Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Sensor Intelligent Data Analysis for Social Networks: Theory and Applications
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Sensor Intelligent Data Analysis for Social Networks: Theory and Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (30 June 2018) | Viewed by 70560

Special Issue Editors

Prof. Dr. Neal Xiong

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, Northeastern State University, Colorado Springs, OK 74464, USA
Interests: security; 6G; green computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Athanasios V. Vasilakos

grade E-Mail Website
Guest Editor
Center for AI Research, University of Agder, Grimstad, Norway
Interests: security & privacy; cryptography; cybersecurity; cryptocurrency protocols; Internet of Things; cloud computing; big data; machine learning; biocomputing
Special Issues, Collections and Topics in MDPI journals
Dr. Sajid Hussain

E-Mail Website
Guest Editor
Department of Computer Science, Fisk University, Nashville, TN, USA
Interests: energy efficient communication protocols and security techniques for mobile, Sensor Networks, and pervasive applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent developments in the theory and application of distributed and wireless/wired sensor, and actuator networks, are vital for social networks with various sensors, and perceive human social information, including the environment, transportation, social activities, etc. Sensor networks is an interdisciplinary field, including fields such as wireless networks and communications, signal processing, embedded systems, information management, and intelligent service. Social networks are the interconnection of uniquely identifiable computing devices, systems, and services within all possible and existing networking infrastructures. Typically, social networks are expected to offer advanced connectivity, which goes beyond machine-to-machine sensor communications and covers a variety of protocols, domains, and applications. Here, we focus on the related theory and applications of sensor intelligent data analysis services for social networks. Intelligent services can deliver all kinds of IT solutions and services, which can help any organization to lead its market with a good cloud Quality of Service (QoS), reduce its costs and increase profit. Intelligent services connect the gap between developers, architect, and build planners, with the best-of-breed intelligent and green building technologies and practice, thus, reducing costs, providing quick returns on investments, adding comfort, obtaining value, and easing build occupants. All lead to sustainable communities.

The major enabling technologies that are providing a jump-start to social networks are intelligent services for ad hoc and wireless sensor networks (WSNs), real-time systems, low power and energy harvesting systems, radio frequency identification, resource-constrained networks, embedded software, and others. The emergence and appearance of various intelligent service of wireless sensor networks have brought about a number of changes, as well as opportunities. However, many new challenges confront researchers.

This Special Issue aims to foster the latest developments in sensor intelligent data analysis services for social networks. Original contributions that provide novel theories, frameworks, and solutions to the challenging problems of service computing, data analysis, trust, security, and privacy are solicited for this Special Issue.

Prof. Dr. Neal N. Xiong
Prof. Dr. Athanasios V. Vasilakos
Dr. Sajid Hussain
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Intelligent service standard

  • Trust models for social networks services

  • Cost/efficiency of intelligent services for social networks

  • Optimization mechanisms for social networks services

  • Social networks resource allocation and indexing

  • Data mining in IoT computing

  • Autonomic services in social networks computing

  • Multimedia services for social networks

  • Virtualization services for social networks

  • Smart home services based on social networks

  • Intelligent services in vehicular social networks

  • Information security in social networks-based intelligent service

  • Social networks in social networks computing

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (15 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

