List Of Nodes Research Articles

An optimized joint medium access control and routing protocol with iterative contention window adjustment mechanism for a guaranteed lifetime in Internet of Things‐based wireless sensor networks

SummaryThe problem of extending the lifespan of wireless sensor networks (WSN) based on the Internet of Things (IoT) has been widely investigated over the last 20 years. This paper proposes an Optimized J‐RMAC (optimized joint routing and media access control protocol) to guarantee the network lifetime in IoT‐based WSN. Initially, all sensor nodes report their position and coverage information to the sink, which uses this information to pick a list of active nodes based on energy usage and active time. Then, the k‐covered network is formed to execute the routing task by selecting the active nodes with the largest sensing areas. A multi‐objective seagull optimization algorithm (MO‐SOA) represents routing paths between the source and destination by considering two objective functions: energy consumption cost and end‐to‐end delay of a routing path. After that, the contention window of the nodes in the routing path is adjusted using a new iterative adaptive adjustment process of the contention window with adjustment parameters (IAACW‐AP) to avoid message conflicts. The proposed protocol is simulated in the NS2 simulator. The performance of the proposed protocol will be compared with existing strategies in terms of network lifetime, packet delivery ratio, communication overhead, energy consumption, and delay.

An integrated approach of designing functionality with security for distributed cyber-physical systems.

In this work, we propose a multi-tier architectural model to separate functionality and security concerns for distributed cyber-physical systems. On the line of distributed computing, such systems require the identification of leaders for distribution of work, aggregation of results, etc. Further, we propose a fault-tolerant leader election algorithm that can independently elect the functionality and security leaders. The proposed election algorithm identifies a list of potential leader capable nodes to reduce the leader election overhead. It keeps identifying the highest potential node as the leader, whenever needed, including the situation when one has failed. We also explain the proposed architecture and its management method through a case study. Further, we perform several experiments to evaluate the system performance. The experimental results show that the proposed architectural model improves the system performance in terms of latency, average response time, and the number of real-time tasks completed within the deadline.

A Wormhole Attack Detection Algorithm Integrated With the Node Trust Optimization Model in WSNs

The openness of Wireless Sensor Networks (WSNs) layout makes them vulnerable to attacks. One of the most threatening attacks is the wormhole attack. The wormhole attack is difficult to be detected since it causes false routing by private tunnels and damages to WSNs in data transmission. Therefore, in order to resist wormhole attacks and improve network performance, this paper proposes a detection algorithm integrated with the node trust optimization model (NTOM-DA) against wormholes in WSNs. First, add the node whose number of neighbors exceeds the threshold to the list of suspicious nodes, and then the exclusive neighbors of suspicious nodes communicate with each other. Mark the path whose hops exceed the wormhole threshold as the path to be tested. Establish a trust model to calculate the node trust and evaluate the path trust. The simulation results show that NTOM-DA is superior in detecting wormhole attacks. Even in the face of a network with high node density and high attack degree, it still has a high detection rate and a low false-positive rate, which effectively guarantees the safe and reliable operation of WSNs.

Improved PBFT algorithm for high-frequency trading scenarios of alliance blockchain

With the continuous development of blockchain technology, the application scenarios of alliance blockchain are also increasing. The consensus algorithm can achieve distributed consensus among nodes in the network. At present, the practical byzantine fault tolerance algorithm (PBFT) consensus algorithm commonly used in alliance blockchain requires all nodes in the network to participate in the consensus process. Experiments show that when the number of consensus nodes in the system exceeds 100, the bandwidth consumption and consensus delay will greatly increase, resulting in the inability of PBFT to be applied. In scenes with many nodes. How to improve the performance of alliance blockchains safely and efficiently has become an urgent problem to be solved at present. For the PBFT commonly used in alliance blockchains, there are some problems, such as large communication overhead, simple selection of master nodes, and inability to expand and exit nodes dynamically in the network. This paper proposes an improved algorithm tPBFT (trust-based practical Byzantine algorithm), which is suitable for high-frequency trading scenarios of consortium chains. By introducing a trust equity scoring mechanism between nodes in the network, the list of consensus nodes can be dynamically adjusted. tPBFT simplifies the pre-prepare stage of the PBFT consensus process, and realizes the verification of the hash transaction list in the reply stage, thereby reducing the interaction overhead between network nodes. Theoretical analysis and experiments show that when the number of nodes in the network is greater than 30, with the further increase of the number of nodes, the improved tPBFT algorithm has a relatively large performance in terms of node communication overhead, consensus efficiency and scalability outperforms the PBFT algorithm.

