Node Selection Research Articles

Federated Learning and Reputation-Based Node Selection Scheme for Internet of Vehicles

With the rapid development of in-vehicle communication technology, the Internet of Vehicles (IoV) is gradually becoming a core component of next-generation transportation networks. However, ensuring the activity and reliability of IoV nodes remains a critical challenge. The emergence of blockchain technology offers new solutions to the problem of node selection in IoV. Nevertheless, traditional blockchain networks may suffer from malicious nodes, which pose security threats and disrupt normal blockchain operations. To address the issues of low participation and security risks among IoV nodes, this paper proposes a federated learning (FL) scheme based on blockchain and reputation value changes. This scheme encourages active involvement in blockchain consensus and facilitates the selection of trustworthy and reliable IoV nodes. First, we avoid conflicts between computing power for training and consensus by constructing state-channel transitions to move training tasks off-chain. Task rewards are then distributed to participating miner nodes based on their contributions to the FL model. Second, a reputation mechanism is designed to measure the reliability of participating nodes in FL, and a Proof of Contribution Consensus (PoCC) algorithm is proposed to allocate node incentives and package blockchain transactions. Finally, experimental results demonstrate that the proposed incentive mechanism enhances node participation in training and successfully identifies trustworthy nodes.

ONDS: Optimum Node and Data Selection From Constrained IoT for Efficient Online Learning

ONDS: Optimum Node and Data Selection From Constrained IoT for Efficient Online Learning

Study the optimization of Dijkstra’s Algorithm

This paper presents an optimized approach to the shortest path problem, a fundamental concern in graph theory, by improving node selection and data storage. The traditional Dijkstra's algorithm is enhanced by introducing a novel node selection strategy that prioritizes nodes with the most significant impact on the shortest path, minimizing redundant calculations and accelerating convergence. Additionally, a compact data storage structure is introduced, reducing memory requirements while maintaining accuracy. This optimized approach offers reduced storage needs, enhanced efficiency, and improved scalability, making it an ideal solution for real-world applications in network optimization, traffic routing, and logistics, enabling faster and more scalable solutions for large-scale graphs and complex networks.

MAPBFT: multilevel adaptive PBFT algorithm based on discourse and reputation models

Abstract The traditional practical Byzantine fault tolerance (PBFT) consensus algorithm has limitations due to its failure to consider node credibility and its static structure, leading to reduced adaptability and increased communication overhead, particularly when dealing with Byzantine nodes. To address these issues, we propose a multilevel adaptive practical Byzantine fault tolerance algorithm (MAPBFT) based on PBFT and incorporating adaptive techniques, including a discourse power mechanism. MAPBFT initially uses a reputation model to evaluate node parameters such as past performance, reliability, availability, and response delay, providing predictive insights for the adaptive algorithm. The adaptive algorithm then employs a multi-layer perceptron to predict the reputation scores of nodes. This enables the selection of high-reputation nodes for consensus participation, narrowing the consensus scope, and reducing communication overhead. Finally, discourse power is distributed differentially based on node reputation scores. The consensus advancement criterion is modified to a threshold achieved through accumulated discourse power, focusing the system on high-reputation nodes and enhancing consensus efficiency and resistance against malicious nodes. We conducted experiments to validate MAPBFT’s performance and compared it with PBFT and APBFT. Experimental results demonstrate that MAPBFT enhances throughput, reduces response delay and communication overhead, and improves security, outperforming the other protocols.

CS-FL: Cross-Zone Secure Federated Learning with Blockchain and a Credibility Mechanism

