Topic
Greedy algorithm
About: Greedy algorithm is a research topic. Over the lifetime, 15347 publications have been published within this topic receiving 393945 citations.
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03 Jul 2006
TL;DR: The Combined Distributed and Centralized (CDC) scheme to combine the distributed scheduling and centralized scheduling mechanisms so that the minislot allocation can be more flexible, and the utilization is increased.
Abstract: The IEEE 80216 standard proposes the Media Access Control (MAC) protocol for the Wireless Metropolitan Area Network (WMAN) Two transmission modes are defined in the IEEE 80216, including Point-to-Multipoint (PMP) mode and mesh mode In the 80216 mesh mode, allocation of minislots can be handled by the centralized and distributed scheduling mechanisms This paper proposes the Combined Distributed and Centralized (CDC) scheme to combine the distributed scheduling and centralized scheduling mechanisms so that the minislot allocation can be more flexible, and the utilization is increased Two scheduling algorithms, Round Robin (RR) and Greedy, are proposed as the baseline algorithms for the centralized scheduling mechanism We conduct simulation experiments to investigate the performance of the CDC scheme with the RR and Greedy algorithms Our study indicates that with CDC scheme, the minislot utilization can be significantly increased
83 citations
20 Sep 2000
TL;DR: It is proved that the greedy algorithm always gives the optimal switching activities of the instruction bus and the problem is NP-hard, and a heuristic algorithm is proposed.
Abstract: In this paper, we investigate the compiler transformation techniques to the problem of scheduling VLIW instructions aimed to reduce the power consumption on the instruction bus. It can be categorized into two types: horizontal and vertical scheduling. For the horizontal case, we propose a bipartite-matching scheme. We prove that our greedy algorithm always gives the optimal switching activities of the instruction bus. In the vertical case, we prove that the problem is NP-hard, and propose a heuristic algorithm. Experimental results show average 13% improvements with 4-way issue architecture and average 20% improvement with 8-way issue architecture for power consumptions of instruction bus as compared with conventional list scheduling for an extensive set of benchmarks.
83 citations
TL;DR: This paper proposes a distributed parallel optimization protocol (POP), where nodes optimize their schedules on their own but converge to local optimality without conflict with one another, and substantially outperforms other schemes in terms of network lifetime, coverage redundancy, convergence time, and event detection probability.
Abstract: Mission-driven sensor networks usually have special lifetime requirements. However, the density of the sensors may not be large enough to satisfy the coverage requirement while meeting the lifetime constraint at the same time. Sometimes, coverage has to be traded for network lifetime. In this paper, we study how to schedule sensors to maximize their coverage during a specified network lifetime. Unlike sensor deployment, where the goal is to maximize the spatial coverage, our objective is to maximize the spatial-temporal coverage by scheduling sensors' activity after they have been deployed. Since the optimization problem is NP-hard, we first present a centralized heuristic whose approximation factor is proved to be 1/2, and then, propose a distributed parallel optimization protocol (POP). In POP, nodes optimize their schedules on their own but converge to local optimality without conflict with one another. Theoretical and simulation results show that POP substantially outperforms other schemes in terms of network lifetime, coverage redundancy, convergence time, and event detection probability.
82 citations
TL;DR: A particle swarm optimization (PSO)-based algorithm called PSO2DT is developed to hide sensitive itemsets while minimizing the side effects of the sanitization process, which performs better than the Greedy algorithm and GA-based algorithms in terms of runtime, fail to be hidden, not to behidden, and database similarity.
82 citations
TL;DR: This paper aims to determine the locations to place the minimal number of nodes used for sensing and relaying such that deployed nodes 1) cover all targets, 2) have a path to the sink, and 3) have energy neutral operation.
Abstract: Wireless sensor networks can be used to monitor targets continuously. This assumes sensor nodes have energy neutral operation, whereby the energy consumed to monitor targets is less than their harvested energy. In this paper, we consider a new problem: minimum energy harvesting node placement for energy neutral coverage and connectivity (MEHNP-ENCC). We aim to determine the locations to place the minimal number of nodes used for sensing and relaying such that deployed nodes 1) cover all targets, 2) have a path to the sink, and 3) have energy neutral operation. We first model MEHNP-ENCC as a mixed integer linear program (MILP). After that we propose an MILP-based approach called greedy MILP (GMILP), whereby a greedy heuristic is used to generate a collection of locations. We also propose two heuristics: 1) DirectSearch considers locations that cover one or more lines connecting targets to the sink, whilst 2) GreedySearch also considers locations farther afield from the said lines that have a high recharging rate. Simulation results show that DirectSearch requires 20% more sensor nodes than the optimal solution whilst this value is 10% for GreedySearch and GMILP.
82 citations