Pub Date : 2022-03-01DOI: 10.1177/15501329221080666
Shanshan Wan, Zhuo Chen, Cheng Lyu, Ruofan Li, Yuntao Yue, Y. Liu
Sudden disaster events are usually unpredictable and uncontrollable, and how to achieve efficient and accurate disaster information dissemination is an important topic for society security. At present, social sensor networks which integrate human mobile sensors and traditional physical sensors are widely used in dealing with emergencies. Previous studies mainly focused on the impact of human mobility patterns on social sensor networks. In this article, based on the inherent autonomy property of human individuals, we propose a social sensor information dissemination model, which mainly focuses on the impact of the individual characteristics, social characteristics, and group information dissemination mode on social sensor networks. Specifically, the human sensor model is first constructed based on the inherent social and psychological attributes of human autonomy. Then, various information dissemination models such as one-to-one, one-to-many, and peer-to-peer are proposed by considering different transmission media and human interaction preferences. We simulate the environment of information dissemination in disaster events based on the NetLogo platform. Evaluation matrix is applied to test the performance of social sensor information dissemination model, such as event dissemination coverage, event delivery time, and event delivery rate. With the comparisons to epidemic model, social sensor information dissemination model shows excellent performance in improving the efficiency and accuracy of information transmission in disaster events.
{"title":"Research on disaster information dissemination based on social sensor networks","authors":"Shanshan Wan, Zhuo Chen, Cheng Lyu, Ruofan Li, Yuntao Yue, Y. Liu","doi":"10.1177/15501329221080666","DOIUrl":"https://doi.org/10.1177/15501329221080666","url":null,"abstract":"Sudden disaster events are usually unpredictable and uncontrollable, and how to achieve efficient and accurate disaster information dissemination is an important topic for society security. At present, social sensor networks which integrate human mobile sensors and traditional physical sensors are widely used in dealing with emergencies. Previous studies mainly focused on the impact of human mobility patterns on social sensor networks. In this article, based on the inherent autonomy property of human individuals, we propose a social sensor information dissemination model, which mainly focuses on the impact of the individual characteristics, social characteristics, and group information dissemination mode on social sensor networks. Specifically, the human sensor model is first constructed based on the inherent social and psychological attributes of human autonomy. Then, various information dissemination models such as one-to-one, one-to-many, and peer-to-peer are proposed by considering different transmission media and human interaction preferences. We simulate the environment of information dissemination in disaster events based on the NetLogo platform. Evaluation matrix is applied to test the performance of social sensor information dissemination model, such as event dissemination coverage, event delivery time, and event delivery rate. With the comparisons to epidemic model, social sensor information dissemination model shows excellent performance in improving the efficiency and accuracy of information transmission in disaster events.","PeriodicalId":50327,"journal":{"name":"International Journal of Distributed Sensor Networks","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47478869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1177/15501329221083168
A. Rajput, Jianqiang Li, F. Akhtar, Zahid Hussain Khand, Jason C. Hung, Yan Pei, A. Börner
The idea of applying integer Reversible Colour Transform to increase compression ratios in lossless image compression is a well-established and widely used practice. Although various colour transformations have been introduced and investigated in the past two decades, the process of determining the best colour scheme in a reasonable time remains an open challenge. For instance, the overhead time (i.e. to determine a suitable colour transformation) of the traditional colour selector mechanism can take up to 50% of the actual compression time. To avoid such high overhead, usually, one pre-specified transformation is applied regardless of the nature of the image and/or correlation of the colour components. We propose a robust selection mechanism capable of reducing the overhead time to 20% of the actual compression time. It is postulated that implementing the proposed selection mechanism within the actual compression scheme such as JPEG-LS can further reduce the overhead time to 10%. In addition, the proposed scheme can also be extended to facilitate network-based compression–decompression mechanism over distributed systems.
