Spatio-Temporal Kronecker Compressive Sensing for Traffic Matrix Recovery

A traffic matrix is generally used by several network management tasks in a data center network, such as traffic engineering and anomaly detection. It gives a flow-level view of the network traffic volume. Despite the explicit importance of the traffic matrix, it is significantly difficult to implement a large-scale measurement to build an absolute traffic matrix. Generally, the traffic matrix obtained by the operators is imperfect, i.e., some traffic data may be lost. Hence, we focus on the problems of recovering these missing traffic data in this paper. To recover these missing traffic data, we propose the spatio-temporal Kronecker compressive sensing method, which draws on Kronecker compressive sensing. In our method, we account for the spatial and temporal properties of the traffic matrix to construct a sparsifying basis that can sparsely represent the traffic matrix. Simultaneously, we consider the low-rank property of the traffic matrix and propose a novel recovery model. We finally assess the estimation error of the proposed method by recovering real traffic

Adaboost‑Based Security Level Classifcation of Mobile Intelligent Terminals

With the rapid development of Internet of Things, massive mobile intelligent terminals are ready to access edge servers for real-time data calculation and interaction. However, the risk of private data leakage follows simultaneously. As the administrator of all intelligent terminals in a region, the edge server needs to clarify the ability of the managed intelligent terminals to defend against malicious attacks. Therefore, the security level classification for mobile intelligent terminals before accessing the network is indispensable. In this paper, we firstly propose a safety assessment method to detect the weakness of mobile intelligent terminals. Secondly, we match the evaluation results to the security level. Finally, a scheme of security level classification for mobile intelligent terminals based on Adaboost algorithm is proposed. The experimental results demonstrate that compared to a baseline that statistically calculates the security level, the proposed method can complete the security level classification with lower latency and high accuracy when massive mobile intelligent terminals access the network at the same time

Determination of the 95% effective dose of remimazolam tosylate in anesthesia induction inhibits endotracheal intubation response in senile patients

Background and Purpose: The prevalence of elderly patients prompts anesthesiologists to determine the optimal dose of medication due to the altered pharmacokinetics and pharmacodynamics of this population. The present study aimed to determine the 95% effective dose (ED95) of remimazolam tosylate in anesthesia induction to inhibit endotracheal intubation-related cardiovascular reaction in frail and non-frail senile patients.Methods: A prospective sequential allocation dose-finding study of remimazolam tosylate was conducted on 80 elderly patients who received general anesthesia between May and June 2022 at the First Affiliated Hospital of Nanchang University. The initial dose was 0.3 mg/kg. The blood pressure and heart rate fluctuations during intubation were either <20% (negative cardiovascular response) or ≥20% (positive cardiovascular response). If positive, the dose of the next patient was increased by 0.02 mg/kg, while if negative, it was reduced by 0.02 mg/kg according to the 95:5 biased coin design (BCD). The ED95 and 95% confidence intervals (CIs) were determined using R-Foundation isotonic regression and bootstrapping methods.Results: The ED95 of remimazolam tosylate to inhibit the response during tracheal intubation was 0.297 mg/kg (95% CI: 0.231–0.451 mg/kg) and 0.331 mg/kg (95% CI: 0.272–0.472 mg/kg) in frail and non-frail senile patients, respectively.Conculation and Implications: The CI of the two groups overlap, and no difference was detected in the ED95 of remimazolam tosylate in inhibiting endotracheal intubation-related cardiovascular response in frail and non-frail senile patients. These results suggested that remimazolam tosylate is an optimal anesthesia inducer for all elderly patients.Clinical Trial Registration:https://www.chictr.org.cn, identifier ChiCTR2200055709

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model’s performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models’ performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors

Fine-granularity inference and estimations to network traffic for SDN.

An end-to-end network traffic matrix is significantly helpful for network management and for Software Defined Networks (SDN). However, the end-to-end network traffic matrix's inferences and estimations are a challenging problem. Moreover, attaining the traffic matrix in high-speed networks for SDN is a prohibitive challenge. This paper investigates how to estimate and recover the end-to-end network traffic matrix in fine time granularity from the sampled traffic traces, which is a hard inverse problem. Different from previous methods, the fractal interpolation is used to reconstruct the finer-granularity network traffic. Then, the cubic spline interpolation method is used to obtain the smooth reconstruction values. To attain an accurate the end-to-end network traffic in fine time granularity, we perform a weighted-geometric-average process for two interpolation results that are obtained. The simulation results show that our approaches are feasible and effective

Wireless Secret Sharing Game for Internet of Things

In the era of Internet of Things (IoT), billions of small but smart wireless devices work together to make our cities more intelligent and sustainable. One challenge is that many IoT devices do not have human interfaces and are very difficult for humans to manage. This creates sustainability and security issues. Enabling automatic secret sharing across heterogeneous devices for cryptography purposes will provide the needed security and sustainability for the underlying IoT infrastructure. Therefore, wireless secret sharing is crucial to the success of smart cities. One secret sharing method is to utilize the effect of the randomness of the wireless channel in the data link layer to generate the common secret between legitimate users. This paper models this secret sharing mechanism from the perspective of game theory. In particular, we formulate a non-cooperative zero-sum game between the legitimate users (Alice and Bob) and an eavesdropper (Eve). Alice and Bob’s strategy is deciding how to exchange packets to protect the secret, and Eve’s strategy is choosing where to stay to better intercept the secret. In a symmetrical game where Eve has the same probability of successfully receiving a packet from Alice and Bob when the transmission distance is the same, we show that both pure and mixed strategy Nash equilibria exist. In an asymmetric game where Eve has different probabilities of successfully receiving a packet from Alice and Bob, a pure strategy may not exist; in this case, we show how a mixed strategy Nash equilibrium can be found. We run simulations to show that our results are better than other approaches