Server Model Research Articles

Nondestructive determination of SSC in an apple by using a portable near-infrared spectroscopy system

The soluble solids content (SSC) is an important factor in the internal quality detection of apples. It is essential to have reliable and high-speed measurement of the SSC. However, almost all traditional equipment is inconvenient and expensive. We designed a handheld nondestructive SSC detector based on near-infrared (NIR) spectroscopy, which is composed of a portable NIR spectrometer, cloud server, smartphone app, and prediction model of SSC. We preprocessed the spectrum with multiplicative scatter correction (MSC), standard normal variable transformation (SNV), and Savitzky-Golay (S-G) smoothing algorithms. Besides, the linear weight reduction of the particle swarm optimization algorithm is carried out, and we establish the model of an extreme learning machine optimized with the improved particle swarm optimization (IPSO-ELM) algorithm. The R2, root mean square error of prediction (RMSEP), and residual prediction deviation (RPD) of the model are 0.993, 0.0155, and 11.6, respectively, which are better than the traditional model obviously. In addition, the number of wavelengths reduced from 228 to 70 as the model is simplified with the uninformative variable elimination (UVE) method. The time of training is reduced by 29.30% compared with the original spectrum. It can be verified that the IPSO-ELM model has good prediction performance, and the NIR diffuse reflectance spectroscopy is a reliable nondestructive measurement of SSC in apples.

Traffic simulation in the LoRaWAN network

LoRaWAN is one of the most commonly used technologies serving the internet of things (IoT) and machine-to-machine (M2M) devices. The traffic growth in the LoRaWAN network gives rise to many problems, which are solved using mathematical modelling. The actual task, in this case, is the development of a traffic simulation model in the LoRaWAN network. This article discusses the issues of traffic simulation in the LoRaWAN network and its research using the MATLAB system. The authors have developed a LoRaWAN network server model as a queuing system with incoming self-similar traffic in the MATLAB system using a separate subsystem for the input traffic modelling allowing to change the number of sources in the LoRaWAN network. The simulation results made it possible to establish the dependences of the network server’s buffer memory, the probability of packet loss from the incoming self-similar traffic parameters, and reveal the possibilities of traffic modelling in the MATLAB system.

Robust Federated Learning With Noisy Labels

Federated learning enables local devices to jointly train the server model while keeping the data decentralized and private. In federated learning, all local data should be annotated by alternative labeling techniques since the annotator in the server cannot access the data. Therefore, it is hardly guaranteed that they are correctly annotated. Under this noisy label setting, local models form inconsistent class decision boundaries with one another, and their weights severely diverge, which are serious problems in federated learning. To solve these problems, we introduce a novel federated learning scheme that allows the server to cooperate with local models by interchanging class-wise centroids. The server aligns the class-wise centroids, which are central features of local data on each device, and broadcasts aligned centroids to selected clients every communication round. Updating local models with the aligned centroids helps us to form consistent class decision boundaries among local models, although the noise distributions in clients’ data are different from each other. Furthermore, we introduce a sample selection approach to filter out data with noisy labels and a label correction method to adjust the labels of noisy instances. Our experimental results show that our approach is noticeably effective in federated learning with noisy labels.

Adaptive Computation Offloading Policy for Multi-Access Edge Computing in Heterogeneous Wireless Networks

