Maximum Number Of Nodes Research Articles

High-speed turbulent flows towards the exascale: STREAmS-2 porting and performance

Exascale High Performance Computing (HPC) represents a tremendous opportunity to push the boundaries of Computational Fluid Dynamics (CFD), but despite the consolidated trend towards the use of Graphics Processing Units (GPUs), programmability is still an issue. STREAmS-2 (Bernardini et al. Comput. Phys. Commun. 285 (2023) 108644) is a compressible solver for canonical wall-bounded turbulent flows capable of harvesting the potential of NVIDIA GPUs. Here we extend the already available CUDA Fortran backend with a novel HIP backend targeting AMD GPU architectures. The main implementation strategies are discussed along with a novel Python tool that can generate the HIP and CPU code versions allowing developers to focus their attention only on the CUDA Fortran backend. Single GPU performance is analyzed focusing on NVIDIA A100 and AMD MI250x cards which are currently at the core of several HPC clusters. The gap between peak GPU performance and STREAmS-2 performance is found to be generally smaller for NVIDIA cards. Roofline analysis allows tracing this behavior to unexpectedly different computational intensities of the same kernel using the two cards. Additional single-GPU comparisons are performed to assess the impact of grid size, number of parallelized loops, thread masking and thread divergence. Parallel performance is measured on the two largest EuroHPC pre-exascale systems, LUMI (AMD GPUs) and Leonardo (NVIDIA GPUs). Strong scalability reveals more than 80% efficiency up to 16 nodes for Leonardo and up to 32 for LUMI. Weak scalability shows an impressive efficiency of over 95% up to the maximum number of nodes tested (256 for LUMI and 512 for Leonardo). This analysis shows that STREAmS-2 is the perfect candidate to fully exploit the power of current pre-exascale HPC systems in Europe, allowing users to simulate flows with over a trillion mesh points, thus reducing the gap between the Reynolds numbers achievable in high-fidelity simulations and those of real engineering applications.

Multi-level optimizing of parameters in stochastic configuration networks based on cloud model and nutcracker optimization algorithm

As a state-of-the-art neural network model, stochastic configuration networks (SCNs) are widely employed in diverse fields due to their exceptional approximation capabilities. Similar to other neural network models, an excessive number of parameters can potentially compromise the generalization ability of SCNs, including hyper-parameters such as the stochastic scale factors (λ) and the maximum number of nodes (Lmax), as well as model parameters like input weight (w) and input bias (b). To tackle this issue, this study proposes a multi-level parameter optimization approach, termed stochastic configuration network with cloud models (CMSCNs). Firstly, the optimal parameter range is determined based on the concept of “cloud droplet” from the cloud model. Herein, mathematical expectation (Ex) is substituted by a polynomial function constructed with λ as the dependent variable and other parameters as independent variables. Secondly, we employ the nutcracker optimization algorithm (NOA) to optimize w and b, using residuals as evaluation indices to identify their optimal combination. Thirdly, singular value decomposition (SVD) is integrated to compress the network structure of CMSCNs for enhanced computational efficiency. Finally, 18 public real datasets and submergence depth data from an oil well are utilized to assess the performance of CMSCNs. The experimental results demonstrate that our proposed method offers improved generalizability and stability while also exhibiting significant potential in practical applications.

Self-organizing hierarchical incremental learning framework and universal approximation analysis based on stochastic configuration mechanism

Conventional machine learning algorithms face significant limitations when dealing with high-dimensional data. Besides, deep learning models often require substantial computational resources and have a high processing time despite their excellent performance. Hence, this paper proposes an expanded stochastic configuration network and a self-organizing hierarchical incremental learning (SHIL) framework to overcome these challenges. Specifically, this study introduces a novel supervised hierarchical clustering tree based on the minimum redundancy maximum correlation algorithm, which mines internal data structures to construct diverse hierarchies. Subsequently, by exploiting the parent-child node relationships in the tree structure, SHIL defines the maximum number of nodes as the switching condition between levels uses the supervisory mechanism as the parameter selection criterion, and adopts the tolerance error as the termination criterion for the training. Furthermore, the universal approximation property of the SHIL framework is provided. The proposed SHIL framework is validated on several benchmark datasets, image datasets, and industrial robot cases, with the corresponding experimental results demonstrating that SHIL significantly improves computational efficiency and ensures high accuracy.

