Eukaryotic genomes contain high volumes of intronic and intergenic regions in which repetitive sequences are abundant. These repetitive sequences represent challenges in genomic assignment of short read sequences generated through next generation sequencing and are often excluded in analysis losing invaluable genomic information. Here we present a method, known as tandem repeat assembler (TRA), for the assembly of repetitive sequences by constructing contigs directly from paired-end reads. Using an experimentally acquired data set for human chromosome 14, tandem repeats >200 bp were assembled. Alignment of the contigs to the human genome reference (GRCh38) revealed that 84.3% of tandem repetitive regions were correctly covered. For tandem repeats, this method outperformed state-of-the-art assemblers by generating correct N50 of contigs up to 512 bp.
{"title":"Greedily assemble tandem repeats for next generation sequences","authors":"Yongqing Jiang, Jinhua Lu, Jingyu Hou, Wanlei Zhou","doi":"10.1504/ijhpcn.2019.103536","DOIUrl":"https://doi.org/10.1504/ijhpcn.2019.103536","url":null,"abstract":"Eukaryotic genomes contain high volumes of intronic and intergenic regions in which repetitive sequences are abundant. These repetitive sequences represent challenges in genomic assignment of short read sequences generated through next generation sequencing and are often excluded in analysis losing invaluable genomic information. Here we present a method, known as tandem repeat assembler (TRA), for the assembly of repetitive sequences by constructing contigs directly from paired-end reads. Using an experimentally acquired data set for human chromosome 14, tandem repeats >200 bp were assembled. Alignment of the contigs to the human genome reference (GRCh38) revealed that 84.3% of tandem repetitive regions were correctly covered. For tandem repeats, this method outperformed state-of-the-art assemblers by generating correct N50 of contigs up to 512 bp.","PeriodicalId":136458,"journal":{"name":"Int. J. High Perform. Comput. Netw.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122266001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-08DOI: 10.1504/ijhpcn.2019.10025212
Shivani Batra, Shelly Sachdeva, S. Bhalla
Standardisation plays an important role in making healthcare application worldwide adaptable. It uses archetypes for semantic interoperability. In addition to the interoperability, a mechanism to handle future evolution is the primary concern for market sustainability. An application should possess dynamism in terms of the front end (user interface) as well as the back end (database) to build a future proof system. Current research aims to extend the functionality of prior work on HEALTHSURANCE with a search efficient generic storage and validation support. At application level, graphical user interface is dynamically built using knowledge provided by standards in terms of archetypes. At the database level, generic storage structure is provided with improved searching capabilities to support faster access, to capture dynamic knowledge evolution and to handle sparseness. A standardised format and content helps to uplift the credibility of data and maintains a uniform and specific set of constraints used to evaluate user's health. Architecture proposed in current research enables implementation of mobile app based on an archetype paradigm that can avoid reimplementation of the systems, supports migrating databases and allows the creation of future-proof systems.
{"title":"Generic data storage-based dynamic mobile app for standardised electronic health records database","authors":"Shivani Batra, Shelly Sachdeva, S. Bhalla","doi":"10.1504/ijhpcn.2019.10025212","DOIUrl":"https://doi.org/10.1504/ijhpcn.2019.10025212","url":null,"abstract":"Standardisation plays an important role in making healthcare application worldwide adaptable. It uses archetypes for semantic interoperability. In addition to the interoperability, a mechanism to handle future evolution is the primary concern for market sustainability. An application should possess dynamism in terms of the front end (user interface) as well as the back end (database) to build a future proof system. Current research aims to extend the functionality of prior work on HEALTHSURANCE with a search efficient generic storage and validation support. At application level, graphical user interface is dynamically built using knowledge provided by standards in terms of archetypes. At the database level, generic storage structure is provided with improved searching capabilities to support faster access, to capture dynamic knowledge evolution and to handle sparseness. A standardised format and content helps to uplift the credibility of data and maintains a uniform and specific set of constraints used to evaluate user's health. Architecture proposed in current research enables implementation of mobile app based on an archetype paradigm that can avoid reimplementation of the systems, supports migrating databases and allows the creation of future-proof systems.","PeriodicalId":136458,"journal":{"name":"Int. J. High Perform. Comput. Netw.","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115621282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-08DOI: 10.1504/ijhpcn.2019.10025210
Weilun Wang, G. Chakraborty
P300 is a strong event related potential (ERP) generated in the brain and observed on the scalp when an unusual event happens. To decipher P300 signal, we have to use the property of P300 to distinguish P300 signal from non-P300 signal. In this work, we used data collected from P300 BCI Speller with 128 probes. Conventional BCI speller uses eight probes at pre-defined locations on the skull. Though P300 is strong in the parietal region of the brain, location of the strongest signal varies from person to person. The idea is that, if we optimise probe locations for an individual, we could reduce the number of probes required. In fact, the process mode for the raw brain wave signals also will affect the classification accuracy. We designed an algorithm to analyse the raw signals. We achieved over 81% classification accuracy on average with only three probes from only one target stimulus and one non-target stimulus.
