Taxonomy of Governance Mechanisms for Trust Management In Smart

Dynamic Ecosystems

Dasa Kusnirakova

and Barbora Buhnova

Faculty of Informatics, Masaryk University, Brno, Czech Republic

Keywords:

Trust, Trust Management, Governance, Taxonomy, Smart Dynamic Ecosystems.

Abstract:

In our evolving society, a future is envisioned where humans and digital systems converge to shape dynamic

and unpredictable ecosystems constantly adapting to ever-changing conditions. Such smart dynamic ecosys-

tems, which seamlessly merge digital agents, physical infrastructure, and human-technology interactions, need

to enable the formation of partnerships between their members to collectively solve complex tasks. This ne-

cessitates the establishment of trust together with effective governance mechanisms on the ecosystem level,

which emerge as crucial elements to ensure the proper functioning, safety, and adherence to established rules.

However, there is currently very little understanding of what such trust-supporting governance mechanisms

could look like. In this paper, we open this promising scientiﬁc ﬁeld with compiling a taxonomy of governance

mechanisms aimed at supporting trust management in smart dynamic ecosystems. By this, we take an initial

step into the development of a comprehensive governance model and stimulate further research to address this

critical aspect of managing the complex and dynamic nature of these ecosystems.

1 INTRODUCTION

Our society is moving towards the future where dig-

ital systems, physical objects and social interactions

among humans and technology all seamlessly merge

to form intelligent and adaptive ecosystems (Liu et al.,

2011; Capilla et al., 2021). These smart dynamic

ecosystems, where all the members interact, collab-

orate, and adapt to the constantly changing needs of

the environment (Xia and Ma, 2011), are however in-

herently unpredictable.

The need of ecosystem members to form partner-

ships and collaborate with others in order to collec-

tively solve complex tasks thus calls for establish-

ing trust, a crucial and yet under-researched concept

necessary to support human-to-machine and machine-

to-machine interactions (Schreieck et al., 2016; Me-

chanic, 1996).

Several studies underscore the key role of building

and maintaining trust among the members of smart

dynamic ecosystems for the successful adoption

of autonomous and intelligent technologies (Capilla

et al., 2021; Beer et al., 2014). The concept of trust

and its importance within the digital world can play

https://orcid.org/0000-0002-5341-902X

https://orcid.org/0000-0003-4205-101X

a major role, for instance, in selecting trusted in-

formation or service providers among various smart

agents in the ecosystem, or as a self-protection mech-

anism against untrusted and potentially malicious

agents (Buhnova et al., 2023), such as those designed

with the intention to cause harm or deceive and ma-

nipulate others.

Besides establishing trust among the ecosystem’s

agents, effective governance is crucial to guarantee

the proper functioning of such dynamic ecosystems

in terms of safety and adherence to established rules.

This governance includes developing strategies and

rules (Schreieck et al., 2016) based on the speciﬁc

needs of the ecosystem in question, i.e. rules for en-

tering the ecosystem, ensuring trustworthy communi-

cation and forming partnerships among ecosystem’s

members. An efﬁcient governance model should also

encompass mechanisms for upholding moral and eth-

ical responsibility and advancing principles like sol-

idarity and fairness. Otherwise, agents might be-

have unethically or perform actions endangering other

members or disrupting the whole ecosystem. Yet, the

current understanding of the mechanisms and compo-

nents that shall form such a governance model is so

far very fragmented.

In this paper, we propose a taxonomy of gover-

nance mechanisms for trust management in smart dy-

700

Kusnirakova, D. and Buhnova, B.

Taxonomy of Governance Mechanisms for Trust Management In Smart Dynamic Ecosystems.

DOI: 10.5220/0012726100003687

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2024), pages 700-710

ISBN: 978-989-758-696-5; ISSN: 2184-4895

namic ecosystems, compiled from a review of exist-

ing literature. We believe that via interconnecting the

fragmented knowledge on the topic, this paper offers

a solid ground for the scientiﬁc community to stimu-

late further research and collaboration to address the

critical aspect of managing the complex and dynamic

nature of smart dynamic ecosystems.

The rest of this paper is organized as follows. Sec-

tion 2 summarizes the related work, while section 3

describes the methodology employed to build the pro-

posed taxonomy of governance mechanisms for smart

dynamic ecosystems. The taxonomy itself is pre-

sented in section 4. Afterward, sections 5 and 6 con-

clude the paper with a discussion of future research

directions.