The Learning Curve for Robotic Lateral Pelvic Lymph Node Dissection for Rectal Cancer: A View from the West.

Lateral pelvic lymph-node dissection is performed for selected patients with rectal cancer with persistent lateral nodal disease after neoadjuvant therapy. This technique has been slow to be adopted in the West due to concerns regarding technical difficulty. This is the first report on the learning curve for lateral pelvic lymph node dissection in the US or Europe. The aim of this study was to analyze the learning curve associated with robotic lateral pelvic lymph node dissection. Retrospective observational cohort. Tertiary academic cancer center. Consecutive patients from 2012 to 2021. All patients underwent robotic lateral pelvic lymph node dissection. The primary endpoints were the learning curves for maximum number of nodes retrieved and urinary retention which was evaluated with simple cumulative-sum and two-sided Bernoulli cumulative-sum charts. Fifty-four procedures were included. A single-surgeon (n = 35) and an institutional learning curve are presented in the analysis. In the single-surgeon learning curve, a turning point marking the end of a learning phase was detected at the 12th procedure for the number of retrieved nodes and at the 20th for urinary retention. In the institutional learning curve analysis, two turning points were identified at the 13th and 26th procedures indicating progressive improvements for the number of retrieved nodes and at the 27th for urinary retention. No sustained alarm signals were detected at any time point. The retrospective nature, small sample size and the referral center nature of the reporting institution that may limit generalizability. In a setting of institutional experience with robotic colorectal surgery including beyond TME resections, the learning curve for robotic lateral pelvic lymph node dissection is acceptably short. Our results demonstrate feasibility of acquisition of this technique in a controlled setting, with sufficient case volume and proctoring can optimize the learning curve. See Video Abstract.

On the approximation capability of GNNs in node classification/regression tasks

Graph neural networks (GNNs) are a broad class of connectionist models for graph processing. Recent studies have shown that GNNs can approximate any function on graphs, modulo the equivalence relation on graphs defined by the Weisfeiler–Lehman (WL) test. However, these results suffer from some limitations, both because they were derived using the Stone–Weierstrass theorem—which is existential in nature—and because they assume that the target function to be approximated must be continuous. Furthermore, all current results are dedicated to graph classification/regression tasks, where the GNN must produce a single output for the whole graph, while also node classification/regression problems, in which an output is returned for each node, are very common. In this paper, we propose an alternative way to demonstrate the approximation capability of GNNs that overcomes these limitations. Indeed, we show that GNNs are universal approximators in probability for node classification/regression tasks, as they can approximate any measurable function that satisfies the 1-WL-equivalence on nodes. The proposed theoretical framework allows the approximation of generic discontinuous target functions and also suggests the GNN architecture that can reach a desired approximation. In addition, we provide a bound on the number of the GNN layers required to achieve the desired degree of approximation, namely 2r-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2r-1$$\\end{document}, where r is the maximum number of nodes for the graphs in the domain.

A Reassessment on Applying Protocol Interference Model Under Rayleigh Fading: From Perspective of Link Scheduling

Link scheduling plays a pivotal role in accommodating stringent reliability and latency requirements. In this paper, we focus on the availability and effectiveness of applying protocol interference model (PIM) under Rayleigh fading model to solve the problem. The motivation is that PIM caters to distributed link scheduling algorithm design, but usually lead to irrationality due to its localization behavior. While Rayleigh fading model can accurately describe the inherent characteristic of wireless signal propagation, but the features of global interference and channel fading make algorithm design more challenging. To be specific, we first remove the effect of channel fading on algorithmic design by establishing the relationship between Rayleigh fading model and non-fading model. We then propose a centralized once link elimination (OLE) algorithm by utilizing local nature of PIM, and achieve its distributed implementation based on the message delivery with time complexity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(\Delta_{\max}\ln\Delta_{\max})$</tex-math> </inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta_{\max}$</tex-math> </inline-formula> is the maximum number of nodes around a given node inside some range. Furthermore, based on random contention resolution, we design another distributed algorithm to schedule all the links within <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(\Delta^{3}_{\max}\ln\Delta_{\max})$</tex-math> </inline-formula> rounds. Simulations show that the PIM is of great confidence as same as Rayleigh fading model, and the proposed algorithms outperform three popular link scheduling algorithms.

