Secure sharing method of network data transmission based on multi-layer encryption algorithm

RCCM: A Novel Method for Data Transmission in Vehicular Sensor Networks

The paper proposes an effective method of data transmission over New Radio (NR) Uplink (UL) channel to improve coverage of 5G wireless communication systems. This method is based on the dedicated time-frequency allocation pattern that makes it possible to combine inter-slot frequency hopping (FH) and demodulation reference signals (DMRS) bundling for joint channel estimation in a newly developed method of multi-slot data transmission over UL channel of 5G NR networks. The proposed approach demonstrates the improvement of coverage by utilizing the benefit of both mentioned coverage enhancement techniques and, therefore, may be recommended for application in future 5G communication networks. The performance improvement, when using the proposed algorithm, was tested using the link level simulator of the communication system based on the 3rd Generation Partnership Project (3GPP) standard for 5G NR mobile networks.

This paper presents an approach of noise-immune data transmission in wireless sensor networks using Frequency-Hopping Spread Spectrum. Redundancy of Residue Number System codes is explored, and adaptive errors correction system is proposed without code algorithm changing by reduction of information modules number or increasing of reference modules number.

The traffic of mobile network environment is increasing day by day. When a large amount of data need to be transmitted, the low-speed wireless network today is hard to meet users' needs. Long response time of operation and high requirements of terminal's performance will keep user annoy and away. Existing independent data compression and transmission algorithm can't solve this problem effectively. In this paper, data compression and transmission considered as a whole, a new kind of data compression and transmission method was designed in this paper to solve the problem and achieved good results finally.

In recent times, a huge amount of data is being created from different sources and the size of the data generated on the Internet has already surpassed two Exabytes. Big Data processing and analysis can be employed in many disciplines which can aid the decision-making process with privacy preservation of users’ private data. To store large quantity of data, Geo-Distributed Data Centres (GDDC) are developed. In recent times, several applications comprising data analytics and machine learning have been designed for GDDC. In this view, this paper presents a hybrid deep learning framework for privacy preservation in distributed DCs. The proposed model uses Deep Neural Network (DNN) for the feature extractor and classifier operations. In addition, Siamese training method is applied to fine-tune the prevention of secondary inference on the data. Moreover, gradient descent approach is employed to reduce the loss function of the DNN model. Furthermore, Glow-worm Swarm Optimization (GSO) algorithm is utilized to fine tune the hyperparameters of the DNN model to improve the overall efficiency. The proposed model is executed on a Hadoop based environment, i.e., Hadoop Distributed File System (HDFS), which has two nodes namely master node and slave nodes. The master node is considered as the main user node to get the services from the service provider. Every slave node behaves as per master node’s instruction for data storage. In order to validate the enhanced performance of the proposed model, a series of simulations take place and the experimental results demonstrate the promising performance of the proposed model. The simple technique has reached a maximum gender recognition accuracy of 95, 90 and 79 on the applied data 1, 2 and 3 respectively. Also, the reduced simple approach has attained reduced gender recognition with the accuracy of 91, 84 and 74 on the applied data 1, 2 and 3 respectively.

This paper shows a development of an RFID (Radio Frequency IDentification) user authorization (access control system) using wireless ZigBee communication network. The system is designed with the topology of one Master and multiple Slave nodes. Idea is to manage a data base on the Master node which is connected to the Slave nodes through a wireless link. Slave node consists of an RFID reader, LCD touch screen and XBee wireless module. Master node communicates with the LabView application through an Ethernet interface. A program development is shown for both nodes, including all the peripheral modules. The proposed system's major advantage over the existing systems is wireless communications. Wireless network uses XBee wireless modules and can be formed as a mesh network, where an end node server as a router, thus extending the range of the network.

