Head Nodes Research Articles

Phenotypical differences of neutrophils patrolling tumour-draining lymph nodes in head and neck cancer.

The complexity and heterogeneity of neutrophils are recognized, especially their roles in modulating inflammation and cancer immune responses. The detailed functions of neutrophils in human tumour-draining lymph nodes (TDLNs), specifically in the context of head and neck cancer, remain inadequately characterized. This study aims to delineate the phenotypic diversity of neutrophils in TDLNs, non-tumour-draining lymph nodes (nTDLNs) from patients with oral squamous cell carcinoma (OSCC), and to evaluate their correlation with clinical outcomes. A flow cytometry-based investigation. Neutrophils manifest a tissue-specific heterogeneity with significant phenotypic differences between compartments. A substantial fraction of neutrophils displayed an activated CD16highCD62Ldim profile in TDLNs, more prominent in patients with advanced T stages, implicating their involvement in the disease's progression. Notably, the presence of this activated neutrophil phenotype in TDLNs was strongly associated with poorer patient prognosis. The study confirms the heterogeneity of neutrophils in human TDLNs, aligning with findings from animal models but extending them to show clinical relevance in human disease. The correlation of neutrophil phenotypes with cancer progression and prognosis emphasizes the importance of these cells in the tumour-microenvironment. The data suggests a future possibility to develop targeted therapies that modulate the neutrophilic response in OSCC.

High-Efficiency Clustering Routing Protocol in AUV-Assisted Underwater Sensor Networks.

Currently, underwater sensor networks are extensively applied for environmental monitoring, disaster prediction, etc. Nevertheless, owing to the complicacy of the underwater environment, the limited energy of underwater sensor nodes, and the high latency of hydroacoustic channels, the energy-efficient operation of underwater sensor networks has become an important challenge. In this paper, a high-efficiency clustering routing protocol in AUV-assisted underwater sensor networks (HECRA) is proposed to address the energy limitations and low data transmission reliability in underwater sensor networks. The protocol optimizes the cluster head selection strategy of the traditional low-energy adaptive clustering hierarchy (LEACH) protocol by introducing the residual energy and node degree in the cluster head selection phase and performs some optimizations in the cluster formation and data transmission phases, including selecting clusters for joining by ordinary nodes based on the residual energy of the cluster head nodes and weight computation based on the depth and residual energy of the cluster head nodes to select the optimal message forwarding nodes. In addition, this paper introduces an autonomous underwater vehicle (AUV) as a dynamic relay node to improve network transmission efficiency. According to the simulation results, compared with the existing LEACH, the energy efficient routing protocol based on layers and unequal clusters in underwater wireless sensor networks (EERBLC) and energy-efficient clustering multi-hop routing protocol in a UWSN (EECMR), the HECRA significantly improves network lifetime, the residual node energy, and the number of successfully transmitted packets, which can effectively prolong network lifetime and ensure efficient data transmission.

GCP: A multi-strategy improved wireless sensor network model for environmental monitoring

Nowadays, smart environmental monitoring devices are widely used in various fields, and one of the most representative tools is the wireless sensor network (WSN). WSNs are easy to deploy and provide real-time information feedback, which is very suitable for environmental monitoring. As we all know, the environmental monitoring network because of the special nature of its work requirements, the need for uninterrupted and real-time transmission of monitoring data, which leads to energy consumption is extremely large, and this cannot meet the needs of its long-term work. Existing traditional routing has problems such as unscientific cluster head election and high redundancy in data transmission, which usually lead to a large amount of energy consumption, which is not conducive to the long-term stable operation of sensor networks. In this paper, we improve the traditional routing protocol and design a cluster head election method based on the genetic algorithm, which proposes a new fitness function in terms of energy, distance, and the number of nodes in the cluster, and performs the selection of cluster head nodes based on this method. In addition, we propose a new grey prediction model, which can realize the real-time update of data queues, and optimize the data transmission process of traditional WSNs based on this prediction model to reduce the amount of intra-cluster data transmission. Combining these improvements, a grey cluster prediction (GCP) model is proposed, and the performance of the model is tested based on real mine and soil data sets. The simulation results show that the model significantly reduces energy loss and extends the network life cycle while ensuring the integrity of data transmission. It can also meet the requirements of long-term stable operation of environmental monitoring equipment.