{"title":"Selection of effective probes for an individual to identify P300 signal generated from P300 BCI speller","authors":"Weilun Wang, G. Chakraborty","doi":"10.1504/ijhpcn.2019.10025210","DOIUrl":"https://doi.org/10.1504/ijhpcn.2019.10025210","url":null,"abstract":"P300 is a strong event related potential (ERP) generated in the brain and observed on the scalp when an unusual event happens. To decipher P300 signal, we have to use the property of P300 to distinguish P300 signal from non-P300 signal. In this work, we used data collected from P300 BCI Speller with 128 probes. Conventional BCI speller uses eight probes at pre-defined locations on the skull. Though P300 is strong in the parietal region of the brain, location of the strongest signal varies from person to person. The idea is that, if we optimise probe locations for an individual, we could reduce the number of probes required. In fact, the process mode for the raw brain wave signals also will affect the classification accuracy. We designed an algorithm to analyse the raw signals. We achieved over 81% classification accuracy on average with only three probes from only one target stimulus and one non-target stimulus.","PeriodicalId":136458,"journal":{"name":"Int. J. High Perform. Comput. Netw.","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133449781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-08DOI: 10.1504/ijhpcn.2019.10025215
A. Tewari, B. Gupta
In spite of being a promising technology which will make our lives a lot easier we cannot be oblivious to the fact IoT is not safe from online threat and attacks. Thus, along with the growth of IoT we also need to work on its aspects. Taking into account the limited resources that these devices have it is important that the security mechanisms should also be less complex and do not hinder the actual functionality of the device. In this paper, we propose an ECC based lightweight authentication for IoT devices which deploy RFID tags at the physical layer. ECC is a very efficient public key cryptography mechanism as it provides privacy and security with less computation overhead. We also present a security and performance analysis to verify the strength of our proposed approach. We have verified the security and authentication session execution of our protocol using the Promela model and SPIN tool.
{"title":"A novel ECC-based lightweight authentication protocol for internet of things devices","authors":"A. Tewari, B. Gupta","doi":"10.1504/ijhpcn.2019.10025215","DOIUrl":"https://doi.org/10.1504/ijhpcn.2019.10025215","url":null,"abstract":"In spite of being a promising technology which will make our lives a lot easier we cannot be oblivious to the fact IoT is not safe from online threat and attacks. Thus, along with the growth of IoT we also need to work on its aspects. Taking into account the limited resources that these devices have it is important that the security mechanisms should also be less complex and do not hinder the actual functionality of the device. In this paper, we propose an ECC based lightweight authentication for IoT devices which deploy RFID tags at the physical layer. ECC is a very efficient public key cryptography mechanism as it provides privacy and security with less computation overhead. We also present a security and performance analysis to verify the strength of our proposed approach. We have verified the security and authentication session execution of our protocol using the Promela model and SPIN tool.","PeriodicalId":136458,"journal":{"name":"Int. J. High Perform. Comput. Netw.","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127173008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-08DOI: 10.1504/ijhpcn.2019.10025204
Zhijian Qu, Hanling Wang, Xiang Peng, Ge Chen
Big data in smart grid dispatch monitoring systems is susceptible to interference from processing delays and slow response times. Hence, a new fault-tolerant flexible lossless cluster compression method is proposed. This paper presents the Five-tuples (S, D, O, T, M) model, and builds a monitoring data processing platform based on hive. By deploying the dispatch host and monitoring servers under the cloud computing environment, where data nodes are respectively transformed by Deflate, Gzip, BZip2 and LZO lossless compression method. Taking the power dispatch automation system of Long-hai line as example, experimental results show that the cluster lossless compression ratio of BZip2 is greater than 81%; when data records reach twelve million, the compression ratio can be further improved to certain extent by using RCFile storage hive format, which has significant flexible features. Therefore, the new method proposed in this paper can improve the flexibility and fault-tolerant ability of big monitoring data processing in smart grid.
