensional setting, and provide a greedy algorithm that determines the minimum number of targets that is consistent with the sensor readings. While these combinatorial arguments bring out the difficulty of target counting based on sensor readings at a given time, they leave open the possibility of accurate counting and tracking by exploiting the evolution of the sensor readings across time. To this end, we develop a particle filtering algorithm based on a cost function that penalizes changes in velocity. An extensive set of simulations, as well as experiments with passive infrared sensors, are reported. We conclude that, despite the combinatorial complexity of target counting, probabilistic approaches based on fairly generic models for the trajectories yield respectable tracking performance.

Submodular Salient Region Detection

Abstract: The problem of salient region detection is formulated as the well-studied facility location problem from operations research. High-level priors are combined with low-level features to detect salient regions. Salient region detection is achieved by maximizing a sub modular objective function, which maximizes the total similarities (i.e., total profits) between the hypothesized salient region centers (i.e., facility locations) and their region elements (i.e., clients), and penalizes the number of potential salient regions (i.e., the number of open facilities). The similarities are efficiently computed by finding a closed-form harmonic solution on the constructed graph for an input image. The saliency of a selected region is modeled in terms of appearance and spatial location. By exploiting the sub modularity properties of the objective function, a highly efficient greedy-based optimization algorithm can be employed. This algorithm is guaranteed to be at least a (e - 1)/e 0.632-approximation to the optimum. Experimental results demonstrate that our approach outperforms several recently proposed saliency detection approaches.