Zaixin Lu

Constant-approximation for target coverage problem in wireless sensor networks

When a large amount of sensors are randomly deployed into a field, how can we make a sleep/activate schedule for sensors to maximize the lifetime of target coverage in the field? This is a well-known problem, called Maximum Lifetime Coverage Problem (MLCP), which has been studied extensively in the literature. It is a long-standing open problem whether MLCP has a polynomial-time constant-approximation. The best-known approximation algorithm has performance ratio 1 + ln n where n is the number of sensors in the network, which was given by Berman et. al [1]. In their work, MLCP is reduced to Minimum Weight Sensor Coverage Problem (MWSCP) which is to find the minimum total weight of sensors to cover a given area or a given set of targets with a given set of weighted sensors. In this paper, we present a polynomial-time (4 + ∈)-approximation algorithm for MWSCP and hence we obtain a polynomial-time (4 + ξ)-approximation algorithm for MLCP, where ∈ >; 0, ξ >; 0.

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.

Efficient data retrieval scheduling for multi-channel wireless data broadcast

Wireless data broadcast is an efficient technique of disseminating data simultaneously to a large number of mobile clients. In many information services, the users may query multiple data items at a time. In this paper, we study the data retrieval scheduling problem from the clients point of view. We formally define the Largest Number Data Retrieval (LNDR) problem with the objective of downloading the largest number of requested data items in a given time duration, and the Minimum Cost Data Retrieval (MCDR) problem which aims at downloading a set of data items with the minimum energy consumption. When the time needed for channel switching can be ignored, a Maximum Matching optimal algorithm is exhibited for LNDR which requires only polynomial time; when the switching time cannot be neglected, LNDR is proven to be NP-hard and a greedy algorithm with constant approximation ratio is developed. We also prove that the MCDR problem is NP-hard to be approximated within to any nontrivial factor and a parameterized heuristic is devised to solve MCDR non-optimally.

Maximum Lifetime Scheduling for Target Coverage and Data Collection in Wireless Sensor Networks

Target coverage and data collection are two fundamental problems for wireless sensor networks (WSNs). Target coverage is needed to select sensors in a given area that can monitor a set of interesting points. Data collection is needed to transmit the sensed data from sensors to a sink. Since, in many applications, sensors are battery powered, it is expected that a WSN can work untended for a long period. This paper addresses the scheduling problems for both target coverage and data collection in WSNs with the objective of maximizing network lifetime. First, a polynomial-time approximation scheme is developed for the case where the density of target points is bounded, and then, a polynomial-time constant-factor approximation algorithm is developed for the general case. It is also proved that it is NP-hard to find a maximum lifetime scheduling of target cover and data collection for a WSN, even if all the sensors have the same sensing radius and the same transmission radius. Further, the practical efficiency of our algorithms is analyzed through simulation. These extensive simulation results show better performances of our algorithms compared with other research findings.

The complexity of influence maximization problem in the deterministic linear threshold model

The influence maximization is an important problem in the field of social network. Informally it is to select few people to be activated in a social network such that their aggregated influence can make as many as possible people active. Kempe et al. gave a

Optimal Data Retrieval Scheduling in the Multichannel Wireless Broadcast Environments

(1-{1 \over e})

Algebraic data retrieval algorithms for multi-channel wireless data broadcast

-approximation algorithm for this problem in the linear threshold model and the independent cascade model. In addition, Chen et al. proved that the exact computation of the influence given a seed set is #P-hard in the linear threshold model. Both of the two models are based on randomized propagation, however such information might be obtained by surveys and data mining techniques. This will make great difference on the complexity of the problem. In this note, we study the complexity of the influence maximization problem in deterministic linear threshold model. We show that in the deterministic linear threshold model, there is no n1−ε-factor polynomial time approximation for the problem unless P=NP. We also show that the exact computation of the influence given a seed set can be solved in polynomial time.

Approximation and Inapproximation for the Influence Maximization Problem in Social Networks under Deterministic Linear Threshold Model

Wireless data broadcast is an efficient way of disseminating data to users in the mobile computing environments. From the servers point of view, how to place the data items on channels is a crucial issue, with the objective of minimizing the average access time and tuning time. Similarly, how to schedule the data retrieval process for a given request at the client side such that all the requested items can be downloaded in a short time is also an important problem. In this paper, we investigate the multi-item data retrieval scheduling in the push-based multichannel broadcast environments. We prove the decision version of this problem is NP-complete, and we devise an algebraic algorithm to search for the best solution. We also develop a heuristic that can employ the algebraic algorithm to download a large number of items efficiently. When there is no replicated item in a broadcast cycle, we show that an optimal retrieval schedule can be obtained in polynomial time. The performances of proposed algorithms are analyzed theoretically and evaluated through simulation. The experimental results show that our algorithms can significantly reduce the access time for multi-item requests.

Routing-efficient CDS construction in Disk-Containment Graphs

Wireless data broadcast is an important data dissemination method for distributing public information to mobile users. Due to the exponentially increasing number of mobile network users, it is necessary to develop efficient data retrieval protocols for end users to download data items effectively. In this paper, we concentrate on investigating scheduling algorithms for retrieving a set of data items from a multichannel wireless data broadcast system. As we know, the most important issues in mobile computing are energy efficiency and query response efficiency. However, in data broadcast the objectives of reducing access latency and energy cost can be contradictive to each other. Consequently, we define a new problem named Minimum Constraint Data Retrieval Problem (MCDR). We prove that MCDR is NP-hard, and then show a fixed parameter tractable algorithm which can balance two factors together. It has computational time O(2^k(hnt)O^(^1^)), where n is the number of channels, k is the number of required data items, t is the maximal time slot, and h is the maximal number of channel switches.

Data Retrieval Scheduling for Multi-Item Requests in Multi-Channel WirelessBroadcast Environments

Influence Maximization is the problem of finding a certain amount of people in a social network such that their aggregation influence through the network is maximized. In the past this problem has been widely studied under a number of different models. In 2003, Kempe \emph{et al.} gave a