Data Transmission Process Research Articles

Cooperative Perception With V2V Communication for Autonomous Vehicles

Occlusion is a critical problem in the Autonomous Driving System. Solving this problem requires robust collaboration among autonomous vehicles traveling on the same roads. However, transferring the entirety of raw sensors' data among autonomous vehicles is expensive and can cause a delay in communication. This paper proposes a method called Realtime Collaborative Vehicular Communication based on Bird's-Eye-View (BEV) map. The BEV map holds the accurate depth information from the point cloud image while its 2D representation enables the method to use a novel and well-trained image-based backbone network. Most importantly, we encode the object detection results into the BEV representation to reduce the volume of data transmission and make real-time collaboration between autonomous vehicles possible. The output of this process, the BEV map, can also be used as direct input to most route planning modules. Numerical results show that this novel method can increase the accuracy of object detection by cross-verifying the results from multiple points of view. Thus, in the process, this new method also reduces the object detection challenges that stem from occlusion and partial occlusion. Additionally, different from many existing methods, this new method significantly reduces the data needed for transfer between vehicles, achieving a speed of 21.92 Hz for both the object detection process and the data transmission process, which is sufficiently fast for a real-time system.

Design and Implementation of Ground Meteorological Data Storage Alarm System for Other Provinces

The provincial information center receives meteorological data from other provinces through satellite broadcasts. In response to the low efficiency and inability to promptly address issues such as data nonexistence and data loss caused by malfunctions in the manual monitoring mode, an analysis of the data transmission process and user access methods was conducted. Based on the Python language, a ground meteorological data storage alarm system was developed for the Northwestern region. This system enables real-time monitoring of the storage status of hourly ground data, facilitates the generation of statistical reports on missing station information within any given time frame, and promptly sends alarm notifications via enterprise WeChat. The results indicate that this system assists operational and maintenance personnel in promptly identifying and addressing faults, ensuring the orderly and timely storage of meteorological data from the Northwestern region into the local database and thereby providing users with higher-quality data services.

Secure and efficient data transmission based on quantum dialogue with hyperentangled states in cloud office

Quantum cloud computing facilitates the rapid processing of vast amounts of data and provides robust network services. To enhance the efficiency and security of data transmission in cloud computing, this study proposes a quantum cloud office model and presents a novel quantum dialogue protocol particularly designed for this model. The proposed protocol enables authenticated communication among office correspondents. Leveraging the unique properties of multiple quantum degrees of freedom, this protocol expands the channel capacity and enhances transmission efficiency. Compared to previous measurement-device-independent quantum dialogue (MDI-QD) protocols, this protocol not only further improves transmission efficiency but also enables mutual authentication between correspondents, effectively defending against the risk of man-in-the-middle attacks. The utilization of a cross-Kerr medium for Bell measurements offers a 50% increase in efficiency compared to linear optical equipment. This novel protocol is tailored to the data transmission process within the cloud office model, thereby enhancing the communication efficiency and reinforcing secure data transmission.

A security optimization scheme for data security transmission in UAV-assisted edge networks based on federal learning

To solve the problem that mobile users cannot handle all data tasks by themselves due to the huge amount of data at present, this paper proposes a four-layer model of efficient and secure multiuser multitask computing offload. Since the user needs to offload the data to the server for processing through the wireless communication link, and users have mobility, it is difficult to achieve dynamic updates of user status and data information based on traditional cloud computing data processing methods, and it is difficult to ensure security during data unloading. In this paper, we studied how to realize the efficient and safe transmission of mobile users' data tasks when deploying Mobile Edge Computing (MEC) servers with artificial intelligence to assist in data processing on Unmanned Aerial vehicles (UAVs). First of all, to ensure the efficient use of computing resources, we use compression algorithms to reduce transmission overhead. Secondly, to enhance the security of the data transmission process, we have designed a security layer in the system model, which mainly uses secure multiparty technology to encrypt sensitive data, and a data transmission algorithm based on artificial intelligence is designed to assure the efficient and secure transmission of data in the network. Finally, through the analysis of the simulation results, we can know that the proposed strategy in this study has better performance than other existing strategies.