14 pages, 751 KiB  
Article
A Privacy Preserving Scheme for Nearest Neighbor Query
by Yuhang Wang, Zhihong Tian, Hongli Zhang, Shen Su and Wei Shi
Sensors 2018, 18(8), 2440; https://doi.org/10.3390/s18082440 - 27 Jul 2018
Cited by 34 | Viewed by 3608
Abstract
In recent years, location privacy concerns that arise when using the nearest neighbor query services have gained increasing attention, as such services have become pervasive in mobile social networks devices and the IoT environments. State-of-the-art privacy preservation schemes focus on the obfuscation of [...] Read more.
In recent years, location privacy concerns that arise when using the nearest neighbor query services have gained increasing attention, as such services have become pervasive in mobile social networks devices and the IoT environments. State-of-the-art privacy preservation schemes focus on the obfuscation of the location information, which has suffered from various privacy attacks and the tradeoff of the quality of service. By noticing the fact that the user’s location could be replaced by their surrounding wireless sensor infrastructures in proximity, in this paper, we propose a wireless sensor access point-based scheme for the nearest neighbor query, without using the location of the user. Then, a noise-addition-based method that preserves user’s location privacy was proposed. To further strengthen the adaptability of the approach to real-world environments, several performance-enhancing methods are introduced, including an R-tree-based Noise-Data Retrieval Algorithm (RNR), and a nearest neighbor query method based on our research. Both performance and security evaluations are conducted to validate our approach. The results show the effectiveness and the practicality of our work. Full article
(This article belongs to the Special Issue Sensor Intelligent Data Analysis for Social Networks: Theory and Applications)
►▼ Show Figures

Figure 1

14 pages, 2392 KiB  
Article
User Access Management Based on Network Pricing for Social Network Applications
by Fuhong Lin, Zhibo Pang, Xingmin Ma and Qing Gu
Sensors 2018, 18(2), 664; https://doi.org/10.3390/s18020664 - 24 Feb 2018
Cited by 3 | Viewed by 4133
Abstract
Social applications play a very important role in people’s lives, as users communicate with each other through social networks on a daily basis. This presents a challenge: How does one receive high-quality service from social networks at a low cost? Users can access [...] Read more.
Social applications play a very important role in people’s lives, as users communicate with each other through social networks on a daily basis. This presents a challenge: How does one receive high-quality service from social networks at a low cost? Users can access different kinds of wireless networks from various locations. This paper proposes a user access management strategy based on network pricing such that networks can increase its income and improve service quality. Firstly, network price is treated as an optimizing access parameter, and an unascertained membership algorithm is used to make pricing decisions. Secondly, network price is adjusted dynamically in real time according to network load. Finally, selecting a network is managed and controlled in terms of the market economy. Simulation results show that the proposed scheme can effectively balance network load, reduce network congestion, improve the user's quality of service (QoS) requirements, and increase the network’s income. Full article
(This article belongs to the Special Issue Sensor Intelligent Data Analysis for Social Networks: Theory and Applications)
►▼ Show Figures

Figure 1

28 pages, 8173 KiB  
Article
The Application of Social Characteristic and L1 Optimization in the Error Correction for Network Coding in Wireless Sensor Networks
by Guangzhi Zhang, Shaobin Cai and Naixue Xiong
Sensors 2018, 18(2), 450; https://doi.org/10.3390/s18020450 - 3 Feb 2018
Cited by 6 | Viewed by 3953
Abstract
One of the remarkable challenges about Wireless Sensor Networks (WSN) is how to transfer the collected data efficiently due to energy limitation of sensor nodes. Network coding will increase network throughput of WSN dramatically due to the broadcast nature of WSN. However, the [...] Read more.
One of the remarkable challenges about Wireless Sensor Networks (WSN) is how to transfer the collected data efficiently due to energy limitation of sensor nodes. Network coding will increase network throughput of WSN dramatically due to the broadcast nature of WSN. However, the network coding usually propagates a single original error over the whole network. Due to the special property of error propagation in network coding, most of error correction methods cannot correct more than C/2 corrupted errors where C is the max flow min cut of the network. To maximize the effectiveness of network coding applied in WSN, a new error-correcting mechanism to confront the propagated error is urgently needed. Based on the social network characteristic inherent in WSN and L1 optimization, we propose a novel scheme which successfully corrects more than C/2 corrupted errors. What is more, even if the error occurs on all the links of the network, our scheme also can correct errors successfully. With introducing a secret channel and a specially designed matrix which can trap some errors, we improve John and Yi’s model so that it can correct the propagated errors in network coding which usually pollute exactly 100% of the received messages. Taking advantage of the social characteristic inherent in WSN, we propose a new distributed approach that establishes reputation-based trust among sensor nodes in order to identify the informative upstream sensor nodes. With referred theory of social networks, the informative relay nodes are selected and marked with high trust value. The two methods of L1 optimization and utilizing social characteristic coordinate with each other, and can correct the propagated error whose fraction is even exactly 100% in WSN where network coding is performed. The effectiveness of the error correction scheme is validated through simulation experiments. Full article
(This article belongs to the Special Issue Sensor Intelligent Data Analysis for Social Networks: Theory and Applications)
►▼ Show Figures

