The main function of Internet of Things is to collect and transmit data.At present,the data transmission in Internet of Things lacks effective trust attestation mechanism and trust traceability mechanism of data sourc...The main function of Internet of Things is to collect and transmit data.At present,the data transmission in Internet of Things lacks effective trust attestation mechanism and trust traceability mechanism of data source.To solve the above problems,a trust attestation mechanism for sensing layer nodes is presented.First a trusted group is established,and the node which is going to join the group needs to attest its identity and key attributes to the higher level node.Then the dynamic trust measurement value of the node can be obtained by measuring the node data transmission behavior.Finally the node encapsulates the key attributes and trust measurement value to use short message group signature to attest its trust to the challenger.This mechanism can measure the data sending and receiving behaviors of sensing nodes and track the data source,and it does not expose the privacy information of nodes and the sensing nodes can be traced effectively.The trust measurement for sensing nodes and verification is applicable to Internet of Things and the simulation experiment shows the trust attestation mechanism is flexible,practical and efficient.Besides,it can accurately and quickly identify the malicious nodes at the same time.The impact on the system performance is negligible.展开更多
The Internet of Things （IoT） implies a worldwide network of interconnected objects uniquely addressable, via standard communication protocols. The prevalence of IoT is bound to generate large amounts of multisource,...The Internet of Things （IoT） implies a worldwide network of interconnected objects uniquely addressable, via standard communication protocols. The prevalence of IoT is bound to generate large amounts of multisource, heterogeneous, dynamic, and sparse data. However, IoT offers inconsequential practical benefits without the ability to integrate, fuse, and glean useful information from such massive amounts of data. Accordingly, preparing us for the imminent invasion of things, a tool called data fusion can be used to manipulate and manage such data in order to improve process efficiency and provide advanced intelligence. In order to determine an acceptable quality of intelligence, diverse and voluminous data have to be combined and fused. Therefore, it is imperative to improve the computational efficiency for fusing and mining multidimensional data. In this paper, we propose an efficient multidimensional fusion algorithm for IoT data based on partitioning. The basic concept involves the partitioning of dimensions （attributes）, i.e., a big data set with higher dimensions can be transformed into certain number of relatively smaller data subsets that can be easily processed. Then, based on the partitioning of dimensions, the discernible matrixes of all data subsets in rough set theory are computed to obtain their core attribute sets. Furthermore, a global core attribute set can be determined. Finally, the attribute reduction and rule extraction methods are used to obtain the fusion results. By means of proving a few theorems and simulation, the correctness and effectiveness of this algorithm is illustrated.展开更多
Since the Internet of Things（IoT） secret information is easy to leak in data transfer,a data secure transmission model based on compressed sensing（CS） and digital watermarking technology is proposed here. Firstly,...Since the Internet of Things（IoT） secret information is easy to leak in data transfer,a data secure transmission model based on compressed sensing（CS） and digital watermarking technology is proposed here. Firstly, for node coding end, the digital watermarking technology is used to embed secret information in the conventional data carrier. Secondly, these data are reused to build the target transfer data by the CS algorithm which are called observed signals. Thirdly, these signals are transmitted to the base station through the wireless channel. After obtaining these observed signals, the decoder reconstructs the data carrier containing privacy information. Finally, the privacy information is obtained by digital watermark extraction algorithm to achieve the secret transmission of signals. By adopting the watermarking and compression sensing to hide secret information in the end of node code, the algorithm complexity and energy consumption are reduced. Meanwhile, the security of secret information is increased.The simulation results show that the method is able to accurately reconstruct the original signal and the energy consumption of the sensor node is also reduced significantly in consideration of the packet loss.展开更多
基金Supported by the National Natural Science Foundation of China(61501007)General Project of Science and Technology Project of Beijing Municipal Education Commission(KM201610005023)
文摘The main function of Internet of Things is to collect and transmit data.At present,the data transmission in Internet of Things lacks effective trust attestation mechanism and trust traceability mechanism of data source.To solve the above problems,a trust attestation mechanism for sensing layer nodes is presented.First a trusted group is established,and the node which is going to join the group needs to attest its identity and key attributes to the higher level node.Then the dynamic trust measurement value of the node can be obtained by measuring the node data transmission behavior.Finally the node encapsulates the key attributes and trust measurement value to use short message group signature to attest its trust to the challenger.This mechanism can measure the data sending and receiving behaviors of sensing nodes and track the data source,and it does not expose the privacy information of nodes and the sensing nodes can be traced effectively.The trust measurement for sensing nodes and verification is applicable to Internet of Things and the simulation experiment shows the trust attestation mechanism is flexible,practical and efficient.Besides,it can accurately and quickly identify the malicious nodes at the same time.The impact on the system performance is negligible.