Devising a method for the interactive arrangement of structural elements of men’s jacket models

This paper has established that one of the directions to correct a designer’s idea is the close relationship between the metric characteristics of a model and the dynamics of updating the shape structure. The need to convolute information about model varieties of a men’s jacket is due to the cyclical nature of fashion. Regression analysis of this study’s results has confirmed the impact of the accumulation of quantitative changes in style attributes on the transition to qualitative changes in shape over 15 years. The periodic repeatability of the five-seam design of a men’s jacket as a typical representative indicates the possibility of using clusters of standard elements. Correlation analysis of the update of mobile attributes (an increase along the waistline and the lapel width) confirms a high level of connection with the silhouette characteristic. Information and analytical material for encoding classification features of the functional components of model designs for an industrial product range collection has been formed. A variant for convoluting the sets of classification features in the process of sampling sorting relative to the basic list of functional nodes has been proposed. The 24×24 compatibility matrix built makes it possible to apply the morphological box method to compare sample sets. The presence of uniformity of the average value of accumulated frequencies Kc.u.=0.72, Kt.u.=0.69 confirms the membership of the sample in the typological series. Having a common encoding system simplifies the selection of models from the internet-based product range collections. The method of sorting the models-proposals of the resulting layout set has been confirmed by the validity coefficient Kv=0.71, which makes it possible to verify the perception of a jacket design as a typical representative of modern structure. Practical recommendations have been compiled on grouping the records of industrial product range collection model codes into industrial series, which enable control over the launch of articles within the manufacturing process.

Dynamic Analysis of Alternative Elements in an Automated Packaging System Based on 5G Internet of Things

The arrival of 5G will usher in an era of “Internet of Everything.” Massive Internet of Things data contains great value in the dynamic analysis of alternative elements of automated packaging systems. From the perspective of the realization of personalized customization functions, this article solves the problem of dynamic analysis of alternative elements in the automated packaging system. We analyze the connection mechanism and interaction method between the cloud service system layer and the mobile terminal service layer, and carry out the corresponding software design. From the perspective of the realization of the intelligent production of the system in this paper, this topic mainly studies the information interaction mechanism and production control mechanism of the cloud service system and the manufacturing system. Based on the hardware of the manufacturing system layer, a flexible production implementation mechanism is formulated to make it the basis for the implementation of intelligent production of the system. Based on the massive data processing capabilities of the cloud service system, the information processing mechanism and the production planning decision-making mechanism are formulated for it, so as to realize the intelligent adjustment of the manufacturing system layer in the production process. For the connection scenario of IoT group paging, based on the application of NB-IoT technology in the next-generation mobile communication network, the focus of network optimization is to ensure the random access performance of IoT devices as much as possible. To this end, this paper proposes a random access optimization strategy for IoT group paging based on time slot scattering. We establish a mathematical model based on queuing theory for the connection scenario of the Io T group paging, then use the mathematical formula to derive the number of IoT devices scattered to each time slot in the initial state, thereby deriving the specific time slot scattering algorithm. This paper establishes a list of credit nodes, changes the participation mode of consensus nodes from static to dynamic, and supports voting to select trusted nodes. We designed a credit evaluation mechanism as a basis for consensus node elections to improve system’s fault tolerance rate. The algorithm process was simplified, and the PBFT algorithm process was simplified from a 3-phase protocol to a 2-phase protocol to further reduce communication bandwidth overhead and algorithm time. Simulation analysis shows that, compared with the PBFT algorithm, the proposed algorithm improves node flexibility and fault tolerance while reducing communication bandwidth overhead by about 45%, packaging throughput by about 4%, and latency by about 3%.