Figure 1

13 pages, 534 KiB  
Article
Hierarchical Discriminant Analysis
by Di Lu, Chuntao Ding, Jinliang Xu and Shangguang Wang
Sensors 2018, 18(1), 279; https://doi.org/10.3390/s18010279 - 18 Jan 2018
Cited by 7 | Viewed by 3561
Abstract
The Internet of Things (IoT) generates lots of high-dimensional sensor intelligent data. The processing of high-dimensional data (e.g., data visualization and data classification) is very difficult, so it requires excellent subspace learning algorithms to learn a latent subspace to preserve the intrinsic structure [...] Read more.
The Internet of Things (IoT) generates lots of high-dimensional sensor intelligent data. The processing of high-dimensional data (e.g., data visualization and data classification) is very difficult, so it requires excellent subspace learning algorithms to learn a latent subspace to preserve the intrinsic structure of the high-dimensional data, and abandon the least useful information in the subsequent processing. In this context, many subspace learning algorithms have been presented. However, in the process of transforming the high-dimensional data into the low-dimensional space, the huge difference between the sum of inter-class distance and the sum of intra-class distance for distinct data may cause a bias problem. That means that the impact of intra-class distance is overwhelmed. To address this problem, we propose a novel algorithm called Hierarchical Discriminant Analysis (HDA). It minimizes the sum of intra-class distance first, and then maximizes the sum of inter-class distance. This proposed method balances the bias from the inter-class and that from the intra-class to achieve better performance. Extensive experiments are conducted on several benchmark face datasets. The results reveal that HDA obtains better performance than other dimensionality reduction algorithms. Full article
(This article belongs to the Special Issue Sensor Intelligent Data Analysis for Social Networks: Theory and Applications)
►▼ Show Figures

Figure 1

1967 KiB  
Article
Spatial-Temporal Data Collection with Compressive Sensing in Mobile Sensor Networks
by Haifeng Zheng, Jiayin Li, Xinxin Feng, Wenzhong Guo, Zhonghui Chen and Neal Xiong
Sensors 2017, 17(11), 2575; https://doi.org/10.3390/s17112575 - 8 Nov 2017
Cited by 21 | Viewed by 5396
Abstract
Compressive sensing (CS) provides an energy-efficient paradigm for data gathering in wireless sensor networks (WSNs). However, the existing work on spatial-temporal data gathering using compressive sensing only considers either multi-hop relaying based or multiple random walks based approaches. In this paper, we exploit [...] Read more.
Compressive sensing (CS) provides an energy-efficient paradigm for data gathering in wireless sensor networks (WSNs). However, the existing work on spatial-temporal data gathering using compressive sensing only considers either multi-hop relaying based or multiple random walks based approaches. In this paper, we exploit the mobility pattern for spatial-temporal data collection and propose a novel mobile data gathering scheme by employing the Metropolis-Hastings algorithm with delayed acceptance, an improved random walk algorithm for a mobile collector to collect data from a sensing field. The proposed scheme exploits Kronecker compressive sensing (KCS) for spatial-temporal correlation of sensory data by allowing the mobile collector to gather temporal compressive measurements from a small subset of randomly selected nodes along a random routing path. More importantly, from the theoretical perspective we prove that the equivalent sensing matrix constructed from the proposed scheme for spatial-temporal compressible signal can satisfy the property of KCS models. The simulation results demonstrate that the proposed scheme can not only significantly reduce communication cost but also improve recovery accuracy for mobile data gathering compared to the other existing schemes. In particular, we also show that the proposed scheme is robust in unreliable wireless environment under various packet losses. All this indicates that the proposed scheme can be an efficient alternative for data gathering application in WSNs . Full article
(This article belongs to the Special Issue Sensor Intelligent Data Analysis for Social Networks: Theory and Applications)
►▼ Show Figures