In heterogeneous wireless networks, massive mobile terminals randomly generate a large number of computation tasks (payloads). How to better manage these mobile terminals located in wireless networks to achieve acceptable quality of service (QoS) such as latency minimization, energy consumption minimization is crucial. A multi-access edge computing (MEC) server can be leveraged to execute the offloaded payloads generated from mobile terminals owing to its powerful processing power and location proximity features. However, an MEC server cannot tackle all offloaded tasks from multiple mobile terminals, and its energy consumption needs further consideration. We introduce an edge server model combined with the unmanned aerial vehicle (UAV) and equipped with the macro base station (MBS-MEC) to process the arrival payloads, and all UAVs and MBS-MECs can harvest renewable energy by using energy harvesting equipment. Furthermore, we model the computation offloading as a deep reinforcement learning scheme without <i>priori</i> knowledge. Considering the infeasibility of deep-reinforcement learning-based centralized learning for the proposed edge computing framework, we propose a distributed computation offloading scheme based on deep reinforcement learning (DCODRL) to minimize the weighted average cost, including the latency cost and the energy cost. Each mobile terminal can be regarded as a learning agent for the DCODRL. To compensate for the lack of cooperation of the DCODRL, we propose a gated-recurrent-unit-assisted multi-agent computation offloading scheme based on deep reinforcement learning (MCODRL) to improve the offloading policy by obtaining global observation information and designing a common reward for all agents. Comprehensive numerical results reflect the convergence and effectiveness of the DCODRL and MCODRL, and the efficacy of the proposed algorithms is further verified through comparisons with two baseline algorithms.

Personalized driving assistance algorithms: Case study of federated learning based forward collision warning

Current designs of advanced driving assistance systems (ADAS) mainly developed uniform collision warning algorithms, which ignore the heterogeneity of driving behaviors, thus lead to low drivers’ trust in. To address this issue, developing personalized driving assistance algorithms is a promising approach. However, current personalization systems were mainly implemented through manually adjusting warning trigger thresholds, which would be less feasible for overall drivers as certain domain expertise is required to set personal thresholds accurately. Other personalization techniques exploited individual drivers’ data to build personalized models. Such approach could learn personal behavior but requires impractical large-scale individual data collections. To fill up the gaps, self-adaptive algorithms for personalized forward collision warning (FCW) based on federated learning were proposed in this study. A baseline model was developed by long short-term memory (LSTM) for FCW. Federated learning framework was then introduced to collect knowledge from multiple drivers with privacy preserving. Specifically, a general cloud server model was trained by collecting updated parameters from individual vehicle server models rather than collecting raw data. Besides, a driver-specific batch normalization (BN) layer was added into each vehicle server model to address the heterogeneity of driving behaviors. Experiments show empirically that the proposed federated-based personalized models with the BN layer showed to have the best performance. The average modeling accuracy has reached 84.88% and the performance is comparable to conventional total data collection training approach, where the additional BN layer could increase the accuracy by 3.48%. Finally, applications of the proposed framework and its further investigations have been discussed.

Design of Embedded Remote Software Update System Based on FPGA+ARM

Software updates are one of the most critical considerations in the production phase of any IoT project at a very early stage. If a production team maintains the program, anything that goes wrong can be corrected, and new characteristics can be implemented promptly, provided remote upgrades are successful, secure, and trusted. The fast growth of embedded and wireless technologies has eventually led to the convergence of embedded and wireless systems. Therefore, products should be pushed into development beforehand to provide the potential for a different approach, including remote upgrades if new enhancements are ready or if bugs have been remedied with the existing version. For the most part, it’s good to have a software upgrade mechanism during the very early stages of the product or application definition. The paper offers an ARM and FPGA-based data acquisition framework. It aims to capture the bandwidth of wireless signals from 70[Formula: see text]MHz to 6[Formula: see text]GHz using the proposed framework in the article. The framework included the user interface to configure sample rate, bandwidth, sample center frequency, sample time, and data storage parameters for sampling — the proposed framework consists of software and software architecture for the hardware framework. The hardware is supported by ARM and FPGA. The software is based on the Xilinx software development kit (SDK). In particular, for fast transmission and storing of data over ETHERNET, a remote data acquisition scheme based on the server and client model is built.

