Optimal Node Research Articles

The Method of Linearization in Optimization Problems of Low-Thrust Spacecraft Trajectories: Optimal Nodes Layout

The Method of Linearization in Optimization Problems of Low-Thrust Spacecraft Trajectories: Optimal Nodes Layout

Software architecture for pervasive critical health monitoring system using fog computing

Because of the existence of Covid-19 and its variants, health monitoring systems have become mandatory, particularly for critical patients such as neonates. However, the massive volume of real-time data generated by monitoring devices necessitates the use of efficient methods and approaches to respond promptly. A fog-based architecture for IoT healthcare systems tends to provide better services, but it also produces some issues that must be addressed. We present a bidirectional approach to improving real-time data transmission for health monitors by minimizing network latency and usage in this paper. To that end, a simplified approach for large-scale IoT health monitoring systems is devised, which provides a solution for IoT device selection of optimal fog nodes to reduce both communication and processing delays. Additionally, an improved dynamic approach for load balancing and task assignment is also suggested. Embedding the best practices from the IoT, Fog, and Cloud planes, our aim in this work is to offer software architecture for IoT-based healthcare systems to fulfill non-functional needs. 4 + 1 views are used to illustrate the proposed architecture.

Kernel-based models for influence maximization on graphs based on Gaussian process variance minimization

The inference of novel knowledge, the discovery of hidden patterns, and the uncovering of insights from large amounts of data from a multitude of sources make data science to an art rather than just a mere scientific discipline. The study and design of mathematical models and signal processing tools able to analyze information represents a central research topic within data science. In this work, we introduce and investigate a model for influence maximization (IM) on graphs using ideas from kernel-based signal approximation, Gaussian process regression, and the minimization of a corresponding variance term. Data-driven approaches can be applied to determine proper kernels for this IM model and machine learning methodologies are adopted to tune the model parameters. Compared to stochastic models for influence maximization that rely on Monte-Carlo simulations, our kernel-based model allows for a simple and cost-efficient update strategy to compute optimal influencing nodes on a graph. In several numerical experiments, we show the properties and benefits of this model.

Location Selection of Metro-Based Distribution Nodes for Underground Logistics System with Bi-Level Programming Model

The main purpose of the study was to apply symmetry principles to general mathematical modeling based on a bi-level programming model in order to select the optimal nodes of the underground metro-based logistics system (M-ULS). The first step was to select the metro stations as alternative logistics distribution nodes based on the existing metro network. Secondly, given the requirements of suppliers and demanders, a bi-level programming model was built based on symmetry principles to minimize the total cost of logistics distribution nodes, including construction cost, transport cost, and fixed cost. The third objective was to use an efficient heuristic algorithm to solve the model to obtain the optimal location of the nodes of the logistics distribution. Lastly, Nanjing’s Metro Line 2 was used as an example to validate the efficacy of the proposed model. The results of the case indicate that it is possible to deliver goods from logistics distribution nodes to demanders using the excess capacity of the metro, and the proposed bi-level programming model for M-ULS can be used to select suitable metro stations as distribution nodes and achieve the lowest cost on both the supply and demand sides of logistics while still ensuring the green and efficient transport of logistics services. References and suggestions for planning and selecting the location of logistics distribution nodes based on the metro network in the future can be found in this article.

FogCom:SDN-enabled fog node selection for early detection of communicable diseases

A communicable illness is one that spreads from person to person by a number of means, including contact with blood and body fluids, inhaling an airborne virus, or being bitten by an insect. Internet of Things (IoT)-oriented medical technologies have proliferated in prior decades, and many more applications depend on wearable sensors for versatility. Modern research has examined the use of software-defined networking (SDN)-oriented fog framework to create intelligent health care services. To choose the best fog nodes within the fog layers, however, to anticipate communicable disease detection.To solve this issue, the SDN-enabled Fog Node Selection for Early Detection of Communicable Diseases (FogCom) is created in this study. The FogCom picks the optimal fog nodes using game theory’s Subgame Perfect Nash Equilibrium (SPNE) method. The suggested method also takes into account the three-layered CNN methodology for detecting communicable illnesses. Efficiency analysis shows that the suggested FogCom outperforms earlier baseline techniques.

