Trust Evaluation Research Articles

A secure and lightweight trust evaluation model for enhancing decision-making in resource-constrained industrial WSNs

The dependability of nodes in an Industrial Wireless Sensor Network (IWSN) is vital for precise decision-making and the overall functioning of the network. Unreliable nodes have the potential to result in incorrect data, which can undermine the reliability of monitoring and control systems. Inaccurate decision-making in IWSNs can result in operational failures, safety risks, inadequate resource utilization, elevated energy consumption, and susceptibility to security vulnerabilities. Trust models tackle these challenges by recognizing and reducing the impact of unreliable nodes, improving the precision of data, and strengthening the security of the IWSNs. To overcome these challenges, this research work introduces a “Novel approach for Trust Utilization and Reliability Enhancement” (NATURE) in IWSNs. The unique approach employed in proposed NATURE model is distinguished by its multi-level clustered model, which improves attack mitigation, reduces communication overhead, and enhances overall network efficiency. This model is particularly effective in industrial environments where the network structure and the nature of data traffic are highly dynamic. The use of a multifactor trust estimation framework allows NATURE to assess the trustworthiness of SNs based on a comprehensive set of criteria, including behavior patterns, energy consumption, and communication reliability. By operating on multiple levels, NATURE can dynamically adjust trust functions to accurately distinguish between trustworthy and faulty SNs, thereby improving network reliability. Moreover, NATURE employs the temporal decay factor to prioritize recent node behaviors, ensuring that obsolete actions have minimal impact. Additionally, it uses the dynamic adjustment factor to balance the influence of negative interactions, encouraging reliable and responsive trust evaluations. Additionally, NATURE integrates a dynamically adjustable logical time window to enhance monitoring precision and adaptability, outperforming fixed-length windows in anomaly detection. NATURE integrates an Optimal Lead Node Election Algorithm (OLNEA) to improve cluster leader selection process. OLNEA considers network density, link quality, and Lead Node (LN), battery life, ensuring competent data aggregation and load balancing. By periodically selecting robust LNs and seamlessly switching to alternatives, NATURE promotes reliability and mitigates the impact of low LN battery levels. Additionally, NATURE employs a trust-based attacks detection algorithm to fortify IWSN security. This algorithm employs keen methods to verify data integrity, monitor energy levels, and ensure message authenticity, effectively safeguarding the IWSN from malevolent attacks and ensuring the safe transmission of data. Experimental results highlight NATURE’s exceptional performance across significant metrics when compared to existing trust schemes. In a WSN of 500 SNs with 30% being malicious, NATURE detects malicious behavior with a 97% accuracy, outperforming other models. Even with 50% malicious SNs, NATURE maintains a high detection accuracy of 91%, again surpassing alternative approaches. Additionally, NATURE significantly reduces energy consumption while achieving efficient throughput rates, underscoring its effectiveness in challenging network conditions.

BN SO12 - Management Strategies for Complications in Common Bile Duct Exploration: A Single Unit Comprehensive Analysis

Abstract Background 10-20% of individuals with systemic gallstones are estimated to have common bile duct stones (CBDS)(1). Laparoscopic common bile duct exploration (LCBDE) and two stage laparoscopic cholecystectomy (LC) with subsequent endoscopic retrograde cholangiopancreatography (ERCP) are clinically equivalent in their purpose but associated with different risks and require different skill sets, training and facilities. This study has retrospectively reviewed all LCBDE in our Benign Upper Gastrointestinal Surgery Unit, in particular analysing complications, the management strategies employed, and the impact associated with these interventions. Method A retrospective analysis was conducted on all patients (n=427) who underwent LCBDE between November 2013 and May 2024 at our benign Upper Gastrointestinal Surgery Department to evaluate complications. Comparison is made with British Benign Upper Gastrointestinal Surgical Society (BBUGSS) standards. Results 16.6% (n=71) experienced complications; 3.28% (n=14) retained stone, 3.74% (n=16) bile leak, 9.6% (n=41) non-specific surgical complications. Bile leak approaches were transcystic (n=2), transcystic converted to cholecdochotomy (n= 3), and choledochotomy n=11. Leaks were diagnosed on ERCP (50%), drains (n=3), CT (n=4) and clinical suspicion (n=1). 50% (n=8) were managed by ERCP, n=6 laparoscopic washout, and n=2 conservatively. Of retained stones, n=5 were indicated intraoperatively, n=7 on readmission with pain, n=1 on tubogram, and n=1 due to increased drain output. 80% (n=12) managed via ERCP with sphincterotomy +/- stenting. Of the remainder n=1 underwent open exploration, and n=1 managed conservatively. Conclusion Bile duct complications can be life altering and thus early identification and management is paramount. BBUGSS’ recommended performance standards for LCBDE state that frequency of retained stones should be <15%, bile leaks <5% and return to theatre <5% (2). In our trust rates are 3.51%, 3.74% and 1.64% respectively. Our stone clearance rate is 96.49%, higher than the BBUGSS standard of >85% (2). Our findings conclude that a benign UGI unit in a District General Hospital can deliver a one stage approach with good management of complications from LCBDE.

