Master Node Research Articles

Development of a Wireless Sensor Network (WSN) Based Energy Efficient Cattle Monitoring System

This study shows how to monitor the movement of cattle using wireless sensor nodes powered by a renewable energy source capable of detecting location. Performance analysis was carried out on the energy consumption pattern of the nodes which indicated that throughout the monitoring period, the average energy consumed by the nodes was thus; master node 6450 joules, node one 1680 joules, node two 1656 joules, node three 1676 joules, node four 1656 joules. The rate of energy consumption was sustained by the renewable energy source. It was equally observed that energy consumption increased depending on how often query was sent and how often the conditions of monitoring was violated. This is to guarantee that information about cattle location gets to the base without delay due to battery failure which has been a major challenge faced with the current existing systems in tackling cattle rustling.

Timely Distributed Computation With Stragglers

We consider a status update system in which the update packets need to be processed to extract the embedded useful information. The source node sends the acquired information to a computation unit (CU) which consists of a master node and n worker nodes. The master node distributes the received computation task to the worker nodes. Upon computation, the master node aggregates the results and sends them back to the source node to keep it updated. We investigate the age performance of uncoded and coded (repetition coded, MDS coded, and multi-message MDS (MM-MDS) coded) schemes in the presence of stragglers under i.i.d. exponential transmission delays and i.i.d shifted exponential computation times. We show that asymptotically MM-MDS coded scheme outperforms the other schemes. Furthermore, we characterize the optimal codes such that the average age is minimized.

Time synchronization and data transfer method for towed electromagnetic receiver.

Deep marine controlled-source electromagnetic (CSEM) prospecting has attracted extensive interest because it enables high efficiency and high horizontal-resolution prospecting of gas hydrate and oil. However, the elimination of time errors between the transmitter and the receiver and realization of long-distance high-speed real-time data transmission (submarine towed body status information and raw electromagnetic field data stream) are worthwhile challenges that require continuous effort. We developed a novel towed CSEM system using double-vessels that have high time synchronization accuracy and real-time data transmission. The near-seafloor-towed CSEM receiver contains a deck user terminal, master node, slave nodes, tail buoy, and neutrally buoyant towed cable. The deck user terminal generated and transmitted a pulse per second to the master node through a fiber converter and optical fiber. The RS-485 transceiver then turned the pulse signal into a differential signal and transmitted it to each slave node for error-free synchronization. The time information was also transmitted from the deck user terminal to various nodes through ethernet switches, optical fibers, and serial to ethernet converters. The deck user terminal can conveniently communicate with each node cascaded by the ethernet switch through ethernet and fiber optic communication technology. During an offshore experiment involving oil and gas exploration in the South China Sea, the towed CSEM receiver continuously acquired all electromagnetic components and status information, which achieved a preliminary prospecting result. The maximum transfer rate of real-time data can reach 10 Mbps with 300 m distance between each slave node, and the time synchronization error between transmitter and receiver is less than ±3 µs.

Energy-Efficient Resource Provisioning Strategy for Reduced Power Consumption in Edge Computing

Edge computing is an emerging paradigm that settles some servers on the near-user side and allows some real-time requests from users to be directly returned to the user after being processed by these servers settled on the near-user side. In this paper, we focus on saving the energy of the system to provide an efficient scheduling strategy in edge computing. Our objective is to reduce the power consumption for the providers of the edge nodes while meeting the resources and delay constraints. We propose a two-stage scheduling strategy which includes the scheduling and resource provisioning. In the scheduling stage, we first propose an efficient scheme based on the branch and bound method. In order to reduce complexity, we propose a heuristic algorithm that guarantees users’ deadlines. In the resource provisioning stage, we first approach the problem by virtualizing the edge nodes into master and slave nodes based on the sleep power consumption mode. After that, we propose a scheduling strategy through balancing the resources of virtual nodes that reduce the power consumption and guarantees the user’s delay as well. We use iFogSim to simulate our strategy. The simulation results show that our strategy can effectively reduce the power consumption of the edge system. In the test of idle tasks, the highest energy consumption was 27.9% lower than the original algorithm.