Design of information security transmission scheme and related authentication services based on Kerberos

A plethora of cloud service companies have sprung up in response to the growing amount of data. Data security protection solutions have been proposed by service providers. As a result, a rising number of people are using the cloud to store their own data. However, as technology advances and hackers' capabilities increase, service providers' protection mechanisms become less effective, and data security cannot be guaranteed. We review the logic of the Kerberos algorithm and discuss the work of each part in this paper. This document also includes and discusses difficulties with Kerberos, as well as improvement strategies.The main focus will be on strengthening the security of the Kerberos secure communication process, which will serve as a reference for Kerberos security transmission and authentication research. It solves the problem of the symmetric encryption algorithm's relatively low security in the authentication process by replacing it with the RSA one-time encryption algorithm; in addition, the Kerberos authentication protocol is added between the client and the cloud service provider to secure the data transmission process. Furthermore, this paper explains some basic authentication principles.

Data Prediction-Based Energy-Efficient Architecture for Industrial IoT

This paper presents an energy-efficient industrial Internet of Things (IIoT) architecture that minimizes the data transmission process based on sensor data prediction. While current IIoT network implementations aim to improve lifetime and reduce maintenance costs, existing data prediction studies have primarily focused on prediction performance, disregarding computing time and energy efficiency. In this paper, we propose a data prediction approach on the base station side to maximize the energy efficiency of sensor nodes. A fast deep learning (DL) model is required to achieve low-latency network communications. Therefore, we exploited a DL-based multi-layer perceptron (MLP) with a deep concatenation method called DC-MLP to ensure data prediction reliability and fast computing time. To demonstrate its robustness, we evaluate the proposed DC-MLP model using six performance metrics with k-fold cross-validation. We varied the sampling rate for data prediction to demonstrate the effectiveness of prediction accuracy and energy efficiency. The performance evaluation results revealed that the proposed architecture successfully reduced energy consumption by up to 33% compared with traditional data transmission while maintaining reliable sensor data and achieving an 81% faster prediction time than existing DL models. Based on these findings, the application of the proposed DC-MLP has the potential to increase the sensor lifetime while satisfying the rigorous requirements of the industrial sector, such as fast prediction times, energy efficiency, and reliable prediction results.

PPO2: Location Privacy-Oriented Task Offloading to Edge Computing Using Reinforcement Learning for Intelligent Autonomous Transport Systems

AI-empowered 5G/6G networks play a substantial role in taking full advantage of the Internet of Things (IoT) to perform complex computing by offloading tasks to edge services deployed in intelligent transport systems. However, offloading behavior has a certain regularity, and the real-time location of users can easily be inferred by attackers who have historical user data during the data transmission process. To address this problem, a privacy-oriented task offloading method that can resist attacks from privacy attackers with prior knowledge is proposed. First, the local computing model, channel model, and privacy loss model are defined and used to quantify evaluation indicators, such those related to privacy, time, and energy. Among them, privacy loss is formalized as the probability of a successful attack by an attacker with prior knowledge. Second, the process of solving an optimal task offloading decision problem is formalized into a Markov decision process (MDP). Finally, the deep reinforcement learning (DRL) method PPO2 is proposed to solve the planning problem of task offloading with good generalization and convergence speed, where we focus on the location privacy requirement. Experiments show that our method can handle large-scale task offloading and obtain offloading policies with reduced privacy loss, energy consumption and time delays.

INTERNET OF THINGS DATA TRANSFER METHOD USING NEURAL NETWORK AUTOENCODER

Background. The number of devices in the Internet of Things is constantly increasing. At the same time, the number of solutions on the market for such technologies is growing. Statistics confirm that these factors lead to an increase in data transfer volumes. This raises the number of resources spent on data transmission. The growing trend in the number of users of the Internet of Things technology leads to the emergence of the problem of a rapid increase in the data transmitted by the network.
 Objective. The purpose of the paper is to improve the process of data transmission in the Internet of Things by modifying the neural network autoencoder to reduce network resources use.
 Methods. Analysis of publications dedicated to Internet of things data transmission. Integration of existing data coding solutions based on a neural network autoencoder in the process of transmitting data from the Internet of things.
 Results. The neural network autoencoder has been improved by using an algorithm that additionally includes an arithmetic encoder and further training a new model on the output of a full-fledged autoencoder.
 Conclusions. The process of data transmission in the Internet of Things network has been modified by improving the neural network autoencoder by using the training of a smaller neural network on the initial data of the main autoencoder, which has reduced the amount of data transmitted and, accordingly, reduced the use of network resources.