智能电网调度监控系统中的大数据容易受到处理延迟和响应时间过慢的干扰。为此，提出了一种新的容错柔性无损聚类压缩方法。本文提出了五元组(S, D, O, T, M)模型，构建了一个基于hive的监测数据处理平台。通过在云计算环境下部署调度主机和监控服务器，其中数据节点分别采用Deflate、Gzip、BZip2和LZO无损压缩方法进行转换。以陇海线路电力调度自动化系统为例，实验结果表明，BZip2的聚类无损压缩率大于81%;当数据记录达到1200万条时，采用RCFile存储hive格式可以在一定程度上进一步提高压缩比，具有显著的灵活性。因此，本文提出的新方法可以提高智能电网大监测数据处理的灵活性和容错能力。
{"title":"Fault-tolerant flexible lossless cluster compression method for monitoring data in smart grid","authors":"Zhijian Qu, Hanling Wang, Xiang Peng, Ge Chen","doi":"10.1504/ijhpcn.2019.10025204","DOIUrl":"https://doi.org/10.1504/ijhpcn.2019.10025204","url":null,"abstract":"Big data in smart grid dispatch monitoring systems is susceptible to interference from processing delays and slow response times. Hence, a new fault-tolerant flexible lossless cluster compression method is proposed. This paper presents the Five-tuples (S, D, O, T, M) model, and builds a monitoring data processing platform based on hive. By deploying the dispatch host and monitoring servers under the cloud computing environment, where data nodes are respectively transformed by Deflate, Gzip, BZip2 and LZO lossless compression method. Taking the power dispatch automation system of Long-hai line as example, experimental results show that the cluster lossless compression ratio of BZip2 is greater than 81%; when data records reach twelve million, the compression ratio can be further improved to certain extent by using RCFile storage hive format, which has significant flexible features. Therefore, the new method proposed in this paper can improve the flexibility and fault-tolerant ability of big monitoring data processing in smart grid.","PeriodicalId":136458,"journal":{"name":"Int. J. High Perform. Comput. Netw.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126349513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-08DOI: 10.1504/ijhpcn.2019.10025211
N. K. Seera, S. Taruna
Hadoop is prominent for its distributed file system (HDFS) and scalability. Hadoop MapReduce framework is extensively used in big data analytics and business-intelligence applications. The analytic queries executed by these applications often include multiple ad hoc queries and aggregate queries with some selection predicates. The cost of executing these queries grows incredibly as the size of dataset grows. The most effective strategy to improve query performance in such applications is to process only relevant data keeping irrelevant data aside, which can be done using index structures. This paper is an attempt to improve query performance by avoiding full scans on data files. The algorithms used in this paper create inverted indexes on HDFS input splits. We show how query processing in MR jobs can benefit in terms of performance by employing these custom inverted indexes. The experiments demonstrate that queries executed using indexed data execute 1.5x faster than the traditional queries.
{"title":"An efficient approach to optimise I/O cost in data-intensive applications using inverted indexes on HDFS splits","authors":"N. K. Seera, S. Taruna","doi":"10.1504/ijhpcn.2019.10025211","DOIUrl":"https://doi.org/10.1504/ijhpcn.2019.10025211","url":null,"abstract":"Hadoop is prominent for its distributed file system (HDFS) and scalability. Hadoop MapReduce framework is extensively used in big data analytics and business-intelligence applications. The analytic queries executed by these applications often include multiple ad hoc queries and aggregate queries with some selection predicates. The cost of executing these queries grows incredibly as the size of dataset grows. The most effective strategy to improve query performance in such applications is to process only relevant data keeping irrelevant data aside, which can be done using index structures. This paper is an attempt to improve query performance by avoiding full scans on data files. The algorithms used in this paper create inverted indexes on HDFS input splits. We show how query processing in MR jobs can benefit in terms of performance by employing these custom inverted indexes. The experiments demonstrate that queries executed using indexed data execute 1.5x faster than the traditional queries.","PeriodicalId":136458,"journal":{"name":"Int. J. High Perform. Comput. Netw.","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124674431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-08DOI: 10.1504/ijhpcn.2019.10025206
C. Phongpensri, Chidchanok Choksuchat
Resource description framework (RDF) is a common representation in semantic web context, including the web data sources and their relations in the URI form. With the growth of data accessible on the Internet, the RDF data currently contains millions of relations. Thus, answering a semantic query requires going through large amounts of data relations, which is time consuming. In this work, we present a representation framework, combined bit map representation (CBM), which compactly represents RDF data while helping speed up semantic query processing using graphics processing units (GPUs). Since GPUs have limited memory size, without compaction the RDF data cannot be entirely stored in the GPU memory; the CBM structure enables more RDF data to reside in the GPU memory. Since GPUs have many processing elements, their parallel use speeds up RDF query processing. The experimental results show that the proposed representation can reduce the size of RDF data by 70%. Furthermore, the search time on this representation using the GPU is 60% faster than with conventional implementation.
