User authorization system using ZigBee WSN and AVR architecture

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.

This paper reviews the expected wide and profound impact of nanotechnology for future wireless devices and communication technologies. This article points out the possibilities of overcoming the same problem set from a device perspective by taking advantage of the merits of nanotechnologies. At the same time, open research issues and challenges are identified to spark new interests and developments in this field. Devices that use wireless communication range from RFID tags to TV receivers, and satellites to mobile phones. The availability of internet access from tablets and mobile phones is growing at an exponential rate, causing increasing demands on the performance of wireless networks and mobile devices. Some passive components necessary in wireless devices, such as inductors and capacitors, cannot be minimized as quickly as transistors and integrated circuits. This imbalance makes it likely that the performance requirements of mobile handsets will exceed the capabilities of current RF technologies within the next 10 to 15 years. As well as the global growth in mobile internet access via tablets and smartphones, wireless sensors, health monitoring systems and other devices dependent on wireless communication are becoming more and more prevalent. Developing more compact, more efficient, and less expensive wireless communication devices will have significant impact on these areas and enable new solutions for healthcare services, logistics and environmental monitoring. The devices that use wireless communication ranges from RFID tags to television set receivers and satellites to mobile phones. The availability of internet access from mobile devices is growing at an exponential rate that causes rising demand on the wireless network and mobile devices' performance. As the type of activities that consumers are engaging in over the wireless connections is changing day in day out, it has been an increased need for the devices to change also. For instance in radios, the increasing quantity of mobile internet traffic there has been increased the need for additional frequency for support. The modern world is becoming an intelligent interactive environment that has needs novel autonomous sensors with wireless communication links that require to be incorporated into an everyday object. This is the reason why sensors that are nanoenabled integrated with small RF transceivers are useful in monitoring air quality, water pollution among other aspects. The main drivers of changing into nanotechnology in wireless devices are needed for high performance, reduced consumption of power as well as reduced compact size.

Radio frequency identification (RFID) uses wireless local area network technology, which limits its scope of application. And the data security isn't well protected because of the limited hardware resource of cheap electronic tag. In this paper, we realize the combination of TCP/IP and RFID technology by the Ethernet interface of SAMSysMP9320 reader/writer. This is accomplished by assigning IP address and port number to the Ethernet interface of the SAMSysMP9320 reader/writer. Therefore the Internet and LAN -based RFID are merged, and RFID tag without power supply can be read and written from afar by the Internet. This provides a customer-oriented, unified and self-adaptive network service. Meanwhile, Snort intrusion detection system is implemented so that the data security is enhanced in RFID system.

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.

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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.

Wireless networks and communications have witnessed tremendous development and growth in recent periods and up until now, as there is a group of diverse networks such as the well-known wireless communication networks and others that are not linked to an infrastructure such as telephone networks, sensors and wireless networks, especially in important applications that work to send and receive important data and information in relatively unsafe environments, cybersecurity technologies pose an important challenge in protecting unsafe networks in terms of their impact on reducing crime. Detecting hacking in electronic networks and penetration testing. Therefore, these environments must be monitored and protected from hacking and malicious attacks. In this manuscript, a correspondence model is designed to discuss the algorithm in the environment of wireless communication systems and distributed computing by adopting the protocol data enhancement to the network using structured construction and diversity algorithm to improve the effectiveness of the intrusion detection system. Next, a multi-label convolutional neural network model is used to detect business transactions. An CNN was trained on the WSN-DS dataset using 5-Fold in CV technique with three hidden layers. The highest Precision values 0.951 of Grayhole attack for multi-classification.

As an information of combining RFID and wireless data communication technologies, Internet of Things is making things all over the world connected. RFID (Radio Frequency Identification) technology is the most basic key technology in Internet of Things, which combines signal processing, wireless communication, embedded calculation and data management. RFID technology is being widely used in various areas, such as object tracking systems, supply chain management, quick disbursement and so on. Especially, deducing semantic relationships of objects in RFID applications has caught worldwide attention, such as inferring an event reminder "somebody lost things" according to the containment-relationship 1 of objects. However, there are rarely any concrete solutions applied to our practical life. In this paper, we study and implement an RFID based object-relationship recognition system to efficiently retrieve the group-relationship and containment-relationship between objects in real-time. Finally, we give a real scenario to implement our research.

