Qilong Han

Mobile cloud sensing, big data, and 5G networks make an intelligent and smart world

Through time, we have seen mobile phones transform into multifaceted devices, adapted to meet and exceed our everyday needs. These needs range from something as personal as a health care manager to something as purely analytical as an environment monitor. In effect, mobile phones have come into our lives, making life easier, smarter, and more efficient. In this article we discuss mobile sensing and cloud computing separately and in detail, then combine the two concepts to form the singular idea of mobile cloud sensing. We will also give an intuitive architectural description of mobile cloud sensing, along with discussions about each of its individual building blocks. There are limitations to mobile cloud sensing today, but with the emergence of 5G coupled with the analysis of big data, we can address the current issues at hand. We believe that with the advent of mobile cloud sensing, 5G, and big data analysis, our lives will continue to see an increase in overall quality.

Brain CT Image Similarity Retrieval Method Based on Uncertain Location Graph

A number of brain computed tomography (CT) images stored in hospitals that contain valuable information should be shared to support computer-aided diagnosis systems. Finding the similar brain CT images from the brain CT image database can effectively help doctors diagnose based on the earlier cases. However, the similarity retrieval for brain CT images requires much higher accuracy than the general images. In this paper, a new model of uncertain location graph (ULG) is presented for brain CT image modeling and similarity retrieval. According to the characteristics of brain CT image, we propose a novel method to model brain CT image to ULG based on brain CT image texture. Then, a scheme for ULG similarity retrieval is introduced. Furthermore, an effective index structure is applied to reduce the searching time. Experimental results reveal that our method functions well on brain CT images similarity retrieval with higher accuracy and efficiency.

Maximizing Influence in Sensed Heterogenous Social Network with Privacy Preservation

Maximising influence to improve marketing performance has a significant impact on targeted advertisements and viral product promotion, which has become a fundamental problem in social data analysis. Most existing works neglect the fact that location data could also play an important role in the influence prorogation. This paper considers maximising influence towards both sensed location data and online social data with privacy concern. We merge location data from cyber-physical networks and relationship data from online social networks into a unified, then propose an efficient algorithm to solve the influence maximisation problem. Furthermore, our privacy-preserving mechanism could protect the sensitive location and link information during the whole process of data analysis. Real-life datasets are empirically tested with our framework and demonstrate the power of sensed and online data combination to influence maximisation. The experiment results suggest that our framework is outperforming most existing alternative resolutions and succeeds in preserving privacy.

Privacy Reserved Influence Maximization in GPS-Enabled Cyber-Physical and Online Social Networks

Influence maximization is one of the most fundamental problems in social network analysis due to its significant impact on viral marketing and targeted advertisements. Considerable amount of works has extensively studied to analyze the influence in social networks, but most of the existing works unfortunately neglected the fact that the location information in the cyber-physical world could also play an important role in the influence prorogation. Furthermore, even though a few works consider a little location information to enhance the influence maximization, they do not discuss any privacy issue of the model and expose users location information directly to the public. This paper considers the problem of influence maximization in both GPS-enabled cyber-physical and online social networks with privacy reservation. We propose one novel model merging both GPS data of cyber-physical network and relationship data of online social network together in a unified framework, then we provide an efficient algorithm to solve the influence maximization problem in the framework. Besides the influence maximization problem, our framework could also support other applications involving both cyber-physical and online social networks. Further more, to protect the sensitive location and link information, we also provide corresponding techniques to protect the privacy during the whole influence maximization process. Empirical studies of real life datasets demonstrate the power of engaging location information to influence maximization, and suggest that our resolution outperforming most existing alternative algorithms with the protection of location privacy.

A game theory-based trust measurement model for social networks

BackgroundIn social networks, trust is a complex social network. Participants in online social networks want to share information and experiences with as many reliable users as possible. However, the modeling of trust is complicated and application dependent. Modeling trust needs to consider interaction history, recommendation, user behaviors and so on. Therefore, modeling trust is an important focus for online social networks.MethodsWe propose a game theory-based trust measurement model for social networks. The trust degree is calculated from three aspects, service reliability, feedback effectiveness, recommendation credibility, to get more accurate result. In addition, to alleviate the free-riding problem, we propose a game theory-based punishment mechanism for specific trust and global trust, respectively.Results and conclusionsWe prove that the proposed trust measurement model is effective. The free-riding problem can be resolved effectively through adding the proposed punishment mechanism.

Analyzing the performance of Aloha in string multi-hop underwater acoustic sensor networks

In this article, we intend to investigate the performance of channel access protocols in multi-hop underwater acoustic sensor networks, which are characterized by long propagation delays and limited channel bandwidth. An analytical model specifically designed for contention-based protocols in multi-hop underwater acoustic networks is identified and validated. The model is based on an underwater network model, called string topology network model, which provides a method for computing the expected network throughput and the probability of packets’ delivery to the gateway from an arbitrary sensor. This study demonstrates an improvement of an existing model, in which a node is implicitly assumed to be able to transmit two packets at the same time, which is not realistic due to the half-duplex character of underwater acoustic channels. Based on our findings, we propose a modified analytical model and evaluate it using NS-3 simulator. Results show that our analytical model is more precise than the existing one.

DFDP: A Distributed Algorithm for Finding Disjoint Paths in Wireless Sensor Networks with Correctness Guarantee

In wireless sensor networks, routing messages through multiple (node) disjoint paths between two sensor nodes is a promising way to increase robustness, throughput, and load balance. This paper proposes an efficient distributed algorithm named distributedly finding disjoint paths (DFDP) to find k disjoint paths connecting two given nodes s and t. A set of paths connecting s and t are disjoint if any two of them do not have any common nodes except s and t. Unlike the existing distributed algorithms, DFDP guarantees correctness; that is, it will output k disjoint paths if there exist k disjoint paths in the network or the maximum number of disjoint paths otherwise. Compared with the centralized algorithms which also guarantee correctness, DFDP is shown to have much better efficiency and load balance by theory analysis and simulation results.

A Clustering Scheme for Trajectories in Road Networks

Trajectories of moving objects contain numerous information to analyze and exploit. Clustering analysis is an important approach to deal with trajectories in data mining technology. However, existing trajectory clustering algorithm barely considers temporal information during clustering procedure. This paper proposes a clustering scheme for trajectories in road network environment. A trajectory on road network is represented by a sequence of interest points that each point indicates a real location on road segment. We define a distance measure to compute the spatial similarity between trajectories, then we propose a clustering algorithm based on DBSCAN and add temporal factor into it at the same time.

A ROI-Based Mining Method with Medical Domain Knowledge Guidance

Image mining is a growing research focus and is more than just an extension of data mining to image domain but an interdisciplinary endeavor. Very few people have systematically investigated this field. Mining association rules in medical images is an important part in domain- specific application image mining because there are several technical aspects which make this problem challenging. In this paper, we firstly incorporate the domain knowledge into the ROI extraction algorithm and ROI clustering algorithm, then we extend the concept of association rule based on ROI and image in medical images, and propose two algorithms to discover frequent item-sets and mine interesting association rules from medical images. Some interesting results are obtained by our program and we believe many of the problems we come across are likely to appear in other domains.

OFDP: a distributed algorithm for finding disjoint paths with minimum total length in wireless sensor networks

This paper investigates the MINimum-length-