Local Scheme Research Articles

WIRELESS SENSOR NETWORK LOCALIZATION TECHNIQUES PERFORMANCE

A large number of inexpensive, small sensors make up a wireless sensor network. The collection and transmission of data is one of the crucial functions of a sensor network. In the greater part of the applications, it is of much interest to figure out the area of the information. Localization methods can be used to obtain this kind of information. Therefore, node localization is very important when using localization algorithms to determine the position of a node. As a result, WSN node localization emerges as one of the primary obstacles. The localization schemes can be broadly divided into two groups based on range measurements, such as: range based and range free plans. Range-based localization techniques cannot be used due to the sensing node's hardware limitations and high cost. Since coarse accuracy is sufficient for most sensor network applications, range-free schemes are being considered an alternative. The performances and accuracy of the range-free algorithms were tested with the application of MATLAB 2017a. The results demonstrated that the amorphous algorithm has the lowest localization error in most cases in comparison to the performance of these four algorithms. Likewise, results demonstrated amorphous, and DV-hop algorithms have 100% coverage rate in every situation that were tested.

A positioning algorithm based on improved robust extended Kalman filter with NLOS identification and mitigation

With the development of the information age and the maturity of Internet of Things technology, wireless sensor network has been widely applied in indoor localization. However, the non-line-of-sight (NLOS) propagation in complicated environment and the inherent noise of the sensor will introduce errors in the measurements, which will seriously lead to inaccurate positioning. In this paper, a novel localization scheme based on the mean reconstruction method is proposed, which reconstructs the distance measurements from all beacon nodes by taking the average twice to weaken the adverse effects of NLOS. At the same time, the noise average is re-estimated when the distance difference is not too large. Next, the robust extended Kalman filter (REKF) is used to process the reconstructed distance measurements to obtain positioning results. To make the positioning results more accurate, hypothesis test is used as NLOS identification to classify the position estimates generated from all distance combinations by least-squares. Then, the residual weighting (RWGH) method is utilized to combine the position estimates that fall into the validation region. At last, we merge the results from RWGH and REKF. The simulation and experimental results show that the proposed algorithm has high positioning accuracy and strong positioning robustness.

Dingo‐optimization‐based task‐offloading algorithm in multihop V2V/V2I‐enabled networks

AbstractIn the Vehicle‐to‐Vehicle‐ (V2V) and Vehicle‐to‐Infrastructure‐ (V2I) enabled edge computing networks, the task vehicle may fail to offload tasks to the nodes outside the one‐hop communication with high computing resources, resulting in longer offloading time. Therefore, in this paper, we first construct a multihop V2V/V2I communications‐enabled edge computing architecture, which innovatively adopts vehicles and roadside units (RSUs) task offloading jointly to expand the communication resources of the task vehicle. Then, for the offloading node selection strategy, we create a vehicle adjacency table and propose a quickly depth‐first search‐based scheme to search the optimal multihop path. Finally, we develop an optimal offloading scheme based on the discrete dingo optimization algorithm (DDOA) to solve the task‐processing‐time minimization problem, which can converge quickly and achieve lower task offloading latency. Each dingo in DDOA is designed to choose one of the group attack, persecution, and scavenger strategies to search the solution space and accelerate the convergence of the DDOA by a survival rule. The numerical experimental results reveal that our proposed algorithm can outperform the other algorithms and reduce the delay time by 80% compared with the local scheme.

A Secure Localization Scheme for UASNs Based on Anchor Node Self-Adaptive Adjustment

The UASNs are widely used in underwater communications and monitoring, and many applications require accurate information regarding the position of nodes. However, intentional attacks against devices or information transmission may exist in the network, and the localization process is periodic, so it is necessary to quickly address attacks and optimize the network structure. This paper proposed an anchor node self-adaptive adjustment localization scheme (ASAL), in which the anchor node can adjust the state and depth of its participation. Two filters were used to adjust the states of referable nodes. The first filter was based on the distance difference of reverse information transmission after direct localization based on anchor nodes. The second was based on the error of the anchor node’s reverse localization after network localization was completed. In addition, a depth-adjustment mechanism of anchor nodes was proposed to optimize the network structure, the virtual force vector was introduced to describe the cost of depth adjustment, and the whale optimization algorithm was used to converge to the depth with the minimum total cost. The simulation results showed that the scheme can ensure localization accuracy and coverage in attack scenarios and reduce localization energy consumption.