Energy Efficient Cooperative Spectrum Sensing with Energy Harvesting for Optimal Node Selection and Channel Access in Cognitive IoT Networks

Energy Efficient Cooperative Spectrum Sensing with Energy Harvesting for Optimal Node Selection and Channel Access in Cognitive IoT Networks

Node Localization in Wireless Sensor Networks using Dynamic Genetic Algorithm

A wireless sensor network is a collection of nodes organized in a cooperative environment. Node localization plays a vital role in many applications. Conventional location detection techniques such as global positioning system (GPS) and infrared are expensive to find the location node. This paper proposes the use of a genetic algorithm (GA) to learn the environment impairments within a wireless sensor network with the purpose of localization for data management. Hence, in this paper, we have presented an efficient and dynamic Genetic algorithm with help of receiving signal strength indicator (RSSI) which gives the optimal node location value with minimal localization error. Our simulation modeled in MATLAB 7.0 shows that the dynamic GA can achieve acceptable node location detection with the aid of three anchors. The simulation results represent th

Enhanced Localization Model in Wireless Sensor Network Using Self Adaptive-Barnacles Mating Optimization

Wireless sensor network (WSN) has more demand nowadays because of their diverse applications. In WSN, sensor node localization has become an interesting topic. The nodes are placed randomly to explore, and the determination of accurate nodes’ positions is required. Fixed cost and energy are the main challenges with the sensor, as the location of all nodes that are not maintained. The main aim of this research work is to plan for a new localization method in WSN using a new meta-heuristic concept. The proposed model focuses on two phases: (a) localizing the target nodes and (b) localizing the rest nodes. The optimal selection of target nodes is initially determined by the proposed meta-heuristic model by considering the displacement among the nodes. For performing the optimal localization of rest nodes regarding anchor nodes, the weight of each anchor node is determined, which is done by recurrent neural network (RNN). Furthermore, an objective function concerning the distance and the weight is derived for localizing the rest nodes. The new algorithm called Self Adaptive-Barnacles Mating Optimization (SA-BMO) is used for performing the optimal node localization. The desired outcome proves the developed method is attaining an increase in efficiency.

CHOP: Maximum Coverage Optimization and Resolve Hole Healing Problem using Sleep and Wake-up Technique for WSN

The Sensor Nodes (SN) play an important role in various hazardous applications environments such as military surveillance, forests, battlefield, etc. The Wireless Sensor Network (WSN) comprised multiple numbers of sensor nodes which are used to perform sensing the physical conditions and subsequently transmitting data to the Base Station (BS). The nodes have limited batteries. The random distribution of nodes in the hazardous areas causes overlapping of nodes and coverage hole issues in the network. The Coverage Optimization and Resolve Hole Healing (CHOP) Protocol is proposed to optimize the network's overlapping and resolve the coverage hole problem. The working phases of the proposed protocol are network initialization, formation of the cluster, Selection of Cluster Head, and sleep and wake-up phase. The issues are optimized, and maximum coverage is achieved for a specific sensing range. Using statistics and probability theory, a link is established between the radius of the node and the coverage area. The protocol used the sleep and wake phase to select optimal nodes active to achieve maximum coverage. The proposed protocol outperformed and showed improvements in the network's performance and lifetime compared to LEACH, TEEN, SEP, and DEEC protocols.

Sustainable Data-Driven Secured Optimization Using Dynamic Programming for Green Internet of Things.

The development of smart applications has benefited greatly from the expansion of wireless technologies. A range of tasks are performed, and end devices are made capable of communicating with one another with the support of artificial intelligence technology. The Internet of Things (IoT) increases the efficiency of communication networks due to its low costs and simple management. However, it has been demonstrated that many systems still need an intelligent strategy for green computing. Establishing reliable connectivity in Green-IoT (G-IoT) networks is another key research challenge. With the integration of edge computing, this study provides a Sustainable Data-driven Secured optimization model (SDS-GIoT) that uses dynamic programming to provide enhanced learning capabilities. First, the proposed approach examines multi-variable functions and delivers graph-based link predictions to locate the optimal nodes for edge networks. Moreover, it identifies a sub-path in multistage to continue data transfer if a route is unavailable due to certain communication circumstances. Second, while applying security, edge computing provides offloading services that lower the amount of processing power needed for low-constraint nodes. Finally, the SDS-GIoT model is verified with various experiments, and the performance results demonstrate its significance for a sustainable environment against existing solutions.

