Independent Nodes Research Articles

Online Partial Offloading and Task Scheduling in SDN-Fog Networks With Deep Recurrent Reinforcement Learning

Smart industries enabling automation and data exchange in manufacturing technologies demanding real-time processing, nearby storage, and reliability, all of which can be satisfied by the fog computing architecture. With the emergence of smart devices coupled with a diverse range of application requirements, it is essential to have an intelligent fog network where intelligence is spread across all network segments, taking network nodes self-aware and self-decision making. In fog networks, an optimal distribution decision faces challenges due to uncertainties associated with user workload and available resources at the fog nodes and also the wide range of node’s computing power. Given this challenge, a computational offloading and CPU resource scheduling method for minimizing energy consumption is proposed. To investigate the characteristics for offloading and optimizing their allocation, we consider two types of tasks, namely, offloadable and nonoffloadable tasks. The independent fog nodes adopt the same strategy without prior knowledge of the dynamic statistics and global observations, aiming to maximize a common goal with cooperative behaviors. Then, the deep recurrent <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network (DRQN) is applied to deal with the partial-observability from limited information. The proposed DRQN-based method requires comparatively less computational complexity than the conventional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning algorithm. The simulation results show that the proposed method can effectively deal with both transmission and CPU energy consumptions while guaranteeing convergence in a limited time.

Quantum Interference between Photons and Single Quanta of Stored Atomic Coherence.

Essential for building quantum networks over remote independent nodes, the indistinguishability of photons has been extensively studied by observing the coincidence dip in the Hong-Ou-Mandel interferometer. However, indistinguishability is not limited to the same type of bosons. For the first time, we hereby observe quantum interference between flying photons and a single quantum of stored atomic coherence (magnon) in an atom-light beam splitter interface. We demonstrate that the Hermiticity of this interface determines the type of quantum interference between photons and magnons. Consequently, not only the bunching behavior that characterizes bosons is observed, but counterintuitively, fermionlike antibunching as well. The hybrid nature of the demonstrated magnon-photon quantum interface can be applied to versatile quantum memory platforms, and can lead to fundamentally different photon distributions from those occurring in boson sampling.

Relay Node Based Fault Recovery Technique in IEEE 802.15.4 based WBAN

Wireless sensor networks comprises of many individual nodes which interconnect to form a system that operates as one. These sensor nodes play the main task of sensing the environmental conditions and maybe control them too. A WBAN consist of portable, miniaturized, and independent sensor nodes for monitoring body function for health, sporting, emergency and entertainment, applications. It provides long term health monitoring of patients under natural physiological states without constraining their normal activities. In body sensor networks it allows communication between implanted devices and remote monitoring.The proposed algorithm reduces the chances the chances of fault in the network. The proposed and existing algorithms are implemented in NS2 and results are analyzed in terms of certain parameters

Collaborative Caching for Energy Optimization in Content-Centric Internet of Things

The development of the content-centric Internet of Things (C2IoT) enriches the services provided by the IoT devices, which diversifies the provided contents. However, system resources are terribly wasted by repeated delivering the same content. Efficiently utilizing caching resources can reduce the link load caused by delivering and forwarding contents, further improving the users’ quality of experience (QoE). However, the collaboration among independent decision nodes is insufficient, which consumes substantial energy redundantly. To solve the mentioned issue, we propose a genetic-algorithm-based collaborative caching scheme integrating on-path strategy and off-path strategy, named cost-oriented caching scheme (CCS), to minimize the energy consumption of requesting contents. First, based on the analysis of content popularity, users’ historical requests, and transmission delay, an optimization problem is formulated to minimize the communication delay of obtaining content. Then, we formulate a submodular function optimization problem to optimize energy consumption, and an improved genetic algorithm is proposed to solve the mentioned problem. Performance evaluations demonstrate that the proposed CCS is superior to other existing caching schemes.

Deep distributed computing to reconstruct extremely large lineage trees.

Phylogeny estimation (the reconstruction of evolutionary trees) has recently been applied to CRISPR-based cell lineage tracing, allowing the developmental history of an individual tissue or organism to be inferred from a large number of mutated sequences in somatic cells. However, current computational methods are not able to construct phylogenetic trees from extremely large numbers of input sequences. Here, we present a deep distributed computing framework to comprehensively trace accurate large lineages (FRACTAL) that substantially enhances the scalability of current lineage estimation software tools. FRACTAL first reconstructs only an upstream lineage of the input sequences and recursively iterates the same produce for its downstream lineages using independent computing nodes. We demonstrate the utility of FRACTAL by reconstructing lineages from >235 million simulated sequences and from >16 million cells from a simulated experiment with a CRISPR system that accumulates mutations during cell proliferation. We also successfully applied FRACTAL to evolutionary tree reconstructions and to an experiment using error-prone PCR (EP-PCR) for large-scale sequence diversification.