Figure 1

6967 KiB  
Article
Design and Analysis of a Data Fusion Scheme in Mobile Wireless Sensor Networks Based on Multi-Protocol Mobile Agents
by Chunxue Wu, Wenliang Wu, Caihua Wan, Ernst Bekkering and Naixue Xiong
Sensors 2017, 17(11), 2523; https://doi.org/10.3390/s17112523 - 3 Nov 2017
Cited by 7 | Viewed by 4642
Abstract
Sensors are increasingly used in mobile environments with wireless network connections. Multiple sensor types measure distinct aspects of the same event. Their measurements are then combined to produce integrated, reliable results. As the number of sensors in networks increases, low energy requirements and [...] Read more.
Sensors are increasingly used in mobile environments with wireless network connections. Multiple sensor types measure distinct aspects of the same event. Their measurements are then combined to produce integrated, reliable results. As the number of sensors in networks increases, low energy requirements and changing network connections complicate event detection and measurement. We present a data fusion scheme for use in mobile wireless sensor networks with high energy efficiency and low network delays, that still produces reliable results. In the first phase, we used a network simulation where mobile agents dynamically select the next hop migration node based on the stability parameter of the link, and perform the data fusion at the migration node. Agents use the fusion results to decide if it should return the fusion results to the processing center or continue to collect more data. In the second phase. The feasibility of data fusion at the node level is confirmed by an experimental design where fused data from color sensors show near-identical results to actual physical temperatures. These results are potentially important for new large-scale sensor network applications. Full article
(This article belongs to the Special Issue Sensor Intelligent Data Analysis for Social Networks: Theory and Applications)
►▼ Show Figures

Figure 1

3106 KiB  
Article
Alternative Opportunistic Alert Diffusion to Support Infrastructure Failure during Disasters
by Farouk Mezghani and Nathalie Mitton
Sensors 2017, 17(10), 2370; https://doi.org/10.3390/s17102370 - 17 Oct 2017
Cited by 4 | Viewed by 3433
Abstract
Opportunistic communications present a promising solution for disaster network recovery in emergency situations such as hurricanes, earthquakes, and floods, where infrastructure might be destroyed. Some recent works in the literature have proposed opportunistic-based disaster recovery solutions, but they have omitted the consideration of [...] Read more.
Opportunistic communications present a promising solution for disaster network recovery in emergency situations such as hurricanes, earthquakes, and floods, where infrastructure might be destroyed. Some recent works in the literature have proposed opportunistic-based disaster recovery solutions, but they have omitted the consideration of mobile devices that come with different network technologies and various initial energy levels. This work presents COPE, an energy-aware Cooperative OPportunistic alErt diffusion scheme for trapped survivors to use during disaster scenarios to report their position and ease their rescue operation. It aims to maintain mobile devices functional for as long as possible for maximum network coverage until reaching proximate rescuers. COPE deals with mobile devices that come with an assortment of networks and aims to perform systematic network interface selection. Furthermore, it considers mobile devices with various energy levels and allows low-energy nodes to hold their charge for longer time with the support of high-energy nodes. A proof-of-concept implementation has been performed to study the doability and efficiency of COPE, and to highlight the lessons learned. Full article
(This article belongs to the Special Issue Sensor Intelligent Data Analysis for Social Networks: Theory and Applications)
►▼ Show Figures