{"title":"A content awareness module for predictive lossless image compression to achieve high throughput data sharing over the network storage","authors":"A. Rajput, Jianqiang Li, F. Akhtar, Zahid Hussain Khand, Jason C. Hung, Yan Pei, A. Börner","doi":"10.1177/15501329221083168","DOIUrl":"https://doi.org/10.1177/15501329221083168","url":null,"abstract":"The idea of applying integer Reversible Colour Transform to increase compression ratios in lossless image compression is a well-established and widely used practice. Although various colour transformations have been introduced and investigated in the past two decades, the process of determining the best colour scheme in a reasonable time remains an open challenge. For instance, the overhead time (i.e. to determine a suitable colour transformation) of the traditional colour selector mechanism can take up to 50% of the actual compression time. To avoid such high overhead, usually, one pre-specified transformation is applied regardless of the nature of the image and/or correlation of the colour components. We propose a robust selection mechanism capable of reducing the overhead time to 20% of the actual compression time. It is postulated that implementing the proposed selection mechanism within the actual compression scheme such as JPEG-LS can further reduce the overhead time to 10%. In addition, the proposed scheme can also be extended to facilitate network-based compression–decompression mechanism over distributed systems.","PeriodicalId":50327,"journal":{"name":"International Journal of Distributed Sensor Networks","volume":"37 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65535113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1177/15501329221082030
Minbo Li, Yu Wu
In recent years, the rapid development of Internet of Things smart hardware has increased the demand for intelligent control of devices, mainly in the smart home industry. The framework to solve the problem of equipment intelligent control is studied. The advantages and disadvantages of existing context modeling strategies are analyzed. According to the characteristics of household context activities, combined with the design principles of graph databases, a new context modeling method based on object and attribute graph is proposed, which is suitable for Internet of Things scenarios with limited resources. Rule control, mode control, and voice control approaches of smart home interaction are designed. An inference engine is used to map the data of context awareness to the Internet of Things control services that executed automatic control of the system, and a framework of smart control system based on context awareness of Internet of Things is proposed. Considering the behavior habits of users, the concept of user preference is introduced to provide more personalized services. Performance tests with simulated data show that the new context modeling method has a faster system response time than the ontology modeling control mode.
{"title":"Intelligent control system of smart home for context awareness","authors":"Minbo Li, Yu Wu","doi":"10.1177/15501329221082030","DOIUrl":"https://doi.org/10.1177/15501329221082030","url":null,"abstract":"In recent years, the rapid development of Internet of Things smart hardware has increased the demand for intelligent control of devices, mainly in the smart home industry. The framework to solve the problem of equipment intelligent control is studied. The advantages and disadvantages of existing context modeling strategies are analyzed. According to the characteristics of household context activities, combined with the design principles of graph databases, a new context modeling method based on object and attribute graph is proposed, which is suitable for Internet of Things scenarios with limited resources. Rule control, mode control, and voice control approaches of smart home interaction are designed. An inference engine is used to map the data of context awareness to the Internet of Things control services that executed automatic control of the system, and a framework of smart control system based on context awareness of Internet of Things is proposed. Considering the behavior habits of users, the concept of user preference is introduced to provide more personalized services. Performance tests with simulated data show that the new context modeling method has a faster system response time than the ontology modeling control mode.","PeriodicalId":50327,"journal":{"name":"International Journal of Distributed Sensor Networks","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46887648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1177/15501477211062835
K. Seng, L. Ang, Ericmoore Ngharamike
The advances and convergence in sensor, information processing, and communication technologies have shaped the Internet of Things of today. The rapid increase of data and service requirements brings new challenges for Internet of Thing. Emerging technologies and intelligent techniques can play a compelling role in prompting the development of intelligent architectures and services in Internet of Things to form the artificial intelligence Internet of Things. In this article, we give an introduction and review recent developments of artificial intelligence Internet of Things, the various artificial intelligence Internet of Things computational frameworks and highlight the challenges and opportunities for effective deployment of artificial intelligence Internet of Things technology to address complex problems for various applications. This article surveys the recent developments and discusses the convergence of artificial intelligence and Internet of Things from four aspects: (1) architectures, techniques, and hardware platforms for artificial intelligence Internet of Things; (2) sensors, devices, and energy approaches for artificial intelligence Internet of Things; (3) communication and networking for artificial intelligence Internet of Things; and (4) applications for artificial intelligence Internet of Things. The article also discusses the combination of smart sensors, edge computing, and software-defined networks as enabling technologies for the artificial intelligence Internet of Things.