Distributed Online Learning for Coexistence in Cognitive Radar Networks

This work addresses the coexistence problem for radar networks. Specifically, we model a network of cooperative, independent, and non-communicating radar nodes which must share resources within the network as well as with non-cooperative nearby emitters. We approach this problem using online Machine Learning (ML) techniques. Online learning approaches are specifically preferred due to the fact that each radar node has no prior knowledge of the environment nor of the positions of the other radar nodes, and due to the sequential nature of the problem. For this task we specifically select the multi-player multi-armed bandit (MMAB) model, which poses the problem as a sequential game, where each radar node in a network makes independent selections of center frequency and waveform with the same goal of improving tracking performance for the network as a whole. For accurate tracking, each radar node communicates observations to a fusion center on set intervals. The fusion center has knowledge of the radar node placement, but cannot communicate to the individual nodes fast enough for waveform control. Every radar node in the network must learn the behavior of the environment, which includes rewards, interferer behavior, and target behavior. Each independent and identical node must choose one of many waveforms to transmit in each Pulse Repetition Interval (PRI) while avoiding <i>collisions</i> with other nodes and interference from the environment. The goal for the network as a whole is to minimize target tracking error, which relies on obtaining high SINR in each time step. Our contributions include a mathematical description of the MMAB framework adapted to the radar network scenario. We conclude with a simulation study of several different network configurations. Experimental results show that iterative, online learning using MMAB outperforms the more traditional sense-and-avoid (SAA) and fixed-allocation approaches.

Performance Analysis of OLSR Protocol in Manet Considering Different Mobility Speed and Network Density

A Mobile Ad Hoc Network (MANET) is created when an independent mobile node network is connected dynamically via wireless links. MANET is a self-organizing network that does not rely on pre-existing infrastructure such as wired or wireless network routers. Mobile nodes in this network move randomly, thus, the topology is always changing. Routing protocols in MANET are critical in ensuring dependable and consistent connectivity between the mobile nodes. They conclude logically based on the interaction between mobile nodes in MANET routing and encourage them to choose the optimum path between source and destination. Routing protocols are classified as proactive, reactive, or hybrid. The focus of this project will be on Optimized Link State Routing (OLSR) protocol, a proactive routing technique. OLSR is known as the optimized variant of link state routing in which packets are sent throughout the network using the multipoint relay (MPR) mechanism. This article evaluates the performance of the OLSR routing protocol under condition of changing mobility speed and network density. The study's performance indicators are average packet throughput, packet delivery ratio (PDR), and average packet latency. Network Simulator 2 (NS-2) and an external patch UM-OLSR are used to simulate and evaluate the performance of such protocol. As a result of research, the approach of implementing the MPR mechanism are able to minimise redundant data transmission during the normal message broadcast. The MPRs enhance the link state protocols’ traditional diffusion mechanism by selecting the right MPRs. Hence, the number of undesired broadcasts can be reduced and limited. Further research will focus on different scenario and environment using different mobility model.

Clinical features and prognostic factors of combined small cell lung cancer: development and validation of a nomogram based on the SEER database.

BackgroundCombined small-cell lung cancer (CSCLC) refers to the simultaneous presence of small cell lung cancer (SCLC) and any subtype of the non-small cell lung cancer (NSCLC). This study aimed to explore the prognosis of CSCLC, NSCLC, and pure SCLC patients, and to develop a nomogram to estimate the overall survival (OS) for CSCLC patients.MethodsPatients diagnosed with NSCLC, CSCLC, and pure SCLC between 2004 and 2015 were identified from the Surveillance Epidemiology and End Results (SEER) database. Survival analyses were performed by using the Kaplan Meier curves and Cox proportional hazards regression. All CSCLC patients were randomly split 7:3 into training and validation sets. A nomogram was developed by integrating all independent predictors for OS. The performance of the nomogram was determined by discrimination, calibration ability, clinical usefulness, and risk stratification ability.ResultsA total of 326,695 lung cancer patients, including 871 with CSCLC, 280,391 with NSCLC, and 45,433 with pure SCLC were enrolled. CSCLC was associated with worse survival compared with NSCLC both in the unmatched and matched cohorts. However, compared to pure SCLC, CSCLC was associated with significantly better survival in the unmatched cohorts only, while showed marginally non-significantly better survival after propensity score matching (PSM). For CSCLC, a nomogram was constructed for the 6-month, 1-year, and 3-year OS prediction by combining the independent risk factors, including age, gender, tumor, node, and metastasis stage, surgery, and chemotherapy. The nomogram showed good discrimination and calibration both in the training and validation sets, and better performance than the tumor–node–metastasis staging system. Risk stratification analysis indicated that the nomogram scores efficiently divided CSCLC patients into low-, intermediate-, and high-risk groups (P<0.001).ConclusionsCSCLC patients presented a significantly worse prognosis than patients with NSCLC, but comparable prognosis when compared with pure SCLC patients in the matched cohorts. In addition, we developed and validated a nomogram for predicting the 6-month, 1-year, and 3-year OS in CSCLC patients.