Federated learning enables multiple intelligent devices to collaboratively perform machine learning tasks while preserving local data privacy. However, traditional FL architectures face challenges such as centralization and lack of effective defense mechanisms against malicious nodes, particularly in large-scale edge computing scenarios, which can lead to system instability. To address these challenges, this paper proposes a cross-zone secure federated learning method with blockchain and credibility mechanism, named CS-FL. By constructing a dual-layer blockchain network and introducing a blockchain ledger between zone servers, CS-FL establishes a decentralized trust mechanism for index detection and model aggregation. In node selection, CS-FL considers multiple dimensions, including node quality, communication resources, and historical credibility, and employs a three-stage mechanism that introduces lightweight probe tasks to assess node status before formal FL training, ensuring high-quality nodes participate. Additionally, the credibility incentive mechanism penalizes nodes that bypass probe mechanism and engage in malicious behaviors, effectively mitigating the impact of deceptive attacks. Experimental results show that CS-FL significantly improves the defense performance of FL, reducing attack success rates from 75–85% to below 5–20% when facing different types of threats, and effectively maintaining the training accuracy of the FL model. This demonstrates the potential of CS-FL to enhance the security and stability of FL systems in complex edge computing scenarios.

Toward optimal disease surveillance with graph-based active learning

Tracking the spread of emerging pathogens is critical to the design of timely and effective public health responses. Policymakers face the challenge of allocating finite resources for testing and surveillance across locations, with the goal of maximizing the information obtained about the underlying trends in prevalence and incidence. We model this decision-making process as an iterative node classification problem on an undirected and unweighted graph, in which nodes represent locations and edges represent movement of infectious agents among them. To begin, a single node is randomly selected for testing and determined to be either infected or uninfected. Test feedback is then used to update estimates of the probability of unobserved nodes being infected and to inform the selection of nodes for testing at the next iterations, until certain test budget is exhausted. Using this framework, we evaluate and compare the performance of previously developed active learning policies for node selection, including Node Entropy and Bayesian Active Learning by Disagreement. We explore the performance of these policies under different outbreak scenarios using simulated outbreaks on both synthetic and empirical networks. Further, we propose a policy that considers the distance-weighted average entropy of infection predictions among neighbors of each candidate node. Our proposed policy outperforms existing ones in most outbreak scenarios given small test budgets, highlighting the need to consider an exploration-exploitation trade-off in policy design. Our findings could inform the design of cost-effective surveillance strategies for emerging and endemic pathogens and reduce uncertainties associated with early risk assessments in resource-constrained situations.

An optimisation model of hierarchical facility location problem for urban last-mile delivery with drones

Drones have, in recent years, been considered as a promising way for the last-mile delivery of urban logistics. In view of limited or short battery life of drones in every operation, the drone recharging stations and their locations are crucial to ensure delivery accessibility and to accomplish the required mission. Therefore, in addition to considering the selection of service centres and demand nodes, the location of recharging stations should also be incorporated into the proposed model of hierarchical facility location problem (HFLP). Many existing researches on drone recharging station locations assume that the delivery demand is known and only a single path is designed to meet the demand by using queuing theory models to describe node-based service waiting time. However, the reality is that we are still in the infrastructure planning stage for drone delivery, in which the demand is unknown in most cases. Our research proposes a novel mix-integer programming model to solve the HFLP for the last-mile drone delivery in urban areas, aiming to minimise the deployment, operation, and maintenance (DOM) costs of recharging stations, service centres, and paths. We have also incorporated a demand satisfaction constraint into the selection of effective alternative paths that meet the delivery accessibility. On the other hand, a sensitivity analysis in our study reveals varying degrees of impact on DOM costs and effective alternative path quantities, whose results would be useful to practical drone logistics delivery in urban areas.

Resilience Analysis of Transportation System Based on Complex Network Theory

In order to ensure the important role of the transportation system in economic development, it is particularly important to evaluate the characteristics and resilience of the network. According to the complex network theory, the original mapping method is used to construct the complex network model, and the topological features node degree, average path length, clustering coefficient and intermediate are analyzed. The improved network efficiency and the maximum connectivity sub-graph size were selected as the resilience indicators, and the random attack and deliberate attack were used to simulate, and the changes of the resilience analysis index were calculated and compared, so as to complete the analysis of the resilience of the transportation system. The node degree, clustering coefficient and intermediate were selected as the evaluation indexes of key nodes, and the coefficient of variation method and TOPSIS algorithm were used to identify the key nodes, which were used as the basis for node selection in the simulation. Finally, taking Tianjin Shugang Highway as an example, an empirical analysis is carried out to verify the accuracy and applicability of the method.