Increasing efficiency for routing in internet of things using Binary Gray Wolf Optimization and fuzzy logic

In the field of information and communication technology, the Internet of Things is regarded as a brand-new and important technology. The introduction of new protocols in this area is caused by the presence of devices in these networks with constrained resources and relatively low computing power. One of the most well-known routing protocols for low-power devices is the RPL protocol. This algorithm cannot take into account all of the required routing goals at once. This article introduces a proposed data-oriented RPL algorithm that divides data during routing according to their content. This can decrease the amount of duplicate data transferred through the network, shorten the communication system's delay, conserve the node's limited energy, and prolong the network's lifespan. The effectiveness of RPL can be increased by selecting the best route utilizing the Binary Gray Wolf Optimization. The best parent node in the routing procedure is chosen using an objective function during the tree construction phase. This objective function is built using fuzzy logic and the Binary Gray Wolf Optimization in the suggested technique. The results of Matlab 2022a and OMNET environment tests have shown that the proposed method has increased the efficiency of energy consumption and reduced the period of instability and end-to-end delay. that the ratio of the instability period in the proposed method is much less than the other three methods, so that the ratio of the instability period is 57% for the proposed method in the ORPL and Qos RPL methods, it is equal to 80%, and in the RPL method it is equal to 89%. This problem shows that the proposed method is more stable, or, in other words, it has been active for a longer period of time with the maximum number of nodes.

Effect of Weed Management Practices on Growth, Yield and Quality of Okra [Abelmoschus esculentus (L.) Moench

The present investigation entitled “Effect of weed management practices on growth, yield and quality of okra Abelmoschus esculentus (L.) Moench)” was conducted at Vegetable Research Farm, Chandra Shekhar Azad University of Agriculture and Technology, Kanpur during kharif season 2022. The experiment was laid out in Randomized Block Design with seven different treatments viz. T1 = Pre-emergence application of Pendimethalin @ 6 ml /L, T2= Post emergence application of Quizalofop-ethyl @ 0.040 kg/ha ,T3 = Pre-emergence application of pendimethalin @ 6 ml/L + one hand weeding at 40 DAS , T4 = Post emergence application of Quizalofop-ethyl @ 0.040 kg ha-1 at 25 DAS + one hand weeding , T5 = Pendimethalin @ 6 ml /L as pre-emergence + Quizalofop-ethyl @ 0.040 kg ha-1 at 25 DAS , T6 = Weed free check (Three hand weeding) 20, 40 and 60 DAS , T7 = weed check control replicated thrice. The result of the study revealed that at 30 DAS maximum plant height (30.96cm) was reported in treatment T3 : Pre-emergence application of Pendimethalin @ 6 ml/L + one hand weeding at 40 days of sowing whereas at 60 and 90 DAS maximum plant height (75.50 and 113.53 cm) was reported in T6 : Weed free check (Three hand weeding) 20, 40 and 60 DAS. Maximum number of branches per plant (4.60)at 60 DAS was found in T3 : Pre-emergence application of Pendimethalin @ 6 ml/L + one hand weeding at 40 DAS and 90 DAS maximum number of branches per plant (5.86) was reported in T6 : Weed free check (Three hand weeding) 20, 40 and 60 DAS. Maximum number of nodes (17.83 ) at 60 days after sowing was noticed in treatment T3 = Pre-emergence application of Pendimethalin @ 6 ml/L + one hand weeding at 40 DAS and 90 Days after of sowing maximum number of nodes () was in treatment T6 = Weed free check (Three hand weeding) 20, 40 and 60 DAS. Maximum Pod diameter( 1.74cm) , fruit weight (12.88gm ), pod length( 14.1cm) , number of pods per plant( 25.66 ) , pod yield per plant ( 304.73gm) , pod yield per hectare (16.91 t), TSS( 3.2 Brix), crude fibre (8.46%) and cholorophyll content (1.18 mg) and relative cholorophyll content (54.39) .