SparkNN: A Distributed In-Memory Data Partitioning for KNN Queries on Big Spatial Data

The increase in GPS-enabled devices and proliferation of location-based applications have resulted in an abundance of geotagged (spatial) data. As a consequence, numerous applications have emerged that utilize the spatial data to provide different types of location-based services. However, the huge amount of available spatial data presents a challenge to the efficiency of these location-based services. Although the advent of big data frameworks like Apache Spark has enabled the processing of large amounts of data efficiently, they are designed for general (non-spatial) data. That is due to the build-in data partitioning mechanism that does not take into account the spatial proximity of the data. Therefore, these big data frameworks cannot be readily used for spatial analytics such as efficiently answering spatial queries. To fill this gap, this paper proposes SparkNN, an in-memory partitioning and indexing system for answering spatial queries, such as K-nearest neighbor, on big spatial data. SparkNN is implemented on top of Apache Spark and consists of three layers to facilitate efficient spatial queries. The first layer is a spatial-aware partitioning layer, which partitions the spatial data into several partitions ensuring that the load of the partitions is balanced and data objects with close proximity are placed in the same, or neighboring, partitions. The second layer is a local indexing layer, which provides a spatial index inside each partition to speed up the data search within the partition. The third layer is a global index, which is placed in the master node of Spark to route spatial queries to the relevant partitions. The efficiency of SparkNN was evaluated by extensive experiments with big spatial datasets. The results show SparkNN significantly outperforms the state-of-the-art Spark system when evaluated on the same set of queries.

A Distributed Low-Complexity Coding Solution for Large-Scale Distributed FFT

In distributed computing, a number of available helper nodes assist in completing a task for the master node. In such setups, the failure or straggling of even a single helper node can significantly increase the processing time. Therefore, coded distributed computing has been the subject of many recent studies. A problem that arises in some setups is that the master’s decoding complexity may exceed the complexity of self-computation, rending distributed computing useless. One such case is distributed large-scale FFT, where many helper nodes are required. In this work, we propose a novel distributed coded FFT, where the master’s load is significantly lower than the existing work. The gain is obtained by (1) using a novel distributed FFT structure which allows for reliable distributed coding at the Shuffle stage, and (2) using Raptor codes which enjoy a linear complexity at the cost of a small number of extra helper nodes. Numerical results are provided to support the benefits of our proposed solution and to optimize design parameters.

Performance Evaluation of an Independent Time Optimized Infrastructure for Big Data Analytics that Maintains Symmetry

Traditional data analytics tools are designed to deal with the asymmetrical type of data i.e., structured, semi-structured, and unstructured. The diverse behavior of data produced by different sources requires the selection of suitable tools. The restriction of recourses to deal with a huge volume of data is a challenge for these tools, which affects the performances of the tool’s execution time. Therefore, in the present paper, we proposed a time optimization model, shares common HDFS (Hadoop Distributed File System) between three Name-node (Master Node), three Data-node, and one Client-node. These nodes work under the DeMilitarized zone (DMZ) to maintain symmetry. Machine learning jobs are explored from an independent platform to realize this model. In the first node (Name-node 1), Mahout is installed with all machine learning libraries through the maven repositories. The second node (Name-node 2), R connected to Hadoop, is running through the shiny-server. Splunk is configured in the third node (Name-node 3) and is used to analyze the logs. Experiments are performed between the proposed and legacy model to evaluate the response time, execution time, and throughput. K-means clustering, Navies Bayes, and recommender algorithms are run on three different data sets, i.e., movie rating, newsgroup, and Spam SMS data set, representing structured, semi-structured, and unstructured data, respectively. The selection of tools defines data independence, e.g., Newsgroup data set to run on Mahout as others cannot be compatible with this data. It is evident from the outcome of the data that the performance of the proposed model establishes the hypothesis that our model overcomes the limitation of the resources of the legacy model. In addition, the proposed model can process any kind of algorithm on different sets of data, which resides in its native formats.

A Novel Blockchain Identity Authentication Scheme Implemented in Fog Computing

In a fog computing environment, lots of devices need to be authenticated in order to keep the platform being secured. To solve this problem, we turn to blockchain techniques. Unlike the identification cryptographic scheme based on elliptic curves, the proposed 2-adic ring identity authentication scheme inherits the high verification efficiency and high key distribution of sequence ciphers of 2-adic ring theory, and this algorithm adds identity hiding function and trading node supervision function by design. The main designed application scenario of this solution is applicable to the consortium blockchain, and the master nodes are mutually trusting cooperative relations. The node transaction verification and block generation consensus algorithm designed in this solution can be implemented in a set of algorithms, which has higher verification efficiency and easier to be deployed than other solutions. This scheme can be widely used in the fog computing environment.