In a sensor network, as we increase the number of nodes, the requirements on network lifetime, and the volume of data traffic across the network, it is often efficient to move towards hierarchical network architectures (e.g., see [5]). In such hierarchical networks, sensor nodes are clustered into groups, and their roles are divided into master and slave nodes for more efficient structuring of network traffic. The opera tional complexity of each sensor node and the amount of data to be transmitted across sensor nodes strongly influence the energy consump tion of the nodes, which ultimately determines the network lifetime. This paper provides a new way of reducing data traffic across nodes by determining and exploiting the lowest data token delivery points within an application graph that is distributed across a network. The technique divides an application graph into two sub-graphs and then distributes each divided subgraph over a master node and its associated slave nodes. The buffer costs of the graph edges over the cutting line corre sponds to the amount of data to be transmitted between nodes after allo cating the two partial subgraphs such that one subgraph executes on a master node, and the other subgraph is distributed across the associated slave nodes. Since the energy consumption on each node is dominated by the transceiver, the reduced data traffic allows for reducing the turn-on time of the transceivers, and thereby leads to high energy savings. This technique also distributes the workload of sensor nodes in a sys tematic manner. The more balanced workload also contributes to effi cient battery usage, and also improves the latency for processing the data frames captured by the sensor nodes.

This Letter investigates and reports on a number of activity recognition methods for a wearable sensor system. The authors apply three methods for data transmission, namely 'stream-based', 'feature-based' and 'threshold-based' scenarios to study the accuracy against energy efficiency of transmission and processing power that affects the mote's battery lifetime. They also report on the impact of variation of sampling frequency and data transmission rate on energy consumption of motes for each method. This study leads us to propose a cross-layer optimisation of an activity recognition system for provisioning acceptable levels of accuracy and energy efficiency.

This article provides a detailed comparative analysis of various methods of data transmission in telemedicine systems in order to determine their effectiveness, reliability and suitability for various medical applications. Wired and wireless data transmission technologies are analyzed, namely, their features, advantages and disadvantages in the context of application in telemedicine systems are investigated.

The problem of designing an effective models and methods for data transmission between group of microsatellites and network of ground stations in the dynamically changing environment is considered. Multi-agent technology for solving the problem by adaptive resource allocation and scheduling is proposed. It is shown that solution of the considered complex problem evolutionary emerges from interaction and trade-offs of many agents which continuously self-organize themselves and change decisions to improve their objectives and the objectives of the system as a whole. The advantages of multi-agent solution are high adaptability, flexibility and efficiency of services. The main classes of agents, ontology of problem domain, interaction protocols, results of first experiments with system prototype and key benefits of proposed system are discussed.

In recent years, intelligent manufacturing has developed rapidly. Industrial IoT technologies are applied to different industrial scenarios. OPC UA is a service-oriented architecture, which has good interoperability. At the same time, it is a cross-platform communication standard that can realize the interconnection between heterogeneous network devices and solve the problem of information silos in the industrial IoT field. However, with the gradual increase of industrial equipment, the amount of data transmission in the network is increasing. The traditional OPC UA communication method based on client/server mode has defects such as tight coupling and performance bottleneck. It cannot meet the high throughput demand of the network. Therefore, these phenomena lead to longer time delays and lower transmission efficiency of network communication systems. To solve the above problems, this paper proposes a publish/subscribe communication model based on OPC UA. We design the overall architecture of the OPC UA publish/subscribe model and adopt a message agent mechanism to realize distributed communication of OPC UA. This model has functions such as message modeling, address space construction, and publish/subscribe. The architecture of this communication model is compatible with the C/S model, which can ensure compatibility and coexistence with the traditional OPC UA communication system. Meanwhile, in order to improve the distinguished service capability of the OPC UA system, a multi-priority data scheduling algorithm is proposed and integrated into the publish/subscribe communication model to improve the efficiency of real-time data transmission in industrial networks. The experimental results show that the communication model can accomplish distributed communication in industrial networks and be better applied in wireless sensor networks. The scheduling algorithm included in the model significantly improves the efficiency of real-time data transmission and reduces the time delay.