A survey on identification of influential users in social media networks using bio inspired algorithms

There are 4.5 billion active social media users who are continuously generating very large amounts of social big data. This data can be a vital source for prescriptive data analytics as it contains various insights about the day-to-day involvements and activities of the user. Among the large number of user profiles in a social media network, it is critical to recognize the best nodes with maximum influentiality in order to propagate messages through them so as to reach maximum number of nodes. After identifying, this seed set of nodes can be used for effective marketing through the platform. Marketing messages which are propagated via social media networks is evaluated to be the most promising form of advertisement for any product. As in the case of any other advertising strategy, the marketing team will be looking for maximum reachability of the advertisement with minimum cost. They will select the influencers in such a way that the advertisement is propagated to the maximum nodes possible, but with only limited number of initial influencers. In this study we are evaluating the suitability of bio inspired algorithms to the influential node identification problem. We are considering a sub class of bio inspired algorithms called as ”Swarm Intelligence” algorithms as they exhibits the collective behaviour of an organized group of bio species analogous to a group of profiles in social networks. This study has attempted to compare 4 algorithms which were already used for predicting the seed-set of influential users. The aim is to identify the most suitable one for the target problem and improve the same in future works.

Towards Latency Bypass and Scalability Maintain in Digital Substation Communication Domain with IEC 62439-3 Based Network Architecture.

Parallel redundancy protocol (PRP) and high-availability redundancy protocol (HSR) are widely adopted protocols based on IEC 61850 standard to support zero recovery communication networks for time-critical and reliable interactions in power system substations. However, hiring these protocols comes with technical and economic constraints that impact the size of the substation network arrangement. Therefore, we will undertake a theoretical analysis of HSR, PRP, and their combinations to reach a maximum number of nodes in different substation communication architectures regarding IEC 61850 standard message time constraint requirements and IEC 62439-3 standard regulations. We will validate our findings through a simulation in the OPNET Modeler environment. In addition, we considered bandwidth efficiency by prohibiting the extra circulation of packets in the redundancy Box (RedBox) and QuadBox implementation as interfaces for HSR and PRP connection and HSR rings interconnection, respectively, which represent the main hindrance in utilizing the combination of these protocols.

IBRE-LEACH: Improving the Performance of the BRE-LEACH for Wireless Sensor Networks

Wireless sensor networks are extensively used in different fields, including severe and harsh environments such as battlegrounds, volcanic areas, healthcare, and so on. The major constraint of WSNs is the limited power supply, which impacts the lifecycle of the entire network. Clustering is a performing method used in WSNs to optimize power consumption and extend the lifetime of WSNs. LEACH is considered the first classical clustering protocol used in WSNs to optimize energy consumption. Many protocols have been developed to enhance the conventional LEACH protocol. BRE-LEACH (Balanced Residual Energy LEACH) is one of them. It is used to enhance the performance of the LEACH protocol. In this paper, we propose a new improved BRE-LEACH protocol called IBRE-LEACH (Improved Balanced Residual Energy LEACH), which improves the performance of the BRE-LEACH approach. The IBRE-LEACH combines clustering and multi-hop techniques. It selects CHs according to the residual energy and limits the maximum number of nodes in each cluster to balance the energy consumption. Thus, IBRE-LEACH allows the Abandoned Nodes to send their data to the BS. Furthermore, it routes the gathered data using CHs, ANs, and a root node, which is a node with maximum residual energy and minimum distance to the BS. The simulation results in MATLAB show that the IBRE-LEACH protocol increases the throughput, optimizes energy consumption, extends network lifetime, and ameliorates stability by 81.99%, 94.33%, and 91.47% compared to BRE-LEACH, LEACH-C, and LEACH, respectively.