{"title":"Artificial intelligence Internet of Things: A new paradigm of distributed sensor networks","authors":"K. Seng, L. Ang, Ericmoore Ngharamike","doi":"10.1177/15501477211062835","DOIUrl":"https://doi.org/10.1177/15501477211062835","url":null,"abstract":"The advances and convergence in sensor, information processing, and communication technologies have shaped the Internet of Things of today. The rapid increase of data and service requirements brings new challenges for Internet of Thing. Emerging technologies and intelligent techniques can play a compelling role in prompting the development of intelligent architectures and services in Internet of Things to form the artificial intelligence Internet of Things. In this article, we give an introduction and review recent developments of artificial intelligence Internet of Things, the various artificial intelligence Internet of Things computational frameworks and highlight the challenges and opportunities for effective deployment of artificial intelligence Internet of Things technology to address complex problems for various applications. This article surveys the recent developments and discusses the convergence of artificial intelligence and Internet of Things from four aspects: (1) architectures, techniques, and hardware platforms for artificial intelligence Internet of Things; (2) sensors, devices, and energy approaches for artificial intelligence Internet of Things; (3) communication and networking for artificial intelligence Internet of Things; and (4) applications for artificial intelligence Internet of Things. The article also discusses the combination of smart sensors, edge computing, and software-defined networks as enabling technologies for the artificial intelligence Internet of Things.","PeriodicalId":50327,"journal":{"name":"International Journal of Distributed Sensor Networks","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46530420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1177/15501329221087183
Ben Wu, Wei Liu, P. Shi, Xiangyang Xu, Yingjing Liu
Due to the continuous expansion of congested urban areas, many new tunnels are inevitable to over-cross the existing subway lines and may even affect the operation of existing lines. It is vital to investigate the response of existing tunnel caused by over-crossing tunneling. In this study, a case history of closely spaced twin tunnels excavated above the existing tunnels in soft soil stratum was presented. The deformation of the existing tunnels induced by the excavation of the new tunnels was automatically monitored. In-situ monitoring results showed that the vertical displacement of the existing tunnels was mainly uplift and its development showed obvious phase characteristics. The increase rate of the vertical displacement in Phase I and II induced by the second over-crossing was smaller than that of the first over-crossing. A superposition method was employed to describe the uplift section characteristics of the existing tunnels. The influence ranges of tunnel excavation on the left and right lines of the existing tunnels were approximately 5.5D and 4.5D, respectively. The torsional deformation of the rail bed and the convergence of the existing tunnels are explored, and the reasons for the changes of the over-crossing sections are analyzed at the same time.
{"title":"A case study of newly tunnels over-crossing the existing subway tunnels","authors":"Ben Wu, Wei Liu, P. Shi, Xiangyang Xu, Yingjing Liu","doi":"10.1177/15501329221087183","DOIUrl":"https://doi.org/10.1177/15501329221087183","url":null,"abstract":"Due to the continuous expansion of congested urban areas, many new tunnels are inevitable to over-cross the existing subway lines and may even affect the operation of existing lines. It is vital to investigate the response of existing tunnel caused by over-crossing tunneling. In this study, a case history of closely spaced twin tunnels excavated above the existing tunnels in soft soil stratum was presented. The deformation of the existing tunnels induced by the excavation of the new tunnels was automatically monitored. In-situ monitoring results showed that the vertical displacement of the existing tunnels was mainly uplift and its development showed obvious phase characteristics. The increase rate of the vertical displacement in Phase I and II induced by the second over-crossing was smaller than that of the first over-crossing. A superposition method was employed to describe the uplift section characteristics of the existing tunnels. The influence ranges of tunnel excavation on the left and right lines of the existing tunnels were approximately 5.5D and 4.5D, respectively. The torsional deformation of the rail bed and the convergence of the existing tunnels are explored, and the reasons for the changes of the over-crossing sections are analyzed at the same time.","PeriodicalId":50327,"journal":{"name":"International Journal of Distributed Sensor Networks","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48727531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1177/15501329221082415
Shanshan Wan, Y. Liu
User social network-based recommender system has achieved significant performance in current recommendation fields. However, the characteristic of openness brings great hidden dangers to the security of recommender systems. Shilling attackers can change the recommendations by foraging user relationships. Most shilling attack detection approaches depend on the explicit user historical data to locate shilling attackers. Some important features such as information propagation and social feedback of users in social networks have not been noticed. We propose a security detection method based on autonomy-oriented user sensor (AOUSD) to identify shilling attackers. Specifically, (1) the user is simulated as a social sensor with autonomous capabilities, (2) the user interaction model is built based on information propagation, information feedback and information disappearance mechanisms of social sensors, and a user dynamic knowledge graph is formed by considering the variable time function, (3) hierarchical clustering method is used to generate preliminary suspicious candidate groups and graph community detection clustering method is applied on the dynamic knowledge graph to detect the attackers. Then, AOUSD is first simulated on NetLogo and it is compared with other algorithms based on the Amazon data. The results prove the advantages of AOUSD in the efficiency and accuracy on shilling attack detection.
