Trust Estimation Research Articles

Using attentive temporal GNN for dynamic trust assessment in the presence of malicious entities

Trust networks are built on the premise that there is a network of trust among entities. Precisely evaluating pairwise trust in such networks is essential for enhancing the quality of service in various applications, including online marketing, recommender systems, and IoT-based applications. However, it remains challenging to consider dynamic trust and account for malicious behavior in a scalable and effective manner.Recently, Graph Neural Networks (GNN)-based methods have emerged as powerful and scalable tools for trust estimation. Nevertheless, they lack thorough consideration for the time-dependent nature of trust, especially in the presence of malicious behaviors, leading to inaccurate trust predictions. To address this gap, we propose a novel trust assessment model called MATA, which handles the time dependency of trust and exhibits robustness against malicious behaviors. MATA leverages an extended Graph Convolutional Network (GCN) integrated with Recurrent Neural Networks (RNNs) and an attention mechanism to jointly capture time dependencies and trace entities’ behavior over time in complex networks. Additionally, by clustering the learned trust representations over time, we assign reputation values to entities. This allows us to monitor their dynamic behavior and mitigate potential adverse effects on trust assessment.In our experiments on real-world datasets, MATA outperforms the state-of-the-art methods by up to 8.4 % in accuracy. This improvement is a testament to the effectiveness of the proposed approach. Furthermore, our work enhances the robustness of trust assessment models, as evidenced by consistently higher performance metrics in the face of various trust-related attacks.

Dynamic and quantitative trust modeling and real-time estimation in human-machine co-driving process

The development of automated vehicles (AVs) will remain in the stage of human–machine co-driving for a long time. Trust is considered as an effective foundation of the interaction between the driver and the automated driving system (ADS). Driver’s trust miscalibration, represented by under-trust and over-trust, is considered to be the potential cause of disuse and misuse of ADS, or even serious accidents. The estimation and calibration of trust are crucial to improve the safety of the driving process. This paper mainly consists of the following two aspects. Firstly, a dynamic and quantitative trust estimation model is established. A framework for trust estimation is constructed. Driver’s perceived risk and behavior features were monitored and a Kalman filter was used to dynamically and quantitatively estimate the driver’s trust. We conducted a driver-in-the-loop experiment and generated model parameters through a data-driven approach. The results demonstrated that the model exhibited precision in trust estimation, with the highest accuracy reaching 74.1%. Secondly, a reminder strategy to calibrate the over-trust of the driver is proposed based on the model from the first part. A scenario with four risky events was designed and the ADS would provide voice reminders to the driver when over-trust was detected. The results demonstrated that the reminder strategy proved to be beneficial for safety enhancement and moderate trust maintenance during the driving process. When the driver is over-trusting, the accident rates of the reminder group and the non-reminder group were 60.6% and 13.0%, respectively. Our contribution in this paper can be concluded by four points: (1) A real-time trust estimation model is proposed, which is dynamic and quantitative, considering the evolution pattern of driver’s trust and the perceived risk; (2) Mathematical modeling and machine learning methods are combined; (3) A trust-based reminder strategy that aims to enhance the safety of human–machine co-driving is designed; (4) Driver-in-loop experiment validates the effectiveness in enhancing the safety, maintaining driver’s trust and reducing trust biases in human–machine co-driving.

Quantifying generalized trust in individuals and counties using language

Trust is predictive of civic cooperation and economic growth. Recently, the U.S. public has demonstrated increased partisan division and a surveyed decline in trust in institutions. There is a need to quantify individual and community levels of trust unobtrusively and at scale. Using observations of language across more than 16,000 Facebook users, along with their self-reported generalized trust score, we develop and evaluate a language-based assessment of generalized trust. We then apply the assessment to more than 1.6 billion geotagged tweets collected between 2009 and 2015 and derive estimates of trust across 2,041 U.S. counties. We find generalized trust was associated with more affiliative words (love, we, and friends) and less angry words (hate and stupid) but only had a weak association with social words primarily driven by strong negative associations with general othering terms (“they” and “people”). At the county level, associations with the Centers for Disease Control and Prevention (CDC) and Gallup surveys suggest that people in high-trust counties were physically healthier and more satisfied with their community and their lives. Our study demonstrates that generalized trust levels can be estimated from language as a low-cost, unobtrusive method to monitor variations in trust in large populations.

