This paper uses P-sets to give the study of abnormal data in information system. P-sets has dynamic characteristics. The paper adds its attribute conjunction characteristics, namely the spread characteristics of the attribute conjunction and the shrinkage characteristics of the attribute conjunction. And the paper proposes F-lost data and F-redundancy data as well as F-lost cognizable degree and F - redundancy cognizable degree. Through using F-lost cognizable degree and F -redundancy cognizable degree, the paper gets recognition methods about F-lost data and F - redundancy data. Finally, the paper provides the application of recognition of loss and redundancy data. The research shows that F-lost data has the spread characteristics of the attribute conjunction and F-redundancy data has the shrinkage characteristics of the attribute conjunction. And P-sets is a new mathematical tool to study the dynamic information system.

Attribute conjunction characteristics and recognition of loss-redundancy data

Information disguising is one of means to insure information transmission security. In order to obtain a way to disguise information, this paper researches a disguising way that is transmitting false information to realize information security. The paper proposes F-disguise carrier information and F-disguise surplus information by using P-sets. By combining them, the paper proposes F-disguise information. The F-disguise information consists of the F-disguise carrier information and the F-disguise surplus information. The paper also presents the structure of F-disguise information, the theorems of F-disguise information and the reduction theorem of true information. Finally, the paper provides the application of F-disguise information. In transmission processes, the F-disguise information is false information (It does not contain true information), but the receiver can convert the F-disguise information into true information by the reduction rule. The research in this paper indicates that true information in transmission is security by using F-disguise information without encrypting, and the way of information disguising is more reliable than encryption cryptogram.

http://ieeexplore.ieee.org/iel7/7569793/7574778/07574858.pdf

F-disguise information and reduction of true information

Long-range(LoRa) backscatter has shown great potential in many applications. However, the narrow bandwidth of LoRa and the unstable backscatter tag make localization challenging in practice. This paper presents LoBaCa, the first super-resolution LoRa backscatter localization system for low-cost tags. To increase the overall bandwidth, LoBaCa utilizes the frequency hopping technique and exploits the phase slope to synchronize multiple frequency bands. We further show that the unstable low-cost backscatter tag causes additional phase error in the weak backscatter signal and thus introduces significant localization error. We use the upper and lower sideband signals to improve the SNR and correct the phase error. Finally, LoBaCa adopts a super-resolution ESPRIT algorithm to solve the complex multipath effect, estimate the angle of arrival (AoA), and localize the backscatter tag. We prototype LoBaCa and conduct extensive experiments to evaluate LoBaCa in both indoor and outdoor scenarios. Our results show that the localization error of LoBaCa is 5.0 cm and 71 cm when the LoBaCa tag is 5m and 40m away, which is 4.3× and 1.7× better than the state-of-the-art.

LoBaCa: Super-Resolution LoRa Backscatter Localization for Low-Cost Tags

Intelligent Energy Management System Based on Reinforcement Learning in the Internet of Things Environment

A Deep Reinforcement Learning Approach towards Distributed Function as a Service (FaaS) based Edge Application Orchestration in Cloud-Edge Continuum

Intelligent Orchestrating of IoT Microservices Based on Reinforcement Learning

The main purpose of this paper is to study abnormal data whose data elements are increased and decreased simultaneously in data transmission. By using a new mathematic model of P-sets which has dynamic characteristics, this paper proposes the concepts of $$ \bar{F} $$
 -abnormal data, $$ F $$
 -abnormal data, $$ (\bar{F},F) $$
 -abnormal data and P-abnormal data, including $$ \bar{F} $$
 -lost data and $$ F $$
 -intrusive data. P-abnormal data consists of $$ \bar{F} $$
 -abnormal data, $$ F $$
 -abnormal data and $$ (\bar{F},F) $$
 -abnormal data. Based on research for P-abnormal data, the paper also gives a separation algorithm for dynamic data by using P-sets and the set difference operation on general set. By using this algorithm, information systems can determine whether the detected data is standard. If the data is not standard, it can be separated into $$ \bar{F} $$
 -abnormal data or $$ F $$
 -abnormal data. In addition, this paper gives a method for recognition of P-abnormal data and two methods for recovery of P-abnormal data. Finally, this paper provides an application for the separation of dynamic data and recovery of P-abnormal data. The study shows that the separation algorithm is a new mathematical tool. It can be applied in data processing which has the internal dynamic characteristics, outer dynamic characteristics or both dynamic characteristics simultaneously.

Separation of dynamic data and recovery of P-abnormal data

A novel research on network security situation prediction based on iteratively optimized RBF-NN

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Intelligent Orchestrating of IoT Microservices Based on Reinforcement Learning

Separation of dynamic data and recovery of P-abnormal data

A novel research on network security situation prediction based on iteratively optimized RBF-NN