Scalable Secure Blockchain based on Proof of Stake Protocol ARAM Blockchain

The main drawback of the Bitcoin cryptocurrency is its low scalability, which somehow is a consequence of the Proof-of-Work (PoW) consensus algorithm. So many other cryptocurrencies use the Proof-of-Stake (PoS) algorithm. In this paper, we present an outline of the architectural design of ARAM technology and arrangements built on PoS, ARAM is a scalable secure blockchain. With sharding technique in which transactions are divided among multiple groups to take the benefits of nodes computational power and process the transaction in parallel which speeds our system and achieves the desired scalability. The scalability of our proposed method is expected to increase linearly with the network size. Group double validation, fast confirmation time and randomization guarantee the security for our architecture. We also present a reward and penalty punishment mechanisms, with these mechanisms, the blockchain has a low probability of forks, to get the best use of ARAM blockchain in addition to the speeding up and fasting the confirmation time for the transaction, moreover, the advantages and contributions of master nodes are fair in the sense that the probability distribution function is ultimately uniform and provides an affirmative direction for other nodes.

An independent time optimized hybrid infrastructure for big data analytics

In Big data domain, platform dependency can alter the behavior of the business. It is because of the different kinds (Structured, Semi-structured and Unstructured) and characteristics of the data. By the traditional infrastructure, different kinds of data cannot be processed simultaneously due to their platform dependency for a particular task. Therefore, the responsibility of selecting suitable tools lies with the user. The variety of data generated by different sources requires the selection of suitable tools without human intervention. Further, these tools also face the limitation of recourses to deal with a large volume of data. This limitation of resources affects the performance of the tools in terms of execution time. Therefore, in this work, we proposed a model in which different data analytics tools share a common infrastructure to provide data independence and resource sharing environment, i.e. the proposed model shares common (Hybrid) Hadoop Distributed File System (HDFS) between three Name-Node (Master Node), three Data-Node and one Client-node, which works under the DeMilitarized zone (DMZ). To realize this model, we have implemented Mahout, R-Hadoop and Splunk sharing a common HDFS. Further using our model, we run [Formula: see text]-means clustering, Naïve Bayes and recommender algorithms on three different datasets, movie rating, newsgroup, and Spam SMS dataset, representing structured, semi-structured and unstructured, respectively. Our model selected the appropriate tool, e.g. Mahout to run on the newsgroup dataset as other tools cannot run on this data. This shows that our model provides data independence. Further results of our proposed model are compared with the legacy (individual) model in terms of execution time and scalability. The improved performance of the proposed model establishes the hypothesis that our model overcomes the limitation of the resources of the legacy model.

Anomaly detection framework for Internet of things traffic using vector convolutional deep learning approach in fog environment

The proliferation of Internet of things (IoT) devices has lured hackers to launch attacks. Therefore, anomalies in IoT traffic must be detected to mitigate these attacks and protect services rendered by smart devices. The lacuna in the existing anomaly detection techniques is the nonscalable nature of anomaly detection systems, resulting in the mishandling of large-scale data generated from IoT devices. The issue of scalability is addressed and an anomaly detection framework in a fog environment is proposed herein using vector convolutional deep learning (VCDL) approach. The anomaly detection system could be scalable if the traffic can be distributed to the nodes in the fog layer for processing. This is effectively captured in the VCDL approach in which the training of IoT traffic is distributed and computations are performed in the fog nodes. The parameters required for training are shared by the master node in the fog layer. Further, the proposed anomaly detection algorithm classifies IoT traffic as either normal or attack and then passes it to the cloud for attack mitigation. Experiments were conducted on UNSW’s Bot-IoT dataset and the results indicate that the proposed distributed deep learning approach can efficiently handle scalable data compared with the existing centralized deep learning approaches. Experimental results show that the proposed approach is significantly better in terms of accuracy, precision, and recall compared with the state-of-the-art anomaly detection systems.

Optical time-frequency transfer across a free-space, three-node network

We demonstrate frequency-comb-based optical two-way time-frequency transfer across a three-node clock network. A fielded, bidirectional relay node connects laboratory-based master and end nodes, allowing the network to span 28 km of turbulent outdoor air while keeping optical transmit powers below 5 mW. Despite the comparatively high instability of the free-running local oscillator at the relay node, the network transfers frequency with fractional precision below 10−18 at averaging times above 200 s and transfers time with a time deviation below 1 fs at averaging times between 1 s and 1 h. The successful operation of this network represents a promising step toward the operation of future free-space networks of optical atomic clocks.