Ripple+: An Improved Scheme of Ripple Consensus Protocol in Deployability, Liveness and Timing Assumption

Ripple acts as a real-time settlement and payment system to connect banks and payment providers. As the consensus support of the Ripple network to ensure network consistency, Ripple consensus protocol has been widely concerned in recent years. Compared with those Byzantine fault tolerant protocols, Ripple has a significant difference that the system can reach an agreement under decentralized trust model. However, Ripple has many problems both in theory and practice, which are mentioned in the previous researches. This paper presents Ripple+, an improved scheme of Ripple consensus protocol, which improves Ripple from three aspects: (1) Ripple+ employs a specific trust model and a corresponding guideline for Unique Node List selection, which makes it easy to deploy in practice to meet the safety and liveness condition; (2) the primary and view change mechanism are joined to solve the problem discussed by the previous research that Ripple may lose liveness in some extreme scenarios; (3) we remove the strong synchrony clock and timeout during consensus periods to make it suitable for weak synchrony assumption. We implemented a prototype of Ripple+ and conducted experiments to show that Ripple+ can achieve the throughput of tens of thousands of transactions per second with no more than half a minute latency, and the view change mechanism hardly incurs additional cost.

Simulated Study of SARS-CoV-2: Contact Tracing and Potential Transmission Networks

During a disease outbreak, contact tracing and epidemiological analysis are of critical importance to analyze disease sources and transmission. To perform this epidemiological analysis, effective data visualization is necessary. In this study, the outbreak of COVID-19 was simulated within three metro Atlanta counties. Data was generated using contact tracing forms and used to create node and edge lists. Each node in the network contained a unique ID representing either a location or individual, as well as any contact tracing information related to each node. Data visualization was performed using MicrobeTrace, an online program developed by the CDC. Visualization of the contact tracing network enabled us to effectively analyze the transmission dynamics of SARS-CoV2. In the simulated network, a singular person node appeared to be linked to the most positive COVID-19 cases in the network. Similarly, a restaurant was identified as the place node with the greatest number of direct connections to positive persons, highlighting it as a potentially major source of exposure to SARS-CoV-2. This study illustrates the benefits of data visualization and demographic analysis using MicrobeTrace, which helps to target mitigation and prevention efforts, while also emphasizing the importance of contact tracing to reduce the transmission of disease.

UNSTRUCTURED MESH SIZE CONTROL METHOD BASED ON ARTIFICIAL NEURAL NETWORK

Automatic mesh generation and adaptation are bottleneck problems restricting computational fluid dynamics (CFD). Grid quality, efficiency, flexibility, automation level, and robustness are several key issues in grid generation. Mesh size control is significant in unstructured mesh generation which directly impacts the mesh quality, efficiency, and solution accuracy. Controlling mesh size by the background grid method requires mesh size defined on a background mesh by solving differential equations and interpolating from background mesh to specific location, which is very tedious and time-consuming in traditional unstructured grid generation. In this paper, two novel mesh size control methods are proposed in terms of efficiency and automation level. Firstly, radial basis function (RBF) interpolation was developed to control mesh size. In order to improve the efficiency of RBF interpolation, the greedy algorithm was applied to reduce the list of reference nodes. Meanwhile, an artificial neural network (ANN) is used to control the mesh size, relative wall distance, and relative mesh size are introduced as input and output parameters for the ANN. Training models are established and samples (2D cylinder and airfoil grids) are generated by commercial software. The relationship is established between wall distance and mesh size by machine learning. Several meshes are generated with the aforementioned three methods, the results demonstrate that the RBF method and the ANN method are 5-10 times more efficient than the background mesh method, which contributes to efficiency improvement of the grid generation process. Finally, the ANN method is extended to mesh size control of anisotropic hybrid grids, which also obtained meshes of good quality.