Investigation of Various Factors for Discriminating Between Cytologic Diagnosis of “Atypical” vs “Suspicious” in Fine Needle Aspiration Biopsy of Head and Neck Lymph Nodes

Investigation of Various Factors for Discriminating Between Cytologic Diagnosis of “Atypical” vs “Suspicious” in Fine Needle Aspiration Biopsy of Head and Neck Lymph Nodes

The Neck-Persistency-Net: a three-dimensional, convolution, deep neural network aids in distinguishing vital from non-vital persistent cervical lymph nodes in advanced head and neck squamous cell carcinoma after primary concurrent radiochemotherapy

PurposeTo evaluate the diagnostic performance (DP) of the high-resolution contrast computed tomography (HR-contrast-CT) based Neck-Persistency-Net in distinguishing vital from non-vital persistent cervical lymph nodes (pcLNs) in patients with advanced head and neck squamous cell carcinoma (HNSCC) following primary concurrent chemoradiotherapy (CRT) with [18F]-fluorodeoxyglucose positron emission tomography and high-resolution contrast-enhanced computed tomography ([18F]FDG-PET-CT). Furthermore, the Neck-Persistency-Net’s potential to justify omitting post-CRT neck dissection (ND) without risking treatment delays or preventing unnecessary surgery was explored.MethodsAll HNSCC patients undergoing primary CRT followed by post-CRT-ND for pcLNs recorded in the institutional HNSCC registry were analyzed. The Neck-Persistency-Net DP was explored for three scenarios: balanced performance (BalPerf), optimized sensitivity (OptSens), and optimized specificity (OptSpec). Histopathology of post-CRT-ND served as a reference.ResultsAmong 68 included patients, 11 were female and 32 had vital pcLNs. The Neck-Persistency-Net demonstrated good DP with an area under the curve of 0.82. For BalPerf, both sensitivity and specificity were 78%; for OptSens (90%), specificity was 62%; for OptSpec (95%), sensitivity was 54%. Limiting post-CRT-ND to negative results would have delayed treatment in 27%, 40%, and 7% for BalPerf, OptSens and OptSpec, respectively, versus 23% for [18F]FDG-PET-CT. Conversely, restricting post-CRT-ND to positive results would have prevented unnecessary post-CRT-ND in 78%, 60%, and 95% for BalPerf, OptSens and OptSpec, respectively, versus 55% for [18F]FDG-PET-CT.ConclusionThe DP of the Neck-Persistency-Net was comparable to [18F]-FDG-PET-CT. Depending on the chosen decision boundary, the potential to justify the omission of post-CRT-ND without risking treatment delays in false negative findings or reliably prevent unnecessary surgery in false positive findings outperforms the [18F]-FDG-PET-CT.

Landscape design of urban culture transmission based on the regional information security of Internet of Things

In the context of the rapid development of Internet of Things technology, urban cultural communication and information security have become a new focus in the field of landscape design. This paper innovatively discusses the landscape design of urban cultural communication based on Internet of Things regional information security, aiming at building a safe and culturally rich urban landscape environment. Taking the unique regional culture of Zhangjiajie as an example, this study evaluated the cultural communication effect of landscape design under information security guarantee through in-depth case analysis and field investigation, combined with Internet of Things information security technology. The results show that the cluster head node strategy has significant advantages in resisting physical capture attacks, especially when the number of sensor nodes captured is less than 2000, the information loss rate is less than 0.1. This discovery not only improves the level of information security in the Internet of Things environment, but also provides technical support for the effective dissemination of urban culture. In addition, through the detailed analysis and evaluation of landscape, this study further reveals the important role of landscape design in regional cultural inheritance. To sum up, this study not only provides a new perspective for urban landscape design, but also provides practical guidance for the protection and dissemination of urban culture in the era of Internet of Things.