Sensing User's Channel and Location With Terahertz Extra-Large Reconfigurable Intelligent Surface Under Hybrid-Field Beam Squint Effect

This paper investigates the sensing of user's uplink channel and location in terahertz extra-large reconfigurable intelligent surface (XL-RIS) systems, where the unique hybrid far-near field effect and the beam squint effect caused by the XL array aperture as well as the XL bandwidth are overcome. Specifically, we first propose a joint channel and location sensing scheme, which consists of a location-assisted generalized multiple measurement vector orthogonal matching pursuit (LA-GMMV-OMP) algorithm for channel estimation (CE) and a complete dictionary based localization (CDL) scheme, where a frequency selective polar-domain redundant dictionary is proposed to overcome the hybrid field beam squint effect. The CE module outputs coarse on-grid angle estimation (respectively observed from the BS and RIS) to the localization module, which returns the fine off-grid angle estimation to improve CE. Particularly, with RIS, CDL can obtain user's location via line intersection, and a polar-domain gradient descent (PGD) algorithm at the base station is proposed to achieve the off-grid angle estimation with super-resolution accuracy. Additionally, to further reduce the sensing overhead, we propose a partial dictionary-based localization scheme, which is decoupled from CE, where RIS is served as an anchor to lock the user on the hyperbola according to time difference of arrival and the user's off-grid location can be obtained by using the proposed PGD algorithm. Simulation results demonstrate the superiority of the two proposed localization schemes and the proposed CE scheme over state-of-the-art baseline approaches.

Improving the efficiency of energy saving through the use of thermal energy

The article analyzes the problem of energy saving in the context of organizational and technical measures. One of the energy saving factors is the use of waste heat of various potential. The advantages of using waste heat in the energy sector and the scope of their application are considered. Power plants for waste processing and their parameters are described. Examples of the creation of special energy devices for the chemical industry are given. The connection of internal (by-product) energy resources (IER) with technologies using melting furnaces is noted. The boundaries of the use of heat recovery boilers (HRB) in various industries are determined. The schemes of HRB application at the wastewater disposal site with quantitative and qualitative characteristics are given. A method for improving economizers using bimetallic pipes and heat exchangers is given. The use of batteries in the disposal and consumption of waste heat is justified. The ways of increasing the efficiency of compressed air systems and the features of using arc steelmaking furnaces to reduce energy consumption are considered. The comparison of local heat supply schemes with regenerative ones is carried out and the prospects for the development of these schemes are noted.

Therapeutic response and safety of different treatments for cutaneous leishmaniasis in patients: A retrospective cross-sectional study

Objective: To analyze the therapeutic response and safety of different treatments for cutaneous leishmaniasis, received by patients in the Program for the Study and Control of Tropical Diseases- PECET-Medellín-Colombia. Methods: This is a retrospective cross-sectional study of patients attended at PECET Research Center during 2016-2021. Relevant information regarding sociodemographic characteristics, history of leishmaniasis, characterization of current infection, treatment received, follow-up of therapeutic response and safety was collected from the medical records. Data were analyzed with Pearson's Chi-square association tests and Mann-Whitney U test using statistical software. Results: A total of 486 clinical records of patients were analyzed, and 356 received treatment. Eight different therapeutic alternatives (systemic, local and in combination) were analyzed. The therapeutic response of the different alternatives used (except thermotherapy) was higher than 50%. Most frequent adverse events were myalgias, arthralgias and headache, and vesicles for systemic and local treatment, respectively. Conclusions: Safety profile and performance of local therapeutic alternatives and combined schemes for the treatment of uncomplicated cutaneous leishmaniasis are an interesting option for the management of the disease.