Red de monitorización para automatizar el sistema de enfriamiento de un centro de datos

El objetivo de este trabajo fue desarrollar una red de monitorización de temperatura, humedad y calidad del aire en un centro de datos para la automatización del encendido y apagado del sistema de enfriamiento, ventilación y filtrado de aire usando Internet de las cosas (IoT-Internet of Things). Se puso en marcha una red con tecnología inalámbrica de largo alcance compuesta por cinco nodos esclavo, un nodo maestro y una interfaz de usuario. Los nodos esclavo transmiten periódicamente al nodo maestro el valor de las tres variables de ambiente. El nodo maestro envía la información recibida de los esclavos a un servidor en la nube para poder ser accedida desde una interfaz de usuario. Cuando el valor de alguna de las variables alcanza el umbral configurado se enciende el sistema de enfriamiento, ventilación y/o filtrado de aire, según sea el caso. Las pruebas mostraron que se logró una precisión menor a ±1.0 °C en la medida de temperatura, menor a ±2 % en la medida de humedad, menor a ±8 μg/m³ en la medida de la calidad del aire y un alcance de 11.5 kilómetros con línea de vista en la transmisión de datos en la red. Según estos resultados, la red puede ponerse en funcionamiento para la monitorización de sensores y procesos en otras instalaciones con este alcance.

Fractional frequency reuse based frequency allocation for 5G HetNet using master–slave algorithm

Frequency allocation in small cell-based green heterogeneous mobile networks is a demanding research domain nowadays. Femtocells are the essential components of small cell networks. In this paper, we propose a low power micro-femtocell network using the master–slave algorithm. In the master–slave algorithm, the master node allocates work to the slave nodes. When a slave ends its task given by a master node, it informs the master node, and it is being assigned a new workload. Slave nodes do not communicate with each other. In our approach, the microcell is divided into three sectors, and each sector is further categorized into three regions: inner region, outer region, and most-outer region. Femtocells are allocated in these three regions. According to the duty cycle, several femtocells are chosen as master femtocells. The rest of the femtocells are assigned under the supervision of the master femtocells. These femtocells are referred to as slave femtocells. The master femtocells communicate with the microcell, and the slave femtocells communicate with the corresponding master femtocell. Frequency allocation for this micro-femtocell network is proposed based on Fractional Frequency Reuse (FFR). The power consumption, signal-to-interference-plus-noise ratio (SINR), and spectral efficiency for the proposed network are calculated. The simulation results exemplify that the proposed scheme reduces the power consumption of the network by approximately 44%–80% than the conventional heterogeneous network. The simulation results also demonstrate that the proposed network has better SINR and spectral efficiency than the existing micro-femtocell network. For experimental analysis, vector signal generator (VSG) and vector signal analyzer (VSA) are used. The experimental results also show that the proposed network is greener compared to the existing micro-femtocell network.

A stochastic quenched disorder model for interaction of network-master node systems

Human beings live in a networked world in which information spreads very fast thanks to advances in technology. This helps to promote conflicts that lead to the dominance of certain ideologies, set of values, belief or opinion. Such conflicts can lead to a decrease in freedom, oppression and to the deterioration of established institutions. Here, we propose a simple model to study, to a certain extent, this class of problems. We look at interacting agents connected in networks to analyze the multi-agent decision stochastic process. Each individual decision may be influenced by others’ decisions. We consider a first neighbor interaction between agents in one-dimensional network. Some agents are also connected to a hub, or master node, who has preferential opinions. The role of the master node is to persuade others to follow a specific orientation, in which there is a probability of successful persuasion. The connections between master node and the network society are quenched disorder. Using a simple model, we found a phase transition from disorder to order for three different control parameters. The evidence for the phase transition in the model was obtained by finite-size scaling analysis. In addition, we present an analytical result via the mean-field approximation. Finally, we discuss how this model may be useful as a framework to study the spread of morality, innovation, opinion formation and consensus.

WSN nodes power consumption using multihop routing protocol for illegal cutting forest

The need for an automation system from a remote area cannot be separated from the role of the wireless sensor network. However, the battery consumption is still a problem that influences the lifetime of the system. This research focused on studying how to characterize the power consumption on each sensor node using multihop routing protocol in the illegal logging field, to get the prediction lifetime of the network. The system is designed by using six sensor nodes in a master-slave connection and implemented in a tree topology. Each sensor node is consisting of a sound sensor, vibration sensor, Xbee communication, current and voltage sensor, and Arduino nano. The system is tested using battery 10050 mAH with several scenarios to have calculated how long the battery lifetime can be predicted. The results stated that the master node on the network depleted the power of the battery faster than the slave node since the more slaves connected to the master, the more energy the battery consumes.