Multi-sensor network tracking research utilizing searchable encryption algorithm in the cloud computing environment.

Presently, the focus of target detection is shifting towards the integration of information acquired from multiple sensors. When faced with a vast amount of data from various sensors, ensuring data security during transmission and storage in the cloud becomes a primary concern. Data files can be encrypted and stored in the cloud. When using data, the required data files can be returned through ciphertext retrieval, and then searchable encryption technology can be developed. However, the existing searchable encryption algorithms mainly ignore the data explosion problem in a cloud computing environment. The issue of authorised access under cloud computing has yet to be solved uniformly, resulting in a waste of computing power by data users when processing more and more data. Furthermore, to save computing resources, ECS (encrypted cloud storage) may only return a fragment of results in response to a search query, lacking a practical and universal verification mechanism. Therefore, this article proposes a lightweight, fine-grained searchable encryption scheme tailored to the cloud edge computing environment. We generate ciphertext and search trap gates for terminal devices based on bilinear pairs and introduce access policies to restrict ciphertext search permissions, which improves the efficiency of ciphertext generation and retrieval. This scheme allows for encryption and trapdoor calculation generation on auxiliary terminal devices, with complex calculations carried out on edge devices. The resulting method ensures secure data access, fast search in multi-sensor network tracking, and accelerates computing speed while maintaining data security. Ultimately, experimental comparisons and analyses demonstrate that the proposed method improves data retrieval efficiency by approximately 62%, reduces the storage overhead of the public key, ciphertext index, and verifiable searchable ciphertext by half, and effectively mitigates delays in data transmission and computation processes.

A secure emotion aware intelligent system for Internet of healthcare

Emotional aware intelligent healthcare mechanism is seen as one of the emergent techniques that can be used to overcome the traditional communication issues like storage overhead and delay in transmission process in healthcare systems. It is a smart technique primarily used to ensure faster connectivity and transmission as well as healthcare data processing through various heterogeneous networks by understanding human emotions. Researchers have focused on various processing and transmitting techniques of emotional aware intelligent systems. However, very few of them have focused on the need for security while transmitting or reading the patients’ record. The aim of this paper is to propose a secure and transparent communication mechanism in intelligent emotional aware systems using Analytical Hierarchical Process (AHP), blockchain system and a mathematical model. AHP and the mathematical model are used to ensure accuracy during the data transmission process by analysing the legitimacy of each device. In addition, the identity based trust and blockchain mechanism is used to ensure the continuous analysis and transparency among network entities while transferring the information. The proposed framework is further analysed against existing models over various security matrices such as data alteration, report generation, accuracy etc. The out performance of proposed mechanism is approximate 89% as compare to existing mechanism.