A Single-Stage Three-Mode Reconfigurable Regulating Rectifier for Wireless Power Transfer

This article proposes a reconfigurable regulating rectifier with a wide operational range for wireless power transfer. The proposed 3-mode rectifier achieves a broad range voltage regulation without global loop control to minimize the chip area occupation. Compared with previous work, more working modes and greater voltage gain allow the proposed rectifier to regulate lower input power, which extends the voltage regulation range. A local loop control scheme is proposed for voltage rectification with three modes. It adaptively senses the duty cycle of the mode signal to determine the working mode of the rectifier, and configure the rectifier to the desired mode for voltage regulation. The proposed system was designed and fabricated in a 180-nm BCD technology with an active area of 1.17 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$mm^{2}$</tex-math></inline-formula> . The measurement results show that the proposed system can rectify wide-range input AC power to a regulated output. The achieved voltage conversion ratio (VCR) is between 0.95X and 2.68X, with a peak power conversion efficiency (PCE) at 87.4%.

A nano-radian precision absolute local slope measurement method for X-ray reflectors.

Ultra-precise reflectors in the advanced light source facilities urgently call for local slope error measurements with nano-radian precision. However, the existing methods currently utilized in the long trace profiler systems struggle to meet the requirements. In this paper, we present a weak-value amplification enhanced absolute local slope measurement scheme, in which the surface height difference between two adjacent points can be measured directly with precision on the pico-meter level. As a result, the absolute local slope measurement reaches a record precision level of 9.7 nrad (RMS) with a small lateral separation of 0.5 mm. Comparing to the existing methods, our scheme is more disturbance-resistant, more compact and cost-effective. The local curvature measuring capability is also validated with two synchronously parallel local slope measurement paths, between which the separation is set as 2mm. A local curvature measurement is obtained with precision of 3.4 × 10-6m-1 (RMS) and its corresponding slope variation is 6.8 nrad. Our method exhibits important application prospects in the field of ultra-precise surface fabrication inspection.

Flock: Accurate Network Fault Localization at Scale

Inferring the root cause of failures among thousands of components in a data center network is challenging, especially for "gray" failures that are not reported directly by switches. Faults can be localized through end-to-end measurements, but past localization schemes are either too slow for large-scale networks or sacrifice accuracy. We describe Flock, a network fault localization algorithm and system that achieves both high accuracy and speed at datacenter scale. Flock uses a probabilistic graphical model (PGM) to achieve high accuracy, coupled with new techniques to dramatically accelerate inference in discrete-valued Bayesian PGMs. Large-scale simulations and experiments in a hardware testbed show Flock speeds up inference by &gt;10000x compared to past PGM methods, and improves accuracy over the best previous datacenter fault localization approaches, reducing inference error by 1.19-11x on the same input telemetry, and by 1.2-55x after incorporating passive telemetry. We also prove Flock's inference is optimal in restricted settings.

High-Throughput Condensed-Phase Hybrid Density Functional Theory for Large-Scale Finite-Gap Systems: The SeA Approach.

