Maximizing Submodular Set Function With Connectivity Constraint: Theory and Application to Networks

Kuo, Tung-Wei; Lin, Kate Ching-Ju; Tsai, Ming‐Jer

doi:10.1109/tnet.2014.2301816

Cited by 49 publications

(23 citation statements)

References 15 publications

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“…It has been proved in previous studies that coverage problem with budget constraint is NP‐hard. The difference in our problem is the spatial and temporal properties in mobile user traces need to be incorporated for optimization, which makes the problem even harder.…”

Section: Problem Formationmentioning

confidence: 99%

Optimizing the interested area coverage with efficient mobile advertisement user selection

Yang

2017

Concurrency and Computation

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Summary Mobile advertisement distribution effects are vitally important for advertisers as well as users. Status quo studies are focusing on efficient distribution especially when user mobilities are involved. Unfortunately, previous studies have shown the interested area property during mobile advertisement propagation. In achieving efficient and effective mobile advertisement applications, this work advocates the concept of location‐centric mobile crowdsourcing network, where locations are vitally important for advertisement distribution, and mobile users need to be carefully selected for efficiency considerations. Different from traditional user‐centric and platform‐centric crowdsourcing networks, this work focuses on the mobile advertisement user selection problem when interested area coverage is considered. There are several fundamentally important challenges needed to be addressed before developing a location‐centric scheme for mobile advertisement user selection. First of all, we need to deal with the spatio‐temporal features for each user, where the interested area coverage ratio needs to be effectively evaluated. Even worse, budget constraint makes this problem intractable. In tackling aforementioned challenges, this work makes the following efforts: First, a budget‐constrained user selection problem is formulated when location sensitive mobile advertisement applications are considered, which is proved NP‐hard. Second, the submodularity feature is explored, and a simple but efficient heuristic algorithm is presented with guaranteed approximation ratio false(1−1efalse). Finally, extensive simulation results show that, our scheme could effectively improve the propagation effects for mobile advertisement with 125%. When considering the user's interest to different advertisements, the real user interest data set has also been used to validate that our proposed method could achieve improved performance than the random method.

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Section: Problem Formationmentioning

confidence: 99%

Optimizing the interested area coverage with efficient mobile advertisement user selection

Yang

2017

Concurrency and Computation

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“…Kuo et al [26] study this problem under the assumption that the communication and the sensing radius of sensors are the same (i.e., R s = R c ). They obtained an O( √ B)-approximation by transforming the problem to a more general connected submodular function maximization problem.…”

Section: Theoremmentioning

confidence: 99%

“…Submodular maximization under various more general combinatorial constraints (in particular, downward monotone set systems) is a vibrant research area in theoretical computer science and there have been a number of exciting new developments in the past few years (see e.g., [3,33] and the references therein). The connectivity constraint has also been considered in some previous work [38,26,24], some of which we mentioned before.…”

Section: Theoremmentioning

confidence: 99%

Approximation Algorithms for the Connected Sensor Cover Problem

Huang

Shi

2015

Lecture Notes in Computer Science

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Abstract. We study the minimum connected sensor cover problem (MIN-CSC) and the budgeted connected sensor cover (Budgeted-CSC) problem, both motivated by important applications in wireless sensor networks. In both problems, we are given a set of sensors and a set of target points in the Euclidean plane. In MIN-CSC, our goal is to find a set of sensors of minimum cardinality, such that all target points are covered, and all sensors can communicate with each other (i.e., the communication graph is connected). We obtain a constant factor approximation algorithm, assuming that the ratio between the sensor radius and communication radius is bounded. In Budgeted-CSC problem, our goal is to choose a set of B sensors, such that the number of targets covered by the chosen sensors is maximized and the communication graph is connected. We also obtain a constant approximation under the same assumption.

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“…In this section, we present a brief overview of the previous work on the coverage of both the stationary and mobile sensor networks. Mobile sensors are widely studied in sensor networks for coverage improvement [3][4][5], load balancing, or lifetime extension [6]. The relationship between coverage area and network connectivity is investigated in [7,8].…”

Section: Related Workmentioning

confidence: 99%

Coverage Capacity Optimization for Mobile Sensor Networks Based on Evolutionary Games

Liu

Yue

Shen

et al. 2014

International Journal of Distributed Sensor Networks

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The optimal and distributed provisioning of high coverage capacity in mobile sensor networks is known as a fundamental but hard problem. The situation is exacerbated in a mobile wireless setting due to the dynamic coverage of a mobile sensor network resulting from continuous movement of sensors. In this paper, we propose an optimization framework for maximizing the coverage capacity in mobile sensor networks that comprise both stationary sensors (SSs) and mobile sensors (MSs). Both the intracoverage capacity and the intercoverage capacity are jointly optimized by considering the control of the power and distance between MSs and SSs and the interference among MSs. We propose a new noncooperative control algorithm that iteratively solves intracoverage capacity optimization between MSs and SSs. We also further formulate intercoverage capacity as evolutionary coalition game and present a new cooperative interference control algorithm that iteratively solves intercoverage capacity optimization among MSs. We prove the existence of a solution for iteration control equation of the power and distance and the interference to maximize the coverage capacity. Finally, we assess the performance of the proposed algorithm and show that proposed control scheme can effectively improve the average coveragecapacity in mobile sensor networks.

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Maximizing Submodular Set Function With Connectivity Constraint: Theory and Application to Networks

Cited by 49 publications

References 15 publications

Optimizing the interested area coverage with efficient mobile advertisement user selection

Optimizing the interested area coverage with efficient mobile advertisement user selection

Approximation Algorithms for the Connected Sensor Cover Problem

Coverage Capacity Optimization for Mobile Sensor Networks Based on Evolutionary Games

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