Matching vs. Mismatching Signals of External Human-Machine Interface and Vehicle Kinematics: An Examination of Pedestrian Crossing Behavior and Trust, Safety, and Affective Ratings in Interactions with Differently Sized Automated Vehicles

Highly automated vehicles (HAVs) will soon be introduced into mixed urban traffic. Pedestrians might have an idea of HAVs. Nevertheless, they probably have never interacted with them before. Moreover, pedestrians will not be able to communicate with HAVs like they are used to with manual vehicles. External human–machine interfaces (eHMIs) are possible design solutions for HAVs to ensure safe interaction with other road users. Light-based eHMIs positively affected pedestrians’ trust ratings, perceived safety, and willingness to cross. However, previous studies often neglected the effect of vehicle size, although larger-sized HAVs could be potentially perceived as the more significant threat. Additionally, the relationship between vehicle kinematics and eHMIs for differently sized HAVs remains an underexplored research topic. This study investigated the effects of vehicle size (small vs. large), eHMI state (dynamic eHMI vs. static eHMI vs. no eHMI), and vehicle kinematics (yielding vs. non-yielding) on pedestrian crossing behavior and their subjective assessment. In virtual reality, we created a shared space traffic scenario, in which the eHMI and vehicle kinematics matched or did not match. For yielding conditions, the results showed that participants felt more aroused with larger HAVs than with smaller HAVs. Moreover, pedestrians initiated their crossing significantly earlier when both vehicle sizes had a dynamic eHMI compared to a static eHMI vs. no eHMI. Additionally, pedestrians evaluated a dynamic eHMI with higher trust ratings, higher perceived safety, and more positive affective reactions. The results manifested that the use of dynamic eHMIs can effectively enhance pedestrian-vehicle communication with a large and a small HAV. For non-matching conditions, the participants tended to rely on the vehicle kinematics for both vehicle sizes. Overall, the study highlighted the potential of eHMIs for pedestrian interactions with HAVs of varying sizes when they are well-coordinated with the vehicle kinematics, aiming to enhance safety and efficiency in mixed-traffic environments.

Public Perceptions of Surgeon Attire in Saudi Arabia.

Surgeon attire significantly affects patients' perceptions and can improve patient-surgeon relationships, which are crucial for patient comfort, experience, satisfaction, and treatment adherence. Understanding patient preferences for surgeon attire is essential, particularly in Saudi Arabia, for establishing appropriate dress codes in healthcare institutions. This national cross-sectional study aimed to fill this gap by assessing patient preferences for surgeon attire and its impact on patients' confidence in their surgeons across various medical settings. The study used a questionnaire with 16 questions and images of surgeons in different attire styles, focusing on trust, care, approachability, and comfort ratings. The questionnaire included options like scrubs with a white coat, formal attire, and traditional Saudi attire for male surgeons, and various options including niqab and skirts for female surgeons. The survey had four sections covering ratings, attire preferences, opinions on white coats, and demographic data. A total of 612 patients completed the questionnaires, mainly aged 35-54 (33%). Skirt and niqab and white coat attire for females received the highest ratings. Attire other than scrubs, especially jeans and heels with a white coat, had significantly lower ratings. Scrubs and white coats were the most preferred attire for male (23.7%) and female (17%) surgeons. Additionally, 71.24% of participants agreed that surgeons should wear white coats. In Saudi Arabia, male surgeons are preferred in scrubs and white coats, while female surgeons are preferred in skirts with white coats and niqabs. These preferences reflect notions of formality, professionalism, and cultural influences. Jeans received the lowest ratings, possibly due to perceptions of informality. Patients favor scrubs and white coats for hygiene benefits and conveying dedication and expertise. Future research should validate these findings across diverse cultures and medical fields.

