Huan Ma

Minimum camera barrier coverage in wireless camera sensor networks

Barrier coverage is an important issue in wireless sensor network. In wireless camera sensor networks, the cameras take the images or videos of target objects, the position and angle of camera sensor impact on the sense range. Therefore, the barrier coverage problem in camera sensor network is different from scalar sensor network. In this paper, based on the definition of full-view coverage, we focus on the Minimum Camera Barrier Coverage Problem (MCBCP) in wireless camera sensor networks in which the camera sensors are deployed randomly in a target field. Firstly, we partition the target field into disjoint subregions which are full-view-covered regions or not-full-view-covered regions. Then we model the full-view-covered regions and their relationship as a weighted directed graph. Based on the graph, we propose an algorithm to find a feasible solution for the MCBCP problem. We also proved the correctness of the solution for the MCBCP problem. Furthermore, we propose an optimal algorithm for the MCBCP problem. Finally, simulation results demonstrate that our algorithm outperforms the existing algorithm.

Least Cost Rumor Blocking in Social Networks

In many real-world scenarios, social network serves as a platform for information diffusion, alongside with positive information (truth) dissemination, negative information (rumor) also spread among the public. To make the social network as a reliable medium, it is necessary to have strategies to control rumor diffusion. In this article, we address the Least Cost Rumor Blocking (LCRB) problem where rumors originate from a community Cr in the network and a notion of protectors are used to limit the bad influence of rumors. The problem can be summarized as identifying a minimal subset of individuals as initial protectors to minimize the number of people infected in neighbor communities of Cr at the end of both diffusion processes. Observing the community structure property, we pay attention to a kind of vertex set, called bridge end set, in which each node has at least one direct in-neighbor in Cr and is reachable from rumors. Under the OOAO model, we study LCRB-P problem, in which α (0 <; α <; 1) fraction of bridge ends are required to be protected. We prove that the objective function of this problem is submodular and a greedy algorithm is adopted to derive a (1-1/e)-approximation. Furthermore, we study LCRB-D problem over the DOAA model, in which all the bridge ends are required to be protected, we prove that there is no polynomial time o(ln n)-approximation for the LCRB-D problem unless P = NP, and propose a Set Cover Based Greedy (SCBG) algorithm which achieves a O(ln n)-approximation ratio. Finally, to evaluate the efficiency and effectiveness of our algorithm, we conduct extensive comparison simulations in three real-world datasets, and the results show that our algorithm outperforms other heuristics.

Energy efficient k-barrier coverage in limited mobile wireless sensor networks

Energy cost and reliability are two main concerns in barrier coverage for wireless sensor networks. In this paper, we take the energy cost and reliability as objectives respectively to study two problems of k-barrier coverage: the minimum energy cost k-barrier coverage problem in static wireless sensor networks and the maximum k-barrier coverage problem in limited mobile wireless sensor networks. For the minimum energy cost k-barrier coverage problem, all sensors are stationary, and each sensor has l+1 sensing power levels in the network, the objective of the problem is to find a sensing level assignment to form k-barrier coverage such that the total power consumed by the k-barrier is minimized. We firstly transform it into a minimum cost flow problem with side constraints and use Lagrangian relaxation technique to solve the minimum cost flow problem. Then, we also propose a heuristic algorithm. For the maximum k-barrier coverage problem, each sensor can move within the limited range, the objective of the problem is to form more barriers while some sensors can move within limited range. We formulate the problem into an integer linear programming (ILP), then propose two heuristic algorithms based on the linear programming (LP) relaxation. The simulation results demonstrate our algorithms are efficient.

Energy-efficient algorithm for the target Q-coverage problem in wireless sensor networks

In this paper we study the target Q-coverage (TQC) problem where each target needs to be covered by different numbers of sensors. We try to find a collection of Q-covsets which satisfy the coverage quality requirement to maximize the network lifetime. We first prove that the problem is NP-Hard. Then we design a greedy algorithm to efficiently compute the Q-covsets. Finally, simulation results are presented to verify our approach.