Recency-based spatio-temporal similarity exploration for POI recommendation in location-based social networks

Point-of-interest (POI) recommendation is one of the primary tasks of location-based social networks (LBSNs). With user data in bulk, extracting useful information and addressing issues such as data sparsity and cold-start problems looming large in collaborative filtering become difficult. One of the plausible solutions is to incorporate contextual information into the recommendation process. In this article, we propose a Recency-based Spatio-Temporal Similarity Exploration (RSTSE) for POI recommendation that utilizes the recency-based trust estimation among the prospective neighbors of the target user. The trust level is categorized into two heads: direct trust, which can be extracted from the peer group information of the user, and indirect trust, which is measured based on venue popularity, temporal recency, radial proximity, and transitivity. The approach consists of two phases. In the incipient phase, POIs are extracted based on the preferences of potential neighbors, including the users who are recognized peers, the users with similar visiting histories in the spatial and temporal context, and the users with friend-of-friend relations. The telic phase involves Neural Collaborative Filtering (NCF) to capture the linear and non-linear user–POI interactions better. RSTSE has been evaluated on three real-world datasets, namely, Gowalla, Foursquare, and Weeplaces, and the results suggest efficacy over other state-of-the-art approaches.

Trust based Routing – A Novel Approach for Data Security in WSN based Data Critical Applications

Wireless technology has changed the way entities communicate with one another. Wireless networks have created several opportunities in fields such as military, health care, and habitat monitoring, to name a few. However, only a few data-critical applications are built on wireless sensor networks, such as border reconnaissance, detecting infringement, and patient monitoring. These applications require the processing of a large amount of private data. Because most applications are data-sensitive, securing data transmission among wireless sensor networks is crucial. While incorporating data security, the most important requirement of wireless sensor nodes being energy optimized also need to be kept in consideration. There are various forms of assaults that are relevant in Wireless Sensor Networks (WSN). Attacks like Black Hole attacks, sink hole attacks, False data Injection attacks etc. are the most commonly seen attacks on WSNs. The common element in all these attacks is the concept of malicious / compromised node - a node which either drops / modifies the data content while forwarding it. Existing techniques for data security generally use cryptographic algorithms, but the use of cryptographic algorithms is in contrast with the energy optimization requirement of sensor nodes. An energy efficient data security scheme needs to be developed. The proposed system analyses several attacks and proposes a multi-layer data security approach to prevent change of data / dropping of data by the compromised nodes. The proposed system is a routing protocol referred as Trust Based Routing (TBR). A concept of trust value of a node is the core idea of TBR. Forwarding node is selected based on highest trust value and thus avoid malicious / compromised nodes from being involved in the routing process. The trust factor is calculated by considering the number of packets dropped, packets rejected, and the node's remaining energy. The idea of TBR is enhanced by incorporating the concept of past trust and trust of node towards a specific destination. This proposed scheme is referred as Extended Trust Based Routing (ETBR). This scheme is further enhanced by including Direct Trust, Indirect Trust and Energy Trust concepts. This scheme is referred as Consolidated Trust Estimation – Trust Based Routing (CTE-TBR). Network Simulator NS2 is used to simulate the proposed schemes. Various network factors are compared to classic Adhoc On-Demand Vector (AODV) and newly proposed schemes. The result indicates the effectiveness of the proposed data security scheme in terms of energy efficiency and Packet Delivery ratio (PDR).

Hybrid Bayesian and modified grey PROMETHEE-AL model-based trust estimation technique for thwarting malicious and selfish nodes in MANETs

Hybrid Bayesian and modified grey PROMETHEE-AL model-based trust estimation technique for thwarting malicious and selfish nodes in MANETs

Depression as a Terminal Illness – is there a place for palliative care?

In 2020, there were 5,224 suicide deaths registered in England and Wales [1]. The Mental Health Foundation reports that ~70% are in patients with depression [2]. The number of attempted suicides is much higher– South West London and St George’s Mental Health Trust estimates that at least 140,000 people attempt suicide in England and Wales every year [3]. In suicidal depression, the psychological pain is often unbearable and feels overwhelmingly incompatible with life. One is no longer living; they are merely surviving and eventually, the exhaustion will lead to decompensation. This is marked by suicide. The goal is to end the suffering permanently and this is achieved through death.