Figure 1

3741 KiB  
Article
Design and Analysis of an Efficient Energy Algorithm in Wireless Social Sensor Networks
by Naixue Xiong, Longzhen Zhang, Wei Zhang, Athanasios V. Vasilakos and Muhammad Imran
Sensors 2017, 17(10), 2166; https://doi.org/10.3390/s17102166 - 21 Sep 2017
Cited by 3 | Viewed by 4000
Abstract
Because mobile ad hoc networks have characteristics such as lack of center nodes, multi-hop routing and changeable topology, the existing checkpoint technologies for normal mobile networks cannot be applied well to mobile ad hoc networks. Considering the multi-frequency hierarchy structure of ad hoc [...] Read more.
Because mobile ad hoc networks have characteristics such as lack of center nodes, multi-hop routing and changeable topology, the existing checkpoint technologies for normal mobile networks cannot be applied well to mobile ad hoc networks. Considering the multi-frequency hierarchy structure of ad hoc networks, this paper proposes a hybrid checkpointing strategy which combines the techniques of synchronous checkpointing with asynchronous checkpointing, namely the checkpoints of mobile terminals in the same cluster remain synchronous, and the checkpoints in different clusters remain asynchronous. This strategy could not only avoid cascading rollback among the processes in the same cluster, but also avoid too many message transmissions among the processes in different clusters. What is more, it can reduce the communication delay. In order to assure the consistency of the global states, this paper discusses the correctness criteria of hybrid checkpointing, which includes the criteria of checkpoint taking, rollback recovery and indelibility. Based on the designed Intra-Cluster Checkpoint Dependence Graph and Inter-Cluster Checkpoint Dependence Graph, the elimination rules for different kinds of checkpoints are discussed, and the algorithms for the same cluster checkpoints, different cluster checkpoints, and rollback recovery are also given. Experimental results demonstrate the proposed hybrid checkpointing strategy is a preferable trade-off method, which not only synthetically takes all kinds of resource constraints of Ad hoc networks into account, but also outperforms the existing schemes in terms of the dependence to cluster heads, the recovery time compared to the pure synchronous, and the pure asynchronous checkpoint advantage. Full article
(This article belongs to the Special Issue Sensor Intelligent Data Analysis for Social Networks: Theory and Applications)
►▼ Show Figures

Figure 1

10503 KiB  
Article
Fuzzy Modelling for Human Dynamics Based on Online Social Networks
by Jesus Cuenca-Jara, Fernando Terroso-Saenz, Mercedes Valdes-Vela and Antonio F. Skarmeta
Sensors 2017, 17(9), 1949; https://doi.org/10.3390/s17091949 - 24 Aug 2017
Cited by 8 | Viewed by 4337
Abstract
Human mobility mining has attracted a lot of attention in the research community due to its multiple implications in the provisioning of innovative services for large metropolises. In this scope, Online Social Networks (OSN) have arisen as a promising source of location data [...] Read more.
Human mobility mining has attracted a lot of attention in the research community due to its multiple implications in the provisioning of innovative services for large metropolises. In this scope, Online Social Networks (OSN) have arisen as a promising source of location data to come up with new mobility models. However, the human nature of this data makes it rather noisy and inaccurate. In order to deal with such limitations, the present work introduces a framework for human mobility mining based on fuzzy logic. Firstly, a fuzzy clustering algorithm extracts the most active OSN areas at different time periods. Next, such clusters are the building blocks to compose mobility patterns. Furthermore, a location prediction service based on a fuzzy rule classifier has been developed on top of the framework. Finally, both the framework and the predictor has been tested with a Twitter and Flickr dataset in two large cities. Full article
(This article belongs to the Special Issue Sensor Intelligent Data Analysis for Social Networks: Theory and Applications)
►▼ Show Figures

