5a - RS-485 overview

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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.

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.

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.

Recently there has been an excessive groundwater pumping and seawater intrusion in the costal aquifers of Oman. To deal with this problem, a fully automated and wireless irrigation control system that avoids subjective decisions about irrigation volumes and timing was developed. All fields in the farm are accessible via TCP/IP protocol. Each farm has a Single Collecting Node for data collection connected to a host computer. All nodes in a crop are connected to Xbee network and considered as Slave Nodes except one termed Master Node. The Master Node includes two sensors and a solenoid valve. The Master Node controls the irrigation of the field with a same crop sequentially using slave nodes. Both the Xbee network and the solenoid valves are powered by a lithium battery with photovoltaic charging panels. The irrigation control and monitoring program is implemented in the host computer in the farm, which monitors the states of all crops and controls the valves using a threshold and timers. The smart irrigation system has been installed in 14 farms.

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.

Intrusion detection in big data using hybrid feature fusion and optimization enabled deep learning based on spark architecture

This paper proposes an effective classification method named Rider Chicken Optimization Algorithm-based Recurrent Neural Network (RCOA-based RNN) to perform big data classification in spark architecture. Initially, the input data are collected from the network by the master node and then forwarded to the slave node. These nodes are responsible for storing the data and performing computations. The features are effectively selected in the slave node using the proposed RCOA. The selected features are forwarded to the master node. The big data classification is achieved in the master node by using the RNN classifier, and the training of the classifier is done using the proposed RCOA algorithm, which is the integration of the Rider optimization algorithm (ROA) with the standard Chicken Swarm Optimization (CSO). The experimentation is done by using the Switzerland dataset, Cleveland dataset, Hungarian dataset and Skin disease dataset, in which the proposed RCOA-based RNN attained better performance based on the quantitative properties, such as sensitivity, accuracy and specificity with the values of 9.3E+01%, 9.4E+01% and 9.3E+01% using Hungarian dataset. The existing learning methods failed to address the complex classification problems at a reasonable time, which is overcome by the proposed method.

Subgraph matching is a key operation on graph data. Social network (SN) providers may want to find all subgraphs within their social network that match certain query graph patterns. Unfortunately, subgraph matching is NP-complete, making its application to massive SNs a major challenge. Past work has shown how to implement subgraph matching on a single processor when the graph has 10-25M edges. In this paper, we show how to use cloud computing in conjunction with such existing single processor methods to efficiently match complex subgraphs on graphs as large as 778M edges. A cloud consists of one master compute node and k slave compute nodes. We first develop a probabilistic method to estimate probabilities that a vertex will be retrieved by a random query and that a pair of vertices will be successively retrieved by a random query. We use these probability estimates to define edge weights in an SN and to compute minimal edge cuts to partition the graph amongst k slave nodes. We develop algorithms for both master and slave nodes that try to minimize communication overhead. The resulting COSI system can answer complex queries over real-world SN data containing over 778M edges very efficiently.

The Local Interconnect Network (LIN) is a low-cost serial communication protocol commonly used in automotive electronics and low-speed embedded systems. This paper details the design, implementation, and verification of a LIN controller that meets the LIN 2.x specification. The proposed architecture adopts a modular approach, including master and slave nodes, synchronization logic, checksum calculation, and frame buffering. The controller does not rely on standard UART peripherals. Instead, it uses custom bit-level transmission and reception logic to provide reliable communication. Functional verification is done using Verilog testbenches and internal loopback setups, followed by synthesis and hardware validation on the DE0-Nano FPGA. Simulation and hardware results demonstrate accurate frame generation, synchronization, identifier decoding, and checksum verification at 19.2 kbps. The design delivers a resourceefficient and fully synthesizable LIN controller that is well-suited for low-cost automotive and industrial applications.

This paper describes the implementation of the mechanisms used to achieve a high level of availability in the SIR-S system through a duplicated Master-Slave configuration. In case of a fault in the Master Node, the Slave Node can carry on the operations as close as possible to the scheduled sequence, in order to match the time optimization criteria with a maximum of overall delay of about 5 msec. >

In network data transmission, the data to be encrypted is not always at the same level. The access structure is embedded in the ciphertext, resulting in excessive communication and computing overhead. Therefore, a secure sharing method of network data transmission is proposed based on multi-layer encryption algorithm. According to the relationship between users, type servers and service providers, the logical structure of data transmission is established, and the data sharing contract is called on the basis of secure communication. By designing the key synchronization mechanism, both sides of the transmission can use the same key for encryption and decryption to ensure the forward security of the data. The key distribution scheme is established based on multi-layer encryption algorithm. The ciphertext information of the next layer is directly embedded in the ciphertext of the previous layer, which realizes the jump transmission of ciphertext and ensures the security of data privacy. In the final stage, both the master node and the slave node will receive the confirmation information, and then check the signature of the confirmation information to complete the secure sharing of data transmission. The test results show that this method reduces the communication overhead and computing overhead, and improves the efficiency of encryption and decryption.

As a distributed system, the research on blockchain related technologies is gradually showing an explosive growth trend. As one of the cores of blockchain, consensus algorithm is mainly responsible for ensuring the security of blockchain system. It has the disadvantages of large overhead and long communication delay. In order to improve the performance of the blockchain system in storing and querying user data, this paper takes the consensus algorithm as the research entry point, and designs the cache optimization raft consensus algorithm (COR), which can effectively shorten the waiting confirmation time in the user data storage stage, so as to improve the efficiency of user data storage. In addition, the system adds a cache layer to its traditional structure, further improve the query efficiency of the system. Finally, the system realizes the goal of system load balancing and improving system query efficiency based on three types: master node, slave node and supervisory information cache node, as well as two data structures: data identifier cache table and resource utilization queue.KeywordsBlockchainConsensus algorithmCache optimizationResource utilization queue

In this paper, we propose an overlapping communication protocol using improved time-slot leasing in the Bluetooth WPANS. One or many slave-master-slave communications usually exist in a piconet of the Bluetooth network. A fatal communication bottleneck is incurred in the master node if many slave-master-slave communications are required at the same time. To alleviate the problem, an overlapping communication scheme is presented to allow slave node directly and simultaneously communicates with another slave node to replace with the original slave-master-slave communication works in a piconet. This overlapping communication scheme is based on the improved time-slot leasing scheme. The key contribution of our improved time-slot leasing scheme additionally offers the overlapping communication capability and we developed an overlapping communication protocol in a Bluetooth WPANs. Finally, simulation results demonstrate that our developed communication protocol achieves the performance improvements on bandwidth utilization, transmission delay time, network congestion, and energy consumption.KeywordsBluetoothtime-slot leasingWPANwireless communication

Nowadays, companies need a lot of storage to store their data. Massive amounts of data can be stored with Hadoop. Studies show that by 2023, 90% of all Fortune 500 companies will have adopted Hadoop. However, it may take hours or even days to set up a Hadoop Cluster for storing data. When businesses manage their time well, they can consistently deliver their products and services on time. To make smart and calculated decisions, they need this data. Business forecasting is a practice that has been around for a long time, but in the past, it was often done with limited data. However, in today’s data-driven world, businesses must utilize data to make informed decisions and stay ahead of their competitors. By analyzing large amounts of data, businesses can make more accurate predictions and decisions about consumer preferences, market trends, and potential fraud activities. The insights gained from data analysis can benefit professionals across all industries, allowing them to make better decisions and improve their business outcomes.The main objective is to automate the formation of a Hadoop Cluster on AWS Cloud with the help of Terraform. The Hadoop Cluster will have the following nodes: Master Node, Slave Nodes, Client Nodes