Modeling of scattering coefficient for biological tissues exposed to near-infrared laser irradiation

Photothermal therapy using a near-infrared laser is a promising noninvasive treatment protocol for metastatic lymph nodes in breast, head, and neck cancers. From a thermal engineering perspective, the key to this therapy is accurately predicting the temperature distribution in the tissue due to laser irradiation. In this study, we focused on quantitatively modeling the scattering coefficient (σs), which changes with heating and significantly impacts the temperature distribution. First, we experimentally determined the change in σs due to heating using biological tissue and attempted to represent σs as a function of thermal damage factor which is based on the Arrhenius equation. Next, this function was incorporated into the radiative transfer equation to perform simulations. The results demonstrated significant agreement with the experimental data compared to those with constant σs. The root mean square error for the simulations with dynamic σs was approximately 2.0 (°C), which was half that of the simulations with constant σs. Although laser scattering in vivo is a complex phenomenon, the method proposed in this study is expected to offer an approach for predicting temperatures and determining appropriate laser power and irradiation time to prevent overheating or underheating in photothermal therapy.

Clustering Uniformity Methods for Energy Efficiency in Wireless Sensor Networks

The wireless sensors that make up a wireless sensor network (WSN) are randomly deployed in nature and cannot be artificially replaced when their batteries are depleted. Failure of communication connection between wireless sensors causes continuous connection attempts, which results in a lot of power dissipation and shortens the lifetime of the WSN. In this paper, we propose to extend the lifetime of WSNs by limiting the appropriate distance between the cluster head (CH) node and the communicating sensor nodes (SNs) so that a group of clusters of appropriate size can be formed on a two-dimensional plane. To equalize cluster size, sensor nodes with the shortest distance communicate with each other to form member nodes, and nodes with closer distances are bring together to form clusters. The simulation results show the improvement rate of cluster uniformity over the shortest distance-based clustering method for clustering based on the proposed cluster uniformity algorithm. The proposed method can improve the cluster uniformity of the network by about 20%. In addition, the power consumption of the proposed method is analyzed according to the difference in the density of sensor nodes in the cluster groups to examine the improvement in power consumption.

Hybrid Salp Swarm and Improved Whale Optimization Algorithm‐based clustering scheme for improving network lifespan in wireless sensor networks

SummaryWireless sensor networks (WSNs) represent the collection of restricted energy sensor nodes that are deployed in an area of target for gathering potential environment data for decision‐making with respect to their objective of application. However, the implementation of energy‐effective data gathering strategies in large‐scale WSNs is the most challenging due to the limited energy resources. Clustering‐based data gathering strategies are identified to be quite effective for energy saving that directly attributes to extended network lifetime. Moreover, optimal path amid the cluster head (CH) and sink node needs to be selected for sustaining energy efficiency and improving network lifespan. In this article, Hybrid Salp Swarm and Improved Whale Optimization Algorithm (HSSIWOA)‐based clustering scheme is proposed for improving the network lifetime and routing optimization with maximized energy efficacy. It integrated the exploration capability of Salp Swarm Optimization Algorithm (SSOA) with exploitation benefits of Improved Whale Optimization Algorithm (IWOA) for balancing the trade‐off between the rate of exploration and exploitation during CH selection process. It utilized the parameters of residual energy, load balance, intra‐cluster distance, inter‐cluster distance, and node centrality into account during the process of fitness evaluation. It performed well by constructing an optimized number of clusters, such that energy stability and network lifetime are maintained in the network. The experimental results of the proposed HSSIWOA scheme confirmed extended network lifetime of 21.64%, minimized energy utilization of 23.42%, and maximized throughput of 18.56%, better than the baseline approaches.