Public Key Encryption with Authorized Equality Test on Outsourced Ciphertexts for Cloud-Assisted IoT in Dual Server Model

In cloud computing, the outsourced data face many privacy and security threats. To allow the cloud server to perform comparison, search, and classification on outsourced ciphertexts while simultaneously providing privacy guarantee, the encryption method that supports the ciphertext equality test is considered as a promising way. Users are able to authorize the cloud server to conduct the ciphertext equality test, so that two ciphertexts can be determined whether they encrypt the same message without being decrypted. In this process, users do not need to retrieve, decrypt, and then perform comparison on data; thus, the computing and communication efficiency can be greatly improved, and the privacy of user data can be guaranteed at the cloud server side. However, existing encryption schemes supporting authorized ciphertext equality test in the single server model cannot resist the keyword guessing attacks, and the solutions in the dual server model do not provide simultaneous authorization on two servers. To address these issues, this paper proposes a public key encryption scheme supporting authorized equality test on ciphertexts in the dual server model (PKE-AUT), where the primary server and secondary server must get the authorization from users before performing a sequential equality test on ciphertexts. Security and performance analysis demonstrate that the proposed PKE-AUT scheme not only guarantees the privacy of user data and authorization but also is practical in cloud-assisted IoT-related applications.

Performance evaluation of IoT networks: A product density approach

Mobile cellular-based Internet of Things (IoT) networking is set to be enhanced with the addition of two important pillars of 5G — massive Machine Type Communications and ultra-Reliable Low Latency Communications. Temporal traffic uncertainty from diverse applications in IoT networks mandates novel modeling approaches for performance evaluation. This paper introduces a novel stochastic point process approach that can be used to evaluate the time-dependent performance of IoT base stations. Special correlation functions called Product Densities are used to (a) evaluate time-dependent offered traffic, and (b) analyze delay performance. These performance measures are evaluated at IoT base station for Poisson as well as non-Homogeneous Poisson (Beta distributed) traffic arrival processes suggested by 3rd Generation Partnership Project(3GPP). The Product Density estimates of offered traffic are found to be more accurate than the point-wise stationary approximation (PSA) under non-stationary traffic arrival rates. Results from the proposed analytical model are compared with results from a simulation of two queuing models of the base station; infinite server model for offered traffic and multi-server model for delay performance. Analytical results from Product Density functions also correlate with the simulation outcomes suggesting that the proposed Product Density technique is effective when modeling the time-dependent performance of IoT networks subjected to non-stationary traffic conditions, which reflects the real-world scenario.

Queueing-Theoretic Performance Analysis of a Low-Entropy Labeled Network Stack

Theoretical modeling is a popular method for quantitative analysis and performance prediction of computer systems, including cloud systems. Low entropy cloud (i.e., low interference among workloads and low system jitter) is becoming a new trend, where the Labeled Network Stack (LNS) based server is a good case to gain orders of magnitude performance improvement compared to servers based on traditional network stacks. However, it is desirable to figure out 1) where the low tail latency and the low entropy of LNS mainly come from, compared with mTCP, a typical user-space network stack in academia, and Linux network stack, the mainstream network stack in industry, and 2) how much LNS can be further optimized. Therefore, we propose a queueing theory-based analytical method defining a bottleneck stage to simplify the quantitative analysis of tail latency. Facilitated by the analytical method, we establish models characterizing the change of processing speed in different stages for an LNS-based server, an mTCP-based server, and a Linux-based server, with bursty traffic as an example. Under such traffic, each network service stage's processing speed is obtained by non-intrusive basic tests to identify the slowest stage as the bottleneck according to traffic and system characteristics. Our models reveal that the full-datapath prioritized processing and the full-path zero-copy are primary sources of the low tail latency and the low entropy of the LNS-based server, with 0.8%-24.4% error for the 9 9 t h percentile latency. In addition, the model of the LNS-based server can give the best number of worker threads querying a database, improving 2.1 × -3.5 × in concurrency.

Query Integrity Meets Blockchain: A Privacy-Preserving Verification Framework for Outsourced Encrypted Data

Cloud outsourcing provides flexible storage and computation services for data users in a low cost, but it brings many security threats as the cloud server may not be fully trusted. Previous secure outsourcing solutions mostly assume that the server is honest-but-curious while the adversary model of a malicious server that may return incorrect results is rarely explored. Moreover, with the increasing popularity of verifiable computations, existing verification schemes are yet not efficient and cannot cater to different scenarios in practice. In this paper, we propose a blockchain-based verifiable search framework in the adversarial cloud outsourcing context. When outsourcing the encrypted data to the cloud or Interplanetary File System (IPFS), we also store the encrypted data index in a decentralized blockchain (i.e., Ethereum in this paper) which is public and cannot be modified. Once a user is authorized, he/she can flexibly obtain the query results and efficiently check the query integrity via the pre-deployed smart contract, without the need of the data owner being online. Moreover, for user&#x0027;s privacy protection, we construct a stealth authorization scheme to deliver the access authorization without any identity disclosure. Finally, theoretical analysis and performance evaluation validate the security and efficiency of our proposed framework.