РОЗРОБКА МОБІЛЬНОЇ КІБЕРФІЗИЧНОЇ СИСТЕМИ ІНТЕЛЕКТУАЛЬНОГО МОНІТОРИНГУ КЛІМАТИЧНИХ ПАРАМЕТРІВ

The development of Mobile Cyber-Physical Systems (MCPS) is a promising research and development direction for many industries, such as manufacturing, healthcare, home automation, and many others. That is why a prototype MCPS based on a smartphone has been developed to collect, process, and transmit data from various devices and sensors in real-time, anywhere. The architecture of a mobile cyber-physical system for monitoring climatic parameters with telegram notifications has been designed. An algorithm for intelligent analysis and optimization of data transmission processes has been proposed for the developed MCPS prototype. The advantage of the developed system is the ability to determine the priority of monitoring parameters, which allows for a quick response to critical temperature changes at the object where the measurements are taken. Additionally, a unique method for measuring end-to-end data transmission delay using a timestamp in the packet header metadata has been implemented. This method enables the determination of the processing time of each component of the MCPS and, in case of exceeding the norms, automatically notifies about the necessary control decisions. Supporting such a method in MCPS is a particularly effective solution for monitoring the quality of real-time service delivery in critical infrastructure objects. Based on the conducted research, it has been established that the proposed algorithm for intelligent data analysis and optimization has reduced the number of messages by 3 times and the amount of transmitted information by 2.3 times. In the future, the developed system, in combination with artificial intelligence, will ensure reliable and high-quality data transmission even in unpredictable situations, making it a promising solution for improving the quality of life and the efficiency of smart infrastructures in various fields.

Spatiotemporal traffic data imputation by synergizing low tensor ring rank and nonlocal subspace regularization

AbstractSpatiotemporal traffic data usually suffers from missing entries in the data acquisition and transmission process. Existing imputation methods only consider the global/local structure of spatiotemporal traffic data, resulting in insufficient estimation performance. Fortunately, it is found that traffic data admits the nonlocal self‐similarity (NSS) prior. This paper incorporates the global and nonlocal low‐rank priors of traffic data and proposes a tensor completion model for spatiotemporal traffic data imputation. To be specific, the proposed method uses tensor ring (TR) decomposition with an enhanced representation capability to characterize the global low‐TR‐rank prior of traffic data, e.g. the correlation of sensor and time modes of the tensor (i.e. traffic data). An implicit plug‐and‐play (PnP)‐based regularization is further utilized to exploit the NSS prior, which depicts the nonlocal similar traffic data patterns. Furthermore, the proximal alternating minimization algorithm under the PnP framework is derived to solve this model. The experiment results on various datasets and missing scenarios show the superiority of the proposed model.

Blockchain-Based Data Access Control and Key Agreement System in IoT Environment.

Recently, with the increasing application of the Internet of Things (IoT), various IoT environments such as smart factories, smart homes, and smart grids are being generated. In the IoT environment, a lot of data are generated in real time, and the generated IoT data can be used as source data for various services such as artificial intelligence, remote medical care, and finance, and can also be used for purposes such as electricity bill generation. Therefore, data access control is required to grant access rights to various data users in the IoT environment who need such IoT data. In addition, IoT data contain sensitive information such as personal information, so privacy protection is also essential. Ciphertext-policy attribute-based encryption (CP-ABE) technology has been utilized to address these requirements. Furthermore, system structures applying blockchains with CP-ABE are being studied to prevent bottlenecks and single failures of cloud servers, as well as to support data auditing. However, these systems do not stipulate authentication and key agreement to ensure the security of the data transmission process and data outsourcing. Accordingly, we propose a data access control and key agreement scheme using CP-ABE to ensure data security in a blockchain-based system. In addition, we propose a system that can provide data nonrepudiation, data accountability, and data verification functions by utilizing blockchains. Both formal and informal security verifications are performed to demonstrate the security of the proposed system. We also compare the security, functional aspects, and computational and communication costs of previous systems. Furthermore, we perform cryptographic calculations to analyze the system in practical terms. As a result, our proposed protocol is safer against attacks such as guessing attacks and tracing attacks than other protocols, and can provide mutual authentication and key agreement functions. In addition, the proposed protocol is more efficient than other protocols, so it can be applied to practical IoT environments.

Multipath Routing Implementation in SD-IoT Network Using OpenFlow-based Routing Metrics