A Data Transmission Scheme Based on Reinforcement-Learning-Aided Two-Stage Trust Evaluation for UASNs

A Data Transmission Scheme Based on Reinforcement-Learning-Aided Two-Stage Trust Evaluation for UASNs

RTrust: toward robust trust evaluation framework for fake news detection in online social networks

RTrust: toward robust trust evaluation framework for fake news detection in online social networks

ZFort: A scalable zero-trust approach for trust management and traffic engineering in SDN based IoTs

The Internet of Things (IoT), is a promising solution, but faces critical security challenges in the backdrop of evolving and sophisticated threats. Traditional security models are not well-adopted to protecting these diverse and resource-constrained devices against evolving threats like Advanced Persistent Threats (APTs). We introduce ZFort, a zero-trust framework that prioritizes the security of critical nodes in IoT networks. ZFort dynamically evaluates the risk status of nodes based on node’s criticality and vulnerability scores derived from Common Vulnerabilities and Exposures (CVE) data ZFort dynamically assesses node risk based on criticality and vulnerability scores derived from Common Vulnerabilities and Exposures (CVE) data, and Common Vulnerability Scoring System (CVSS). ZFort uses a stochastic differential equation model for dynamic and continuous trust evaluation between nodes. Based on this evaluation, it dynamically adjusts security measures and routing decisions in real-time. Additionally, ZFort quickly isolates nodes that are likely compromised and prevents routing across them. ZFort uses Mixed Integer Linear Programming (MILP) and efficient heuristics, guaranteeing scalability and resource efficiency even in large networks and enhances the resilience and trustworthiness of key IoT infrastructure.

Deep Artificial Immune System With Malicious Node Detection and Secure Routing Protocol in MANET

ABSTRACTIn the context of Mobile Ad Hoc Networks (MANETs), the dynamic and decentralized topology poses significant challenges like unreliable connectivity, limited bandwidth, node mobility, and vulnerability to security threats from malicious nodes. Ensuring secure and energy‐efficient data transmission in such environments is crucial for mission‐critical applications. This research addresses these pressing challenges by introducing a robust routing protocol capable of detecting and mitigating malicious nodes, thereby enhancing MANET's Quality of Service (QoS). The proposed approach, the Dendritic Cell with Adaptive Trust Q‐learning Protocol (dDC‐ATQP), integrates several innovative techniques to tackle these issues. Firstly, the trust evaluation mechanism assesses the behavior of nodes to identify potential malicious actors, mitigating the effect of malicious nodes on system execution. Secondly, the adaptive routing strategy optimizes data transmission paths based on real‐time network conditions, reducing latency and packet loss. To evaluate the effectiveness of this approach, extensive simulations are conducted using a range of performance metrics. The results demonstrate significant improvements over existing methods, including a throughput increase of (79.2% in 50 s), lower end‐to‐end‐delay (0.075 s for 20 nodes), energy consumption of (38.55/J), higher packet delivery ratio (98% for 20 nodes), reduced packet loss ratio (5% for 100 nodes), enhanced security (80% for 70 nodes).

Research on Network Security Protection Technology Based on P2AEDR in New Low-Voltage Control Scenarios for Power IoT and Other Blockchain-Based IoT Architectures.

In the construction of new power systems, the traditional network security protection mainly based on boundary protection belongs to static defense and still relies mainly on manual processing in vulnerability repair, threat response, etc. It is difficult to adapt to the security protection needs in large-scale distributed new energy, third-party aggregation platforms, and flexible interaction scenarios with power grid enterprise systems. It is necessary to conduct research on dynamic security protection models for IoT and other Blockchain-based IoT architectures. This article proposes a network security comprehensive protection model P2AEDR based on different interaction modes of cloud-edge interaction and cloud-cloud interaction. Through continuous trust evaluation, dynamic access control, and other technologies, it strengthens the internal defense capabilities of power grid business, shifting from static protection as the core mode to a real-time intelligent perception and automated response mode, and ultimately achieving the goal of dynamic defense, meeting the security protection needs of large-scale controlled terminal access and third-party aggregation platforms. Meanwhile, this article proposes a dynamic trust evaluation algorithm based on deep learning, which protects the secure access and use of various resources in a more refined learning approach based on the interaction information monitored in the system. Through experimental verification of the dynamic trust evaluation algorithm, it is shown that the proposed model has good trust evaluation performance. Therefore, this research is beneficial for trustworthy Power IoT and other Blockchain-based IoT architectures.