Toward Privacy-Preserving Aggregate Reverse Skyline Query With Strong Security

It has been witnessed that Aggregate Reverse Skyline (ARS) query has recently received a wide range of practical applications due to its marvelous property of identifying the influence of query requests. Nevertheless, the query users may hesitate to participate in such query services as the query requests and query results may leak sensitive personal data or valuable business data assets to the service providers. To tackle the concerns, a promising solution is to encrypt the query requests, conduct the ARS queries over encrypted query requests without decrypting, and return the encrypted query results. Unfortunately, many existing solutions are either deployed over a two-server model or unable to fully preserve query privacy. In this paper, we propose a novel privacy-preserving aggregate reverse skyline query (PPARS) scheme on a single server model while ensuring full query privacy. Specifically, we first transform the problem of ARS query into a combination of set membership test and logical expressions. Then, by employing the prefix encoding technique, bloom filter technique, and fully homomorphic encryption, we run the transformed logical expressions to obtain the encrypted aggregate values without leaking query requests, query results, and access patterns. Furthermore, we propose an interpolation-based packing technique to improve the communication efficiency of PPARS. Detailed and formal security analysis demonstrates that our proposed schemes can guarantee strong security. In addition, extensive experiments are conducted, and the results validate the efficiency of our proposed schemes.

SIMULATION MODELING AND OPTIMIZATION OF THE OPERATION OF A PARALLEL SERVER WITH FAILURES IN ANYLOGIC

Modern scientific research is increasingly raising issues of the processing of large amounts of data. The widespread use of client-server interaction technology and cloud computing at the moment raises questions about the efficiency of a parallel server, as well as the ability to predict results depending on the degree of load and characteristics of the equipment. This article simulates a parallel server with failures in the AnyLogic environment, and then performs multidimensional optimization by the weighted sum method. As part of the study, a simulation model of a queuing system with failures was built. It contains a server simulator, terminals, a failure simulator and statistics collection segments. The used parallel server model is abstract and rather generalized and makes it possible to concretize it by introducing additional dependencies and refining characteristics. The experiment with optimal parameters allowed to obtain the following gain in system efficiency indicators: processor load parameter (by memory) — a gain of 7%; processor load parameter (by load factor) — a gain of 8%; probability of terminal downtime — a gain of 5.7%; the failure rate of the main computer — 36 times less than the initial configuration; the number of interrupted programs — 7 less. In addition, it should be noted that the total number of completed requests remained at the same level — 462-465, for the reason that the intensity of the terminals did not vary. Since the results of replications (“runs”) are unique and the values of the optimized function vary for different replications, the built-in possibility of a variable number of replications (from 5 to 10) with a confidence probability of 95% and an error level of 0.5 was used. The obtained results suggest the possibility of further research of the model and its development in the AnyLogic environment.

Application of Queuing Theory and Management of Waiting Time Using Multiple Server Model: Empirical Evidence From Ahmadu Bello University Teaching Hospital, Zaria, Kaduna State, Nigeria

In many hospitals, time spent by the patients to get service in the healthcare facility is always the main problem faced by the health care providers. This trend is on the increase and it is a potential threat to healthcare services. In Nigeria, general hospitals with large number of patients have cases where patients may not be attended to on time. The consequences of keeping patients in a queue for too long in order to get medical care can cause a slew of problems or even lead to death. In this study, the multiple servers queuing models were used to analyze service efficiency of the Ahamadu Bello University Teaching Hospital Zaria, Kaduna State. Primary data was collected through observation and questionnaire methods at the hospital over a two-week period to determine the queuing model that minimizes patients waiting time. The results showed that a significant percentage of the patients were not satisfied with how queues management strategies put in place by the hospital. Regression analysis shows that all the dimensions of service quality have significant positive relationship with the patient’s satisfaction.