Cervical lymph node dissection fails to improve survival in T2N0M0 glottic squamous cell carcinoma: a large-scale real-world study.

This study aimed to evaluate the therapeutic efficacy of cervical lymph node dissection in patients with T2N0M0 glottic squamous cell carcinoma. Among 260,068 head and neck cancer patients, 861 cases of T2N0M0 glottic squamous cell carcinoma were selected, of which 113 underwent selective lymph node dissection, and 748 did not receive dissection. The overall survival rates and disease-specific survival rates were compared between the two groups to assess efficacy. There was no significant difference in overall survival rates (median survival time of 57 months vs. 62 months, p = 0.34) and disease-specific survival rates (p = 0.79) between the two groups. Patients aged ≥ 60 years were at a higher risk, whereas neck dissection, gender, and degree of differentiation did not show significant risk differences in this study. Cervical lymph node dissection in patients with T2N0 glottic squamous cell carcinoma did not demonstrate a significant advantage in survival rates.

An improving secure communication using multipath malicious avoidance routing protocol for underwater sensor network

The Underwater Sensor Network (UWSN) comprises sensor nodes with sensing, data processing, and communication capabilities. Due to the limitation of underwater radio wave propagation, nodes rely on acoustic signals to communicate. The data gathered by these nodes is transmitted to coordinating nodes or ground stations for additional processing and analysis. The characteristics of UWSN with underwater channels make them vulnerable to malicious attacks. UWSN communication networks are particularly susceptible to malicious attacks owing to high bit error rates, significant propagation delay variations, and low bandwidth. Moreover, because of the challenging and erratic underwater conditions, limited bandwidth, slow data transmission speed, and power constraints of underwater sensor nodes establishing secure communication in UWSN presents a significant challenge. To address the issues mentioned above, we have introduced the Multipath Malicious Avoidance Routing Protocol (M2ARP) and Foldable Matrix based Padding Rail Fence Encryption Scheme (FM-PRFES) methods to enhance secure communication in UWSNs. The proposed FM-PRFES method encrypts the input data to prevent unauthorized access during transmission within the network. Subsequently, the proposed Energy Efficiency Node Selection (EENS) method is used to identify the significant nodes in the network. Additionally, the Cuckoo Search Optimization (CSO) method is utilized to select the Cluster Head (CH) for data transmission. Subsequently, M2ARP is employed to analyze various routes and avoid adversarial nodes in the network. As a result, the proposed experimental analysis yields more efficient results regarding security, Packet Delivery Ratio (PDR), and throughput performance than traditional approaches.

Enhanced lion swarm optimization and elliptic curve cryptography scheme for secure cluster head selection and malware detection in IoT-WSN

Wireless Sensor Networks present a significant issue for data routing because of the potential use of obtaining data from far locations with greater energy efficiency. Networks have become essential to modern concepts of the Internet of Things. The primary foundation for supporting diverse service-centric applications has continued to be the sensor node activity of both sensing phenomena in their local environs and relaying their results to centralized Base Stations. Malware detection and inadequate Cluster Heads node selection are issues with the current technology, resulting in a drastic decrease in the total Internet of Things-based performance of sensor networks. The paper proposes an Enhanced Lion Swarm Optimization (ELSO) and Elliptic Curve Cryptography (ECC) scheme for secure cluster head selection and malware detection in IoT-based Wireless Sensor Networks (WSNs). The paper includes network models, choice of Cluster Head (CH) and attack detection procedures. The proposed method chooses the Cluster Head with the best fitness function values, increasing data transmission speeds and energy efficiencies. Minimum Hop Detection has been implemented to provide the best routing paths against attack nodes. Security level for quick data transmissions via the Internet of Things using Wireless Sensor Networks strengthen sinkhole attacks and black hole nodes, which are successfully removed using this method. The proposed method integrates the use of Lion Swarm Optimization and Elliptic Curve Cryptography (ECC) enhances network security by ensuring secure data transmission and preventing unauthorized access, which is particularly important in IoT-WSN environments. The proposed method achieves less End delay, increased throughput of 93%, lower energy utilization of 4%, increased network lifetime of up to 96%, Packet Delivery Ratio of up to 98% and 97% of malicious node detection efficiently compared to existing methods.