Entropy analysis of Boolean network reduction according to the determinative power of nodes

Boolean networks are utilized to model systems in a variety of disciplines. The complexity of the systems under exploration often necessitates the construction of model networks with large numbers of nodes and unwieldy state spaces. A recently developed, entropy-based method for measuring the determinative power of each node offers a new method for identifying the most relevant nodes to include in subnetworks that may facilitate analysis of the parent network. We develop a determinative-power-based reduction algorithm and deploy it on 36 network types constructed through various combinations of settings with regards to the connectivity, topology, and functionality of networks. We construct subnetworks by eliminating nodes one-by-one beginning with the least determinative node. We compare entropy ratios between these subnetworks and the parent network and find that, for all network types, the change in network entropies (sums of conditional node entropies) follows a concave down decreasing curve, and the slightest reductions in network entropy occur with the initial reductions which eliminate the nodes with the least determinative power. Comparing across the three network characteristics, we find trends in the rates of decrease in the entropy ratios. In general, the decline occurs more slowly in networks with degree values assigned from a power-law distribution and canalyzing functions of higher canalization depth. We compare results of the determinative-power-based reduction with those of a randomized reduction and find that, in forming subnetworks with maximal network entropy, the determinative-power-based method performs as well as or better than the random method in all cases. Lastly, we compare findings based on this conditional-entropy-based calculation of network entropy with those of an alternative calculation using simple sums of (independent) node entropies to demonstrate the vast differences resulting from the two approaches.

Design and Implementation of Efficient MOSFET’s Utilization Based Proposed Voltage Controlled Oscillator

Ring oscillator is a device which consists of NOT gates connected in the form of ring. This ring oscillator’s output oscillates between the true and false stages controlled by applied voltage. Now days this voltage controlled oscillator (VCO) becomes the heart of modern electronic devices and communication systems. Earlier five-stage complementary metal oxide semiconductor (CMOS) based VCO for the Phase Locked Loop (PLL) was implemented. High frequency oscillations are required for many applications and further it is observed that a very general technique is normally adopted by researchers to achieve high frequency that if number of transistors is increased then the frequency can be increased. But the consequences of increase in number of transistors are the increase in delay and more number of MOSFET occupies more area and more power dissipation. So, in this paper VCO is designed with efficient utilization of MOSFETs. There is a balance between frequency and number of transistors, so that the area and power dissipation can be reduced. From the obtained results it can observed that the number of MOSFET’s, Independent Nodes, boundary nodes total nodes and power are reduced compared to five stage VCO and VCO based Ring oscillator.

Collaborative Machine Learning for Energy-Efficient Edge Networks in 6G

To fulfill the diversified requirements of the emerging Internet of Everything (IoE) applications, the future sixth generation (6G) mobile network is envisioned as a heterogeneous, ultra-dense, and highly dynamic intelligent network. Edge intelligence is a vital solution to enable various intelligent services to improve the quality of experience of resource-constrained end users. However, it is very challenging to coordinate the independent but interrelated edge nodes in a decentralized learning manner to improve their strategies. In this article, we propose a decentralized and collaborative machine learning architecture for intelligent edge networks to achieve ubiquitous intelligence in 6G. Considering energy efficiency to be an essential factor in building sustainable edge networks, we design a multi-agent deep reinforcement learning (DRL)-empowered computation offloading and resource allocation scheme to minimize the overall energy consumption while ensuring the latency requirement. Further, to decrease the computing complexity and signaling overhead of the training process, we design a federated DRL scheme. Numerical results demonstrate the effectiveness of the proposed schemes.