Real-time service integration of defense information system

This study designs a reference model of the Defense REST API server based on the representational state transfer (REST) architecture style to present the most efficient, stable, and sustainable technical criteria for real-time service integration of defense information systems in Korea. The purpose of this component is to provide evidence to be stipulated as part of the Korean Defense Ministry's instructions and regulations, such as the Defense Interoperability Management Directive and the Interoperability Guide, and to support the development of the National Defense Interworking Technology and Interoperability. As the defense information system was subdivided and developed by the army, navy, air force, or business functions, interworking between information systems has become one of the most important factors. However, despite the need for advanced service integration and interworking, various interconnection service modules based on enterprise application integration (EAI), EAI hubs, and spokes were developed at a level that met local requirements (simple data transmission) without specific criteria for each network or information system. As a result, most of the interconnection modules currently in operation suffer from the absence of a technical spectrum, such as not meeting the military's demands for real-time interconnection and service integration, which increases with time. Therefore, this study seeks to identify the above problems by integrating the defense information system into one service and presenting a reference model of the defense REST API server to meet various real-time interworking requirements, analyze the technical basis, and pursue a model that fits military reality.

A data-assisted first-principle approach to modeling server outlet temperature in air free-cooled data centers

The server outlet temperature is an important thermal condition to the operation of an air free-cooled data center that uses fans to continuously pass the outside air through the server room to cool the computing devices. However, the standard server’s management and monitoring tool cannot read the server’s built-in outlet temperature sensors fast enough to catch up the fast dynamics of the server outlet thermal condition caused by the changing server workload. Moreover, many server models do not have built-in sensors that can measure the server outlet temperature. In this paper, we develop a data-assisted first-principle model that leverages available built-in sensors and server’s operating monitoring tools to achieve low-latency estimation of the server outlet temperature. Specifically, the developed model takes the inlet and processor core temperatures, server’s fan speed, and processor utilization which are measured by hardware/software sensors as inputs to predict the outlet temperature with low latencies. Our extensive evaluation based on real data traces collected from a real air free-cooled data center testbed shows that our model can accurately predict the outlet temperature with an average root mean squared error ranging from 1.21 °C to 1.46 °C under various cold supply air temperatures and processor utilization levels.

Distributed Monitoring for Energy Infrastructures: A Two-Tier Analysis over Wireless Networks

Wireless networks (e.g., 5G networks) enable distributed energy infrastructures to be connected even when they are geometrically isolated. Intelligent monitoring from remote sites therefore becomes possible, allowing decision makers to examine the status of distributed energy infrastructures from a central location. The major challenge is when local devices cannot perform the monitoring independently; transmitting every signal back to the central server triggers enormous amounts of wireless communication. To address this, we propose a two-tier AI system by offloading computations to multiple devices. Specifically, we build lightweight AI models for deployment on edge clients (i.e., edge sensors) and a large-scale AI model for the central server. These two types of AI models are trained with different criteria: the models on the edges act as the filtering tools to detect abnormal events and maximally avoid making false negative predictions, whereas the server model is supposed to be an expert for accurate predictions. By validating on a power theft dataset, we show that such a cascading methodology could filter out sufficient negative examples on the edge side while still being able to provide precise predictions on the second-round analysis.

