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The massive growth in the popularity of Internet of Things (IoT) and hence expansion in the number of IoT devices has led to network control issues. The heterogeneity observed in the generated data from each device has further contributed to latency delays and network traffic concerns. An integral part of current network research encompasses the mo...
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... network analysis tools, and techniques for characterizing the network traffic are presented. A comprehensive account of some relevant studies using machine learning approaches, statistical methods and software defined network (SDN) based approaches for capturing the network traffic generated from IoT based devices and applications are discussed. Fig. 1 provides a generalized high-level architecture for the network traffic flow mechanism between different devices. ...
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... Sujith Bebortta. et al. [4] implemented a method addressing the challenge of network traffic administration for diverse IoT devices. The focus was on efficiently characterizing inter-arrival rates through packet-level and flow-level analysis, facilitating the crucial identification and management of IoT devices for stable network activities and enhanced cybersecurity. ...
... Nevertheless, in extremely restricted computational systems, this approach imposes extra CPU workloads and resource outages, potentially deteriorating the integrity of the collected packets. 65 To limit the quantity of data transferred across gateways and avoid circulation, the routing topology is often a tree structure topology across the network. The routing topology among resources has been determined by research using different methodologies. ...
The widespread use of Internet of Things (IoT) in various wireless sensor networks applications has increased their importance in recent years. IoT is a smart technology that connects anything anywhere at any time. These smart objects, which connect the physical world with the world of computing infrastructure, are expected to permeate all aspects of our daily lives and revolutionize a number of application domains such as healthcare, energy conservation, and transportation. As wireless networking expands, the disadvantage of wireless communication is clearly obvious. People's apprehension over the IoT's dependability has therefore skyrocketed. IoT networks' key requirements are dependability, channel security, fault tolerance, and reliability. Monitoring the IoT networks depends on the availability and correct functioning of all the network nodes. Recent research has proposed promising solutions to address these challenges. This article systematically examines recent articles that use meta‐heuristic and nature‐inspired algorithms to establish reliable IoT networks. Eighteen articles were analyzed in four groups. Results showed that reliable enhancement mechanisms in IoT networks increase fault node detection, network efficiency, and lifetime and attain energy optimization results in the IoT concept. Additionally, it was discovered in the literature that the current studies focus on how to effectively use edge network capabilities for IoT application executions and support, along with the related needs. Reviewing literature to show how valuable “an application of meta‐heuristic and nature‐inspired algorithms for designing reliable networks based on the Internet of things” is. Reviewing the literature to indicate the major orientation of the articles and the solutions provided. Reviewing articles to see how real‐world solutions are implemented. Proposing future tasks to improve the reliable networking of objects.
... The empirical analysis of data acquired from real-time IoT traffic [6] is illustrated in Figs. 2-4. ...
... The massive growth in smart devices have largely influenced the perception of data acquisition and processing in the Industrial Internet of Things (IIoT). IIoT is becoming an increasingly emerging application area of IoT which entails a large number of computing devices and is capable of generating voluminous industrial data [1]. In recent years, IIoT has tremendously transformed the workforce associated with execution of industrial workloads. ...
Abstract—The extensive growth in Industrial Internet of Things (IIoT) applications have tremendously increased the demands for low latency and resource-sensitive computing to accomplish critical industrial automations. This has leveraged the use of some proficient computing paradigms like Multi-access Edge Computing (MEC) which facilitates a low latency and scalable solution for execution of industrial workloads. However, the continual generation of industrial data has imposed a substantial amount of stress on the resource-constrained MEC systems. In this perspective, our study proposes a Consolidated Stochastic Computation Offloading (CSCO) framework to address the increasing computational demands of MEC-based IIoT systems. The proposed framework efficiently handles industrial workloads by modeling them as stochastic processes to observe the number of data packets denied service due to finite number of busy
MEC servers. We provide an analytical solution corresponding to the loss probability of data packets denied service at the MEC servers. This leads to the development of a computation offloading mechanism for time-critical tasks. Further, we provide the expression for Conditional Waiting Time (CWT) and Unconditional Waiting Time (UWT) of the data packets waiting to be offloaded to the remote cloud servers. Through extensive numerical simulations it is inferred that the proposed CSCO framework provides promising results in characterizing the stochastic behavior of MEC-based IIoT systems thereby providing a low-latency, and resource sensitive solution for the considered system.
Index Terms—Industrial Internet of Things (IIoT), Queuing theory, Multi-access Edge Computing (MEC), Computation Offloading, MEC Server Bursts, Big Data.