A Multi-Robot-Based Architecture and a Trust Model for Intelligent Fault Management and Control Systems

One of the most important challenges in robotics is the development of a Multi-Robot-based control system in which the robot can make intelligent decisions in a changing environment. This paper proposes a robot-based control approach for dynamically managing robots in such a widely distributed production system. A Multi-Robot-based control system architecture is presented, and its main features are described. Such architecture facilitates the reconfiguration (either self-reconfiguration ensured by the robot itself or distributed reconfiguration executed by the Multi-Robot-based system). The distributed reconfiguration is facilitated through building a trust model that is based on learning from past interactions between intelligent robots. The Multi-Robot-based control system architecture also addresses other specific requirements for production systems, including fault flexibility. Any out-of-control fault occurring in a production system results in the loss of production time, resources, and money. In these cases, robot trust is critical for successful job completion, especially when the work can only be accomplished by sharing knowledge and resources among robots. This work introduces research on the construction of trust estimation models that experimentally calculate and evaluate the trustworthiness of robots in a Multi-Robot system where the robot can choose to cooperate and collaborate exclusively with other trustworthy robots. We compare our proposed trust model with other models described in the literature in terms of performance based on four criteria, which are time steps analysis, RMSD evaluation, interaction analysis, and variation of total feedback. The contribution of the paper can be summarized as follows: (i) the Multi-Robot-based Control Architecture; (ii) how the control robot handles faults; and (iii) the trust model.

Intelligent analysis of landscape architecture based on digital technology under 5G network

In the wake of the wide promotion of 5G network, the era of super-high-speed networks and the Internet of Everything is approaching. Combining digital technologies led by 5G with landscape architecture has become an important way for the sustainable development of garden ecology. In order to achieve refined management of gardens and improve the accuracy and consistency of garden environmental data monitoring, this study constructs a new IoT sensor multi data fusion algorithm model. Considering the high redundancy and large error data collected by multiple sensors, this paper proposes a multi data fusion algorithm based on adaptive trust estimation and improved D-S evidence theory. The experimental data demonstrates that matched with IGA-BP, algorithm in this paper obtained the largest fitness value and the fastest convergence speed in three sensor application scenarios with different numbers of nodes. The lowest values were obtained in terms of unit energy consumption and network latency indicators. In the monitoring experiment for environmental data of landscape architecture, the algorithm obtained lower relative error and mean square error than IGA-BP in four environmental parameters of temperature, humidity, light intensity and carbon dioxide concentration. Therefore, the algorithm is effective in real-time monitoring of landscape garden environmental data, and can provide decision-making data for garden management as a reference.

A Lightweight Trust Mechanism with Attack Detection for IoT.

In this paper, we propose a lightweight and adaptable trust mechanism for the issue of trust evaluation among Internet of Things devices, considering challenges such as limited device resources and trust attacks. Firstly, we propose a trust evaluation approach based on Bayesian statistics and Jøsang's belief model to quantify a device's trustworthiness, where evaluators can freely initialize and update trust data with feedback from multiple sources, avoiding the bias of a single message source. It balances the accuracy of estimations and algorithm complexity. Secondly, considering that a trust estimation should reflect a device's latest status, we propose a forgetting algorithm to ensure that trust estimations can sensitively perceive changes in device status. Compared with conventional methods, it can automatically set its parameters to gain good performance. Finally, to prevent trust attacks from misleading evaluators, we propose a tango algorithm to curb trust attacks and a hypothesis testing-based trust attack detection mechanism. We corroborate the proposed trust mechanism's performance with simulation, whose results indicate that even if challenged by many colluding attackers that can exploit different trust attacks in combination, it can produce relatively accurate trust estimations, gradually exclude attackers, and quickly restore trust estimations for normal devices.