Figure 1

3539 KiB  
Article
Joint Mobile Data Collection and Wireless Energy Transfer in Wireless Rechargeable Sensor Networks
by Ping Zhong, Ya-Ting Li, Wei-Rong Liu, Gui-Hua Duan, Ying-Wen Chen and Neal Xiong
Sensors 2017, 17(8), 1881; https://doi.org/10.3390/s17081881 - 16 Aug 2017
Cited by 40 | Viewed by 5362
Abstract
In wireless rechargeable sensor networks (WRSNs), there is a way to use mobile vehicles to charge node and collect data. It is a rational pattern to use two types of vehicles, one is for energy charging, and the other is for data collecting. [...] Read more.
In wireless rechargeable sensor networks (WRSNs), there is a way to use mobile vehicles to charge node and collect data. It is a rational pattern to use two types of vehicles, one is for energy charging, and the other is for data collecting. These two types of vehicles, data collection vehicles (DCVs) and wireless charging vehicles (WCVs), are employed to achieve high efficiency in both data gathering and energy consumption. To handle the complex scheduling problem of multiple vehicles in large-scale networks, a twice-partition algorithm based on center points is proposed to divide the network into several parts. In addition, an anchor selection algorithm based on the tradeoff between neighbor amount and residual energy, named AS-NAE, is proposed to collect the zonal data. It can reduce the data transmission delay and the energy consumption for DCVs’ movement in the zonal. Besides, we design an optimization function to achieve maximum data throughput by adjusting data rate and link rate of each node. Finally, the effectiveness of proposed algorithm is validated by numerical simulation results in WRSNs. Full article
(This article belongs to the Special Issue Sensor Intelligent Data Analysis for Social Networks: Theory and Applications)
►▼ Show Figures

Figure 1

5164 KiB  
Article
RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization
by Yuling Fang, Qingkui Chen, Neal N. Xiong, Deyu Zhao and Jingjuan Wang
Sensors 2017, 17(8), 1799; https://doi.org/10.3390/s17081799 - 4 Aug 2017
Cited by 15 | Viewed by 4561
Abstract
This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we [...] Read more.
This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU (Graphics Processing Unit) cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSNs. Then, using the CUDA (Compute Unified Device Architecture) Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes’ diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services. Full article
(This article belongs to the Special Issue Sensor Intelligent Data Analysis for Social Networks: Theory and Applications)
►▼ Show Figures

Figure 1

1040 KiB  
Article
Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks
by Junxing Zhu, Jiawei Zhang, Quanyuan Wu, Yan Jia, Bin Zhou, Xiaokai Wei and Philip S. Yu
Sensors 2017, 17(8), 1786; https://doi.org/10.3390/s17081786 - 3 Aug 2017
Cited by 10 | Viewed by 6216
Abstract
Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different [...] Read more.
Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a = ( u , v ) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages. Full article
(This article belongs to the Special Issue Sensor Intelligent Data Analysis for Social Networks: Theory and Applications)
►▼ Show Figures

Figure 1

2241 KiB  
Article
Non-Convex Sparse and Low-Rank Based Robust Subspace Segmentation for Data Mining
by Wenlong Cheng, Mingbo Zhao, Naixue Xiong and Kwok Tai Chui
Sensors 2017, 17(7), 1633; https://doi.org/10.3390/s17071633 - 15 Jul 2017
Cited by 18 | Viewed by 4465
Abstract
Parsimony, including sparsity and low-rank, has shown great importance for data mining in social networks, particularly in tasks such as segmentation and recognition. Traditionally, such modeling approaches rely on an iterative algorithm that minimizes an objective function with convex l1-norm or [...] Read more.
Parsimony, including sparsity and low-rank, has shown great importance for data mining in social networks, particularly in tasks such as segmentation and recognition. Traditionally, such modeling approaches rely on an iterative algorithm that minimizes an objective function with convex l1-norm or nuclear norm constraints. However, the obtained results by convex optimization are usually suboptimal to solutions of original sparse or low-rank problems. In this paper, a novel robust subspace segmentation algorithm has been proposed by integrating lp-norm and Schatten p-norm constraints. Our so-obtained affinity graph can better capture local geometrical structure and the global information of the data. As a consequence, our algorithm is more generative, discriminative and robust. An efficient linearized alternating direction method is derived to realize our model. Extensive segmentation experiments are conducted on public datasets. The proposed algorithm is revealed to be more effective and robust compared to five existing algorithms. Full article
(This article belongs to the Special Issue Sensor Intelligent Data Analysis for Social Networks: Theory and Applications)
►▼ Show Figures