High-throughput electronic structure calculations (often performed using density functional theory (DFT)) play a central role in screening existing and novel materials, sampling potential energy surfaces, and generating data for machine learning applications. By including a fraction of exact exchange (EXX), hybrid functionals reduce the self-interaction error in semilocal DFT and furnish a more accurate description of the underlying electronic structure, albeit at a computational cost that often prohibits such high-throughput applications. To address this challenge, we have constructed a robust, accurate, and computationally efficient framework for high-throughput condensed-phase hybrid DFT and implemented this approach in the PWSCF module of Quantum ESPRESSO (QE). The resulting SeA approach (SeA = SCDM + exx + ACE) combines and seamlessly integrates: (i) the selected columns of the density matrix method (SCDM, a robust noniterative orbital localization scheme that sidesteps system-dependent optimization protocols), (ii) a recently extended version of exx (a black-box linear-scaling EXX algorithm that exploits sparsity between localized orbitals in real space when evaluating the action of the standard/full-rank operator), and (iii) adaptively compressed exchange (ACE, a low-rank approximation). In doing so, SeA harnesses three levels of computational savings: pair selection and domain truncation from SCDM + exx (which only considers spatially overlapping orbitals on orbital-pair-specific and system-size-independent domains) and low-rank approximation from ACE (which reduces the number of calls to SCDM + exx during the self-consistent field (SCF) procedure). Across a diverse set of 200 nonequilibrium (H2O)64 configurations (with densities spanning 0.4-1.7 g/cm3), SeA provides a 1-2 order-of-magnitude speedup in the overall time-to-solution, i.e., ≈8-26× compared to the convolution-based PWSCF(ACE) implementation in QE and ≈78-247× compared to the conventional PWSCF(Full) approach, and yields energies, ionic forces, and other properties with high fidelity. As a proof-of-principle high-throughput application, we trained a deep neural network (DNN) potential for ambient liquid water at the hybrid DFT level using SeA via an actively learned data set with ≈8,700 (H2O)64 configurations. Using an out-of-sample set of (H2O)512 configurations (at nonambient conditions), we confirmed the accuracy of this SeA-trained potential and showcased the capabilities of SeA by computing the ground-truth ionic forces in this challenging system containing >1,500 atoms.

Behaviour of UHPFRC-retrofitted RC beams with varying strengthening configurations under single and repeated blast loading

The objective of this study was to characterize the effects of various UHPFRC strengthening configurations on the blast performance of reinforced concrete (RC) beams. The methodology involved blast tests on eight as-built and UHPFRC-retrofitted RC beams using a pneumatically-driven shock-tube. The 150 mm × 200 mm × 2440 mm beams were first cast with 40 MPa concrete and were retrofitted by replacing the 20 mm cover with UHPFRC. The studied parameters included the influence of UHPFRC applied on the tension face, or in the form of U, Full or localized jackets, under single or repeated blast loads. The first set included one as-built beam and three specimens with tension-sided (T), U-jacket (UJ) or full jacket (FJ) retrofits, and were tested under repeated and gradually increasing blast pressures (Pr = 16, 38 and 50 kPa, for Blasts 1, 2 and 3). The second set included one as-built beam and two specimens with full-jacket retrofits applied over the full span (FJ) or in the middle hinge region (FJ-Hinge). The final beam had a hybrid of the T-sided and FJ-Hinge schemes. These beams were tested under single Blast 3 loads, and then tested under static bending to assess their residual capacity. In the first set, all retrofits resulted in reduced displacements (ranging from 30% to 60%) when compared to the as-built beam; however, the repeated blasts ultimately led to crack localization and rupture of the tension steel bars. In the second set, both the full-span and localized hinge schemes led to reductions in damage and displacements (ranging from 20% to 40%), without bar fracture, along with significant residual capacity. Finite element (FE) modelling using LS-DYNA was used to predict the blast behavior of the beams. After validation, the models were used to study the effects of the longitudinal steel ratio and blast load scenario on the behaviour and failure mode of the beams. Increasing the steel ratio was found to be effective in preventing bar rupture, while this failure mode was more likely under repeated blast loading.

Barycentric coordinate-based distributed localization for wireless sensor networks subject to random lossy links

Distributed localization in wireless sensor networks is essential for verifying the positions of the sensor nodes deployed in the sensing field based on information interactions. However, the information transmitted from the controller to the actuator for each sensor node may be lost owing to communication noise, channel interference, or network congestion. With this in mind, this paper aims at investigating the distributed localization for wireless sensor networks subject to random lossy links. The barycentric coordinate, which can be determined by the distance measurements between node pairs in the network, is used as the basic scheme for range-based localization. First, based on the characterization of random link loss in wireless sensor networks with a Bernoulli random variable, a distributed iterative localization algorithm is proposed. And then, the global convergence of the proposed localization algorithm which ensures accurate localization is theoretically proved by using the convergence of sub-stochastic matrices’ product. Finally, numerical examples are conducted to illustrate the effectiveness of the proposed localization algorithm.