A self-organized priority-based MAC protocol in wireless sensor networks based on SDR-RHSO optimal relay node selection and HL-ANN wake-up scheduling

A self-organized priority-based MAC protocol in wireless sensor networks based on SDR-RHSO optimal relay node selection and HL-ANN wake-up scheduling

A Hybrid Modified Ant Colony Optimization - Particle Swarm Optimization Algorithm for Optimal Node Positioning and Routing in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have been widely deployed in hostile locations for environmental monitoring. Sensor placement and energy management are the two main factors that should be focused due to certain limitations in WSNs. The nodes in a sensor network might not stay charged when energy draining takes place; therefore, increasing the operational lifespan of the network is the primary purpose of energy management. Recently, major research interest in WSN has been focused with the essential aspect of localization. Several types of research have also taken place on the challenges of node localization of wireless sensor networks with the inclusion of range-free and range-based localization algorithms. In this work, the optimal positions of Sensor Nodes (SNs) are determined by proposing a novel Hybrid M-ACO – PSO (HMAP) algorithm. In the HMAP method, the improved PSO utilizes learning strategies for estimating the relay nodes' optimal positions. The M-ACO assures the data conveyance. A route discovers when it relates to the ideal route irrespective of the possibility of a system that includes the nodes with various transmission ranges, and the network lifetime improves. The proposed strategy is executed based on the energy, throughput, delivery ratio, overhead, and delay of the information packets.

Dynamic Programming BN Structure Learning Algorithm Integrating Double Constraints under Small Sample Condition

The Bayesian Network (BN) structure learning algorithm based on dynamic programming can obtain global optimal solutions. However, when the sample cannot fully contain the information of the real structure, especially when the sample size is small, the obtained structure is inaccurate. Therefore, this paper studies the planning mode and connotation of dynamic programming, restricts its process with edge and path constraints, and proposes a dynamic programming BN structure learning algorithm with double constraints under small sample conditions. The algorithm uses double constraints to limit the planning process of dynamic programming and reduces the planning space. Then, it uses double constraints to limit the selection of the optimal parent node to ensure that the optimal structure conforms to prior knowledge. Finally, the integrating prior-knowledge method and the non-integrating prior-knowledge method are simulated and compared. The simulation results verify the effectiveness of the method proposed and prove that the integrating prior knowledge can significantly improve the efficiency and accuracy of BN structure learning.

317. ABSOLUTE LYMPH NODE YIELD AND LYMPH NODE RATIO ARE PREDICTORS OF SURVIVAL AND IMPORTANT QUALITY METRICS IN ESOPHAGEAL CANCER SURGERY

Abstract Surgical resection following neoadjuvant therapy remains the cornerstone of curative management of esophageal cancer. In spite of this, there remains uncertainty regarding the optimal radicality of lymphadenectomy, and whether increasing lymph node yields confer a true survival benefit. This study aims to assess the impact of lymph node (LN) yield and LN ratio on survival following surgery for esophageal cancer, in addition to identifying factors that may influence LN yield and radicality of resection. All patients undergoing esophagectomy with curative intent from January 1, 2010 to December 31, 2020 were reviewed. Clinical and pathological variables were assessed, with univariable, multivariable, regression and survival analyses performed as appropriate. Cutpoint analysis was used to determine the optimal lymph node ratio. 397 patients underwent esophagectomy, with 288 having a minimally invasive operation (MIE). Stage (stage 3 HR: 1.64 (1.02–2.62), p = 0.04, stage 4 HR: 2.50 (1.43–5.01), p = 0.001), margin status (HR: 2.62 (1.57–4.36), p < 0.001), LN yield <15 (HR: 2.62 (1.57–4.36), p < 0.001) and elevated LN ratio (HR: 8.42 (2.85–24.90, p < 0.001) predicted survival. Patients undergoing MIE had higher LN yields compared with open (30.7 vs 25.3, p < 0.001). Patients undergoing neoadjuvant chemoradiation had lower LN yields compared with those without neoadjuvant therapy and those with neoadjuvant chemotherapy (26.4 vs 30.6 vs 36.8 respectively, p < 0.001). LN ratio < 0.05 was associated with a survival benefit. Lymphadenectomy is a cornerstone of resection of esophageal cancer. Low LN yield and high LN ratio are associated with reduced overall survival. Maintaining a LN yield >15 should remain a key quality metric. A LN ratio of <0.05 is associated with a significant survival benefit.