English

The ubiquitous nature of wireless sensor networks (WSNs) means that their underlying network architecture may be used to serve a broad range of use cases, from "smart cities" to "smart homes" and beyond. Other serious problems, such as energy use and security, have persisted. When it comes to WSN security, the blockchain might be the answer. Traditional blockchains have limitations, such as inefficient transaction processing and limited scalability. When Wireless Sensor Networks (WSNs) are deployed, most of the time, the nodes are left unmanaged and vulnerable to various security threats. Reliable data distribution in WSNs is nontrivial because of the limited resources and dynamic behavior of sensor nodes. Traditional cryptography and authentication-based methods have been shown to be unsuitable for countering node misbehavior attacks due to their high cost and incapability. The lightweight authentication encryption system was developed to solve the problems of sensor resource scarcity and data security (data integrity and secrecy). Current trust-based solutions, on the other hand, increase traffic congestion and decrease the network's lifespan due to the high costs associated with trust estimates and network-wide dissemination. As a result, the objective of this study is to present an intrusion prevention architecture for mobile Internet of Things devices, along with its integration into WSN, to ensure data security while simultaneously improving the network delivery ratio. The architecture that has been suggested is made up of two different discrete components. To begin, the ambiguity principle is used to construct non-overlapping and independently structured clusters, which are then used to preserve the clusters' stability. Second, based on the architecture of the blockchain, encrypted multi-hop routing pathways with end-to-end coverage are being established. By reducing end-to-end latency, time consumption, packet loss rate, and energy usage while increasing throughput, our recommended model's simulation results reveal good results and security gains.

It's Not Only What You Say, But Also How You Say It: Machine Learning Approach to Estimate Trust from Conversation.

The objective of this study was to estimate trust from conversations using both lexical and acoustic data. As NASA moves to long-duration space exploration operations, the increasing need for cooperation between humans and virtual agents requires real-time trust estimation by virtual agents. Measuring trust through conversation is a novel and unintrusive approach. A 2 (reliability) × 2 (cycles) × 3 (events) within-subject study with habitat system maintenance was designed to elicit various levels of trust in a conversational agent. Participants had trust-related conversations with the conversational agent at the end of each decision-making task. To estimate trust, subjective trust ratings were predicted using machine learning models trained on three types of conversational features (i.e., lexical, acoustic, and combined). After training, model explanation was performed using variable importance and partial dependence plots. Results showed that a random forest algorithm, trained using the combined lexical and acoustic features, predicted trust in the conversational agent most accurately . The most important predictors were a combination of lexical and acoustic cues: average sentiment considering valence shifters, the mean of formants, and Mel-frequency cepstral coefficients (MFCC). These conversational features were identified as partial mediators predicting people's trust. Precise trust estimation from conversation requires lexical cues and acoustic cues. These results showed the possibility of using conversational data to measure trust, and potentially other dynamic mental states, unobtrusively and dynamically.

P078 Bone health in men with prostate cancer on androgen deprivation therapy: audit and service development

Abstract Background/Aims Androgen deprivation therapy (ADT) is commonly used in the treatment of prostate cancer. A significant complication of ADT use is osteoporosis. National and international guidelines recommend a fracture risk assessment (FRA) based on clinical factors and bone mineral density (BMD) to guide bone agent (BA) use and bone protection advice. We audit our current performance, and subsequently offer a novel interdisciplinary service to optimise patients’ bone health. Methods Retrospective hospital analysis of patients with metastatic prostate cancer or pre-radiotherapy seen in 2 hospitals over a 6-month period. Patients intended for >6 months treatment were assessed against guideline standards and a retrospective FRA was performed. Results 82 patients were captured. Age range 62 to 92, median age 77.3. 4.9% patients were already known to the osteoporosis service, but none had been referred for a bone health assessment with the commencement of ADT. Table 1 illustrates our performance against guidelines and the likely need for a BA in a post-hoc analysis at one site. As a result of the collaborative audit undertaken between the urology and osteoporosis services that demonstrated poor recognition of bone health in patients on ADT, the teams worked collaboratively to design a guideline for assessing and managing these patients. This included reviewing the information given to patients about the risk of ADT to bone health, resources that the urology team can signpost patients to, and the initiation of appropriate BA to be undertaken by an appropriately trained person/team. Conclusion ADT use can accelerate bone loss, therefore users need FRA and BMD assessments to appropriately guide management. Self-management to optimise bone health by patients can be improved by providing appropriate bone protection advice (regular exercise and adequate vitamin D and calcium intake). Bone health assessments in patients on ADT is a major unmet need that requires attention to minimise clinical risk to patients. The new assessment tool and management pathway in our Trust estimates 114 new patients annually requiring FRA with a maximum number of 372 DXA scans per year accounting for those requiring 2-yearly scans to monitor BMD until the age of 80. Disclosure Z. Sheng: None. P. Srivastava: None. J. Shah: None. S. Yaqub: None. R. Laxminarayan: None. A. Ugur: None. S. Williams: None. S. Broadhurst: None. J. Tipper: None. C. Haynes: None. M. Hui: None.