On extensive agricultural land, a system for monitoring the quality of land that is spread over many points and uses data communication wirelessly needs to be made. This will facilitate the initial installation and maintenance of the system. This study aims to create a pH and humidity monitoring system for agriculture’s soil with wireless sensor network technology based on internet of things (IoT). This system consists of two slave nodes and one master node. The master node and slave node use the RF433MHz radio module as a communication tool. Each slave node consists of a soil pH sensor and YL69 sensor to measure soil moisture. All data for each slave node is sent to the master node to be processed and then sent to the database using the Ethernet shield. Data contained in the database will be displayed on a web application that can be accessed anywhere. The maximum range of RF433MHz radio modules in open spaces is capable of receiving data from the transmitter at 50 meters. The monitoring system able to display pH and soil moisture values in real-time with an average error value of the soil pH sensor which is equal to 1.66% and YL69 sensor error average is 1% compared to commercial soil analyzer.

Serverless computing has gained widespread popularity due to its rapid deployment, elastic scaling, and pay-as-you-go advantages. Large-scale pipeline applications designed for big data processing, video analytics, and machine learning are gradually evolving toward the serverless architecture. However, the inefficiency of large data transmission among functions has limited the further adoption of the serverless paradigm in these applications. With the emergence of capability Instruction Set Architecture (ISA), the multi-tenant security isolation model and data transmission methods in the cloud have ushered in new opportunities. This paper proposes Hummer, an efficient communication and data transmission scheme based on the capability ISA. The capability ISA allows multiple functions to run in separate isolation domains in a single address space. Function instances can efficiently share memory by directly passing hardware capabilities. This paper extends the CHERI capability ISA, introducing the novel shared capability and shared capability store tracking (SCST) mechanism to manage the shared memory access rights, enabling a zero-copy communication mechanism. We compare Hummer with existing communication mechanisms and show that it improves data transmission rate by 6 – 40 ×. Redis implemented with Hummer APIs doubles its throughput.

The aim of this review was to assess the current viable technologies for wireless power delivery and data transmission through metal barriers. Using such technologies sensors enclosed in hermetical metal containers can be powered and communicate through exterior power sources without penetration of the metal wall for wire feed-throughs. In this review, we first discuss the significant and essential requirements for through-metal-wall power delivery and data transmission and then we: (1) describe three electromagnetic coupling based techniques reported in the literature, which include inductive coupling, capacitive coupling, and magnetic resonance coupling; (2) present a detailed review of wireless ultrasonic through-metal-wall power delivery and/or data transmission methods; (3) compare various ultrasonic through-metal-wall systems in modeling, transducer configuration and communication mode with sensors; (4) summarize the characteristics of electromagnetic-based and ultrasound-based systems, evaluate the challenges and development trends. We conclude that electromagnetic coupling methods are suitable for through thin non-ferromagnetic metal wall power delivery and data transmission at a relatively low data rate; piezoelectric transducer-based ultrasonic systems are particularly advantageous in achieving high power transfer efficiency and high data rates; the combination of more than one single technique may provide a more practical and reliable solution for long term operation.

Energy Efficiency plays a vital role in Wireless Sensor Networks (WSN) because of the limited battery power of nodes. Data Aggregation techniques help in reducing the redundant data transmissions which results in reduced energy consumption. Compressive Sensing (CS) is one of the effective signal processing techniques which helps in reducing the number of transmissions resulting in improved energy efficiency. Clustering is also a proven method of energy efficient data transmission. Employing the CS technique in cluster based WSN gains advantages of both and the outcome of which is further reduction in energy consumption. In this paper, a review of algorithms which employ CS in cluster based WSN is made and explanation of the same is given in brief. Some algorithms employ plain-CS and some employ hybrid-CS techniques. Using the sparse representation of the data vector in some orthonormal basis, even with smaller number of measurements, the data can be reconstructed accurately. First, we have given the details of compressive sensing and the possible sparse transforms, measurement matrices and data reconstruction algorithms. Then, recent works which exploit CS in clustering algorithms are discussed and comparison of the same is provided.