Five-year outcomes of moderately hypofractionated proton therapy incorporating elective pelvic nodal irradiation for high-risk prostate cancer.

To assess the efficacy of moderately hypofractionated intensity-modulated proton therapy (IMPT) targeting the prostate/seminal vesicles and pelvic lymph nodes for high-risk (HR) or unfavorable intermediate-risk (UIR) prostate cancer (PCa) MATERIALS/METHODS: A prospective study (ClinicalTrials.gov: NCTXXX) of moderately hypofractionated IMPT accrued a target sample size of 56 patients with HR or UIR-PCa . The prostate/seminal vesicles and pelvic lymph nodes were treated simultaneously with 67.5 Gy and 45 Gy, respectively, in 25 daily fractions. All received androgen deprivation therapy (ADT). Its primary objective was late gastrointestinal (GI) and genitourinary (GU) adverse events (AEs), and secondary objectives were a recurrence-free rate (RFR) including freedom from PSA relapse and disease-free survival (DFS) at 5 years. PSA and AEs were evaluated at 3, 6, and 12 months post-IMPT, then every 6 months for 5 years and then yearly thereafter. The actuarial rates of late GI and GU AEs, RFR, and DFS were estimated with Kaplan-Meier method. Median age was 75 years. Median PSA was 10.5 ng/mL. Fifty-three patients had HR-PCa; 2 had UIR-PCa. Median ADT duration was 18 months. Median follow-up was 62 months. Late grade ≥ 2 and 3 GI AEs at 5 years were 16% and 4%, respectively. Late grade ≥ 2 and 3 GU AEs at 5 years were 41% and 0%, respectively. None had a grade ≥ 4 late AE. At 5 years, RFR and DFS were 90% and 89%, respectively. Seven patients had PCa recurrence, all detected by PSA relapse initially. Three patients died with PSA < 0.1 ng/mL at last follow-up. None died of PCa or treatment-related AEs. This regimen of moderately hypofractionated IMPT for HR or UIR-PCa yielded encouraging 5-year RFR, DFS, and late AE outcomes. A phase III study is needed to assess any therapeutic gain of IMPT compared with photon-based radiotherapy.

Long-term outcome in cervical lymph node-positive nasopharyngeal carcinoma following IMRT with one-step cervical nodal CTV delineation by geometric-anatomic expansion from nodal GTV: A double-center experience.

To report long-term results of cervical node-positive (CLN+) nasopharyngeal carcinoma (NPC) patients treated with IMRT with one-step nodal clinical target volume (CTVn) delineation by geometric-anatomic expansion from the nodal gross target volume (GTVn). CLN+ NPC treated with the same one-step-CTVn delineation in two Chinese academic centers were pooled for this study. GTVn was prescribed to 70 Gy equivalent, CTVn1 was omitted, CTVn2 was prescribed to 45-55 Gy equivalent and defined as GTVn + 3 mm geometric expansion (5 mm if radiological extranodal extension-positive, rENE+) + elective nodal regions defined by anatomic boundary of cervical nodal levels. Regional-control (RC) and overall survival (OS) were analyzed. Fifteen randomly selected cases were recontoured for CTVn according to 2018 International Guidelines (2018-IG). Dose/volume was compared between the two CTV delineation methods. A total of 807 patients were included (Center 1, n=459; Center 2, n=348). Five-year RC and OS were 95.8% and 86.2%, respectively. Thirty-four patients developed regional failure, and 13/34 (38%) were outside CTVn2: level VIII (parotid node) (9/13), Ib (4/13), and IV (2/13). Seven out of these 9 level VIII failures had preexisting "equivocal" nodes. All 4 level 1b failures had "equivocal" nodes with very advanced rENE or large (>5 cm) nodal mass in level II. Compared with the 2018-IG, our strategy resulted in significant reduction in nodal volumes received therapeutic (V70) (mean: 100.7 vs 27.5 cc, p<0.001) and prophylactic (V45) (mean: 343.5 vs 261.2 cc, p<0.001) doses, and further dose reduction in surrounding organs-at-risks. Our one-step-CTVn delineation by geometric-anatomic expansion from GTVn appears to be a safe and efficient approach in CLN+ NPC with excellent RC and potential dosimetric benefit in selected patients. Caution is needed for parotid sparing in patients with preexisting "equivocal" nodes or level Ib sparing in cases with advanced rENE or large (>5 cm) nodal mass in level II.