Effect estimate of time-varying social support and trust on the physical and mental health of mothers at 2.5 years postpartum: The Japan Environment and Children's Study (JECS).

Many epidemiological studies have reported the association between various social factors and health status in mothers during and after pregnancy. However, little is known about their joint and longitudinal impact. We therefore examined the association of lack of social support and trust during pregnancy and at 2.5 years postpartum with health status in mothers. To adequately address time-varying exposure, marginal structural models were fitted to a pseudopopulation constructed by inverse probability weighting. The model included records of 90,071 mothers participating in the Japan Environment and Children's Study. Social support and trust were measured using a 9-item questionnaire (Q1-9). Mental and physical health were measured using Mental and Physical Component Summary scores from the 8-item Short-Form Health Survey. For the Mental Component Summary, the magnitude of the effect estimate was largest when participants lacked close friends/neighbors (Q4) at only 2.5 years postpartum (= -6.23), followed by a lack in emotional support (Q2) at the same time point (= -4.94). For the Physical Component Summary, effect estimates were negligible. The magnitude of the effect estimates of lack of social support and trust tended to be larger when there was a lack at only 2.5 years postpartum than at both time points. These findings suggest that, after childbirth, a loss in social support, particularly in a concrete or instrumental aspect, carries high risk, especially for mental health. Our results highlight the importance of supporting mothers for more than a few years after pregnancy.

Immigration and Public Support for Political Systems in Europe

Immigration and growing diversity have been linked with pathologies such as lower social capital, the rise of authoritarian populists, intergroup conflict, and perhaps the breakdown of democracy itself. At the heart of this complex is a question relating to migration and political culture: whether immigration erodes the attitudes that sustain and legitimize democratic political systems. This paper takes a time-series, cross-sectional approach to this question by analyzing the effects of a comprehensive set of measures of immigration on dynamic estimates of trust in democratic institutions, satisfaction with democracy, and democratic support from 30 European countries. The results show that immigration does not undermine any of these forms of public support for political systems. Indeed, under some circumstances, immigration may increase public trust in democratic institutions.

How Much Should We Trust Estimates of Firm Effects and Worker Sorting?

Many studies use matched employer-employee data to estimate a statistical model of earnings determination with worker and firm fixed effects. Estimates based on this model have produced influential yet controversial conclusions. The objective of this paper is to assess the sensitivity of these conclusions to the biases that arise because of limited mobility of workers across firms. We use employer-employee data from the United States and several European countries while taking advantage of both fixed effects and random effects methods for bias correction. We find that limited mobility bias is severe and that bias correction is important.

Energy Efficient Secure-Trust-Based Ring Cluster Routing in Wireless Sensor Network

Typically, wireless sensor networks (WSNs) are used to monitor as well as detect different kinds of objects in realistic monitoring, where security remains as a major confront. Estimation of node trust is established to be an effectual way of enhancing the security, thus aiding in nodes collaboration and decision-making in wireless and wired networks. Nevertheless, conventional methods of trust management generally highlight on trust modeling and fail to notice the overhead issues. In this paper, a security aware ring cluster routing technique is introduced. The routing is undergone based on the multi-objectives including trust (security) parameters, energy, and distance. Here, the trust parameters include both the direct trust evaluation and indirect trust evaluation. Thereby, the lifetime of the network gets maximized even with secured manner. An innovative Self-Adaptive Deer Hunting Optimization (SA-DHO) is presented in this study because the optimization plays a significant role in selecting the neighbors as ring nodes. Finally, the superiority of the suggested approach is demonstrated in relation to various measures.