Tooth root crack detection of planet gear in industrial robot RV reducer

The windowed synchronous averaging (WSA) is widely utilized for planetary structures. However, it cannot be applied for the fault detection of the planetary structure in the industrial robot rotate vector (RV) reducer. The robot usually works within a specified angle range, which causes the RV reducer rotates incompletely. To address this issue, an angle compensation local synchronous fitting scheme is proposed. To detect the localized planet gear fault in the RV reducer, the observed vibration is equi-angle resampled. And the synchronous interference contained in the resampled vibration is constructed and removed according to the angle compensation strategy. The residual data is used to construct the synthetic vibration of the planet gear. Then, the fault feature of the planet gear can be detected. Experiments on the RV reducer test rig under the robot running conditions support the effectiveness of the proposed scheme positively.

NLOS node localization for improving warning message distribution in VANETS

SummaryVehicular Ad‐hoc Network (VANET) is a vehicle‐to‐vehicle communication technology, which is highly promising due to its continuous demand, applicability, and suitability for public safety. The vehicular nodes establish communication with each other for exchanging traffic data and road maps, especially when warning messages need to be broadcasted on the network during emergency situations. Therefore, accurate vehicle information is necessary in such circumstances to improve the accuracy of Non‐Line‐of‐Sight (NLOS) node localization. In this paper, a Driving Training‐Based Optimization for NLOS Node Localization (DTO‐NL) Scheme is proposed for facilitating better warning message delivery during emergency situations in vehicular networks through a dynamic increase and decrease in the search based on the position of unknown NLOS nodes from the considered anchor nodes employed for localization. The performance of the suggested technique has been analyzed based on parameters like warning message delivery rate, latency, localization error, channel utilization rate, energy consumption, and packet delivery ratio. The proposed DTO‐NL scheme enhances the warning message delivery rate by 9.81%, 11.28%, 14.38%, and 17.64% compared to WIDVBA , ROA‐APS , ILL‐WOA‐NLOS , and WDHPBDS techniques .

Advanced Bio-Inspired computing paradigm for nonlinear smoking model

Smoking has emerged as one of the leading global factors that is the source of health issues. It damages almost all of the body's organs. It damages various muscles and causes lung cancer. Additionally, it causes ulcers, pulmonary disease, and vascular deterioration. Except for the financial benefit to tobacco companies, manufacturers, and marketing companies, smoking has no advantages. Due to these factors, the present study exploited a feed forward neural networking based global optimization procedure with a local scheme to solve a mathematical model of smoking. A genetic based algorithm and sequential quadratic programming (GA-SQP) are utilized as hybridized global and local strategies. The model is categorized into five classes: potential smokers, occasional smokers, smokers, temporary quit, and permanent quit smokers. An objective optimization function is constructed to minimize the mean square error using the designed smoking model in form of feed forward neural networking. The comparative evaluation of hybrid GA-SQP and Adam numerical scheme is also assessed to authenticate the precision and correctness of the solution of the smoking model. The robustness, perfection, and convergence stability of GA-SQP are verified by establishing various statistical performance indicators. The quantitative analysis provides the minimum, mean, and semi-inter quartile range values for absolute errors up to 6 to 13 decimal places, demonstrating the worthiness and precision of the proposed GA-SQP.

A neuro Meyer wavelet neural network procedure for solving the nonlinear Leptospirosis model

The aim of such work is to design a Meyer wavelet neural network (WNN) for solving the mathematical form of the Leptospirosis disease model (LDM). The global and local search optimization schemes based on the genetic algorithm (GA) and active-set algorithm (ASA) are used in this study. Leptospirosis is an infection spread by rodents, which is found in the world and causes fatalities in humans. The mathematical LDM model form consists of susceptible-infected-recovered (SIR), which is based on the disease spread processes. A fitness function is designed by using the mathematical LMD and then optimized by the hybridization of the GAASA. For the correctness, and capability of the Meyer WNN along with the procedures of GAASA, the comparison of the obtained and reference results is provided. Moreover, the reducible absolute error provides the efficiency of the proposed Meyer WNN along with the procedures of GAASA. The statistical observations are also provided to authenticate the convergence of the stochastic Meyer WNN along with the procedures of GAASA.