Internet of Things (IoT) Device Management in Rural Areas to Support Precision Agriculture

Internet of Things (IoT) devices have an important role in supporting precision agriculture systems, namely acting as the first layer consisting of several nodes in a limited network group as a producer of land, environmental and plant parameter data that will be processed at the next level. This node is an embedded system consisting of sensors, microcontrollers, power supplies and wireless communication media. The challenge that arises is when the number of new nodes that will be connected to the concentrator in a limited network group is quite large and is still done manually by the user, so that it has the potential to cause complexity and errors in configuration. The purpose of this research is to design a self-registration scheme from each new node to the concentrator in a restricted network group automatically. The method used by new nodes for self-registration is to read the RSSI (Received Signal Strength Indicator) value from the concentrator, then exchange metadata according to the OCF (Open Connectivity Foundation) standard. The concentrator will refer to the maximum number of nodes allowed to join the group and check the metadata of the new node. If the registration process is successful, the new node will gain access rights to communicate with the concentrator and its closest neighbours in the network group. This mechanism will help farmers in rural areas to be able to configure Internet of Things (IoT) devices easily and provide benefits in supporting precision agriculture.

A Computer-Based IEC 61850 Sampled Values Analyzer for Parallel Power Quality Analysis

This paper introduces a system capable of characterizing on-site digital instrument transformers from the analysis of the IEC 61850/IEC 61869-9 frames injected by the Stand Alone Merging Units (SAMU) located in digital substations. Besides, the second objective of this work is to implement a suitable computation architecture to monitor distributed Power Quality (PQ) nodes. For that aim, capacity of calculating PQ from the information sent by the SAMU will be leveraged. In a first step, this approach relies on the implementation of an IEC 61000-4-30 class-A PQ library on an industrial-based computer. Concerning the first objective, several experimental tests were driven for a solid timestamping characterization of the received data. After that, different PQ tests were carried out to verify the suitability of the proposed solution by comparing the system against one traceable generator. The calculation of normalized error indexes (En) revealed that the developed system is compatible with respect to the reference generator. In this regard, the system can be used to calibrate digital instrument transformers (via SAMUs) against one analogue reference transformer. Later, the library is migrated to a more constrained hardware to identify the maximum number of parallel nodes that the analyzer can process. On this basis, several tests were conducted to stress the low-cost platform until reaching the thermal limit on CPU. In light of the results, the maximum number of nodes was 20. This fact indicates that the proposed low-cost analyzer meets the stringent class-A requirements of IEC 61000-4-30 for real-time parallel evaluations.

Data Dissemination in VANETs Using Clustering and Probabilistic Forwarding Based on Adaptive Jumping Multi-Objective Firefly Optimization

Data dissemination in a VANETs network requires a meticulous process to ensure a high quality of service and eliminate hazardous conditions due to congestion or a broadcast storm. Considering multi-metric approaches and their implicit conflicting nature, it is necessary to handle this through effective multi-objective optimization algorithms. An effective optimization can be handled using a meta-heuristic approach with a high level of solution interactions. For this purpose, firefly was selected, which is a type of meta-heuristic search algorithm. Several developments of the firefly optimization were added to increase its capability to find more dominating solutions, namely, objective decomposition, archive management, and controlled mutation for exploration and exploitation balance. This developed multi-objective optimization was designated as adaptive jumping multi-objective firefly algorithm (AJ-MOFA). Afterwards, AJ-MOFA was integrated with a clustering and forwarding mechanism (CFM). This mechanism includes three main components. The first is clustering, which uses arbitration based on the cluster head score; the second is a forwarding mechanism that uses probabilistic forwarding and the third is AJ-MOFA. The solution space design in CFM combined two variables: the first is the probability of forwarding and the second is the maximum number of nodes within one cluster. The metrics to be incorporated in the multi-objective optimizations are the packet delivery ratio (PDR), the end-to-end delay (E2E-delay) and the number of dropped packets. Comparing both AJ-MOFA and CFM with benchmarks using multi-objective optimization and networking metrics reveals the superiority in most evaluation measures, which makes them promising algorithms for data dissemination in VANETs. The results showed an accomplished PDR of 60% and an E2E delay of 6.6 seconds, while the number of dropped packets was almost nine for the entire running time of the experiment, comparing a similar or lower performance of the benchmarks for these metrics.