Lightweight capability-token for consent-based authentication protocol for smart sensor nodes

This paper highlights a lightweight token-based authentication scheme for smart sensor nodes in a smart home network. The token holds the capability information of a sensor node in a secure and trusted environment. The capability information adds another layer of security by incorporating the user's consent when registering in the network. The registration is a one-time activity in the node's life cycle, but it prevents unauthorized users or devices from intruding into the network. The method also mitigates the user and token impersonation attacks in the network. It does not need any manual intervention like entering user-id or password, which helps in seamless authentication, identity management, and secure communication. It also incorporates the position of the sensor nodes during the authentication phase. The capability-token mitigates the threats that arise from an independent node attempting to intrude whose position is near the smart home environment. The authors have demonstrated the communication time gain and compared it with the existing methods.

Evaluation Platform for DDM Algorithms With the Usage of Non-Uniform Data Distribution Strategies

Huge amounts of data are collected in numerous independent data storage facilities around the world. However, how the data is distributed between physical locations remains unspecified. Downloading all of the data for the purpose of processing it is undesirable and sometimes even impossible. Various methods have been proposed for performing data mining tasks, but the main problem is the lack of an objective strategy for comparing them. The authors present current research on a novel evaluation platform for distributed data mining (DDM) algorithms. The proposed platform opens up a new field to evaluate algorithms in terms of the quality of the results, transfer used, and speed, but also for the use of a non-uniform data distribution among independent nodes during algorithm evaluation. This work introduces a ‘data partitioning strategy’ term referring to a specific, not necessarily uniform data distribution. A brief evaluation for three clustering algorithms is also reported, showing the usability and simplicity of identifying differences in processing with the use of the platform.

Risk factors for lymph node metastasis and surgical scope in patients with cN0 non-small cell lung cancer: a single-center study in China

BackgroundIt is difficult to determine the lymph node metastasis of patients with clinically negative lymph nodes (cN0) non-small cell lung cancer (NSCLC) before surgery. The purpose of this study is to investigate risk factors of lymph node metastasis in cN0 NSCLC, thereby to identify the surgical indications for lymph node dissection in cN0 NSCLC.MethodsWe conducted a retrospective study of patients with tumor size ≤ 30 mm who underwent radical resection of NSCLC. Binary logistic regression analysis was applied to predict risk factors for lymph node metastasis, and subject operating characteristics (ROC) curve was used to evaluate the independent risk factors.ResultsOverall, 44 patients (6.8%) with cN0 NSCLC had lymph node metastasis. Factors of tumor consolidation diameter (p < 0.001) and preoperative serum carcinoembryonic antigen (CEA) level (p = 0.017) are independent risk factors lymph node metastasis in cN0 NSCLC. The ROC curve showed that the cut-off value of consolidation diameter was 16.5 mm, and the area under the curve (AUC) was 0.825 (p < 0.001, 95% CI 0.780–0.870); the cut-off value of serum CEA level was 1.765 μg/L, and the AUC was 0.661 (p < 0.001, 95% CI: 0.568–0.754). Moreover, 8 of 461 patients with tumor parenchyma ≤ 16.5 mm had lymph node metastasis, and 36 of 189 patients with tumor parenchyma > 16.5 mm had lymph node metastasis.ConclusionTumor consolidation diameter and preoperative serum CEA are independent factors to predict cN0 NSCLC with tumor size ≤ 30 mm. For patients with tumor parenchyma > 16.5 mm, the probability of lymph node metastasis is higher and lymph node dissection is recommended. For patients with tumor parenchyma ≤ 16.5 mm, the probability of lymph node metastasis is lower and lymph node sampling is feasible.

A method for calculation of the operational availability factor of technical systems using the reliability models based on the continuous-time Markov chains

This scientific paper deals with the reliability models of technical systems based on the continuous-time Markov chains. An offered by the author calculation method for the operational availability factor of technical systems based on the methods for calculation of the probabilities of the system states and probability of system faultless functioning using the reliability models based on the continuous-time Markov chains is discussed. Finally, an example of calculation of the operational availability factor for the fault-tolerant data processing system with two identical and independent nodes is also given.

Safe Data Transfer Using Logic Gate Based Cryptographic Technique in Wireless Sensor Network

Wireless Sensor Networks consist of independent sensor nodes attached to one base station. In wireless sensor networks, nodes are connected to sensing environment and communicate the data to the base station. As WSNs continues to grow, they become vulnerable to attacks and hence the need for operative security techniques. Applications of wireless sensor networks demands for the well-organized and secure communication. For the solution of well-organized and reliable security, we need cryptography algorithms which provide good solutions. For providing reliable security techniques mainly data confidentiality, key management is used. Identification of suitable cryptographic techniques for WSNs is an important challenge due to limitation of energy, computation capability and memory of the sensor nodes. Symmetric cryptography techniques do not act well when the number of sensor nodes increases. Hence asymmetric key cryptographic techniques are widely used. Here we propose an electronic logic gate based symmetric Cryptographic technique which is more suitable for small and medium WSNs.