5G Wireless Network Security Strategies and Security Issues or its Uses in 5G Networks

Future production networks (5G) will power employ new technological technologies toward convene the supplies of broadband admittance and wireless access ubiquitously and anywhere elevated frequency with mobile availability, and convergence of huge numbers of policy (Internet of Things (IoT)) within an especially consistent plus reasonable means. The potential attack with safety services inside 5G wireless network be then outline by the understanding of novel repair values and original practice cases. We hit 5G using open partners through aphasia approaches industries. The eNB LTE is a master node, and gNB is a master node as well as Massive MIMO (Beam Formulation) for Mobile use. In this 5G Technology use and Smart City and IoT Extending the current Internet and linking, contact, and inter-networking between computer and physical object or' things' is a growing trend which is offending referred to as the Internet of Things. This paper provides approaches to these problems and roadmap for stable 5G networks in the future.

A Parallel Algorithm Framework for Feature Extraction of EEG Signals on MPI

In this paper, we present a parallel framework based on MPI for a large dataset to extract power spectrum features of EEG signals so as to improve the speed of brain signal processing. At present, the Welch method has been wildly used to estimate the power spectrum. However, the traditional Welch method takes a lot of time especially for the large dataset. In view of this, we added the MPI into the traditional Welch method and developed it into a reusable master-slave parallel framework. As long as the EEG data of any format are converted into the text file of a specified format, the power spectrum features can be extracted quickly by this parallel framework. In the proposed parallel framework, the EEG signals recorded by a channel are divided into N overlapping data segments. Then, the PSD of N segments are computed by some nodes in parallel. The results are collected and summarized by the master node. The final PSD results of each channel are saved in the text file, which can be read and analyzed by Microsoft Excel. This framework can be implemented not only on the clusters but also on the desktop computer. In the experiment, we deploy this framework on a desktop computer with a 4-core Intel CPU. It took only a few minutes to extract the power spectrum features from the 2.85 GB EEG dataset, seven times faster than using Python. This framework makes it easy for users, who do not have any parallel programming experience in constructing the parallel algorithms to extract the EEG power spectrum.

PySpark-Based Optimization of Microwave Image Reconstruction Algorithm for Head Imaging Big Data on High-Performance Computing and Google Cloud Platform

For processing large-scale medical imaging data, adopting high-performance computing and cloud-based resources are getting attention rapidly. Due to its low–cost and non-invasive nature, microwave technology is being investigated for breast and brain imaging. Microwave imaging via space-time algorithm and its extended versions are commonly used, as it provides high-quality images. However, due to intensive computation and sequential execution, these algorithms are not capable of producing images in an acceptable time. In this paper, a parallel microwave image reconstruction algorithm based on Apache Spark on high-performance computing and Google Cloud Platform is proposed. The input data is first converted to a resilient distributed data set and then distributed to multiple nodes on a cluster. The subset of pixel data is calculated in parallel on these nodes, and the results are retrieved to a master node for image reconstruction. Using Apache Spark, the performance of the parallel microwave image reconstruction algorithm is evaluated on high-performance computing and Google Cloud Platform, which shows an average speed increase of 28.56 times on four homogeneous computing nodes. Experimental results revealed that the proposed parallel microwave image reconstruction algorithm fully inherits the parallelism, resulting in fast reconstruction of images from radio frequency sensor’s data. This paper also illustrates that the proposed algorithm is generalized and can be deployed on any master-slave architecture.

On the Fundamental Limits of Coded Data Shuffling for Distributed Machine Learning

We consider the data shuffling problem in a distributed learning system, in which a master node is connected to a set of worker nodes, via a shared link, in order to communicate a set of files to the worker nodes. The master node has access to a database of files. In every shuffling iteration, each worker node processes a new subset of files, and has excess storage to partially cache the remaining files, assuming the cached files are uncoded. The caches of the worker nodes are updated every iteration, and they should be designed to satisfy any possible unknown permutation of the files in subsequent iterations. For this problem, we characterize the exact load-memory trade-off for worst-case shuffling by deriving the minimum communication load for a given storage capacity per worker node. As a byproduct, the exact load-memory trade-off for any shuffling is characterized when the number of files is equal to the number of worker nodes. We propose a novel deterministic coded shuffling scheme, which improves the state of the art, by exploiting the cache memories to create coded functions that can be decoded by several worker nodes. Then, we prove the optimality of our proposed scheme by deriving a matching lower bound and showing that the placement phase of the proposed coded shuffling scheme is optimal over all shuffles.