ENHANCED LION SWARM OPTIMIZATION ALGORITHM WITH CENTRALIZED AUTHENTICATION APPROACH FOR SECURED DATA TRANSMISSION OVER WSN

Securing data accuracy in WSNs (Wireless Sensor Networks) is a major problem. Aggregation techniques for improving accuracy in data processing have been gaining attention of scholars, recently. Existing security systems using singular paths for transmission of data have delays in transmissions while being open to intrusions. Moreover, increased computational overheads and processing time increases the delay of data transmission in the given networks. To overcome these issues, this work proposes ELSOA-CA (Enhanced Lion Swarm Optimization Algorithm and Centralized Authentication) method. This method focuses on an optimal, faster and energy efficient data transmissions while ensuring accurate decisions on tomato crops. Multipath routing introduced in the proposed method ensures faster data transmissions by selecting optimal forwarder nodes which satisfy the constraints of delay and energy. Optimal forwarder node selection is done using ELOSA algorithm. Data transmissions are secured by centralized authentication involving third party nodes. Each and every node in a region gets registered with the authentication node where a node’s genuineness is checked the node is allowed into the forwarder node list, used for data routing. CA (Centralized Authentication) enhances the security level of data transmissions over WSN multi path routing. ELSOA-CA framework’s simulation results provide higher throughputs, reduced energy consumptions, improved network lifetimes PDRs (Packet Drop Ratios) and lesser delay times.

Reactive Power Support in Radial Distribution Network Using Mine Blast Algorithm

The passive power distribution networks are prone to imperfect voltage profile and higher power losses, especially at the far end of long feeders. The capacitor placement is studied in this article using a novel Mine Blast Algorithm (MBA). The voltage profile improvement and reduction in the net annual cost are also considered along with minimizing the power loss. The optimization problem is formulated and solved in two steps. Firstly, the Voltage Stability Index (VSI) is used to rank the nodes for placement of the capacitors. Secondly, from the priority list of nodes in the previous step, the MBA is utilized to provide the optimal location and sizes of the capacitors ensuring loss minimization, voltage profile improvement, and reduced net annual cost. Finally, the results are tested on 33 and 69 radial node systems in MATLAB. The results for the considered variables are presented which show a significant improvement in active and reactive power loss reduction and voltage profile with lesser reactive power injection.

Research on the Construction of Smart Tourism System Based on Wireless Sensor Network

With the rapid development of information technology represented by the Internet, the economic development of various countries in the world is closely related to information technology represented by computers. As a new growth point of economic development, tourism has attracted more and more attention from all parties. Traditional artificial travel agencies can no longer meet people’s needs. In the network information environment, tourists need systematic and large amounts of online travel information to provide support for their decision-making. This article first discusses the research significance and research status of wireless sensor networks at home and abroad and analyzes and optimizes the sensor network algorithms. Based on the existing wireless sensor network hardware experimental system, a tourism management system is designed and developed. Various user interfaces of the tourism management system are designed, including topology tree structure view, node list, program status and program package view, data management, real-time curve display, and node positioning functions. The simulation experiment results show that the efficiency of this system for tourism information integration is 20% higher than other systems, and it has certain practical value.

Differentially Private Link Prediction with Protected Connections

Link prediction (LP) algorithms propose to each node a ranked list of nodes that are currently non-neighbors, as the most likely candidates for future linkage. Owing to increasing concerns about privacy, users (nodes) may prefer to keep some of their connections protected or private. Motivated by this observation, our goal is to design a differentially private LP algorithm, which trades off between privacy of the protected node-pairs and the link prediction accuracy. More specifically, we first propose a form of differential privacy on graphs, which models the privacy loss only of those node-pairs which are marked as protected. Next, we develop DPLP, a learning to rank algorithm, which applies a monotone transform to base scores from a non-private LP system, and then adds noise. DPLP is trained with a privacy induced ranking loss, which optimizes the ranking utility for a given maximum allowed level of privacy leakage of the protected node-pairs. Under a recently introduced latent node embedding model, we present a formal trade-off between privacy and LP utility. Extensive experiments with several real-life graphs and several LP heuristics show that DPLP can trade off between privacy and predictive performance more effectively than several alternatives.