The implementation growth of the Internet of Things (IoT) may increase the complexity of the data transmission process between smart devices. The route generation process between available nodes on the network will burden the intermediary node. One of the possible solutions for resolving the problem is the integration of Software Defined Networks and IoT (SD-IoT) to provide network automation and management. The separation of networking control and data forwarding functions may provide a multipath delivery path between each node in the IoT environment. In addition, the controller can directly extract the resource usage of the intermediary devices, which can be utilized as the routing metric variable in order to maintain the resource utilization on the intermediary devices. Instead of using traditional routing, this paper aims to develop multipath routing based on Deep First Search (DFS) and Dijkstra algorithms for acquiring an efficient path using OpenFlow-based routing metrics. The traffic monitoring module delivered the metrics extraction process, which obtained the variables using Port and Aggregate Flow Statistic features. The metrics calculation aimed to provide the multipath, which was constructed based on switches resource usage. Each selected path was chosen based on the smallest cost and probability provided by the group table feature in OpenFlow. The results showed that the Dijkstra algorithm could create the multipath more swiftly than DFS with a time difference of 0.6 s. The Quality of Service (QoS) results also indicated that the proposed routing metric variables could maintain the transmission process efficiently.

Optimization of BBR Congestion Control Algorithm Based on Pacing Gain Model.

In 2016, Google proposed a congestion control algorithm based on bottleneck bandwidth and round-trip propagation time (BBR). The BBR congestion control algorithm measures the network bottleneck bandwidth and minimum delay in real-time to calculate the bandwidth delay product (BDP) and then adjusts the transmission rate to maximize throughput and minimize latency. However, relevant research reveals that BBR still has issues such as RTT unfairness, high packet loss rate, and deep buffer performance degradation. This article focuses on its most prominent RTT fairness issue as a starting point for optimization research. Using fluid models to describe the data transmission process in BBR congestion control, a fairness optimization strategy based on pacing gain is proposed. Triangular functions, inverse proportional functions, and gamma correction functions are analyzed and selected to construct the pacing gain model, forming three different adjustment functions for adaptive adjustment of the transmission rate. Simulation and real experiments show that the three optimization algorithms significantly improve the fairness and network transmission performance of the original BBR algorithm. In particular, the optimization algorithm that employs the gamma correction function as the gain model exhibits the best stability.

Development of methods for managing energy consumption and energy efficiency in a common system

<span lang="EN-US">The work aims to analyze and examine renewable energy sources (RES) to develop interconnected energy efficiency and energy consumption management system by integrating the software-defined machine-to-machine (M2M) communication. The article’s objectives include analysis of using RES as alternative raw materials for electricity production, the study of intelligent technologies for integrating RES into monitoring and control systems, research of devices and methods for monitoring energy production and consumption, analysis of sensor application for automation of control systems in the energy sector, a study of data transmission and information processing rates. The study results showed that the data transfer rate was delayed by 6 seconds to process 1,000 MB of information. It has been proven that wind energy can be used most efficiently within a 12-hour daily cycle, in contrast to tidal energy and solar energy. It is shown that due to the cyclical nature of obtaining energy from renewable sources, they do not fully provide energy to a large city, on the basis of which it is necessary to additionally use other energy sources. Three different types of power generation facilities were examined and compared. Wind farms were found to have the highest potential for electricity generation, amounting to 1,600-1,700 kW.</span>

A modified whale‐dragonfly algorithm and self‐adaptive cuckoo search‐based clustering strategy for augmenting network lifetime in wireless sensor networks

SummaryDesigning an energy efficient and durable wireless sensor networks (WSNs) is a key challenge as it personifies potential and reactive functionalities in harsh antagonistic environment at which wired system deployment is completely infeasible. Majority of the clustering mechanisms contributed to the literature concentrated on augmenting network lifetime and energy stability. However, energy consumption incurred by cluster heads (CHs) are high and thereby results in minimized network lifetime and frequent CHs selection. In this paper, a modified whale‐dragonfly optimization algorithm and self‐adaptive cuckoo search‐based clustering strategy (MWIDOA‐SACS) is proposed for sustaining energy stability and augment network lifetime. In specific, MWIDOA‐SACS is included for exploiting the fitness values that aids in determining two optimal nodes that are selected as optimal CH and cluster router (CR) nodes in the network. In MWIDOA, the search conduct of dragon flies is completely updated through whale optimization algorithm (WOA) for preventing load balancing at CHs. It minimized the overhead of CH by adopting CHs and CR for collecting information from cluster members and transmitting the aggregated data from CHs to the base station (BS). It included self‐adaptive cuckoo search (SACS) for achieving sink mobility using radius, energy stability, received signal strength, and throughput for achieving optimal data transmission process after partitioning the network into unequal clusters. Simulation experiments of the proposed MWIDOA‐SACS confirmed better performance in terms of total residual energy by 21.28% and network lifetime by 26.32%, compared to the competitive CH selection strategies.