{"title":"Combined bit map representation and its applications to query processing of resource description framework on GPU","authors":"C. Phongpensri, Chidchanok Choksuchat","doi":"10.1504/ijhpcn.2019.10025206","DOIUrl":"https://doi.org/10.1504/ijhpcn.2019.10025206","url":null,"abstract":"Resource description framework (RDF) is a common representation in semantic web context, including the web data sources and their relations in the URI form. With the growth of data accessible on the Internet, the RDF data currently contains millions of relations. Thus, answering a semantic query requires going through large amounts of data relations, which is time consuming. In this work, we present a representation framework, combined bit map representation (CBM), which compactly represents RDF data while helping speed up semantic query processing using graphics processing units (GPUs). Since GPUs have limited memory size, without compaction the RDF data cannot be entirely stored in the GPU memory; the CBM structure enables more RDF data to reside in the GPU memory. Since GPUs have many processing elements, their parallel use speeds up RDF query processing. The experimental results show that the proposed representation can reduce the size of RDF data by 70%. Furthermore, the search time on this representation using the GPU is 60% faster than with conventional implementation.","PeriodicalId":136458,"journal":{"name":"Int. J. High Perform. Comput. Netw.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131142121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-19DOI: 10.1504/ijhpcn.2019.102350
Qi Liu, Zhen Wang, Xiaodong Liu, N. Linge
In the wake of the developments in science and technology, cloud computing has obtained more attention in different fields. Meanwhile, outlier detection for data mining in cloud computing is playing significant role in different research domains and massive research works have been devoted to outlier detection. However, the existing available methods require lengthy computation time. Therefore, the improved algorithm of outlier detection, which has higher performance to detect outliers, is presented. In this paper, the proposed method, which is an improved spectral clustering algorithm (SKM++), is fit for handling outliers. Then, pruning data can reduce computational complexity and combine distance-based method Manhattan distance (distm) to obtain outlier score. Finally, the method confirms the outlier by extreme analysis. This paper validates the presented method by experiments with real collected data by sensors and comparison against the existing approaches. The experimental results show that our proposed method outperforms the existing.
{"title":"Outlier detection of time series with a novel hybrid method in cloud computing","authors":"Qi Liu, Zhen Wang, Xiaodong Liu, N. Linge","doi":"10.1504/ijhpcn.2019.102350","DOIUrl":"https://doi.org/10.1504/ijhpcn.2019.102350","url":null,"abstract":"In the wake of the developments in science and technology, cloud computing has obtained more attention in different fields. Meanwhile, outlier detection for data mining in cloud computing is playing significant role in different research domains and massive research works have been devoted to outlier detection. However, the existing available methods require lengthy computation time. Therefore, the improved algorithm of outlier detection, which has higher performance to detect outliers, is presented. In this paper, the proposed method, which is an improved spectral clustering algorithm (SKM++), is fit for handling outliers. Then, pruning data can reduce computational complexity and combine distance-based method Manhattan distance (distm) to obtain outlier score. Finally, the method confirms the outlier by extreme analysis. This paper validates the presented method by experiments with real collected data by sensors and comparison against the existing approaches. The experimental results show that our proposed method outperforms the existing.","PeriodicalId":136458,"journal":{"name":"Int. J. High Perform. Comput. Netw.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122392898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-19DOI: 10.1504/ijhpcn.2019.102354
Yajie Sun, Feihong Gu, S. Ji
Structural health monitoring technology has been widely used in the detection and identification of plate structure damage. Ultrasonic phased array technology has become an important method for structural health monitoring because of its flexible beam scanning and strong focusing performance. However, a large number of phased array signals will be produced, which leads to difficulty in storing, transmitting and processing. Therefore, under the condition of the signal being sparse, compressive sensing theory can make signal acquisition with much lower sampling rate than traditional Nyquist sampling theorem. Firstly, the sparse orthogonal transformation is used to make the sparse representation. Then, the measurement matrix is used for the projection observation. Besides, the reconstruction algorithm is used for sparse reconstruction. In this paper, the experimental verification of the antirust aluminium plate material is carried out. The experiment shows that the proposed method is useful for reconstructing the signal of phased array structure health monitoring.