{"title":"A security detection approach based on autonomy-oriented user sensor in social recommendation network","authors":"Shanshan Wan, Y. Liu","doi":"10.1177/15501329221082415","DOIUrl":"https://doi.org/10.1177/15501329221082415","url":null,"abstract":"User social network-based recommender system has achieved significant performance in current recommendation fields. However, the characteristic of openness brings great hidden dangers to the security of recommender systems. Shilling attackers can change the recommendations by foraging user relationships. Most shilling attack detection approaches depend on the explicit user historical data to locate shilling attackers. Some important features such as information propagation and social feedback of users in social networks have not been noticed. We propose a security detection method based on autonomy-oriented user sensor (AOUSD) to identify shilling attackers. Specifically, (1) the user is simulated as a social sensor with autonomous capabilities, (2) the user interaction model is built based on information propagation, information feedback and information disappearance mechanisms of social sensors, and a user dynamic knowledge graph is formed by considering the variable time function, (3) hierarchical clustering method is used to generate preliminary suspicious candidate groups and graph community detection clustering method is applied on the dynamic knowledge graph to detect the attackers. Then, AOUSD is first simulated on NetLogo and it is compared with other algorithms based on the Amazon data. The results prove the advantages of AOUSD in the efficiency and accuracy on shilling attack detection.","PeriodicalId":50327,"journal":{"name":"International Journal of Distributed Sensor Networks","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47658399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1177/15501329221080417
Qi Wu, Qiang Li, Dong Guo, Xiangyu Meng
In recent years, the Internet of Things has been widely used in modern life. Advanced persistent threats are long-term network attacks on specific targets with attackers using advanced attack methods. The Internet of Things targets have also been threatened by advanced persistent threats with the widespread application of Internet of Things. The Internet of Things device such as sensors is weaker than host in security. In the field of advanced persistent threat detection, most works used machine learning methods whether host-based detection or network-based detection. However, models using machine learning methods lack robustness because it can be attacked easily by adversarial examples. In this article, we summarize the characteristics of advanced persistent threats traffic and propose the algorithm to make adversarial examples for the advanced persistent threat detection model. We first train advanced persistent threat detection models using different machine learning methods, among which the highest F1-score is 0.9791. Then, we use the algorithm proposed to grey-box attack one of models and the detection success rate of the model drop from 98.52% to 1.47%. We prove that advanced persistent threats adversarial examples are transitive and we successfully black-box attack other models according to this. The detection success rate of the attacked model with the best attacked effect dropped from 98.66% to 0.13%.