The error rate analyze and parameter measurement on LoRa communication for health monitoring

During LoRaWAN data transmission, the Bit Rate transmitted from the transmitter to the Receiver experiences attenuation from the object being passed, so Packet Loss (Bytes) may occur due to diffraction, scattering, and reflection in the LoRa data transmission process. LoRa data that has errors is researched and further developed with a formula and method to minimize data rate errors in LoRa communication. Analysis development lies in the gateway's ability to accommodate LoRa data from end nodes. This condition requires a development method of Adaptive Data Rate, which is helpful for Battery Power Saving or power consumption of sensor nodes, especially in data communication with LoRa servers, i.e., the uplink and downlink processes that are correct, regular, and on-time. We use pulse data in the existing case examples to monitor the patient's health status. This data is applied to elderly patients and patients from victims of natural disasters. The development is the use of extra small and comfortable LoRa for all patients in the hospital. One of the mechanism developments is Blind-ADR, which is used for unpredictable Mobile LoRa data transmission, e.g., Blind-ADR is intended to monitor the movement of the patient's changing position. This research also examines the effects of increasing the number of end-nodes that cause health data collisions and the mechanisms used to avoid collisions, including the Adaptive Data Rate.

SCDAP –secured cluster based data aggregation protocol for energy efficient communication in wireless sensor networks

A network of real time devices that can sense and transmit the information from the deployed environment by using multi hop communication is called as Wireless Sensor Network (WSNs). Despite the rapid advancement of WSN, where an increasing number of physical devices so called as sensors nodes are connected with each other, providing the improved security with optimized energy consumption during data transmission, communication and computation remains huge challenge. In wireless sensor networks, numerous sensor nodes are deployed in the physical environment to sense and collect the required information from the given environment. The sensed information is needed to be transmitted from the nodes to the control station in an energy efficient manner. Data aggregation is one kind of techniques which will optimize the energy usage in wireless sensor networks during the data transmission. In data aggregation, the unnecessary data is removed which will significantly reduce energy of the nodes during data transmission. However, collected data during the data aggregation should be completely protected and there are various threats that can be launched by the intruders to carry out unauthorised data access and can cause threat to the integrity of the network. Therefore, ensuring data security during the data aggregation process is very important and essential for the security of the network. In this paper, a Secure Cluster based Data Aggregation Protocol (SCDAP) have been proposed to provide better security through secure authentication and verification process, and to reduce overall energy consumption of the network by implementing secure clustering process to eliminate the redundant data in the network. Moreover, the proposed system is more efficient in generating public and private keys for effective and secure data transmission and verification process. The proposed system is experimentally tested in NS-3 tool and proves that the proposed system reduces high energy consumption, computational and communicational cost, end-to-end delay and improves the packet delivery ratio. Moreover, the proposed system provides better security in the network when compared to other existing systems during the data aggregation.

CAP2M..÷Contingent Anonymity Preserving Privacy Method for the Internet of Things Services

The Internet of Things (IoT) consists of a collection of inter-connected devices that are used to transmit data. Secure transactions that guarantee user anonymity and privacy are necessary for the data transmission process. One of the major constraints of IoT Healthcare is the issue of maintaining security. These problems are addressed in this study by the Contingent Anonymity-Preserving Privacy Method (CAP2M), which takes into account the various security needs. The CAP2M method uses the session deployment and user requirements to preserve user privacy. The initial and final privacy settings are modified in the session deployment depending on the session interval and the service provider's recommendation. Federated learning is employed for training privacy settings toward the session's final interval. The suggested approach maintains privacy on various sharing intervals with minimum computation complexity. In the privacy-preserving process, two-factor authentication is used for concealing the sharing and shared ends. The performance is validated using the metrics failure, complexity, and latency.