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This paper considers distributed estimation methods to enable the formation of Unmanned-Aerial-Vehicles (UAVs) that track a moving target. The UAVs (or agents) are equipped with communication devices to receive a beacon signal from the target and share information with neighboring UAVs. The shared information includes the time-of-arrival (TOA) of the beacon signal from the target and estimates on the target’s location. Every UAV processes the received information from the neighbors using a single-time-scale distributed estimation protocol. This differs from multi-time-scale protocols that require (i) many consensus iterations on a-priori estimates, (ii) fast communication among agents (in general, much faster than the sampling rate of the target dynamics), and thus, more-costly communication equipment and processing units. Further, our approach outperforms most single-time-scale methods in terms of observability assumption as these methods assume that the target is observable via the measurement data received from neighboring UAVs (referred to as local-observability). This requires more communications among the sensors. In contrast, our approach is only based on global-observability assumption, and thus, requires less networking (only strong-connectivity) and communication traffic along with less computational load by data-processing once at the same time-scale of sampling target dynamics. We consider modified time-difference-of-arrival (TDOA) measurements with a constant output matrix for the linearized model. UAVs make a pre-specified formation, and by estimating the target’s location via these measurements, move along with the target. Note to Practitioners—Inspired by recent development in industrial UAVs along with emerging progress in low-cost processing units, fog computing systems, and wireless communications, this paper considers mobile tracking of a moving target via a group of wireless-connected autonomous drones. In the classical tracking methods, which are prone to single-point-of-failure, all the sensors need to send their information to a ground central station over a long-range and costly data-transmission channel. In contrast, by collaborative tracking the processing and decision-making are distributed among a swarm of drones equipped with onboard miniaturized electronic parts such as sensors, microcontrollers, microprocessors, and communication units. This article provides an efficient algorithm to enable such drones to autonomously track the moving target in real-time. Note that the cost and tracking ability of the UAV swarm are directly determined by the computational efficiency and communication burden of the estimation algorithm. In this regard, most available estimation algorithms are over budget and even infeasible due to the need for fast data-transmission channels, fast CPUs, and high network traffic. Our proposed estimation technique outperforms similar algorithms in terms of required communication bandwidth, data-transmission rate, and computational resources, which considerably reduces the hardware cost and improves tracking efficiency in real-time large-scale applications. We show the feasibility and efficiency of our distributed tracking method by simulation. [ABSTRACT FROM AUTHOR]

Todays, Wireless Body Sensor Networks (WBSNs) are used as a useful way in health monitoring. One of the most important problems regarding Wireless Body Sensor Network (WBSNs) is network lifetime. This factor mainly relies on the energy consumption of sensors. In fact, during capturing vital sign data and also communicating them to the coordinator, the biosensors consume energy. In this article, we are interested to propose an energy efficient Adaptive Sampling (AS) rate specification algorithm to set the amount of sensed data. According to the National Early Warning Score (NEWS), the sensors gather data and detect emergency data. Two scenarios have been used; the first is utilizing context recognition to indicate the active and sleep sensors in different time slices and the second is using watchdog sensors for checking patient situation in critical condition. Simulation results show that the proposed method can save energy and increase network lifetime by up to 4 times more than the previous work. In addition, our methods allow on average 75% improvement in overhead data reduction while maintaining more than 90% data integrity. [ABSTRACT FROM AUTHOR]

This research focuses on resource assignment in cooperative energy heterogeneous systems with non-orthogonal multiple access in which cells are powered via a common grid network and alternative energy resources and all base stations have the ability to cover a group of subscribers simultaneously at a specific frequency band. In order to consider the local limitations of alternative energy resources, it was assumed that the alternative energy would be shared among the base stations by the dynamic grid network. In this architecture, resource allocation and user association frameworks should be reconfigured because conventional schemes use orthogonal multiple access. Hence, this paper suggests a novel approach joint optimal power allocation and user association techniques to achieve the maximum degree of energy efficiency for the whole system in which the quality of experience parameters are assumed to be bounded during multi-cell multicast sessions. The solution to the introduced problem in a scenario with fixed transmission power is an improved decentralized algorithm that supplies effective user association framework. The model has been modified to develop joint multi-layered resource control and user association that can distinguish the service pattern in cooperative energy heterogeneous systems with non-orthogonal multiple access to obtain more resource optimality than current approaches. The effectiveness of the suggested approach has been confirmed by the numerical results. Also, the results reveal that non-orthogonal multiple access can provide greater energy efficiency than orthogonal multiple access in heterogeneous wireless networks. [ABSTRACT FROM AUTHOR]