{"title":"Sparse reconstruction of piezoelectric signal for phased array structural health monitoring","authors":"Yajie Sun, Feihong Gu, S. Ji","doi":"10.1504/ijhpcn.2019.102354","DOIUrl":"https://doi.org/10.1504/ijhpcn.2019.102354","url":null,"abstract":"Structural health monitoring technology has been widely used in the detection and identification of plate structure damage. Ultrasonic phased array technology has become an important method for structural health monitoring because of its flexible beam scanning and strong focusing performance. However, a large number of phased array signals will be produced, which leads to difficulty in storing, transmitting and processing. Therefore, under the condition of the signal being sparse, compressive sensing theory can make signal acquisition with much lower sampling rate than traditional Nyquist sampling theorem. Firstly, the sparse orthogonal transformation is used to make the sparse representation. Then, the measurement matrix is used for the projection observation. Besides, the reconstruction algorithm is used for sparse reconstruction. In this paper, the experimental verification of the antirust aluminium plate material is carried out. The experiment shows that the proposed method is useful for reconstructing the signal of phased array structure health monitoring.","PeriodicalId":136458,"journal":{"name":"Int. J. High Perform. Comput. Netw.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128648175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-19DOI: 10.1504/ijhpcn.2019.102347
Mingzhong Wang
Mobile crowd sensing becomes a promising solution for massive data collection with the public participation. Besides the challenges of user incentives, and diversified data sources and quality, the requirement of sharing spatial-temporal data drives the privacy concerns of contributors as one of the top priorities in the design and implementation of a sound crowdsourcing platform. In this paper, FollowMe is introduced as a use case of mobile crowd sensing platform to explain possible design guidelines and solutions to address these challenges. The incentive mechanisms are discussed according to both the quantity and quality of users' contributions. Then, a k-anonymity based solution is applied to protect contributors' privacy in both scenarios of trustworthy and untrustworthy crowdsourcers. Thereafter, a reputation-based filtering solution is proposed to detect fake or malicious reports, and finally a density-based clustering algorithm is introduced to find hotspots which can help the prediction of future events. Although FollowMe is designed for a virtual world of the popular mobile game Pokemon Go, the solutions and discussions are supposed to be applicable to more complex applications sharing spatial-temporal data about users.
{"title":"FollowMe: a mobile crowd sensing platform for spatial-temporal data sharing","authors":"Mingzhong Wang","doi":"10.1504/ijhpcn.2019.102347","DOIUrl":"https://doi.org/10.1504/ijhpcn.2019.102347","url":null,"abstract":"Mobile crowd sensing becomes a promising solution for massive data collection with the public participation. Besides the challenges of user incentives, and diversified data sources and quality, the requirement of sharing spatial-temporal data drives the privacy concerns of contributors as one of the top priorities in the design and implementation of a sound crowdsourcing platform. In this paper, FollowMe is introduced as a use case of mobile crowd sensing platform to explain possible design guidelines and solutions to address these challenges. The incentive mechanisms are discussed according to both the quantity and quality of users' contributions. Then, a k-anonymity based solution is applied to protect contributors' privacy in both scenarios of trustworthy and untrustworthy crowdsourcers. Thereafter, a reputation-based filtering solution is proposed to detect fake or malicious reports, and finally a density-based clustering algorithm is introduced to find hotspots which can help the prediction of future events. Although FollowMe is designed for a virtual world of the popular mobile game Pokemon Go, the solutions and discussions are supposed to be applicable to more complex applications sharing spatial-temporal data about users.","PeriodicalId":136458,"journal":{"name":"Int. J. High Perform. Comput. Netw.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130990730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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