A selfish node trust aware with Optimized Clustering for reliable routing protocol in Manet

Reliable routing in MANET is a pivotal element in real-world scenarios. The former works introduced a novel routing protocol namely, Trustable Energy-based Clustering and Optimized Routing (TECROP). However, this failed to concentrate on the selfish nodes. The proposed research focuses on the progress of a routing protocol that could handle the presence of selfish nodes. The proposed Selfish Node aware Trustable and Optimized Clustering-based Routing (SN-TOCRP) follows hierarchical clustering for creating node clusters. The selection of cluster Heads (CH) is done through the nature-inspired Fuzzy based Crow Search Algorithm. Selfish node detection is done by using introducing the authentication method which will authenticate the cluster heads. Bandwidth-aware Trust-based Routing Protocol (BTRP) is deployed to detect as well as isolate both misbehaving as well as faulty nodes. This also helps in diagnosing common misbehaviour attacks like a wormhole, Sybil, and selfishness with better results. Trust estimation and trust-based routing will be based on Modified Glow swarm optimization. The detailed experimental analysis done on the NS2 simulator proves that the proposed work achieves improved results with Packet delivery ratio of 96%, loss ratio of 0.045%, average delay of 0.325 ms with throughput is 76 Kbps, end to end delay of 0.425 ms and of energy consumption of 88 mJ.

A Trust-Based Secure Neuro Fuzzy Clustering Technique for Mobile Ad Hoc Networks

A MANET consists of a group of mobile nodes. In a MANET, scalability and mobility have a greater influence on routing performance. The clustering technique plays a vital role in enhancing the routing mechanism and improving the network lifetime of a large-scale network like a MANET. The clustering process will degrade network performance if the malicious node is chosen as the Cluster Leader (CL). Thus, the secure clustering process in a MANET is a very challenging task. To overcome this problem, the following key factors like Trust Value (TV), Residual Energy Level (REL), and Mobility (M) of the node are used as decision-making parameters to elect a Cluster Leader (CL). In this work, we have proposed a soft computing-based neuro-fuzzy model, ANFIS-based Energy-Efficient Secure Clustering Model (ANFIS-EESC), with a primary objective of forming energy-aware stable trust-based clustering in a MANET. Moreover, we have proposed two working novel algorithms: Weight-Based Trust Estimation (WBTE) algorithm and the Fuzzy-Based Clustering (FBC) algorithm. The primary objective of the WBTE algorithm is to measure the trustworthiness of the nodes and to mitigate the malicious nodes. Fuzzy-Based Clustering (FBC) algorithm is a fuzzy logic-based cluster formation algorithm. In our proposed work, each non-CL in the system applies the cluster density of CL and mobility for each CL node using the Mamdani Fuzzy Inference system, and makes the decision to join as a member with a CL that holds maximum value. Simulation results show that the proposed work enhances the network performance by electing a more stable trust-aware and energy-aware node as Cluster leader (CL). We compare the performance parameters of the proposed work, such as packet delivery rate, energy consumption, detection rate, and reaffiliation, with the existing work, Weighted Clustering Algorithm (WCA). The network lifetime is 39% greater in the proposed ANFIS-EESC model than in the other existing work, WCA. Moreover, ANFIS-EESC shows an enhancement of 22% to 32% in packet delivery ratio and 32% and 39% in throughput. From the above analysis, it has been proved that the proposed work gives a better performance in terms of reliability and stability when compared to the existing work, WCA.

Fuzzy Reputation Based Trust Mechanism for Mitigating Attacks in MANET

Mobile Ad-hoc Networks (MANET) usage across the globe is increasing by the day. Evaluating a node’s trust value has significant advantages since such network applications only run efficiently by involving trustable nodes. The trust values are estimated based on the reputation values of each node in the network by using different mechanisms. However, these mechanisms have various challenging issues which degrade the network performance. Hence, a novel Quality of Service (QoS) Trust Estimation with Black/Gray hole Attack Detection approach is proposed in this research work. Initially, the QoS-based trust estimation is proposed by using a Fuzzy logic scheme. The trust value of each node is estimated by using each node’s reputation values which are determined based on the fuzzy membership function values and utilizing QoS parameters such as residual energy, bandwidth, node mobility, and reliability. This mechanism prevents only the black hole attack in the network during transmission. But, the gray hole attacks are not identified which in turn increases the packet drop rate significantly. Hence, the gray hole attack is also detected based on the Kullback-Leibler (KL) divergence method used for estimating the statistical measures. Additional QoS metrics are considered to prevent the gray hole attack, such as packet loss, packet delivery ratio, and delay for each node. Thus, the proposed mechanism prevents both black hole and gray hole attacks simultaneously. Finally, the simulation results show that the effectiveness of the proposed mechanism compared with the other trust-aware routing protocols in MANET.