An energy-aware cluster-based routing in the Internet of things using particle swarm optimization algorithm and fuzzy clustering

The effectiveness and longevity of IoT infrastructures heavily depend on the limitations posed by communication, multi-hop data transfers, and the inherent difficulties of wireless links. In dealing with these challenges, routing, and data transmission procedures are critical. Among the fundamental concerns are the attainment of energy efficiency and an ideal distribution of loads among sensing devices, given the restricted energy resources at the disposal of IoT devices. To meet these challenges, the present research suggests a novel hybrid energy-aware IoT routing approach that mixes the Particle Swarm Optimization (PSO) algorithm and fuzzy clustering. The approach begins with a fuzzy clustering algorithm to initially group sensor nodes by their geographical location and assign them to clusters determined by a certain probability. The proposed method includes a fitness function considering energy consumption and distance factors. This feature guides the optimization process and aims to balance energy efficiency and data transmission distance. The hierarchical topology uses the advanced PSO algorithm to identify the cluster head nodes. The MATLAB simulator shows that our method outperforms previous approaches. Various metrics have demonstrated significant improvements over DEEC and LEACH. The method reduces energy consumption by 52% and 16%, improves throughput by 112% and 10%, increases packet delivery rates by 83% and 15%, and extends the network lifespan by 48% and 27%, respectively, compared to DEEC and LEACH approaches.

An efficient dual layer data aggregation scheme in clustered wireless sensor networks

AbstractIn wireless sensor network (WSN) monitoring systems, redundant data from sluggish environmental changes and overlapping sensing ranges can increase the volume of data sent by nodes, degrade the efficiency of information collection, and lead to the death of sensor nodes. To reduce the energy consumption of sensor nodes and prolong the life of WSNs, this study proposes a dual layer intracluster data fusion scheme based on ring buffer. To reduce redundant data and temporary anomalous data while guaranteeing the temporal coherence of data, the source nodes employ a binarized similarity function and sliding quartile detection based on the ring buffer. Based on the improved support degree function of weighted Pearson distance, the cluster head node performs a weighted fusion on the data received from the source nodes. Experimental results reveal that the scheme proposed in this study has clear advantages in three aspects: the number of remaining nodes, residual energy, and the number of packets transmitted. The data fusion of the proposed scheme is confined to the data fusion of the same attribute environment parameters.

A Distributed Deployment Method for Wireless Sensor Networks Based on Multi Agent Systems

This paper proposes a distributed deployment method for wireless sensor networks based on multi-agent systems, which addresses the issues of poor self adaptive deployment capability and high deployment costs caused by numerous and diverse types of nodes in the deployment process of wireless sensor networks, leading to blind spots in coverage and redundant nodes. Due to the characteristics of intelligent perception, intelligent communication, and clustering of nodes in wireless sensor network, sensor nodes can be seen as sensing agents, and sensor networks can be seen as distributed multi-agent systems composed of numerous sensing agents. Through the sensing agents' perception of the surrounding environment and communication interaction, a unified deployment model and distributed deployment algorithm with multiple nodes (cluster head nodes) as leaders are established, achieving effective integration of autonomous and leadership mechanisms, distributed management, and centralized management. Taking the problem of sensor network coverage as an example, simulation experiments were conducted, including 12 sensor agents and 1 20 * 20 monitoring areas, to verify how the sensor agents perceive the environment and optimize the migration and deployment process under the coordination of cluster head nodes. The experimental results confirmed the effectiveness of the above models and algorithms. The research results provide a theoretical model and practical approach for the distributed deployment and scheduling of sensor nodes in wireless sensor networks, and can be extended to intelligent Internet of Things systems, such as urban intelligent agent systems, logistics intelligent agent systems, etc., to improve the intelligence of wireless sensor networks deployment and scheduling.