Fuzzy based Energy Efficient Rider Remora Routing protocol for secured communication in WSN network

Due to the resource constraint nature of WSN, ensuring secured communication in WSN is a challenging problem. Moreover, enhancing the network lifetime is one of the major issues faced by the existing studies. So, in order to secure the communication between WSNs and achieve improved network lifetime, a novel trust enabled routing protocol is proposed in this study. Initially, the clusters are constructed using Direct, Indirect, and Total Trust evaluations, which helps to identify the faulty nodes. After, an Improved Fuzzy-based Balanced Cost Cluster Head Selection (IFBECS) method is used to choose the cluster head (CH). Finally, to determine the best path from source to destination, a hybrid bionic energy-efficient routing model known as an Energy Efficient Rider Remora Routing (EERRR) protocol is introduced. To improve the network lifetime and throughput, the parameters like remaining energy of the CH, sensor node space, CH, etc., are considered by the utilized protocol. The proposed mechanism is implemented in NS-2 programming tool. The simulation results show that the proposed routing protocol has attained improved PDR of 97.92 % at the time period of 50 ms, reduced energy consumption of 3.336 at the time period of 100 ms, higher throughput of 86262.7 at the time period of 250 ms, and enhanced network lifetime of 1028.08 rounds in 200 nodes. Therefore, by attaining better results as compared with other existing protocols, it is clearly revealed that the proposed routing protocol is highly suitable for secured energy efficient WSN communication.

An energy-aware secure routing scheme in internet of things networks via two-way trust evaluation

The rapid advancement of technology has led to the proliferation of devices connected to the Internet of Things (IoT) networks, bringing forth challenges in both energy management and secure data communication. In addition to energy constraints, IoT networks face threats from malicious nodes, which jeopardize the security of communications. To address these challenges, we propose an Energy-aware secure Routing scheme via Two-Way Trust evaluation (ERTWT) for IoT networks. This scheme enhances network protection against various attacks by calculating trust values based on energy trust, direct trust, and indirect trust. The scheme aims to enhance the efficiency of data transmission by dynamically selecting routes based on both energy availability and trustworthiness metrics of fog nodes. Since trust management can guarantee privacy and security, ERTWT allows the service requester and the service provider to check each other's safety and reliability at the same time. In addition, we implement Generative Flow Networks (GFlowNets) to predict the energy levels available in nodes in order to use them optimally. The proposed scheme has been compared with several advanced energy-aware and trust-based routing protocols. Evaluation results show that ERTWT more effectively detects malicious nodes while achieving better energy efficiency and data transmission rates.

A trust model for VANETs using malicious-aware multiple routing

Vehicular ad hoc networks (VANETs) enable multi-hop communication among vehicles, promoting information sharing and smarter collaborative driving. However, VANETs are facing several challenges due to the open wireless communication environment. Attackers may maliciously drop or alter packets so that the receiver cannot obtain correct information. In addition, the high mobility of vehicles may lead to link failures, consequently resulting in packet loss. In this paper, we propose a multipath-based trust model (MPTM), in which the reliability of packet transmission is guaranteed by data redundancy and the detection of potential attackers is achieved by trust evaluation. Specifically, we present a route discovery mechanism to find multiple routes that avoid potential attackers, which reduces the risk of attacks on redundant packets. The receivers identify correct information based on two factors including content consistency and route information. An attacker detection module is presented to evaluate the trustworthiness of vehicles involved in packet transmission and vehicles with trust value below a threshold are detected as attackers. We conducted extensive experiments using OMNeT++ simulation platform, considering various attack scenarios. Experiment results show that MPTM can reach 90% packet delivery ratio and effectively detect attackers in terms of 90% detection precision.

TrustGNN: Graph Neural Network-Based Trust Evaluation via Learnable Propagative and Composable Nature.