Effective proactive routing protocol using smart nodes system

Small Power Restricted Unit (PRU) platform known as the Wireless Sensor Network (WSN) to monitor a Large Region of Interest (ROI) and send data to the Base Station (BS). Accurately capturing the ROI and communicating observed information to the BS over the longest period is indeed the main problem facing WSN. Despite the latest introduction of many power routing algorithms in regular monitoring applications, the variable environment and complex environment for WSN applications end up creating these procedures as an important task. This study Degree Restricted Tree (DRE) nodes for such networks, including a BS outside of the ROI in a homogeneous pre-emptive WSN. The optimal degree of a node with low DRT energy consumption is determined because the degree of a node affects the network lifespan of these forms of connections. To provide an equitable distribution of the burden in terms of transmission power, this study then suggests a Joint Decentralized Antenna (JDA) algorithm which is based on several antenna theories. With an optimum node density and DRT base, JDA is made for frequent surveillance systems with real-time applications. The results validate our research, which emphasizes that the network throughput of DRT is doubled when utilizing optimum node angles as opposed to certain other node degrees. Additionally, it has been demonstrated that introducing JDA into DRT with ideal network density increases the network's latency thus eliminating the proportion between the unstable period and the lifetime of the network in halves. Additionally, it displays a 25% improvement in network lifespan and the lowest rate of node loss when compared to the existing system ensuring that halves of nodes are still alive just a few rounds even before the lifetime of the network expires.

Research on the application of time series ARIMA model in trade strategy

Market trading has always been a popular choice for bold investors, but market trading doesn't rely on pure luck. More need for daily experience and data. In this article, we will model the random forest algorithm for a specific situation and find the best strategy. Then it is proved that the model can provide the best trading strategy, which can be understood as proving the fit of the model established above. Using the real value of bit coin and gold prices in 5 years, establish a time series ARIMA model, analyze the smoothness and pure randomness of the series.Then get the ARIMA model fitting of bit coin and gold, indicating that the prediction model is very suitable. For the dynamic programming model, this paper adopts a genetic algorithm for the judgment of the optimal buy-sell node, which accelerates the convergence speed. To understand how the trading price affects the strategy and the results, sensitivity analysis is also done in this paper. Considering giving a certain perturbation to the results and giving a perturbation margin of 5% to the buying and selling fees respectively, the algorithm is iterated to calculate the sensitivity of the investment strategy to the trading cost and analyze how the change in the trading cost would affect the buying and selling strategy.

Chance-constrained optimization of distributed power and heat storage in integrated energy networks

Energy storage is widely recognized as an effective alternative to match the instantaneous imbalance between distributed renewable energy sources and loads. However, suitable installation sites and capacities are necessarily determined in integrated energy networks. This paper constructs a two-layer optimization model of integrated power and heat networks to obtain the optimal installation nodes and capacities of electric energy storage (EES) and thermal energy storage (TES) units. In the optimization model, the interchange of power to heat with storage units is collaborated to unitize distributed renewable energy sources and decrease comprehensive costs, including investment cost of storage units and operation cost. A chance-constrained optimization model is proposed to transform the uncertain variables of electric and heat loads into a deterministic optimization problem. The impacts of confidence levels involved in load probability distributions on the optimal nodes and capacities are analyzed. The results demonstrate that the optimal installation nodes are determined by the distributions of distributed power sources and load ratios in networks. The increased confidence level globally raises the optimal installation capacities of EES and TES. When the confidence level increases from 0.50 to 0.99, the utilization rate of wind power increases by 3.91 %, whereas the comprehensive cost increases by 22.34 %. The hybrid integrations of EES and TES consume more uncertain renewable power and improve the economic performances by the interchange of power to heat.