2 RELATED WORK

The governance of trust management in smart dy-

namic ecosystems represents a complex research ﬁeld

in its scope (i.e., what research challenges need to

be addressed), breadth (i.e., what mechanisms and in

which interplay are needed to address the challenges),

and depth of the individual mechanisms (i.e., what

are the effective ways to address the individual chal-

lenges). While attempts to the depth aspect of the

challenge exist in the literature, unless there is an un-

derstanding of the breadth and scope of the problem,

which is currently very fragmented, we can hardly

hope for an effective solution to the problem.

A notable pillar of knowledge in terms of tax-

onomies addressing the governance of quality aspects

in complex ecosystems can be traced in Social Inter-

net of Things (SIoT) (Alkhabbas et al., 2019), which

however focuses on technical-quality aspects, such

as ensuring resilience (Berger et al., 2021), secu-

rity (Williams et al., 2019; Rizvi et al., 2018), or ser-

vice discovery (Roopa et al., 2019), instead of trust.

On the other hand, the works that focus on categoriz-

ing the aspects of trust and trust management within

SIoT (Ahmed et al., 2019; Chahal et al., 2020; Ahmed

et al., 2020), recognizing trust as the fundamental

building block of SIoT (Khan et al., 2020) needed for

effective interactions and collaboration of SIoT mem-

bers, focus on particular aspects of trust such as prop-

erties, metrics, and trust attacks, leaving the gover-

nance mechanisms for trust management largely un-

explored.

Governance in the context of Internet of Things

(IoT) has been researched from the direction of

decision-making (Almeida et al., 2017), and roles

and responsibilities management (Gerber and Kansal,

2020), while unfortunately overlooking trust-based

governance. Besides, considerable research effort

has been dedicated to developing governance mech-

anisms and frameworks for Cyber-Physical-Social

Systems (CPSS) (Katina and Keating, 2018; Katina

et al., 2017). These works predominantly focus on in-

dividual systems, though, rather than holistically ad-

dressing the governance needs of entire ecosystems in

which CPSS operate, and thus lack systematic orga-

nization of the necessary mechanisms.

As for the ﬁeld of software ecosystems, there ex-

ist studies addressing trust management (Hou and

Jansen, 2023) and governance (Alves et al., 2017) is-

sues. However, it is crucial to recognize that smart

dynamic ecosystems diverge from software ecosys-

tems as the former encompasses a blend of physi-

cal and digital entities, adapting to real-world con-

ditions, while the latter predominantly involves dig-

ital components and applications operating in virtual

spaces. Due to this key difference, the principles and

strategies employed in trust management and gov-

ernance within software ecosystems cannot directly

translate to the complexities presented by smart dy-

namic ecosystems but need to be addressed sepa-

rately.

To sum up, while notable sources of knowledge

exist on the fragments of the topic, there is a lack of

(1) a comprehensive taxonomy of governance mecha-

nisms, (2) tailored for smart dynamic ecosystems and

(3) centered around trust. In this paper, we ﬁll the

gap by introducing an initial version of the taxonomy

of governance mechanisms for trust management in

smart dynamic ecosystems.

3 METHODOLOGY

To identify relevant papers, we conducted an

exploratory search across electronic academic

databases. The search utilized combinations of

keywords on trust, trust management, govern*, IoT,

and SIoT to retrieve an initial set of papers. This

collection was further expanded by incorporating

selected reference papers cited in the initial set. The

collected papers were then examined with a focus

on the identiﬁcation of mechanisms essential for

the governance of trust management within smart

dynamic ecosystems.

In order to classify the collected governance

mechanisms, a classiﬁcation scheme was developed

following the methodology proposed by Usman et

al. (Usman et al., 2017). Thus, we applied the fol-

lowing four phases: (1) Planning, (2) Identiﬁcation

and Extraction, (3) Design and Construction, and (4)

Validation.

Taxonomy of Governance Mechanisms for Trust Management In Smart Dynamic Ecosystems

701

Table 1: References for Trust Score Management and Ecosystem Wellbeing Management mechanisms.