{"title":"Exploring the vulnerability in the inference phase of advanced persistent threats","authors":"Qi Wu, Qiang Li, Dong Guo, Xiangyu Meng","doi":"10.1177/15501329221080417","DOIUrl":"https://doi.org/10.1177/15501329221080417","url":null,"abstract":"In recent years, the Internet of Things has been widely used in modern life. Advanced persistent threats are long-term network attacks on specific targets with attackers using advanced attack methods. The Internet of Things targets have also been threatened by advanced persistent threats with the widespread application of Internet of Things. The Internet of Things device such as sensors is weaker than host in security. In the field of advanced persistent threat detection, most works used machine learning methods whether host-based detection or network-based detection. However, models using machine learning methods lack robustness because it can be attacked easily by adversarial examples. In this article, we summarize the characteristics of advanced persistent threats traffic and propose the algorithm to make adversarial examples for the advanced persistent threat detection model. We first train advanced persistent threat detection models using different machine learning methods, among which the highest F1-score is 0.9791. Then, we use the algorithm proposed to grey-box attack one of models and the detection success rate of the model drop from 98.52% to 1.47%. We prove that advanced persistent threats adversarial examples are transitive and we successfully black-box attack other models according to this. The detection success rate of the attacked model with the best attacked effect dropped from 98.66% to 0.13%.","PeriodicalId":50327,"journal":{"name":"International Journal of Distributed Sensor Networks","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44427350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1177/15501477211069903
Minghua Wang, Ran Ou, Yan Wang
Large-scale deployment of mobile wireless sensor networks has been widely used in some dangerous and hostile urban security surveillance scenarios. As a new network architecture, software-defined networks was introduced into wireless sensor networks to form a new software-defined wireless sensor networks to solve the problem of balanced large-scale deployment of sensor networks and simplify the complexity of network management. In this article, we first develop an original confident information coverage–based multiplicatively weighted Voronoi diagram through sensor clustering and sensor collaborative sensing. And then, we propose two sensor collaborative redeployment algorithms based on the novel confident information coverage–based multiplicatively weighted Voronoi diagram and software-defined wireless sensor networks architecture to provide high-confidence coverage and improve the coverage ratio. Finally, we demonstrate the superiority of the confident information coverage–based multiplicatively weighted Voronoi diagram and the effectiveness and efficiency of our proposed algorithms via a series of experiments.
{"title":"Multiplicatively weighted Voronoi-based sensor collaborative redeployment in software-defined wireless sensor networks","authors":"Minghua Wang, Ran Ou, Yan Wang","doi":"10.1177/15501477211069903","DOIUrl":"https://doi.org/10.1177/15501477211069903","url":null,"abstract":"Large-scale deployment of mobile wireless sensor networks has been widely used in some dangerous and hostile urban security surveillance scenarios. As a new network architecture, software-defined networks was introduced into wireless sensor networks to form a new software-defined wireless sensor networks to solve the problem of balanced large-scale deployment of sensor networks and simplify the complexity of network management. In this article, we first develop an original confident information coverage–based multiplicatively weighted Voronoi diagram through sensor clustering and sensor collaborative sensing. And then, we propose two sensor collaborative redeployment algorithms based on the novel confident information coverage–based multiplicatively weighted Voronoi diagram and software-defined wireless sensor networks architecture to provide high-confidence coverage and improve the coverage ratio. Finally, we demonstrate the superiority of the confident information coverage–based multiplicatively weighted Voronoi diagram and the effectiveness and efficiency of our proposed algorithms via a series of experiments.","PeriodicalId":50327,"journal":{"name":"International Journal of Distributed Sensor Networks","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43208309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1177/15501329221088450
Zixiang Nie, YuanZhenTai Long, Senlin Zhang, Yueming Lu
With the in-depth integration of traditional industries and information technology in Internet of things, wireless sensor networks are used more frequently to transmit the data generated from various application scenarios. Structural health monitoring is a scene that requires recurrent data transmission in Internet of things, and the wireless sensor networks in Internet of things not only have storage and communication capabilities, but also have computing capabilities. Therefore, the demand for intelligent and decentralized data exchange between them has increased significantly which brings challenges with respect to low data reliability, chaotic data circulation, provenance tracking, and data accountability investigating, threatening the data security of structural health monitoring in Internet of things utilization. In this article, we propose a controllable data transmission mechanism based on the consortium blockchain to content the requirements of the Internet of things scenario. We identify a version-based, fine-grained, and privacy-protected data structure and propose the corresponding smart contracts for our mechanism to ensure the trusted data transmission. To prove the feasibility of our mechanism, a prototype system is implemented based on the Hyperledger Fabric, an open-source consortium blockchain framework. Our experimental results show in practice the usability and scalability of the approach in this article.