Influence maximization in social networks with user attitude modification

The aim of influence maximization problem is to find a k-size seed set that has the maximum influence. In previous works the modification of users attitude is seldom paid attention to. However from the psychology research, we know that peoples opinions are affected by their friends. Base on this, we present a new Linear Threshold model with Instant Opinions (LT-IO). We devise an attitude function Atu that describes node us attitude at time t, and the broadcast attitude which is the attitude when a node becomes active. To simulate information propagation in real world, we define a trust threshold η to justify whether a node follows or opposes the influence from its neighbor. We propose a heuristic algorithm IMLT-IOA to solve our problem, prove its submodularity and monotonicity and then obtain its approximation ratio which is (1 - 1/e). To the best of our knowledge, this is the first work that focuses on the influence maximization with users attitude modification. To verify our IMLT-IOA algorithm, we conduct extensive experiments on a large data collection obtained from real social networks, the results show that IMLT-IOA reduces the running time and meanwhile keeps effectiveness comparing to other algorithms.

Mining hidden links in social networks to achieve equilibrium

Although more connections between individuals in a social network can be identified with the development of high techniques, to obtain the complete relation information between individuals is still hard due to complex structure and individual privacy. However, the social networks have communities. In our work, we aim at mining the invisible or missing relations between individuals within a community in social networks. We propose our algorithm according to the fact that the individuals exist in communities satisfying Nash equilibrium, which is borrowed from game-theoretic concepts often used in economic researches. Each hidden relation is explored through the individuals loyalty to their community. To the best of our knowledge, this is the first work that studies the problem of mining hidden links from the aspect of Nash equilibrium. Eventually we confirm our approachs superiority from extensive experiments over real-world social networks.

Energy efficient broadcast in multiradio multichannel wireless networks

The broadcast is a fundamental operation in computer and communication networks. We study broadcast in multiradio multichannel multi-hop wireless networks. Suppose through configuration, each node is already assigned with a transmission power level and a set of radio channels for receiving and forwarding data. Our problem is to select a forward scheme for broadcasting from a given source node and to minimize total energy consumption. This is a known NP-hard minimization problem. In this paper, we construct a polynomial-time (1.35 + ϵ)(1+ln(n-1))-approximation algorithm where n is the number of nodes in given network and ϵ is any positive constant. We also show that there is no polynomial-time (ρ ln n)-approximation for 0 <; ρ <; 1 unless NP ⊆ DTIME(nO(log log n)).

Improving the influence under IC-N model in social networks

The influence maximization problem in social networks is to find a set of seed nodes such that the total influence effect is maximized under certain cascade models. In this paper, we propose a novel task of improving influence, which is to find strategies to allocate the investment budget under IC-N model. We prove that our influence improving problem is 𝒩𝒫-hard, and propose new algorithms under IC-N model. To the best of our knowledge, our work is the first one that studies influence improving problem under bounded budget when negative opinions emerge. Finally, we implement extensive experiments over a large data collection obtained from real-world social networks, and evaluate the performance of our approach.

Minimum energy multicast/broadcast routing with reception cost in wireless sensor networks

In this paper, we study the minimum energy multicast/broadcast problem with reception cost in wireless sensor networks. Suppose there are n sensors in the network. Each node v has l(v) transmission power levels to choose and its reception cost is B(v) if it receives a message. The problem of our concern is: given a multicast (broadcast) request, how to find a multicast (broadcast) tree such that the total energy cost of the multicast tree including transmitting cost and reception cost is minimized. There are two cases for reception cost: one is that for any node v, the reception cost of v only relies on v itself and is irrelevant with its transmitting node, the other is that the reception cost of v relies on not only itself but also its transmitting node. For the first case, we firstly propose a general approximation algorithm MEB-R-G for the broadcast problem. Moreover, for the multicast problem, we propose a general algorithm MEM-R-G and prove its approximation ratio, we also present a greedy algorithm. For the second case, we also propose a general approximation algorithm MEM-RT-G, and prove its approximation ratio.

A Nash Equilibrium Based Algorithm for Mining Hidden Links in Social Networks

With the advance of high techniques, more and more connections between individuals in a social network can be identified, but it is still hard to obtain the complete relation information between individuals for complex structure and individual privacy. However, the social networks have communities. In our work, we aim at mining the invisible or missing relations between individuals within a community in social networks. We propose our algorithm according to the fact that the individuals exist in communities satisfying Nash Equilibrium, which is borrowed from game-theoretic concepts often used in economic researches. Each hidden relation is explored through the individual’s loyalty to their community. To the best of our knowledge, this is the first work that studies the problem of mining hidden links from the aspect of Nash Equilibrium. Eventually we confirm the superiority of our approach from extensive experiments over real-world social networks.