Trusted Head Node for Node Behaviour Analysis for Malicious Node Detection in Wireless Sensor Networks

There has been a rise in the use of Wireless Sensor Networks (WSNs) in several fields. Depending on the malicious actions, attacks on WSN can take many forms. This research demonstrates how to leverage request and response data from each node to pinpoint the location of malicious nodes. If a node is malevolent, it will prohibit data from being sent to other nodes. They do an excellent job of pretending to be someone else. To enhance route quality, packet dropping can be used to perform a Denial of Service (DoS) assault by rejecting data packets and cutting them off from their destinations. The primary goal of this research is to expose malicious nodes within the network. To avoid being attacked by the false information fed by the adversary through compromised nodes, it is crucial to discover and isolate the nodes that have been compromised. Unfortunately, flat topology networks are difficult to keep safe because of their limited scalability and substantial communication overhead. By computing the packet drop and throughput values with valid nodes, efficient security architecture for the WSN can be proposed, allowing for the detection of malicious activity. If sensor networks are susceptible to attacks, they will be unable to fulfill their duty in a variety of event detection applications. Malicious nodes can cause unacceptable drops in event detection effectiveness and spikes in false alarm rates by producing fake readings and deceptive information. In this research, a method for identifying malicious nodes in WSNs is proposed. This research considers a Trusted Head Node (THN) for monitoring the nodes behaviour in the network to detect the malicious actions in the network. This research proposes a Trusted Head Node for Node Behaviour Analysis for Malicious Node Detection (THN-NBA-MND) in WSN. The proposed model based on node behavior analysis in frequent time intervals; detect the malicious nodes to improve the network performance in data transmission. The proposed model when contrasted with the existing model performs better in malicious node detection.

CHEABC-QCRP: A novel QoS-aware cluster routing protocol for industrial IoT

Clustering routing protocols currently have problems such as Single point of failure of cluster head nodes, poor network dynamics, uneven data transmission, etc., which are critical to the optimization of energy efficiency, network lifespan and network topology control. However, this optimization problem is an NP hard problem that conventional algorithms are difficult to solve. This paper proposes a new multi-objective cluster routing protocol (CHEABC-QCRP) aimed at optimizing network energy consumption, system lifespan, and quality of services (QoS). The protocol is based on a new chaotic hybrid elite artificial bee colony algorithm (CHEABC) proposed in this paper, which has strong search ability and greatly reduces convergence time. At the same time, a new chaotic strategy was designed to effectively prevent falling into local optima and premature convergence. In simulation experiments, compared with multiple routing protocols, a large number of test results show that this protocol significantly reduces network energy consumption, greatly improves system lifespan, and effectively improves QoS in IWSN.

Distinguishing Kikuchi-Fujimoto disease from lymphoma in patients by clinical and PET/CT features.

To develop a scheme for distinguishing Kikuchi-Fujimoto disease (KFD) from lymphoma in patients presenting enlarged lymph nodes (LNs) predominantly on the upper side of the diaphragm. From November 2015 to August 2023, 32 KFD patients and 38 lymphoma patients were pathologically confirmed and enrolled in this retrospectively study. Clinical and 18F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography (CT) features were collected. When comparing those PET/CT parameters, we set 5 models with different research objects: (1) all affected LNs; (2) the 5 largest affected LNs in terms of maximum diameter; (3) the 5 largest affected LNs in terms of maximum standard uptake values (SUVmax); (4) the largest affected LNs in terms of maximum diameter; (5) the largest affected LNs in terms of SUVmax. Compared to lymphoma patients, KFD patients were younger; and with higher incidence of fever, arthralgia, abnormal serum white blood cell, lactate dehydrogenase (LDH) and splenomegaly; lower incidence of affected LNs perinodal infiltration, necrosis and conglomeration; more affected LNs in Head and Neck nodes (particularly in level II) and Axillary in KFD (P ˂ .05). PET/CT parameters presented as various difference in each model. Finally, 11 clinical and PET/CT features (age ≤ 34, with fever, arthralgia, abnormal white blood cell, abnormal LDH, and without node necrosis and node conglomeration have a score of 2 each; splenomegaly, perinodal infiltration, median maximum diameter ≤ 20.5 and median SUVmax ≤ 7.1 of affected LNs in model 2 have score of 1 each) were selected as scheme items for distinguishing KFD from lymphoma. Individuals who have a total score > 8, meet the criteria for KFD. Sensitivity and specificity were high: 86.8% (95% CI: 71.9%, 95.5%) and 96.9% (95% CI: 83.7%, 99.5%), AUC = 0.975 (95% CI: 90.5%, 99.6%), respectively. It can effectively distinguish KFD from lymphoma by clinical and PET/CT parameters.