Trust evaluation is critical for many applications such as cyber security, social communication, and recommender systems. Users and trust relationships among them can be seen as a graph. Graph neural networks (GNNs) show their powerful ability for analyzing graph-structural data. Very recently, existing work attempted to introduce the attributes and asymmetry of edges into GNNs for trust evaluation, while failed to capture some essential properties (e.g., the propagative and composable nature) of trust graphs. In this work, we propose a new GNN-based trust evaluation method named TrustGNN, which integrates smartly the propagative and composable nature of trust graphs into a GNN framework for better trust evaluation. Specifically, TrustGNN designs specific propagative patterns for different propagative processes of trust, and distinguishes the contribution of different propagative processes to create new trust. Thus, TrustGNN can learn comprehensive node embeddings and predict trust relationships based on these embeddings. Experiments on some widely-used real-world datasets indicate that TrustGNN significantly outperforms the state-of-the-art methods. We further perform analytical experiments to demonstrate the effectiveness of the key designs in TrustGNN.

Evaluation of Patients' Trust in Dentists According to Generations

Evaluation of Patients' Trust in Dentists According to Generations

Comprehensive Analysis Of Background Screening Impact On Employee Experience And Organizational Outcomes

This comprehensive study examines the multifaceted impact of background checks on employee experience and organizational outcomes, with a particular focus on the implementation of a nationwide, standardized employment screening system. The research is guided by six interconnected themes: trust, transparency, fairness, diversity and inclusion, engagement, and retention. In the contemporary business landscape, background checks have transcended their traditional role as a mere formality, evolving into critical assets for enhancing employee satisfaction, organizational trust, and retention rates. This review synthesizes a wide array of studies to analyze the complex interplay between background screening practices and employer-employee relationships. It establishes robust evidence for how fair, transparent, and reasonable background checks can significantly enhance organizational commitment and employee loyalty. The results underscore the critical importance of awareness and transparency in the background check process, highlighting how these factors foster trust and perceptions of fairness, ultimately leading to positive employee attitudes and improved organizational outcomes. The study also delves into the potential adverse effects of excessive or biased background checks on workforce diversity, with a particular emphasis on historically marginalized groups and individuals with criminal records. It proposes that employers who embrace equality in the workplace and ensure that their background check processes are free from unlawful discrimination can cultivate and maintain a diverse, inclusive, and highperforming workforce. The implications for practice and research are far-reaching. To optimize employee experience and key outcomes such as organizational commitment, job satisfaction, and reduced turnover intention, employers must prioritize fairness, transparency, and inclusiveness in their background check processes. The study identifies critical areas for further research, including sector-specific effects of background screening practices and the impact of emerging technologies and data privacy concerns on background check processes. By reconceptualizing background checks as a strategic tool for building trust and enhancing employee commitment, rather than a mere compliance exercise, organizations can achieve superior outcomes across multiple dimensions of performance. This research provides a comprehensive framework for understanding and implementing effective background screening practices that balance organizational risk management with positive employee experiences.

Understanding Fluctuations in Public Opinion toward COVID-19 Vaccines: Insights from Social Media Analysis

As of early 2019, the COVID-19 outbreak has ensued in millions of deaths, making it one of the worst pandemics in history. In addition to wearing masks, increasing sanitation, and avoiding crowds, widespread vaccination is crucial for preventing virus transmission. Despite significant progress in vaccine research and policy implementations, widespread immunization remains challenging. Analysis of exchanges on social media regarding COVID-19 vaccines has revealed significant uncertainty and mistrust in vaccines. As a result, ongoing evaluation of trust and confidence in COVID-19 vaccines is critical to crafting successful communication approaches for promoting extensive vaccination. This study aims to use content analysis of tweets about COVID-19 vaccines while also examining the user accounts generating them to provide evidence of fluctuations in public views toward COVID-19 vaccines. The proposed framework collects and processes data from social media networks, particularly Twitter, before presenting various analytics based on the different analyses performed through machine learning and deep learning algorithms. We hypothesize that a qualitative study starting from the pandemic would identify themes in public discourses (particularly those with negative sentiment or evidence of misleading information) that circulated during the developmental and mass release phases of COVID-19 vaccines. Therefore, it could inform and aid healthcare officials, public health agencies, and policymakers in increasing awareness and educational interventions for COVID-19 vaccines.