One of the problematic concerns in petroleum industries is the deposition of heavy fractions of crude oil such as asphaltene fraction during production and transportation. The utilization of inhibitors is known as a relative low cost and effective method for asphaltene inhibition. In this study, Radial basis function artificial neural network (RBF-ANN) was applied to predict asphaltene precipitation reduction in terms of structure and concentration of inhibitor and oil properties. In order to training and testing of RBF-ANN the required data are extracted from reliable sources. The predicted asphaltene precipitation reduction values were compared with the actual data statistically and graphically. The coefficients of determination for training and testing phases of RBF-ANN were determined as 0.995906 and 0.994853 respectively. These evaluations showed that the RBF-ANN as a predictive tool has great capacity to estimate effect of asphaltene inhibitors on reduction of asphaltene precipitation. [ABSTRACT FROM AUTHOR]

Summary: Recently, the growth of Internet of Things (IoT) and its remarkable impacts on human well‐being life style are deniable. On the connectivity side, IoT is highly related to Wireless Sensor Network (WSN) concept. The key elements include the data, which is machine‐produced, specifically by sensors, and the data communication through connectivity technologies. On the security side, primary user emulation attack (PUE) is one of the well‐defined attacks in cognitive radio (CR)–based WSN. Here, we investigate a smart primary user emulation attacker that has the most destructive effect on the spectrum sensing unit of cognitive radio users. To deal with this attack, a soft cooperative spectrum sensing using an energy detector is proposed. In the proposed method, the values of sensing information of each secondary user are sent to a fusion center. Once the values are received, they will be combined with some appropriate coefficients in order to minimize spectrum sensing probability of error for a given probability of false alarm. The coefficients are the variables of a constrained optimization problem. Based on simulation results, our method has a lower error probability in spectrum sensing in comparison to hard combination schemes (eg, OR rule) and soft combination schemes (eg, CSINR method). [ABSTRACT FROM AUTHOR]

Viscosity is known as one of major properties of fluids which have straight effects on different parts of chemical and petroleum industries. Due to this importance, in the present work, adaptive neuro-fuzzy interference system (ANFIS) was coupled with genetic algorithm (GA) to predict dynamic viscosity of normal alkane in terms of molecularweight of n-alkane, temperature and pressure. In order to prepare and validate the predicting model 228 experimental data points were extracted from the literature. The outputs of this predictive tool were compared with the experimental data and comparisons showed that predicted dynamic viscosities have good agreement with experimental data. According to the statistical and graphical analyses this simple tool can be used as a rigorous and accurate method for prediction of dynamic viscosity of n-alkane, especially at reservoir conditions. [ABSTRACT FROM AUTHOR]

It is essential to satisfy class-specific QoS constraints to provide broadband services for new generation wireless networks. A self-optimization technique is introduced as the only viable solution for controlling and managing this type of huge data networks. This technique allows control of resources and key performance indicators without human intervention, based solely on the network intelligence. The present study proposes a big data based self optimization networking (BD-SON) model for wireless networks in which the KPI parameters affecting the QoS are assumed to be controlled through a multidimensional decision-making process. Also, Resource Management Center (RMC) was used to allocate the required resources to each part of the network based on made decision in SON engine, which can satisfy QoS constraints of a multicast session in which satisfying interference constraints is the main challenge. A load-balanced gradient power allocation (L-GPA) scheme was also applied for the QoS-aware multicast model to accommodate the effect of transmission power level based on link capacity requirements. Experimental results confirm that the proposed power allocation techniques considerably increase the chances of finding an optimal solution. Also, results confirm that proposed model achieves significant gain in terms of quality of service and capacity along with low complexity and load balancing optimality in the network. [ABSTRACT FROM AUTHOR]

Summary: The rapid growth of distributed computing systems that heavily communicate and interact with each other has raised the importance of confrontation against cyber intruders, attackers, and subversives. With respect to the emergence of cloud computing and its deployment all over the world, and because of its distributed and decentralized nature, a special security requirement is needed to protect this paradigm. Intrusion detection systems could differentiate usual and unusual behaviors by means of supervising, verifying, and controlling the configurations, log files, network traffic, user activities, and even the actions of different processes by which they could add new security dimensions to the cloud computing systems. The position of the intrusion detection mechanisms in cloud computing systems as well as the applied algorithms in those mechanisms are the 2 main factors in which many researches have focused on. The goal of those researches is to uncover intrusions as much as possible and to increase the rate and accuracy of detections while reducing the false warnings. Those solutions, however, mainly have high computational loads, low accuracy, and high implementation costs. In this paper, we present a comprehensive and accurate solution to detect and prevent intrusions in cloud computing systems by using a hybrid method, called HIDCC. The implementation results of the proposed method show that the intrusion coverage, intrusion detection accuracy, reliability, and availability in cloud computing systems are considerably increased, and false warnings are significantly reduced. [ABSTRACT FROM AUTHOR]