Figure 1

3112 KiB  
Article
Node Scheduling Strategies for Achieving Full-View Area Coverage in Camera Sensor Networks
by Peng-Fei Wu, Fu Xiao, Chao Sha, Hai-Ping Huang, Ru-Chuan Wang and Nai-Xue Xiong
Sensors 2017, 17(6), 1303; https://doi.org/10.3390/s17061303 - 6 Jun 2017
Cited by 40 | Viewed by 5406
Abstract
Unlike conventional scalar sensors, camera sensors at different positions can capture a variety of views of an object. Based on this intrinsic property, a novel model called full-view coverage was proposed. We study the problem that how to select the minimum number of [...] Read more.
Unlike conventional scalar sensors, camera sensors at different positions can capture a variety of views of an object. Based on this intrinsic property, a novel model called full-view coverage was proposed. We study the problem that how to select the minimum number of sensors to guarantee the full-view coverage for the given region of interest (ROI). To tackle this issue, we derive the constraint condition of the sensor positions for full-view neighborhood coverage with the minimum number of nodes around the point. Next, we prove that the full-view area coverage can be approximately guaranteed, as long as the regular hexagons decided by the virtual grid are seamlessly stitched. Then we present two solutions for camera sensor networks in two different deployment strategies. By computing the theoretically optimal length of the virtual grids, we put forward the deployment pattern algorithm (DPA) in the deterministic implementation. To reduce the redundancy in random deployment, we come up with a local neighboring-optimal selection algorithm (LNSA) for achieving the full-view coverage. Finally, extensive simulation results show the feasibility of our proposed solutions. Full article
(This article belongs to the Special Issue Sensor Intelligent Data Analysis for Social Networks: Theory and Applications)
►▼ Show Figures

Figure 1

3681 KiB  
Article
Distributed and Modular CAN-Based Architecture for Hardware Control and Sensor Data Integration
by Diego P. Losada, Joaquín L. Fernández, Enrique Paz and Rafael Sanz
Sensors 2017, 17(5), 1013; https://doi.org/10.3390/s17051013 - 3 May 2017
Cited by 12 | Viewed by 6459
Abstract
In this article, we present a CAN-based (Controller Area Network) distributed system to integrate sensors, actuators and hardware controllers in a mobile robot platform. With this work, we provide a robust, simple, flexible and open system to make hardware elements or subsystems communicate, [...] Read more.
In this article, we present a CAN-based (Controller Area Network) distributed system to integrate sensors, actuators and hardware controllers in a mobile robot platform. With this work, we provide a robust, simple, flexible and open system to make hardware elements or subsystems communicate, that can be applied to different robots or mobile platforms. Hardware modules can be connected to or disconnected from the CAN bus while the system is working. It has been tested in our mobile robot Rato, based on a RWI (Real World Interface) mobile platform, to replace the old sensor and motor controllers. It has also been used in the design of two new robots: BellBot and WatchBot. Currently, our hardware integration architecture supports different sensors, actuators and control subsystems, such as motor controllers and inertial measurement units. The integration architecture was tested and compared with other solutions through a performance analysis of relevant parameters such as transmission efficiency and bandwidth usage. The results conclude that the proposed solution implements a lightweight communication protocol for mobile robot applications that avoids transmission delays and overhead. Full article
(This article belongs to the Special Issue Sensor Intelligent Data Analysis for Social Networks: Theory and Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-15
Back to TopTop