{"title":"A controllable privacy data transmission mechanism for Internet of things system based on blockchain","authors":"Zixiang Nie, YuanZhenTai Long, Senlin Zhang, Yueming Lu","doi":"10.1177/15501329221088450","DOIUrl":"https://doi.org/10.1177/15501329221088450","url":null,"abstract":"With the in-depth integration of traditional industries and information technology in Internet of things, wireless sensor networks are used more frequently to transmit the data generated from various application scenarios. Structural health monitoring is a scene that requires recurrent data transmission in Internet of things, and the wireless sensor networks in Internet of things not only have storage and communication capabilities, but also have computing capabilities. Therefore, the demand for intelligent and decentralized data exchange between them has increased significantly which brings challenges with respect to low data reliability, chaotic data circulation, provenance tracking, and data accountability investigating, threatening the data security of structural health monitoring in Internet of things utilization. In this article, we propose a controllable data transmission mechanism based on the consortium blockchain to content the requirements of the Internet of things scenario. We identify a version-based, fine-grained, and privacy-protected data structure and propose the corresponding smart contracts for our mechanism to ensure the trusted data transmission. To prove the feasibility of our mechanism, a prototype system is implemented based on the Hyperledger Fabric, an open-source consortium blockchain framework. Our experimental results show in practice the usability and scalability of the approach in this article.","PeriodicalId":50327,"journal":{"name":"International Journal of Distributed Sensor Networks","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45618609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1177/15501477211064755
Qiang Liu, Baoyan Song, Junlu Wang
The blockchain runs in a complex topological network which is affected by the principle of consensus, and data storage between nodes needs to maintain global consistency in the entire network, which causes the data storage inefficient. At the same time, the information exchange between large-scale communication node groups leads to the problems of bandwidth expropriation and excessive network load. In response to these problems, this article proposes a second-degree branch structure blockchain expansion model. First, a ternary storage structure is established. Data use the way of fully integrated storage, multi-cell storage, and fully split storage, and data are classified and stored in parallel between the structures. Second, a second-degree branch chain model is constructed. The main chain forks into multiple sub-chains, and a free competition chain structure and a Z-type chain structure are defined; a two-way rotation mechanism is introduced to realize the integration and transition between chain structures. Finally, a set of malicious attacks is simulated to realize the security constraints of the blockchain, to verify the security of the second-degree branch chain model. Experiment shows that the second-degree branch structure expansion model proposed in this article has great advantages in data storage efficiency and network load.
{"title":"Second-degree branch structure blockchain expansion model","authors":"Qiang Liu, Baoyan Song, Junlu Wang","doi":"10.1177/15501477211064755","DOIUrl":"https://doi.org/10.1177/15501477211064755","url":null,"abstract":"The blockchain runs in a complex topological network which is affected by the principle of consensus, and data storage between nodes needs to maintain global consistency in the entire network, which causes the data storage inefficient. At the same time, the information exchange between large-scale communication node groups leads to the problems of bandwidth expropriation and excessive network load. In response to these problems, this article proposes a second-degree branch structure blockchain expansion model. First, a ternary storage structure is established. Data use the way of fully integrated storage, multi-cell storage, and fully split storage, and data are classified and stored in parallel between the structures. Second, a second-degree branch chain model is constructed. The main chain forks into multiple sub-chains, and a free competition chain structure and a Z-type chain structure are defined; a two-way rotation mechanism is introduced to realize the integration and transition between chain structures. Finally, a set of malicious attacks is simulated to realize the security constraints of the blockchain, to verify the security of the second-degree branch chain model. Experiment shows that the second-degree branch structure expansion model proposed in this article has great advantages in data storage efficiency and network load.","PeriodicalId":50327,"journal":{"name":"International Journal of Distributed Sensor Networks","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43170182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：