Deep-Reinforcement-Learning-Based Joint Energy Replenishment and Data Collection Scheme for WRSN.

With the emergence of wireless rechargeable sensor networks (WRSNs), the possibility of wirelessly recharging nodes using mobile charging vehicles (MCVs) has become a reality. However, existing approaches overlook the effective integration of node energy replenishment and mobile data collection processes. In this paper, we propose a joint energy replenishment and data collection scheme (D-JERDG) for WRSNs based on deep reinforcement learning. By capitalizing on the high mobility of unmanned aerial vehicles (UAVs), D-JERDG enables continuous visits to the cluster head nodes in each cluster, facilitating data collection and range-based charging. First, D-JERDG utilizes the K-means algorithm to partition the network into multiple clusters, and a cluster head selection algorithm is proposed based on an improved dynamic routing protocol, which elects cluster head nodes based on the remaining energy and geographical location of the cluster member nodes. Afterward, the simulated annealing (SA) algorithm determines the shortest flight path. Subsequently, the DRL model multiobjective deep deterministic policy gradient (MODDPG) is employed to control and optimize the UAV instantaneous heading and speed, effectively planning UAV hover points. By redesigning the reward function, joint optimization of multiple objectives such as node death rate, UAV throughput, and average flight energy consumption is achieved. Extensive simulation results show that the proposed D-JERDG achieves joint optimization of multiple objectives and exhibits significant advantages over the baseline in terms of throughput, time utilization, and charging cost, among other indicators.

Abstract 3528: Constructing knowledge graph of N6-methyladnosion regulations in cancer from literature using ChatGPT-4

Abstract N6-methyladenosine (m6A) is the most abundant mRNA modification with a crucial role in cellular processes. Its involvement in cancers is well-established. Dysregulation of m6A is linked to cancer initiation, progression, and therapy resistance, impacting the tumor microenvironment (TME) and promoting metastasis. Understanding m6A's regulatory role in cancer is essential for identifying new therapeutic targets. We considered the construction of a knowledge graph (KG) representing molecular regulatory mechanisms (MRMs) of m6A in healthy conditions and different cancers from PubMed papers. Such a KG consists of interconnected triplets of (head node, regulatory relationship, tail node), each denoting regulation between genes or a gene and a phenotype/process. Advances in natural language processing (NLP) have provided tools such as SemRep and GNBR to automate the extraction of these triplets from the literature. However, these methods don’t extract contextual information, such as the type of cancers or cells in which these MRMs occur, leading to contradictory regulations in the constructed KG. In addition, existing approaches were designed to extract generic regulatory relations and therefore struggled in properly capturing unique concepts associated with m6A regulatory mechanisms. To tackle this challenge, we designed a novel ChatGPT-4 prompt to extract relational graphs from papers tailored for m6A regulatory mechanisms. An annotated dataset of 400 titles on m6A-related MRMs were created to evaluate the performance. We then applied the prompt to 1023 papers to create a m6A Molecular Regulatory Mechanism Knowledge Graph (m6A-MRM-KG). This graph illuminates m6A's roles in gene expression regulation, especially in cancer and immunity. Anticipated to enhance our understanding of cancer development, it provides insights into potential therapeutic strategies. The m6A-MRM-KG not only organizes information but also empowers researchers to uncover novel insights in the dynamic relationships of m6A modifications in diverse biological contexts. Citation Format: Sumin Jo, Xidong Wu, Yiming Zeng, Arun Das, Ting-He Zhang, David Alexander Spellman, Adam Ferris, Shou-Jiang Gao, Jianqiu (Michelle) Zhang, Yu-Chiao Chiu, Yufei Huang. Constructing knowledge graph of N6-methyladnosion regulations in cancer from literature using ChatGPT-4 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3528.