Defining Optimal Nodal Dose for Stage IIIC Cervical Cancer: An Institutional Audit of Patients Treated with Simultaneous Integrated Nodal Boost

ObjectiveTo assess radiation dose-response relationship for individual lymph nodes in patients with stage IIIC cervical cancer who are treated with simultaneous integrated boost (SIB).MethodsThis is a retrospective study in which patients with stage IIIC cervical cancer treated with (chemo)radiation who received simultaneous integrated nodal boost (SIB) and brachytherapy were evaluated with the primary aim of determining in-field nodal control. Secondary aims included estimation of local control (LC), disease free survival (DFS) and adverse events. Univariate analysis and multivariate analysis was performed for factors impacting nodal control, LC and DFS.ResultsOne hundred and fifty-one (151) nodes in 65 patients were included. The median number of lymph nodes and nodal volume was 2 (1–6) and 3.4 cc (1.8–6.8 cc) respectively. Median SIB dose was 55Gy/25# (53.7–55.0 Gy). At a median follow-up of 43 months the in-field nodal control was 99.3% (1/151). The 5-year LC, DFS and regional nodal control was 88.7, 66 and 85.6% respectively. The 5-year DFS for FIGO stage IIIC1 was 67.7% versus 43.5% for stage IIIC2 (p = 0.03) and lymph node volume >3cc was associated with reduced 5-year DFS (80.8% vs. 55%, p = 0.03) on univariate analysis. No factor was statistically significant on multivariate analysis. A vast majority of lymph node recurrences were observed in elective nodal regions outside the true pelvis that received lower contribution from brachytherapy.ConclusionNodal SIB doses of up to 55Gy/25#/ 5 weeks administered during chemoradiation are associated with 99.3% 5-year infield nodal control. A vast majority of nodal recurrences were observed in elective volumes that received a lower contribution from brachytherapy.

Protocol of a single-arm, multicenter, phase III trial for selective lymph node dissection in cT1N0M0 invasive non-small cell lung cancer with consolidation-tumor ratio >0.5 located in the apical segment: Eastern Cooperative Thoracic Oncology Projects ECTOP-1018 (SELLAS study).

Systemic lymph node dissection (LND) is the standard procedure for operable invasive non-small cell lung cancer (NSCLC), for radical cure and lymph node (LN) staging. However, its necessity is controversial in early-stage patients without LN metastasis, as evidence shows it could not improve prognosis and may cause surgical complications. We initiated a prospective, multi-center, single-arm, phase III trial to confirm the non-inferior survival rate of surgery sparing the lower mediastinal LND in apical cT1N0M0 invasive NSCLC with consolidation-tumor ratio (CTR) >0.5 at high-resolution computed tomography (CT) scan. We plan to enroll a total of 634 patients with invasive NSCLC with predominantly non-lepidic subtype confirmed by intraoperative frozen pathology, who will receive radical lung cancer surgery but waiver lower mediastinal LND. The primary endpoint is recurrence-free survival (RFS), the secondary endpoints are overall survival (OS) rate of these patients, metastatic distribution of LNs in different histological subtypes of NSCLC, and consistency between frozen and paraffin pathology reports in determining the tumor invasiveness. The results of this study will validate the necessity of LND in the lower mediastinum in the patients with early-stage NSCLC located in the apical segment with CTR >0.5 at CT scan. ClinicalTrials.gov Identifier NCT06031246.