A Segment-based Tree Traversal Algorithm for Enhancing Data Gathering in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are sink-based networks in which assigned sinks gather all data sensed by lightweight devices that are deployed in natural areas. The sensor devices are energyscarce, therefore, energy-efficient protocols need to be designed for this kind of technology. PowerEfficient GAthering in Sensor Information Systems (PEGASIS) protocol is an energy-efficient data gathering protocol in which a chain is constructed using a greedy approach. This greedy approach has appeared to have unbalanced distances among the nodes which result in unfair energy consumption. Tree traversal algorithms have been used to improve the constructed chain to distribute the energy consumption fairly. In this research, however, a new segmentbased tree traversal approach is introduced to further improve the constructed chain. Our new proposed algorithm first constructs initial segments based on a list of nodes that are sorted according to post-order traversal. Afterwards, it groups these segments and concatenates them one by one according to their location; thus, our proposed approach uses location-awareness to construct a single balanced chain in order to use it for the data gathering process. This approach has been evaluated under various numbers of sensor devices in the network field with respect to various crucial performance metrics. It is shown in our conducted simulation results that our proposed segment-based chain construction approach produces shorter chains and shorter transmission ranges which as a result has improved the overall energy consumption per round, network lifetime, and end-to-end delay.

A food web including parasites for kelp forests of the Santa Barbara Channel, California

We built a high-resolution topological food web for the kelp forests of the Santa Barbara Channel, California, USA that includes parasites and significantly improves resolution compared to previous webs. The 1,098 nodes and 21,956 links in the web describe an economically, socially, and ecologically vital system. Nodes are broken into life-stages, with 549 free-living life-stages (492 species from 21 Phyla) and 549 parasitic life-stages (450 species from 10 Phyla). Links represent three kinds of trophic interactions, with 9,352 predator-prey links, 2,733 parasite-host links and 9,871 predator-parasite links. All decisions for including nodes and links are documented, and extensive metadata in the node list allows users to filter the node list to suit their research questions. The kelp-forest food web is more species-rich than any other published food web with parasites, and it has the largest proportion of parasites. Our food web may be used to predict how kelp forests may respond to change, will advance our understanding of parasites in ecosystems, and fosters development of theory that incorporates large networks.

Multi-Goal Path Planning Using Multiple Random Trees

In this paper, we propose a novel sampling-based planner for multi-goal path planning among obstacles, where the objective is to visit predefined target locations while minimizing the travel costs. The order of visiting the targets is often achieved by solving the Traveling Salesman Problem (TSP) or its variants. TSP requires to define costs between the individual targets, which - in a map with obstacles - requires to compute mutual paths between the targets. These paths, found by path planning, are used both to define the costs (e.g., based on their length or time-to-traverse) and also they define paths that are later used in the final solution. To enable TSP finding a good-quality solution, it is necessary to find these target-to-target paths as short as possible. We propose a sampling-based planner called Space-Filling Forest (SFF*) that solves the part of finding collision-free paths. SFF* uses multiple trees (forest) constructed gradually and simultaneously from the targets and attempts to find connections with other trees to form the paths. Unlike Rapidly-exploring Random Tree (RRT), which uses the nearest-neighbor rule for selecting nodes for expansion, SFF* maintains an explicit list of nodes for expansion. Individual trees are grown in a RRT* manner, i.e., with rewiring the nodes to minimize their cost. Computational results show that SFF* provides shorter target-to-target paths than existing approaches, and consequently, the final TSP solutions also have a lower cost.