Enhancing Worker Recruitment in Collaborative Mobile Crowdsourcing: A Graph Neural Network Trust Evaluation Approach

Enhancing Worker Recruitment in Collaborative Mobile Crowdsourcing: A Graph Neural Network Trust Evaluation Approach

Trust-aware and improved density peaks clustering algorithm for fast and secure models in wireless sensor networks

Many trust-based models for wireless sensor networks do not account for trust attacks, which are destructive phenomena that undermine the security and reliability of these models. Therefore, a trust-based fast security model fused with an improved density peaks clustering algorithm (TFSM-DPC) is proposed to quickly identify trust attacks in this paper. First, when calculating direct trust values, TFSM-DPC designs the adaptive penalty factors based on the state of received and sent packets and behaviors, and introduces the volatilization factors to reduce the effect of historical trust values. Second, TFSM-DPC improved density peaks clustering (DPC) algorithm to evaluate the trustworthiness of each recommendation value, thus filtering malicious recommendations before calculating the indirect trust values. Moreover, to filter two types of recommendations, the improved DPC algorithm incorporates artificial benchmark data along with trust values recommended by neighbors as input data. Finally, based on the relationship between direct trust and indirect trust, a secure formula for calculate the comprehensive trust is designed. Therefore, the proposed TFSM-DPC can improve the accuracy of trust evaluation and speed up the identification of malicious nodes. Simulation results show that TFSM-DPC can effectively identify on-off, bad-mouth and collusion attacks, and improve the speed of excluding malicious nodes from the network, compared to other trust-based algorithms.

Medical Metaphors That May Reinforce Misconceptions Are Associated With Increased Trust in the Clinician.

This study measured patient reactions to medical metaphors used in musculoskeletal specialty offices and asked: (1) Are there any factors associated with patient thoughts and emotions in response to common metaphors? (2) Is there a difference between patient ratings of metaphors rated as potentially reinforcing misconceptions and those that are more neutral? In a cross-sectional study, 228 patients presenting to multiple musculoskeletal specialty offices rated reactions to 4 metaphors presented randomly from a set of 14. Two were categorized as potentially reinforcing common misconceptions and 2 as relatively neutral. Bivariate tests and multivariable regression identified factors associated with patient ratings of levels of emotion (using the standard assessment manikins) and aspects of experience (communication effectiveness, trust, and feeling comfortable rated on 11-point ordinal scales) in response to each metaphor. Levels of patient unhelpful thinking or distress regarding symptoms were not associated with patient ratings of patient emotion and experience in response to metaphors. Metaphors that reinforce misconceptions were associated with higher ratings of communication effectiveness, trust, and comfort (P < .05). The observation that metaphors that validate a person's understanding of his or her illness may elicit trust even if those metaphors have the potential to reinforce misconceptions may account for the common usage of such metaphors. Clinicians can work to incorporate methods for building trust without reinforcing misconceptions.

A Dynamic Trust evaluation and update model using advance decision tree for underwater Wireless Sensor Networks

Underwater wireless sensor networks (UWSNs) are an emerging research area that is rapidly gaining popularity. However, it has several challenges, including security, node mobility, limited bandwidth, and high error rates. Traditional trust models fail to adapt to the dynamic underwater environment. Thus, to address these issues, we propose a dynamic trust evaluation and update model using a modified decision tree algorithm. Unlike baseline methods, which often rely on static and generalized trust evaluation approaches, our model introduces several innovations tailored specifically for UWSNs. These include energy-aware decision-making, real-time adaptation to environmental changes, and the integration of multiple underwater-specific factors such as water currents and acoustic signal properties. Our model enhances trust accuracy, reduces energy consumption, and lowers data overhead, achieving a 96% accuracy rate with a 2% false positive rate. Additionally, it outperforms baseline models by improving energy efficiency by 50 mW and reducing response time to 20 ms per packet. These innovations demonstrate the proposed model’s effectiveness in addressing the unique challenges of UWSNs, ensuring both security and operational efficiency goals. The proposed model effectively enhances the trust evaluation process in UWSNs, providing both security and operational benefits. These key findings validate the potential of integrating modified decision tree algorithms to improve the performance and sustainability of UWSNs.