基金the National High-Tech Research and Development (863) Program of China (No. 2011AA010101)the National Natural Science Foundation of China (Nos. 61103197, 61073009, and 61240029)+5 种基金the Science and Technology Key Project of Jilin Province (No. 2011ZDGG007)the Youth Foundation of Jilin Province of China (No. 201101035)the Fundamental Research Funds for the Central Universities of China (No. 200903179)China Postdoctoral Science Foundation (No. 2011M500611)the 2011 Industrial Technology Research and Development Special Project of Jilin Province (No. 2011006-9)the 2012 National College Students' Innovative Training Program of China, and European Union Framework Program: MONICA Project under the Grant Agreement Number PIRSES-GA-2011-295222
文摘The Internet of Things （IoT） implies a worldwide network of interconnected objects uniquely addressable, via standard communication protocols. The prevalence of IoT is bound to generate large amounts of multisource, heterogeneous, dynamic, and sparse data. However, IoT offers inconsequential practical benefits without the ability to integrate, fuse, and glean useful information from such massive amounts of data. Accordingly, preparing us for the imminent invasion of things, a tool called data fusion can be used to manipulate and manage such data in order to improve process efficiency and provide advanced intelligence. In order to determine an acceptable quality of intelligence, diverse and voluminous data have to be combined and fused. Therefore, it is imperative to improve the computational efficiency for fusing and mining multidimensional data. In this paper, we propose an efficient multidimensional fusion algorithm for IoT data based on partitioning. The basic concept involves the partitioning of dimensions （attributes）, i.e., a big data set with higher dimensions can be transformed into certain number of relatively smaller data subsets that can be easily processed. Then, based on the partitioning of dimensions, the discernible matrixes of all data subsets in rough set theory are computed to obtain their core attribute sets. Furthermore, a global core attribute set can be determined. Finally, the attribute reduction and rule extraction methods are used to obtain the fusion results. By means of proving a few theorems and simulation, the correctness and effectiveness of this algorithm is illustrated.
基金Supported by the Foundation of Tianjin for Science and Technology Innovation(10FDZDGX00400,11ZCKFGX00900)Key Project of Educational Reform Foundation of Tianjin Municipal Education Commission(C03-0809)
文摘Since the Internet of Things（IoT） secret information is easy to leak in data transfer,a data secure transmission model based on compressed sensing（CS） and digital watermarking technology is proposed here. Firstly, for node coding end, the digital watermarking technology is used to embed secret information in the conventional data carrier. Secondly, these data are reused to build the target transfer data by the CS algorithm which are called observed signals. Thirdly, these signals are transmitted to the base station through the wireless channel. After obtaining these observed signals, the decoder reconstructs the data carrier containing privacy information. Finally, the privacy information is obtained by digital watermark extraction algorithm to achieve the secret transmission of signals. By adopting the watermarking and compression sensing to hide secret information in the end of node code, the algorithm complexity and energy consumption are reduced. Meanwhile, the security of secret information is increased.The simulation results show that the method is able to accurately reconstruct the original signal and the energy consumption of the sensor node is also reduced significantly in consideration of the packet loss.