Appropriate Lymph Node Dissection Sites for Cancer in the Body and Tail of the Pancreas: A Multicenter Retrospective Study.

Simple SummaryDistal pancreatectomy has become the standard surgery for patients with pancreatic body–tail cancers. The optimal area for dissection remains controversial, and there is an emphasis on preserving as much tissue as possible while eradicating the cancer. This multicenter, retrospective study evaluated the frequency and patterns of lymph node metastasis based on tumor site among 235 patients with pancreatic body–tail cancers to determine the optimal lymph node dissection area for distal pancreatectomy. Patients with pancreatic body cancer tumors showed no metastasis to the splenic hilum lymph node. Spleen-preserving pancreatectomy might be feasible for Pb cancer.Distal pancreatectomy (DP) with lymphadenectomy is the standard surgery for pancreatic body–tail cancer. However, the optimal lymph node (LN) dissection area for DP remains controversial. Thus, we evaluated the frequency and patterns of LN metastasis based on the tumor site. In this multicenter retrospective study, we examined 235 patients who underwent DP for pancreatic cancer. Tumor sites were classified as confined to the pancreatic body (Pb) or pancreatic tail (Pt). The efficacy index (EI) was calculated by multiplying the frequency of metastasis to each LN station by the five-year survival rate of patients with metastasis to that station. LN metastasis occurred in 132/235 (56.2%) of the patients. Patients with Pb tumors showed no metastasis to the splenic hilum LN. Distal splenic artery LNs and anterosuperior/posterior common hepatic artery LNs did not benefit from dissection for Pb and Pt tumors, respectively. In multivariate analysis, splenic artery LN metastasis was identified as an independent predictor of poor overall survival in patients with pancreatic body–tail cancer. In conclusion, differences in metastatic LN sites were evident in pancreatic body–tail cancers confined to the Pb or Pt. Spleen-preserving pancreatectomy might be feasible for Pb cancer.

Deployment and Allocation Strategy for MEC Nodes in Complex Multi-Terminal Scenarios.

Mobile edge computing (MEC) has become an effective solution for insufficient computing and communication problems for the Internet of Things (IoT) applications due to its rich computing resources on the edge side. In multi-terminal scenarios, the deployment scheme of edge nodes has an important impact on system performance and has become an essential issue in end–edge–cloud architecture. In this article, we consider specific factors, such as spatial location, power supply, and urgency requirements of terminals, with respect to building an evaluation model to solve the allocation problem. An evaluation model based on reward, energy consumption, and cost factors is proposed. The genetic algorithm is applied to determine the optimal edge node deployment and allocation strategies. Moreover, we compare the proposed method with the k-means and ant colony algorithms. The results show that the obtained strategies achieve good evaluation results under problem constraints. Furthermore, we conduct comparison tests with different attributes to further test the performance of the proposed method.

Analysis of the Role of 5G Communication Technology Based on Intelligent Sensor Network in the Construction and Design of the Internet of Things in Free Trade Zones

With the rapid development of the Internet today, the number of various mobile communication terminals has increased rapidly, and 5G has also appeared. With the popularization of 5G technology, new development trends have emerged in more business scenarios. For example, in the 5G era, there will be a lot of room for development in traditional business applications. On this basis, we design a resource allocation that utilizes DQN technology to achieve 5G high-band services. First of all, considering the characteristics of boundary operation, the connection between the base station and the user, the transmission capacity of the base station to the user, and other factors are used as judgment factors, the total energy efficiency is maximized as the goal, and the requirements of mobile users are the constraints. Based on customer service quality assurance, QoS assurance is the object. On the basis of DQN, the convex optimization method is used to solve the given maximum transmission energy between nodes, and DQN is used for iterative iteration to obtain the optimal node and optimal power distribution. Through simulation experiments, the results show that the algorithm has high learning efficiency and convergence and can effectively optimize the allocation of network resources under the premise of ensuring the mobile terminal’s requirements for service quality.