5a - RS-485 overview

Analyzing massive amounts of data using complex machine learning models requires significant computational resources. The conventional approach to such problems involves centralizing training data and inference processes in the cloud, i.e., in data centers. However, with the proliferation of mobile devices and increasing application of the Internet-of-Things (IoT) paradigm, very large amounts of data are collected at the edges of wireless networks, and due to privacy constraints and limited communication resources, it is undesirable or impractical to upload this data from mobile devices to the cloud for centralized learning. This problem can be solved by distributed learning at the network edge, by which edge devices collaboratively train a shared learning model using real-time mobile data. The avoidance of raw-data uploading not only helps to preserve privacy but may also alleviate network-traffic congestion and minimize latency. With that said, distributed training still requires a substantial amount of information exchange between devices and edge servers over wireless links. In the process, wireless impairments such as noise, interference, and imperfect knowledge of channel states can significantly slow down distributed learning (e.g., convergence speed) and degrades its performance (e.g., learning accuracy). This makes it crucial to optimize wireless network performance so as to support the efficient deployment of distributed learning algorithms. On the other hand, distributed learning algorithms provide a powerful tool-set for solving complex problems in wireless communication and networking. One important framework, called federated learning (FL), enables users to collaboratively learn a shared model while helping to preserve local data privacy. The application of FL can endow edge devices with capabilities of user behavior prediction, user identification, and wireless environment analysis. As another example, distributed reinforcement learning is capable of leveraging distributed computation power and data to solve complex optimization and control problems that arise in various use cases, such as network control, user clustering, resource management, and interference alignment. To cover this paradigm of distributed learning over wireless networks, this two-part Special Issue features papers dealing with two main research challenges: a) optimization of wireless network performance for efficient implementation of distributed learning in wireless networks, and b) distributed learning for solving communication problems and optimizing network performance.

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.

Remote control and monitoring systems provide many useful operating advantages in industrial automation and demand highly reliable and secure communication means. Their widespread use is, however, limited because of the high cost of current communications in use making them economically infeasible. Hence, there is a need to develop new low cost communication and control concepts by making such solutions economically feasible. In this paper, we present a scheme for implementing reliable wireless communication links for application of remote control and monitoring system based on current available commercial wireless communication networks, e.g. GPRS/EDGE/UMTS. The reliability enhancement over wireless networks has been proposed and its performance has been studied. This paper examines the proposed solution by means of both analytical and numerical evaluation, and has shown that the demanded reliability performance can be met by using only commercial wireless communication networks

Authentication is an essential issue for any organization. Previously, automated authentication was present using punch cards system, but it had security issues. Now days, organizations prefer to have biometrics based authentication. But using biometrics applications is very costly and time consuming. We can derive a time and cost effective solution for this problem using RFID (Radio Frequency Identification). Using feature provided by RFID to detect multiple individuals with very short amount of time and provide security with image pattern matching algorithm. To implement this system, we will interface camera with RFID enabled system which will use Low Frequency (LF) RFID tags. On every detection the camera will capture photograph. Then authentication is processed by using image pattern matching algorithm. Such system will be cost and time effective. Radio Frequency Identification (RFID) is old technology but more widely used in location tracing and tracking applications, due to wider reading ranges, and larger memory capacities, making it a key contributor to the growth of the radio technology industry. Many large corporations are interested in research and development within the field of RFID due to potential growth in various RFID applications. The people management system is an application where masses data needs to be collected and managed in a short duration. But due to authentication issues with this technology makes it less effective. RFID is an automated data collection technology in which radio communication for data transfer across two entities: a reader and a tag. The tag has two sections: one for radio communication and other for data storage. The tags are broadly classified into 3 categories namely active, passive and semi passive. The active tags have an internal power source e.g. a battery, which limits the life time. A passive tag doesn't have a power source and obtain the energy from the magnetic field of the reader. These types of tags are smaller, cheaper and can be used for long time. The scope of the paper is limited to passive tags. The functionality of RFID Passive tags is very simple. When a tag comes in the vicinity of the reader, it detects the radio signals generated by the reader and start to transmit the data stored in the memory. The radio signal generated by the reader offers the power needed to function and the synchronization data for communication between two entities. RFID has emerged as a key technology for automatic data collection. Using this, one can speed up authentication process. Every individual having RFID enabled identity can be detected in very short amount of time. But problem of secured authentication comes into the picture here. One way is proceeding with camera and matching the pattern for individual's photograph.

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