Mingyuan Yan

Using crowdsourced data in location-based social networks to explore influence maximization

Online social networks have gained significant popularity recently. The problem of influence maximization in online social networks has been extensively studied. However, in prior works, influence propagation in the physical world, which is also an indispensable factor, is not considered. The Location-Based Social Networks (LBSNs) are a special kind of online social networks in which people can share location-embedded information. In this paper, we make use of mobile crowdsourced data obtained from location-based social network services to study influence maximization in LBSNs. A novel network model and an influence propagation model taking influence propagation in both online social networks and the physical world into consideration are proposed. An event activation position selection problem is formalized and a corresponding solution is provided. The experimental results indicate that the proposed influence propagation model is meaningful and the activation position selection algorithm has high performance.

An exploration of broader influence maximization in timeliness networks with opportunistic selection

The goal of classic influence maximization in Online Social Networks (OSNs) is to maximize the spread of influence with a fixed budget constraint, e.g. the size of seed nodes is pre-determined. However, most existing works on influence maximization overlooked the information timeliness. That is, these works assume that the influence will not decay with time and the influence could be accepted immediately, which are not practical. Second, even the influence could be passed to a specific node in time, whether the influence could be delivered (influence take effect) or not is still an unknown question. Furthermore, if let the number of users who are influenced as the depth of influence and the area covered by influenced users as the breadth, most of research results only focus on the influence depth instead of the influence breadth. Timeliness, acceptance ratio and breadth are three important factors neglected before but strongly affect the real result of the influence maximization. In order to fill the gap, a novel algorithm that incorporates time delay for timeliness, opportunistic selection for acceptance ratio, and broad diffusion for influence breadth has been investigated in this paper. In our model, the breadth of influence is measured by the number of communities, and the tradeoff between depth and breadth of the influence could be balanced by a parameter ?. Empirical studies on different large real-world social networks show that high depth influence does not necessarily imply broad information diffusion. Our model, together with its solutions, not only provides better practicality but also gives a regulatory mechanism for the influence maximization. It also outperforms most of the existing classical algorithms.

Genetic-algorithm-based construction of Load-Balanced CDSs in Wireless Sensor Networks

A Connected Dominating Set (CDS) is used as a virtual backbone for efficient routing and broadcasting in Wireless Sensor Networks (WSNs). Most existing works focus on constructing a Minimum CDS (MCDS), a k-connect m-dominating CDS, a minimum routing cost CDS or a bounded-diameter CDS. However, no work considers the load-balance factor of CDSs in WSNs. In this paper, we propose a novel problem — the Load-Balanced CDS (LBCDS) problem, in which constructing an LBCDS and load-balancedly allocating dominatees to dominators are investigated simultaneously. A Genetic Algorithm (GA) based strategy called LBCDS-GA is proposed to construct an LBCDS in a WSN. As a matter of fact, constructing a CDS as a virtual backbone in a WSN is an efficient way to extend network lifetime through reducing the number of the nodes involved in communication, while building an LBCDS and load-balancedly allocating dominatees to dominators can further prolong network lifetime through balancing the workloads of all the dominators. Through extensive simulations, we demonstrate that our proposed methods extend network lifetime by 65% on average compared with the best and latest MCDS construction algorithm.

Influence maximization by probing partial communities in dynamic online social networks

With the rapid development of online social networks, exploring influence maximization for product publicity and advertisement marketing has attracted strong interests from both academia and industry. However, because of the continuous change of network topology, updating the variation of an entire network moment by moment is resource intensive and often insurmountable. On the other hand, the classical influence maximization models Independent Cascade and Linear Threshold together with their derived varieties are all computationally intensive. Thus, developing a solution for dynamic networks with lower cost and higher accuracy is in an urgent necessity. In this paper, a practical framework is proposed by only probing partial communities to explore the real changes of a network. Our framework minimizes the possible difference between the observed topology and the real network through several representative communities. Based on the framework, an algorithm that takes full advantage of our divide-and-conquer strategy, which reduces the computational overhead, is proposed. The systemically theoretical analysis shows that the proposed effective algorithm could achieve provable approximation guarantees. Empirical studies on synthetic and real large-scale social networks demonstrate that our framework has better practicality compared with most existing works and provides a regulatory mechanism for enhancing influence maximization. Copyright

Truthful Incentive Mechanisms for Social Cost Minimization in Mobile Crowdsourcing Systems

With the emergence of new technologies, mobile devices are capable of undertaking computational and sensing tasks. A large number of users with these mobile devices promote the formation of the Mobile Crowdsourcing Systems (MCSs). Within a MCS, each mobile device can contribute to the crowdsourcing platform and get rewards from it. In order to achieve better performance, it is important to design a mechanism that can attract enough participants with mobile devices and then allocate the tasks among participants efficiently. In this paper, we are interested in the investigation of tasks allocation and price determination in MCSs. Two truthful auction mechanisms are proposed for different working patterns. A Vickrey–Clarke–Groves (VCG)-based auction mechanism is proposed to the continuous working pattern, and a suboptimal auction mechanism is introduced for the discontinuous working pattern. Further analysis shows that the proposed mechanisms have the properties of individual rationality and computational efficiencies. Experimental results suggest that both mechanisms guarantee all the mobile users bidding with their truthful values and the optimal maximal social cost can be achieved in the VCG-based auction mechanism.