Linear interpolation of shape operators for umbilical points through local parametrization

Curvature analysis is frequently employed in shape interrogation. Umbilical points are of particular interest in curvature analysis because of their identical normal curvatures in all tangential directions. Locating the umbilical points is the basis of geometric analysis. On the one hand, as singularities, umbilical points severely hinder the analysis (e.g., in nets of curvature lines). On the other hand, they provide qualitative information about the intrinsic shape of a surface and are therefore desirable quantities in some applications. In this study, we develop a straightforward and effective method to detect generic umbilical points on triangular meshes. This method is applicable to any type of admissible parametrization. We propose two local parametrization schemes–orthogonal projection and conformal transformation–to be used with the proposed method. Furthermore, we systematically analyze our method and prove its convergence behavior. The algorithm used in our approach is flexible and straightforward to implement for triangular meshes of arbitrary topology.

Localized Validation Accelerates Distributed Transactions on Disaggregated Persistent Memory

Persistent memory (PM) disaggregation significantly improves the resource utilization and failure isolation to build a scalable and cost-effective remote memory pool in modern data centers. However, due to offering limited computing power and overlooking the bandwidth and persistence properties of real PMs, existing distributed transaction schemes, which are designed for legacy DRAM-based monolithic servers, fail to efficiently work on the disaggregated PM. In this article, we propose FORD, a F ast O ne-sided R DMA-based D istributed transaction system for the new disaggregated PM architecture. FORD thoroughly leverages one-sided remote direct memory access to handle transactions for bypassing the remote CPU in the PM pool. To reduce the round trips, FORD batches the read and lock operations into one request to eliminate extra locking and validations for the read-write data. To accelerate the transaction commit, FORD updates all remote replicas in a single round trip with parallel undo logging and data visibility control. Moreover, considering the limited PM bandwidth, FORD enables the backup replicas to be read to alleviate the load on the primary replicas, thus improving the throughput. To efficiently guarantee the remote data persistency in the PM pool, FORD selectively flushes data to the backup replicas to mitigate the network overheads. Nevertheless, the original FORD wastes some validation round trips if the read-only data are not modified by other transactions. Hence, we further propose a localized validation scheme to transfer the validation operations for the read-only data from remote to local as much as possible to reduce the round trips. Experimental results demonstrate that FORD significantly improves the transaction throughput by up to 3× and decreases the latency by up to 87.4% compared with state-of-the-art systems.

A Reliable Low-Latency Multipath Routing Algorithm for Urban Rail Transit Ad Hoc Networks

With the advancement of urban rail transit towards intelligence, the demand for urban rail transit communication has increased significantly, but the traditional urban rail transit vehicle–ground communication system has been unable to meet the future vehicle–ground communication requirements. To improve the performance of vehicle–ground communication, the paper proposes a reliable low-latency multipath routing (RLLMR) algorithm for urban rail transit ad hoc networks. First, RLLMR combines the characteristics of urban rail transit ad hoc networks and uses node location information to configure a proactive multipath to reduce route discovery delay. Second, the number of transmission paths is adaptively adjusted according to the quality of service (QoS) requirements for vehicle–ground communication, and then the optimal path is selected based on the link cost function to improve transmission quality. Third, in order to enhance the reliability of communication, a routing maintenance scheme has been added, and the static node-based local repair scheme is used in routing maintenance to reduce the maintenance cost and time. The simulation results show that compared with traditional AODV and AOMDV protocols, the proposed RLLMR algorithm has good performance in improving latency and is slightly inferior to the AOMDV protocol in improving reliability. However, overall, the throughput of the RLLMR algorithm is better than that of the AOMDV.