Multiobjective, trust‐aware, artificial hummingbird algorithm‐based secure clustering and routing with mobile sink for wireless sensor networks

AbstractWireless sensor networks (WSNs) are composed of numerous nodes distributed in geographical regions. Security and energy efficiency are challenging tasks due to an open environment and a restricted battery source. The multiobjective trust‐aware artificial hummingbird algorithm (M‐TAAHA) is proposed to achieve secure and reliable transmission over a WSN with a mobile sink (MS). The M‐TAAHA selects secure cluster head (SCH) nodes based on trust, energy, interspace between sensors, interspace between SCH and MS, and the CH balancing factor. A secure route is found by M‐TAAHA with trust, energy, and interspace between SCH and MS. The M‐TAAHA avoids the malicious nodes to improve data delivery and avoid unwanted energy consumption. The M‐TAAHA is analyzed using energy consumption, alive nodes, life expectancy, delay, data packets received in MS, throughput, packet delivery ratio, and packet loss ratio. Existing techniques (LEACH‐TM, EATMR, FAL, Taylor‐spotted hyena optimization [Taylor‐SHO], TBEBR, and TEDG) are used for comparison with the M‐TAAHA. Findings show that the energy consumption of the proposed M‐TAAHA for 1000 rounds is 0.56 J (1.78 × smaller than that of the Taylor‐SHO).

Research on WSN Intelligent Routing Algorithm based on Bayesian Learning and Particle Swarm Optimization

Background: Wireless sensor networks have the characteristics of strong scalability, easy maintenance, and self-organization, but the energy of nodes is limited and it is difficult to replace the energy supply module. The survival time of the network has always been the key to restricting the development of wireless sensor networks. Objective: Aiming at the problems of short network lifetime and low coverage, a multi-objective optimization routing algorithm has been proposed, focusing on how to balance the communication energy consumption of each node in the network and improve the coverage area of the remaining nodes. Method: Firstly, the node region was divided into several fan ring subregions. Then, the particle swarm optimization algorithm was used to find the fan angles and radii of each fan ring subregion. Next, Bayesian learning was used to select the appropriate cluster head. Results: The simulation results showed the convergence speed of the proposed algorithm to be improved, solving the problems of cluster head election and node routing planning, improving the utilization of node energy, and verifying the effectiveness. Conclusion: The particle swarm optimization algorithm and Bayesian learning have been introduced to cluster network nodes, and a multi-objective fitness function compatible with the energy consumption and coverage of network nodes has been designed. By optimizing the selection method of convergence nodes, the network communication cost of each node can be effectively balanced, and the speed of network coverage area reduction can be effectively reduced in the later period of node communication.

Asymmetric Learning for Graph Neural Network based Link Prediction

Link prediction is a fundamental problem in many graph-based applications, such as protein-protein interaction prediction. Recently, graph neural network (GNN) has been widely used for link prediction. However, existing GNN-based link prediction (GNN-LP) methods suffer from scalability problem during training for large-scale graphs, which has received little attention from researchers. In this paper, we first analyze the computation complexity of existing GNN-LP methods, revealing that one reason for the scalability problem stems from their symmetric learning strategy in applying the same class of GNN models to learn representation for both head nodes and tail nodes. We then propose a novel method, called a sym m etric l earning (AML), for GNN-LP. More specifically, AML applies a GNN model to learn head node representation while applying a multi-layer perceptron (MLP) model to learn tail node representation. To the best of our knowledge, AML is the first GNN-LP method to adopt an asymmetric learning strategy for node representation learning. Furthermore, we design a novel model architecture and apply a row-wise mini-batch sampling strategy to ensure promising model accuracy and training efficiency for AML. Experiments on three real large-scale datasets show that AML is 1.7×∼7.3× faster in training than baselines with a symmetric learning strategy while having almost no accuracy loss.