Predicting the Robustness of Large Real‐World Social Networks Using a Machine Learning Model

Computing the robustness of a network, i.e., the capacity of a network holding its main functionality when a proportion of its nodes/edges are damaged, is useful in many real applications. The Monte Carlo numerical simulation is the commonly used method to compute network robustness. However, it has a very high computational cost, especially for large networks. Here, we propose a methodology such that the robustness of large real‐world social networks can be predicted using machine learning models, which are pretrained using existing datasets. We demonstrate this approach by simulating two effective node attack strategies, i.e., the recalculated degree (RD) and initial betweenness (IB) node attack strategies, and predicting network robustness by using two machine learning models, multiple linear regression (MLR) and the random forest (RF) algorithm. We use the classic network robustness metric R as a model response and 8 network structural indicators (NSI) as predictor variables and trained over a large dataset of 48 real‐world social networks, whose maximum number of nodes is 265,000. We found that the RF model can predict network robustness with a mean squared error (RMSE) of 0.03 and is 30% better than the MLR model. Among the results, we found that the RD strategy has more efficacy than IB for attacking real‐world social networks. Furthermore, MLR indicates that the most important factors to predict network robustness are the scale‐free exponent α and the average node degree &lt;k&gt;. On the contrary, the RF indicates that degree assortativity a, the global closeness, and the average node degree &lt;k&gt; are the most important factors. This study shows that machine learning models can be a promising way to infer social network robustness.

Scalability Enhancement on Software Defined Industrial Wireless Sensor Networks Over TSCH

Industry 4.0 digitization requirements have led to widespread adoption of industrial wireless sensor networks (IWSNs) in manufacturing environments. These require strict control of the traffic flowing over the network to ensure the quality of service. Software-defined networks (SDNs) can address this challenge, as their centralized operation allows routing and media access control (MAC) protocols to be optimized. In addition, in many cases a massive deployment of metering devices is necessary, therefore the network must be scalable, to allow an increase in the number and density of nodes in the network. This implies an increase in the control traffic required by SDNs, generating a significant reduction in the throughput available for data. This is because the length of the timeslot limits the throughput of the network in protocols that uses time division, such as the Time Slotted Channel Hopping (TSCH), conditioning the number of nodes that the network can have and its data sending rate. In this paper, we propose to exploit the integration and reconfiguration capabilities of SDN to extend the number of nodes in the network through the use of multi-radio sinks called virtual sinks, orchestrated from the SDN controller, and extending their use to multiple SD-IWSNs to take full advantage of the radio spectrum. To optimize bandwidth usage, the SDN controller will allocate network slices, allowing nodes to send data with a high frequency using dedicated channels. The results obtained show that the use of multi-radio sinks provides for increasing the maximum number of nodes that can be deployed while preserving the quality of service (QoS) requirements. A linear increase in nodes at least 0.7 times for each radio interface is obtained compared to an SD-IWSN with a traditional sink. In addition, the use of virtual sinks increases the packet frequency of the network by a factor equal to the number of radio interfaces. Moreover, the total number of nodes is improved through the coexistence of multiple SD-IWSNs, where up to 13.7 times more nodes can be scheduled using all the available spectrum orchestrated from the same SDN controller.

LoRaWAN vs. 6TiSCH: Which one scales better?

Internet of Things (IoT) has been a hot topic in both academia and industry for the last few years. There are many possible applications, in fields as smart cities, home automation, smart buildings, agriculture, automated metering, logistic, industrial automation, among others. Such a wide range of applications resulted in various technological solutions in order to enable Machine-to-Machine (M2M) communications, in which we highlight those for wide area networks. In that sense, two widely used sub-GHz protocols for Low Power Wide Area (LPWA) communications are: Long Range (LoRa), using the upper layers defined by Long Range Wide Area Network (LoRaWAN); and IEEE 802.15.4g, using the upper layers defined by IPv6 over the Time-Slotted Channel Hopping (TSCH) mode of IEEE 802.15.4e (6TiSCH). While LoRaWAN is a well-known and widespread protocol, 6TiSCH offers IPv6 connectivity to LPWA networks and is used in important standards such as Wireless Smart Ubiquitous Network (Wi-SUN). This paper aims at determining how well each protocol scales, analyzing different aspects such as packet loss, delay, and the maximum number of nodes per square area. In order to achieve such results, computer simulations are performed using open-source simulators. The obtained results demonstrate that the best scalability depends on which scenarios are considered. For scenarios with multiple gateways requiring low latency or low packet transmission rates, LoRaWAN demonstrates better results. However, in scenarios with high packet transmission rates and where the latency is not a major concern, 6TiSCH is more appropriate. Moreover, even in the best case, the latency associated with the LoRaWAN technology may be considerably smaller than associated with 6TiSCH. Such novelty results are obtained in a fair and realistic way and, noting that both technologies could be used to build LPWA networks, it can help system designers to decide between the two technologies.