Design of IoT based Platform Development for Smart Home Appliances Control

The advent of industrial automation has brought about a substantial change in the human lifestyle and the way human interaction with machines. One of the major technological interventions in day-to-day life is the role of automation, which has uplifted the living standards in developed countries and fostered humans’ dependency for accomplishing recurring tasks. The latest and most adopted technology for remotely monitoring and controlling any machine or device is the Internet of Things-which has become one of the industry standards for automation. Multiple edge computation capable embedded devices and machines are connected to the internet as independent nodes, which can be monitored and controlled remotely with the use of IoT. Adapting this cutting-edge technology for household appliances to monitor and control them is often seen nowadays. This is fast and reliable but arrives with its own snags such as availability of internet at place where user and also the device/appliance is located, use of single control strategy which hampers and restricts the user always to carry the remote or to stay in the vicinity of the device to be controlled in case of wifi or Bluetooth controlled automation. The proposed research focuses on the adoption of multiple strategies to control each device/appliance to empower the end-user without being restricted to depend on a single strategy remote controlling system. The core part of the proposed research revolves around the Message Queuing Telemetry Transport (MQTT) protocol but can be activated with multiple routes such as touch-based sensing panels, cellphone-based controlling, web-enabled controlling or even gesture-based controlling of the appliances resulting in an ameliorative user experience and interactions with machines.

Neural Networks in the Modern Age

The idea to apply representations about the work of the human brain as mathematical models, which can be used in a variety of applied tasks is shown. It is shown that mathematical and physiological models have quite little in common, but the basic idea - to apply neurons as some independent computational nodes and combine them into layers - has been developed to the current state of technology. The historical background of the development of neural network science is given and the main researchers, whose works have influenced the vector and the pace of technology development the most, are mentioned. It is shown that neural networks had different support from investors during their development and the peak of mass interest in them depended on the emergence of necessary computing power or a breakthrough network architecture. Such networks were in its time perceptrons, networks with feedback, networks applying convolution operation for image analysis and classification. It is shown that the so-called deep learning has developed based on weight optimization methods by gradient descent. A review of known solutions of supervised learning, feedback, and language-based learning networks is conducted. Generative models look like the most promising direction in the development of scientific thought and the creation of interpretive solutions based on neural networks. It is shown that in learning with a teacher, which is typical for deep neural networks, the optimization for it is performed by regularization procedures, which help to avoid diversity and provide error minimization during error back propagation

Performance Evaluations of Distributed File Systems for Scientific Big Data in FUSE Environment

Data are important and ever growing in data-intensive scientific environments. Such research data growth requires data storage systems that play pivotal roles in data management and analysis for scientific discoveries. Redundant Array of Independent Disks (RAID), a well-known storage technology combining multiple disks into a single large logical volume, has been widely used for the purpose of data redundancy and performance improvement. However, this requires RAID-capable hardware or software to build up a RAID-enabled disk array. In addition, it is difficult to scale up the RAID-based storage. In order to mitigate such a problem, many distributed file systems have been developed and are being actively used in various environments, especially in data-intensive computing facilities, where a tremendous amount of data have to be handled. In this study, we investigated and benchmarked various distributed file systems, such as Ceph, GlusterFS, Lustre and EOS for data-intensive environments. In our experiment, we configured the distributed file systems under a Reliable Array of Independent Nodes (RAIN) structure and a Filesystem in Userspace (FUSE) environment. Our results identify the characteristics of each file system that affect the read and write performance depending on the features of data, which have to be considered in data-intensive computing environments.

On the algebra of data sketches

We consider the problem of designing a distributed data sketch for scenario in which data stream is observed by many independent network nodes. We require that a sketch apart from being computationally and memory efficient should also be mergeable in a way that mimics set theory operations on related data sets. For example, when monitoring network traffic, one may consider how many distinct packets passed through a given node (sum of sets for different time windows) or passed through two given nodes (intersection of sets from two locations) and what is their total size (intersection of weighted sets). In this paper we propose a sketch that allows to efficiently summarize sets constructed from a sequence of set theory operations. We also provide an analytical control over the trade-off between the accuracy and storage/computational requirements. In comparison to the previous works the proposed solution 1) allows the weights of elements, 2) allows performing set theory operations simultaneous on a large number of sketches, 3) does not require computationally expensive numerical calculations and guarantees low overheads.