As technology shrinks, multiple bits upsets (MBUs) are becoming an important problem in the reliability of digital designs exposed to radiation effects. In this paper, a new technique for the implementation of finite impulse response (FIR) filters is presented that provides protection against single and multi-unidirectional bit upsets (SEUs and MBUs), which have a lower circuit complexity and cost than traditional techniques like N-modular redundancy. Most of previous works has focused on single event upset (SEU), however, in this paper, a coding method based on Berger codes are presented and implemented on FPGA-based FIR filter. As the Berger coding covers all unidirectional faults, the MBUs are also handled in the proposed schemes. The effectiveness of the proposed technique has been evaluated using a dynamic partial reconfiguration-based fault injection platform. The implementation results of the proposed mitigation technique in comparison with traditional TMR method and previous mitigation techniques in the literature represent the effectiveness in terms of protection and implementation cost. [ABSTRACT FROM AUTHOR]

Single-time-scale distributed estimation of dynamic systems via a network of sensors/estimators is addressed in this letter. In single-time-scale distributed estimation, the two fusion steps, consensus and measurement exchange, are implemented only once, in contrast to, e.g., a large number of consensus iterations at every step of the system dynamics. We particularly discuss the problem of failure in the sensor/estimator network and how to recover for distributed estimation by adding new sensor measurements from equivalent states. We separately discuss the recovery for two types of sensors, namely $\alpha$ and $\beta$ sensors. We propose polynomial-order algorithms to find equivalent state nodes in graph representation of the system to recover for distributed observability. The polynomial-order solution is particularly significant for large-scale systems. [ABSTRACT FROM AUTHOR]

Accurate prediction of the solid deposition is petroleum industry can result in increasing the production efficiency. This can also result in the elimination of a major industrial problem, namely the wax deposition. In this study, application of intelligent methods in prediction of the wax deposition is investigated by developing a radial basis function artificial neural network. Levenberg Marquardt algorithm is also applied to determine the optimum predictions. Results from the proposed model are also compared to Kamari et al. model revealing the better performance of the proposed RBF-ANN. The validity of the proposed model is also investigated using statistical and graphical approaches, illustrating the great capability of the proposed RBF-ANN in accurate prediction of the wax deposition. R-squared and mean squared error values of 0.9975 and 0.029251 are obtained for the proposed model, revealing the validity of the RBF-ANN in estimating the wax deposition. [ABSTRACT FROM AUTHOR]

It is essential to satisfy class-specific QoS constraints to provide broadband services for new generation networks. The present study proposes a QoS-driven multicast scheme for wireless networks in which the transmission rate and end-to-end delay are assumed to be bounded during a multiple multicast session. A distributed algorithm was used to identify a costefficient sub-graph between the source and destination which can satisfy QoS constraints of a multicast session. The model was then modified as to be applied for wireless networks in which satisfying interference constraints is the main challenge. A discrete power control scheme was also applied for the QoS-aware multicast model to accommodate the effect of transmission power level based on link capacity requirements. We also proposed random power allocation (RPA) and gradient power allocation (GPA) algorithms to efficient resource distribution each of which has different time complexity and optimality levels. Experimental results confirm that the proposed power allocation techniques decrease the number of unavailable links between intermediate nodes in the sub-graph and considerably increase the chance of finding an optimal solution. [ABSTRACT FROM AUTHOR]

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In this paper, a cognitive machine-to-machine (CM2M) network is considered in which underlaid on a macro cell, there exist some co-existing small cell based M2M networks. In each small cell, M2M network is comprised of some machine type devices (MTDs) and an MTD gateway. The MTD gateway collects data of MTDs within each small cell and transmits it to the small cell base station reusing the spectrum of the macro cell. To avoid data loss, MTD gateway is equipped with a buffer. In the considered CM2M network, MTDs with delay sensitive application are prioritized over MTDs having delay tolerant packets. In order to minimize the average delay of the MTDs, a novel packet adjustment scheme is proposed. The average delay and the average throughput of the CM2M network are computed. Simulation results show that the proposed scheme outperforms conventional policy without packet adjustment in terms of average delay of MTDs. [ABSTRACT FROM AUTHOR]

Arrhythmias using electrocardiogram (ECG) signal is important in medical and computer research due to the timely diagnosis of dangerous cardiac conditions. The current study used the ECG to classify cardiac signals into normal heartbeats, congestive heart failure, ventricular arrhythmias, atrial fibrillation arrhythmias, atrial flutter, malignant ventricular arrhythmias, and premature atrial fibrillation. A deep learning algorithm was used to identify and diagnose cardiac arrhythmias. We proposed a new ECG signal classification method to increase signal classification sensitivity. We smoothed the ECG signal with noise removal filters. A discrete wavelet transform based on an arrhythmic database was applied to extract ECG features. Feature vectors were obtained based on wavelet decomposition energy properties and calculated values of PQRS morphological features. We used the genetic algorithm to reduce the feature vector and determine the input layer weights of the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Proposed methods for classifying ECG signals were in different classes of rhythm to diagnose heart rhythm diseases. Training data was with 80% of the data set and test data was with 20% for the whole data set. The learning accuracy for the results of training and test data in the ANN classifier was calculated as 99.9% and 88.92% and in ANFIS as 99.8% and 88.83% respectively. Based on these results, good accuracy was observed., (© 2023 IOP Publishing Ltd.)