ORCID Profile
0000-0002-7036-5158
Current Organisation
Nanyang Technological University
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2022
Publisher: Elsevier BV
Date: 11-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2011
Publisher: ACM
Date: 11-11-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2014
Publisher: Association for Computing Machinery (ACM)
Date: 03-2021
Abstract: Given an undirected graph and a number of vertex groups, the group Steiner trees problem is to find a tree such that (i) this tree contains at least one vertex in each vertex group and (ii) the sum of vertex and edge weights in this tree is minimized. Solving this problem is useful in various scenarios, ranging from social networks to knowledge graphs. Most existing work focuses on solving this problem in vertex-unweighted graphs, and not enough work has been done to solve this problem in graphs with both vertex and edge weights. Here, we develop several algorithms to address this issue. Initially, we extend two algorithms from vertex-unweighted graphs to vertex- and edge-weighted graphs. The first one has no approximation guarantee, but often produces good solutions in practice. The second one has an approximation guarantee of |Γ| - 1, where |Γ| is the number of vertex groups. Since the extended (|Γ| - 1)-approximation algorithm is too slow when all vertex groups are large, we develop two new (|Γ| - 1)-approximation algorithms that overcome this weakness. Furthermore, by employing a dynamic programming approach, we develop another (|Γ| - h + 1)-approximation algorithm, where h is a parameter between 2 and |Γ|. Experiments show that, while no algorithm is the best in all cases, our algorithms considerably outperform the state of the art in many scenarios.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2013
Publisher: Association for Computing Machinery (ACM)
Date: 13-02-2018
DOI: 10.1145/3173039
Abstract: Whereas a lot of efforts have been put on energy conservation in wireless sensor networks (WSNs), the limited lifetime of these systems still h ers their practical deployments. This situation is further exacerbated indoors, as conventional energy harvesting (e.g., solar) may not always work. To enable long-lived indoor sensing, we report in this article a self-sustaining sensing system that draws energy from indoor environments, adapts its duty-cycle to the harvested energy, and pays back the environment by enhancing the awareness of the indoor microclimate through an “energy-free” sensing. First of all, given the pervasive operation of heating, ventilation, and air conditioning (HVAC) systems indoors, our system harvests energy from airflow introduced by the HVAC systems to power each sensor node. Secondly, as the harvested power is tiny, an extremely low but synchronous duty-cycle has to be applied whereas the system gets no energy surplus to support existing synchronization schemes. So, we design two complementary synchronization schemes that cost virtually no energy. Finally, we exploit the feature of our harvester to sense the airflow speed in an energy-free manner. To our knowledge, this is the first indoor wireless sensing system that encapsulates energy harvesting, network operating, and sensing all together.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2013
Publisher: IEEE
Date: 06-2012
Publisher: Association for Computing Machinery (ACM)
Date: 2020
Abstract: Bump hunting is an important approach to the extraction of insights from Euclidean datasets. Recently, it has been explored for graph datasets for the first time, and a single bump is hunted in an unweighted graph in this exploration. Here, we extend this exploration by hunting multiple bumps in a weighted graph. Given a weighted graph and a set of query nodes exhibiting a property of interest, our objective is to find k non-overlapping and connected subgraphs, i.e., bumps, in which the discrepancy between the numbers of query and non-query nodes is maximized and the sum of edge costs is minimized simultaneously. We prove that our extended bump hunting problem can be transformed to a recently formulated Prize-Collecting Steiner Forest Problem (PCSFP). We further prove that PCSFP is NP-hard even in trees. Then, we propose a fast approximation algorithm for solving PCSFP in trees. Based on this algorithm, we improve the state-of-the-art approximation algorithm for solving PCSFP in graphs, and prove that the solutions of our improvement are always better than or equal to those of the state-of-the-art algorithm. Moreover, we adapt the existing bump hunting algorithms for solving our extended bump hunting problem. We evaluate our methodology via real datasets, and show that 1) our improvement scales well to large graphs, while producing solutions that dominate those of the state-of-the-art algorithm and 2) our adaptation of an existing bump hunting algorithm can also produce solutions that are better than those of the state-of-the-art algorithm in some cases.
Publisher: ACM
Date: 11-11-2013
Publisher: IEEE
Date: 06-2011
No related grants have been discovered for Jun Luo.