... It has been challenging to find out a simple and tight mathematical expression for cumulative distribution function. Therefore, various approximation techniques have been proposed for the computation of different performance measures in wireless communication [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. Karagiannidis and Lioumpas [19] have improved the approximation of Q-function and defined integer power of Q-function to evaluate the error probabilities of fading channels. ...
The high-frequency trading (HFT) uses the discrete and finite precision Gaussian cumulative distribution table, which leads to the erroneous computation of modern option pricing models. In this context, this paper provides two fast and tight approximations for the Gaussian cumulative distribution function which takes continuous input values and provides a wide range of precision values correct up to (10−5). These approximations eliminate the existing finite precision and discrete tabulated values with the real-valued computations. We derive accurate and tight option prices by exploiting these approximations, which are in excellent agreement with the exact option price under a wide range of volatilities and time periods. It also operates well on the risk hedging metrics such as option greeks. Further, this framework can be employed for accurate and fast computation of pricing, designing of replicating portfolio, and dynamic hedging purposes which in turn reduces arbitrage in HFT. Finally, we discuss the time-dependent Fokker-Planck equation corresponding to the geometric Brownian motion (GBM) stochastic differential equation(SDE) and derive a steady-state solution in terms of lognormal probability density function (PDF) which helps to compute the aforementioned option price models.KeywordsStochastic differential equation·Fokker- Planck equation·high-frequency trading (HFT)·Gaussian Q-function·option price·option greeks.
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The goal of this study is to create a strong categorization system specifically designed for Internet of Things (IoT) device profiling. The main goal is to supplement current studies that use a wide range of machine learning techniques to identify anomalous behavior in Smart Home IoT devices with an exceptionally high accuracy rate. The intended framework is positioned to play a crucial function in bolstering IoT security in the future because it is made to include several types of abnormal activity detection. Our technological motivation stems from IoT smart sensors’ high processing power and advanced connectivity capabilities. Notably, these sensors have the potential to be manipulated for malicious purposes only on a single sensed data point rather than the complete collection of collected data from sensors, such as temperature, humidity, light, and voltage measurements. Such a threat lowers the detection effectiveness of many machine learning algorithms and has a substantial impact on the accuracy of aberrant behavior detection. To identify occurrences of alteration in one specific data point among the four potential data points collected by a single sensor, we compared and used different classifiers in our investigations, including the Decision Tree Classifier, KNeighbors Classifier, Support Vector Classifier (SVC), Logistic Regression, AdaBoost Classifier, Random Forest with Extreme Gradient Boost (XGBRF) Classifier, Random Forest Classifier, Light Gradient Boosting Machine (LGBM) Classifier, Gradient Boosting Classifier, and XGB Classifier. The results showed that the Gradient Boosting Classifier algorithm using random search attained an 85.96% detection accuracy, indicating a somewhat lower vulnerability to such changes. As a result, the Gradient Boosting Classifier algorithm with random search was the foundation for the carefully constructed suggested framework, which used four hyperparameter tuning mechanisms for comparison.
In recent times, several strategies to minimize the spread of 2019 novel coronavirus disease (COVID-19) have been adopted. Some recent technological breakthroughs like the drone-based tracking systems have focused on devising specific dynamical approaches for administering public mobility and providing early detection of symptomatic patients. In this paper, a smart real-time image processing framework converged with a non-contact thermal temperature screening module was implemented. The proposed framework comprised of three modules viz., smart temperature screening system, tracking infection footprint, and monitoring social distancing policies. This was accomplished by employing Histogram of Oriented Gradients (HOG) transformation to identify infection hotspots. Further, Haar Cascade and local binary pattern histogram (LBPH) algorithms were used for real-time facial recognition and enforcing social distancing policies. In order to manage the redundant video frames generated at the local computing device, two holistic models, namely, event-triggered video framing (ETVF) and real-time video framing (RTVF) have been deduced, and their respective processing costs were studied for different arrival ratess of the video frame. It was observed that the proposed ETVF approach outperforms the performance of RTVF by providing optimal processing costs resulting from the elimination of redundant data frames. Results corresponding to autocorrelation analysis have been presented for the case study of India pertaining to the number of confirmed COVID-19 cases, recovered cases, and deaths to obtain an understanding of epidemiological spread of the virus. Further, choropleth analysis was performed for indicating the magnitude of COVID-19 spread corresponding to different regions in India.