An efficient propagation algorithm based on node clustering and seed node selection in a blockchain network

An efficient propagation algorithm based on node clustering and seed node selection in a blockchain network

Enhancing Stability and Efficiency in Mobile Ad Hoc Networks (MANETs): A Multicriteria Algorithm for Optimal Multipoint Relay Selection

Mobile ad hoc networks (MANETs) are autonomous systems composed of multiple mobile nodes that communicate wirelessly without relying on any pre-established infrastructure. These networks operate in highly dynamic environments, which can compromise their ability to guarantee consistent link lifetimes, security, reliability, and overall stability. Factors such as mobility, energy availability, and security critically influence network performance. Consequently, the selection of paths and relay nodes that ensure stability, security, and extended network lifetimes is fundamental in designing routing protocols for MANETs. This selection is pivotal in maintaining robust network operations and optimizing communication efficiency. This paper introduces a sophisticated algorithm for selecting multipoint relays (MPRs) in MANETs, addressing the challenges posed by node mobility, energy constraints, and security vulnerabilities. By employing a multicriteria-weighted technique that assesses the mobility, energy levels, and trustworthiness of mobile nodes, the proposed approach enhances network stability, reachability, and longevity. The enhanced algorithm is integrated into the Optimized Link State Routing Protocol (OLSR) and validated through NS3 simulations, using the Random Waypoint and ManhattanGrid mobility models. The results indicate superior performance of the enhanced algorithm over traditional OLSR, particularly in terms of packet delivery, delay reduction, and throughput in dynamic network conditions. This study not only advances the design of routing protocols for MANETs but also significantly contributes to the development of robust communication frameworks within the realm of smart mobile communications.

Social relationships and location information based routing algorithm in opportunistic networks

In opportunistic networks, the lack of a stable route between nodes necessitates the use of relay nodes for message transmission. In social attribute-based routing, the selection of relay nodes is mainly done according to historical encounter information among nodes. This historical information is used to compute utility values between nodes, which are then used to select relay nodes. However, when there is insufficient historical encounter information between source and destination nodes, the utility value between nodes may not be accurately calculated by traditional social routing strategies. This results in difficulties in selecting appropriate relay nodes and hence a low delivery ratio. To address this shortcoming, a routing algorithm based on social relationships and location information (SRLI) is proposed. Initially, historical encounter information among nodes is used to identify social relationships among nodes. Messages are forwarded to the relay nodes possessing higher social relationships with the destination nodes. To enhance the coverage of messages, the geographic cosine similarity between nodes is calculated. Messages are preferentially transmitted to adjacent nodes with lower similarity to the current node. In addition, a dynamic buffer management strategy is utilized to facilitate the appropriate discarding of messages. Lower priority messages are prioritized for deletion when a node buffer is full. Experimental results demonstrate that the proposed routing algorithm significantly outperforms the Epidemic, Prophet, and CHOP-NET algorithms in terms of message delivery ratio, average message forwarding latency and routing overhead. Our proposed SRLI is superior to CHOP-NET, Prophet and Epidemic by at least 16.9% in terms of average message forwarding latency and 7.7% in terms of message delivery ratio, while still being able to achieve 1.5% lower routing overhead.

An efficient ACO-inspired multi-path routing for source location privacy with dynamic phantom node selection scheme in IoT environments

An efficient ACO-inspired multi-path routing for source location privacy with dynamic phantom node selection scheme in IoT environments

Adaptive federated learning for resource-constrained IoT devices through edge intelligence and multi-edge clustering

In the rapidly growing Internet of Things (IoT) landscape, federated learning (FL) plays a crucial role in enhancing the performance of heterogeneous edge computing environments due to its scalability, robustness, and low energy consumption. However, one of the major challenges in such environments is the efficient selection of edge nodes and the optimization of resource allocation, especially in dynamic and resource-constrained settings. To address this, we propose a novel architecture called Multi-Edge Clustered and Edge AI Heterogeneous Federated Learning (MEC-AI HetFL), which leverages multi-edge clustering and AI-driven node communication. This architecture enables edge AI nodes to collaborate, dynamically selecting significant nodes and optimizing global learning tasks with low complexity. Compared to existing solutions like EdgeFed, FedSA, FedMP, and H-DDPG, MEC-AI HetFL improves resource allocation, quality score, and learning accuracy, offering up to 5 times better performance in heterogeneous and distributed environments. The solution is validated through simulations and network traffic tests, demonstrating its ability to address the key challenges in IoT edge computing deployments.