Automatic Algorithm for Fractal Plant Art Image Similarity Feature Generation

With the popularity of smart devices and the Internet, the volume of multimedia data is growing rapidly, and content-based image retrieval (CBIR) can search for similar images from large-scale images to realize the utilization of the data. For data owners, outsourcing the management and maintenance of image data to cloud service providers can effectively reduce costs, but there is a privacy leakage problem. In this paper, we focus on image feature extraction, index design, and image similarity recognition methods under a dual server model with content-based image security similarity recognition as the research topic, the work done such as proposing a BOVW (Bag of Visual Word) feature-based image security similarity recognition scheme. The scheme combines SIFT (scale-invariant feature transform) feature secure extraction and locally sensitive hashing algorithm to achieve secure extraction of BOVW features of images. To protect the BOVW features of images, an inverted index based on word frequency division is designed, the index is stored in chunks, and an image secure similarity recognition scheme based on CNN (convolutional neural networks) features is proposed. The scalable hash index based on dimensional division is designed based on the image CNN features secure extraction algorithm. The security and performance of the proposed scheme are theoretically analyzed and experimentally verified. Based on different image datasets, the impact of different parameters on the performance of the scheme is tested, and optimized parameters are given. The experimental results show that the proposed scheme in this paper can effectively improve the efficiency of analyzing the similarity of plant botanical art images compared to the existing schemes.

Real-Time Facial Emotion Recognition Framework for Employees of Organizations Using Raspberry-Pi

There is a significant interest in facial emotion recognition in the fields of human–computer interaction and social sciences. With the advancements in artificial intelligence (AI), the field of human behavioral prediction and analysis, especially human emotion, has evolved significantly. The most standard methods of emotion recognition are currently being used in models deployed in remote servers. We believe the reduction in the distance between the input device and the server model can lead us to better efficiency and effectiveness in real life applications. For the same purpose, computational methodologies such as edge computing can be beneficial. It can also encourage time-critical applications that can be implemented in sensitive fields. In this study, we propose a Raspberry-Pi based standalone edge device that can detect real-time facial emotions. Although this edge device can be used in variety of applications where human facial emotions play an important role, this article is mainly crafted using a dataset of employees working in organizations. A Raspberry-Pi-based standalone edge device has been implemented using the Mini-Xception Deep Network because of its computational efficiency in a shorter time compared to other networks. This device has achieved 100% accuracy for detecting faces in real time with 68% accuracy, i.e., higher than the accuracy mentioned in the state-of-the-art with the FER 2013 dataset. Future work will implement a deep network on Raspberry-Pi with an Intel Movidious neural compute stick to reduce the processing time and achieve quick real time implementation of the facial emotion recognition system.

Creating One Time Password Programming for the Security of Data for Remote Location Treatment in Body Area Network

Wireless body area network can manage patient through doctors through app on smart phones and security to data can be given by one-time password (OTP generation) like banking system. 6G protocols can make one time password more effective as all financial technology are dependent on one time password. Because transfer is mutual and one time password is best secure handshake between servers and containers are best security tempo for client server programmers. It depends on size of transfer means in the form of money in the transaction that size of container security code used for three way handshakes like third party or on the emergency for group client server model. Control logic is conditional enforcement of profitable rules to follow that is word access in computer science and that in the form of Intel processors till now but these Intel processors are in production in as general for as possible means one kind of Intel processor for whole world as processor generation paradigm. Can finance technological companies is eligible for creating more secure hardware for their trillions of transactions, I think word trillion is taken from word truth. Smartphone can handle data from sensor on body sensor through wireless with in distance limit and as data get uploaded it can reach to doctor by protecting it through OTP. In this as banking system can depend on OTP in the same way wireless health system may depend on this system.

Availability and maintenance modeling of a batch service queuing system

PurposeThe purpose of the paper is to analyze reliability characteristics of batch service queuing system with a single server model that envisages Poisson input process and exponential service times under first come, first served (FCFS) queue discipline.Design/methodology/approachWith the help of renewal theory and stochastic processes, a model has been designed to discuss the reliability and its characteristics.FindingsThe instantaneous and steady-state availability along with the maintenance model of the systems subject to generalized M/Mb/1 queuing model is derived, and a few particular cases for availability are obtained as well. For supporting the developed model, a case study on electrical distribution system (EDS) has been illustrated, which also includes a comparison for the system subject to M/Mb/1 queuing model and the system without any queue (delay).Originality/valueIt is a quite realistic model that may aid to remove congestion in the system while repairing.