The Component Diagnosability of General Networks

The processor failures in a multiprocessor system have a negative impact on its distributed computing efficiency. Because of the rapid expansion of multiprocessor systems, the importance of fault diagnosis is becoming increasingly prominent. The [Formula: see text]-component diagnosability of [Formula: see text], denoted by [Formula: see text], is the maximum number of nodes of the faulty set [Formula: see text] that is correctly identified in a system, and the number of components in [Formula: see text] is at least [Formula: see text]. In this paper, we determine the [Formula: see text]-component diagnosability of general networks under the PMC model and MM[Formula: see text] model. As applications, the component diagnosability is explored for some well-known networks, including complete cubic networks, hierarchical cubic networks, generalized exchanged hypercubes, dual-cube-like networks, hierarchical hypercubes, Cayley graphs generated by transposition trees (except star graphs), and DQcube as well. Furthermore, we provide some comparison results between the component diagnosability and other fault diagnosabilities.

Turf establishment quality as affected by planting density and variety of Doobgrass (Cynodon dactylon L.)

The experiment was laid out in a Completely Randomized Design with Factorial concept which replicated thrice and comprised of two factors viz., factor-I Varieties of Cynodon dactylon turf (V1: Tifdwarf, V2: Selection-1, V3: Locally available variety and factor-II Planting desnsity (P1: 100.00 dibble/m2, P2: 44.44 dibble/m2, P3: 25.00 dibble/m2). The interaction effect of varieties and planting desnsity were influence on different characters viz., highest number (196.08) of runners per 100 cm2, earliness for fully establishment of turfgrass (48.67 days), dry biomass (13.24 g/100 cm2) of doobgrass at 60 DAP. Moreover, maximum root depth (84.27 mm) and longest runner length (37.87 cm) was noted in V3P3. Maximum number of nodes (17.20) present on runner found maximum in V1P3.In case minimum fresh weed (197.20 g/plot and 43.40 g/plot) and dry biomass of weed (24.64 g/plot and 5.05 g/plot) observed with combination V1P1 and V2P1 From the results it was revealed that dibble of Cynodon dactylon var. Tifdwarf or Selection-1 grown at 100.00 dibble/m2 planting desnsity favoured quick establishment of turf, appropriate growth habit for less frequency of lawn mowing and minimal weed growth.

Distributed bare-bones communication in wireless networks

We consider wireless networks operating under the SINR model of interference. Nodes have limited individual knowledge and capabilities: they do not know their positions in a coordinate system in the plane, further they do not know their neighborhoods, nor do they know the size of the network n, and finally they cannot sense collisions resulting from simultaneous transmissions by at least two neighbors. Each node is equipped with a unique integer name, where N as an upper bound on the a range of names. We refer as a backbone to a subnetwork induced by a diameter-preserving dominating set of nodes. Let \(\Delta \) denote a maximum number of nodes that can successfully receive a message transmitted by a node when no other nodes transmit concurrently. We study distributed algorithms for communication problems in three settings. In the single-node-start case, when one node starts an execution and other nodes are awoken by receiving messages from already awoken nodes, we present a randomized broadcast algorithm that wakes up all nodes in \(\mathcal {O}(n \log ^2 N)\) rounds with high probability. For the synchronized-start case, when all nodes start an execution simultaneously, we give a randomized algorithm computing a backbone in \(\mathcal {O}(\Delta \log ^{7} N)\) rounds with high probability. In the partly-coordinated-start case, when a number of nodes start an execution together and other nodes are awoken by receiving messages from the already awoken nodes, we develop an algorithm that creates a backbone in time \(\mathcal {O}(n\log ^2 N +\Delta \log ^{7} N)\) with high probability