Minimum latency scheduling for Multi-Regional Query in Wireless Sensor Networks

Query scheduling as one of the most important technologies used in query processing has been widely studied recently. Unfortunately, to the best of our knowledge, no previous work focuses on the Minimum Latency Multi-Regional Query Scheduling (ML-MRQS) problem. In this paper, we investigate the ML-MRQS problem in Wireless Sensor Networks (WSNs), which aims to generate a scheduling plan with minimum latency for a more practical query model called Multi-Regional Query (MRQ). A MRQ targets at user interested data from multiple region-sofa WSN, where each region is a subarea of the WSN. We claim that the ML-MRQS problem is NP-hard. Therefore, we propose a heuristic scheduling algorithm Multi-Regional Query Scheduling Algorithm (MRQSA) to solve this problem. Theoretical analysis shows that the latency of MRQSA is upper bounded by 23A + B + C for a MRQ with m query regions R₁, R₂..., R_m, where A = max_i=1^m D_i^left, B = max_i=1^m {(23D_i+5Δ+21)k_i}, C = Σ_i=1^m H_i+5Δ-m+17, m is the number of regions, Δ is the maximum node degree in the WSN, A is the diameter of R_i, k_i is the maximum overlapped degree of sensor nodes in TU, Hi represents the distance of R_i with respect to the sink, and D_i^left is the diameter of the non-overlapped part of R_i. Extensive simulations are conducted to verify the performance of our algorithm, which show that MRQSA significantly reduces the query latency when compared with the most recently published multi-query scheduling algorithm.

Neighborhood-based uncertainty generation in social networks

Imprecision, incompleteness and dynamic exist in a wide range of network applications. It is difficult to decide the uncertainty relationship among nodes since traditional models are not meaningful in uncertain networks, and the inherent computational complexity of the problems with uncertainty is always intractable. In this paper, we study how to capture uncertainty in networks by transforming a series of snapshots of a network to an uncertain graph. A novel sampling scheme is also proposed which enables the development of efficient algorithms to measure uncertainty in networks. Considering the practical aspects of neighborhood relationship in real networks, a framework is introduced to transform an uncertain network into a deterministic weighted network where the weights on edges can be measured by Jaccard-like index. The comprehensive experimental evaluation results on real data demonstrate the effectiveness and efficiency of our algorithms.

Data Aggregation Scheduling in Probabilistic Wireless Networks with Cognitive Radio Capability

Transitional Region Phenomenon leads to the existence of lossy links in wireless networks, which results in a transmission between two users who are theoretically connected under the Deterministic Network Model cannot be guaranteed. Therefore, we focus on a more practical network model - Probabilistic Network Model (PNM) which can better characterize the lossy links in wireless networks. To be specific, we focus on the investigation of accelerating data aggregation process in probabilistic wireless networks with the cognitive radio technology. By involving cognitive radio technology, users in the wireless networks can seek extra transmission opportunity if other spectrum resource is available. Otherwise, the data aggregation process still can be done on the default working spectrum. Particularly, we are interested in the time efficient data aggregation scheduling problem. In this work, a two phase scheduling algorithm is proposed. The first phase is finding an efficient routing structure considering the speciality of the network model under investigation. In the second phase, a dynamic scheduling algorithm is introduced. Theoretical analysis is provided to estimate the lower latency bound for the scheduling algorithm, followed by the experimental simulation verification.

Semi-Structure Routing and Performance Analysis for Cognitive Radio Networks

Routing is one of the most important and fundamental issues in Cognitive Radio Networks (CRNs). In this paper, we propose an effective routing scheme. Our main contributions are threefold. First, we propose a spectrum-aware Semi-Structure Routing (SSR) framework which incorporates power control. By employing forwarding zones and routing zones, SSR can effectively utilize the local real-time spectrum dynamics and meanwhile guarantee the global routing performance. Second, considering the lack of analytical models for routing protocol performance [1] in CRNs, we analyze the upper bound of the induced latency and scalability of SSR. Finally, extensive simulation results are presented to validate the performance of SSR.

Semi-Structure Routing and Analytical Frameworks for Cognitive Radio Networks

Routing is one of the most important and fundamental issues in cognitive radio networks (CRNs). However, most of the existing routing algorithms for CRNs either cannot fully take account of the spectrum dynamics or are resource aided which might introduce too much cost. Therefore, in this paper, we study to design an effective routing scheme with respect to induced latency and energy consumption for CRNs. Our main contributions are threefold. First, we propose a spectrum-aware semi-structure routing (SSR) framework which also incorporates power control for CRNs. By employing forwarding zones and routing zones, SSR can utilize the local real-time spectrum dynamics effectively and meanwhile guarantee the global routing performance. In addition, without sacrificing spectrum opportunities, SSR achieves energy efficiency by completing each data transmission with the lowest allowed working power. Second, aiming at closing the gap of lacking of analytical models for routing protocol performance [4] in CRNs, we propose a mathematical framework for SSR which includes a latency analytical model and an energy consumption analytical model. Under the dense scaling network distribution model, we demonstrate (i) the upper bound of the induced latency and scalability of SSR; and (ii) the optimality of SSR with respect to energy consumption, which is approximately optimal. Finally, extensive simulations are conducted to validate the performance of SSR. Simulation results indicate that SSR can utilize spectrum dynamics effectively and has better performance than state-of-the-art methods.