Some new perspectives for solving 0–1 integer programming problems using Balas method

Egon Balas’s additive algorithm, also known as implicit enumeration, is a technique that uses a branch-and-bound (B&B) approach to finding optimal solutions to 0–1 integer programming problems. Three common search strategies in B&B are depth-first search, breadth-first search and best-first search. The B&B approach generates a list of pending nodes to be evaluated and storage of these nodes becomes a memory issue for larger problems. In this paper, we propose a simple bookkeeping method that tracks the state of the problem using a single array when performing a depth-first search, dramatically reducing memory requirements. The method also provides the ability to calculate, at any point of the search, the theoretical maximum number of remaining nodes to be evaluated. We note in this paper that when using the best-first search strategy, the first candidate solution found is the optimal solution.

Identifying vital nodes by Achlioptas process

The vital nodes are the ones that play an important role in the organization of network structure or the dynamical behaviors of networked systems. Previous studies usually applied the node centralities to quantify the importance of nodes. Realizing that the percolation clusters are dominated by local connections in the subcritical phase and by global connections in the supercritical phase, in this paper we propose a new method to identify the vital nodes via a competitive percolation process that is based on an Achlioptas process. Compared with the existing node centrality indices, the new method performs overall better in identifying the vital nodes that maintain network connectivity and facilitate network synchronization when considering different network structure characteristics, such as link density, degree distribution, assortativity, and clustering. We also find that our method is more tolerant of noisy data and missing data. More importantly, compared with the unique ranking list of nodes given by most centrality methods, the randomness of the percolation process expands the possibility space of the optimal solutions, which is of great significance in practical applications.

Is Single Scan based Restructuring Always a Suitable Approach to Handle Incremental Frequent Pattern Mining?

Incremental mining of frequent patterns has attracted the attention of researchers in the last two decades. The researchers have explored the frequent pattern mining from incremental database problems by considering that the complete database to be processed can be accommodated in systems’ main memory even after the database gets updated very frequently. The FP-tree-based approaches were able to draw more interest because of their compact representation and requirement of a minimum number of database scans. The researchers have developed a few FP-tree based methods to handle the incremental scenario by adjusting or restructuring the tree prefix paths. Although the approaches have managed to solve the re-computation problem by constructing a complete pattern tree data structure using only one database scan, restructuring the prefix paths for each transaction is a computationally costly task, leading to the high tree construction time. If the FP-tree construction process can be supported with suitable data structures, reconstruction of the FP-tree from scratch may be less time consuming than the restructuring approaches in case of incremental scenario. In this study, we have proposed a tree data structure called Improved Frequent Pattern tree (Improved FP-tree). The proposed Improved FP-tree construction algorithm has immensely improved the performance of tree construction time by resourcefully using node links, maintained in header table to manage the same item node list in the FP-tree. The experimental results emphasize the significance of the proposed Improved FP-tree construction algorithm over a few conventional incremental FP-tree construction algorithms with prefix path restructuring.

A novel IoT-based health and tactical analysis model with fog computing.

In sports competitions, depending on the conditions such as excitement, stress, fatigue, etc. during the match, negative situations such as disability or loss of life may occur for players and spectators. Therefore, it is extremely important to constantly check their health. In addition, some strategic analyzes are made during the match. According to the results of these analyzes, the technical team affects the course of the match. Effects can have positive and sometimes negative results. In this article, fog computing and an Internet of Things (IoT) based architecture are proposed to produce new technical strategies and to avoid disabilities. Players and spectators are monitored with sensors such as blood pressure, body temperature, heart rate, location etc. The data obtained from the sensors are processed in the fog layer and the resulting information is sent to the devices of the technical team and club doctors. In the architecture based on fog computing and IoT, priority processes are computed with low latency. For this, a task management algorithm based on priority queue and list of fog nodes is modified in the fog layer. Authentication and data confidentiality are provided with the Federated Lightweight Authentication of Things (FLAT) method used in the proposed model. In addition, using the Software Defined Network controller based on blockchain technology ensures data integrity.