Trust Score Management

Trust Evidence

Collection

Trust Metrics

QoS Metrics Quality of Service (QoS) trust metrics (Xiao et al., 2015; Bao and Chen, 2012b)

Social Metrics

social interactions (Yan et al., 2016), social metrics (Buhnova et al., 2023), honesty (Yan

et al., 2016; Nitti et al., 2013), openness (Iqbal and Buhnova, 2022), fairness (Nwebonyi

et al., 2019)

social trust parameters (Chen et al., 2014; Bao and Chen, 2012a)

Time Dimension

Past Behaviour

past subjective experiences (Gwak et al., 2017), past behaviours (Meena Kowshalya and

Valarmathi, 2017)

Present Behaviour present experience (Buhnova, 2023), present behavior (Mehdizadeh and Farzaneh, 2022)

Future Behaviour futuristic behaviours (Meena Kowshalya and Valarmathi, 2017)

Trust Score

Computation

Local

subjective trust calculation (Ghafari et al., 2020; Bo et al., 2017), distributed computing

(Asiri and Miri, 2016)

Global centralized authority for computations (Asiri and Miri, 2016), guarantor (Clarke et al., 2013)

Trust Score

Propagation

From Members

to Central Authority

global share (Nitti et al., 2013), centralized (Resnick et al., 2000), reputation centre (Jøsang

et al., 2007)

From Members

to Members

distributed collaborating ﬁltering (Chen et al., 2014), distributed (Kamvar et al., 2003; Men-

doza and Kleinschmidt, 2015), distributed stores (Jøsang et al., 2007)

From Central Au-

thority to Members

from central authority (Jøsang et al., 2007), intermediate or provider (Nitti et al., 2013)

Trust Score

Lifecycle

Initialization initial trust value (Chen et al., 2015), entrance of a new object (Atzori et al., 2012)

Update

trust update (Chen et al., 2014; He et al., 2020; Peng et al., 2008), value update (Namal

et al., 2015)

Erosion trust erosion (Sagar et al., 2022; Truong et al., 2017; Rana et al., 2022)

Ecosystem Wellbeing Management

Incentive Mechanisms

Reward Mechanisms

reward mechanisms (Bangui et al., 2023a; Bangui et al., 2023b; Guo et al., 2021; Zhaofeng

et al., 2019; Malik et al., 2019; Xiaoxue et al., 2010), reward system (Singh and Kim, 2018)

Punishment Mechanisms

punishment mechanisms (Bangui et al., 2023a; Bangui et al., 2023b; Guo et al., 2021; Xi-

aoxue et al., 2010), punishment (Etalle et al., 2007), penalties (Malik et al., 2019)

Safety Assurance

Isolation of Untrusted Mem-

bers

isolate untrusted devices (Banerjee et al., 2018), isolation module (Hategekimana et al.,

2020)

Isolation of Trust Management

Disruptors

isolation of attacking nodes (Muzammal et al., 2020; Alsumayt et al., 2017), isolating mali-

cious devices (Nandhini et al., 2022; Seshadri et al., 2020; Ahmed et al., 2015)

Detection of Trust

Management Disruptors

Detection of Disruptive

Members

detection of malicious nodes (Liu et al., 2019; She et al., 2019; Li et al., 2020; Wang and

Wei, 2021; Khatun et al., 2019; Illi et al., 2023)

Detection of Trust Attacks

trust attack detection (Caminha et al., 2018; Abdelghani et al., 2019; Marche and Nitti,

2020; Masmoudi et al., 2020; Magdich et al., 2021)

Trade-off Analysis in

Decision Making

Resolving Conﬂicting Values,

Interests and Goals

conﬂicting preferences (Zavvos et al., 2021), conﬂicting information (K

okciyan and Yolum,

2020)

Detection of Discrimination

discrimination of objects (Jafarian et al., 2020; Illi et al., 2023), discrimination attack

(Marche and Nitti, 2020)

Corrective Mechanisms

Trust Score/Decision Re-

assessment

self-correction (Lochner and Smilek, 2023), trust miscomputation (Khan et al., 2015), feed-

back loop (Bangui et al., 2023a)

Correction of Trust Score

Miscomputation

self-correction (Lochner and Smilek, 2023), trust miscomputation (Khan et al., 2015)

Reparation/Compensation of

Affected Members

trust compensation (Yu et al., 2017)

The initial phase involved the planning process,

where the ideas for the classiﬁcation scheme were

collected. In the second phase, the dimensions for

the classiﬁcation of governance mechanisms were de-

veloped, drawn from the grouping of the mechanisms

found in the literature. This was performed iteratively

by the authors and each dimension was discussed and

agreed by the authors. Moving into the third phase,

the taxonomy was constructed by combining pro-

posed dimensions and validated in the fourth phase

by correspondence and backward snowball analysis

searches that have been used in the taxonomy descrip-

tions, as elaborated in the discussion in section 5.2.

The complete list of references for individual mecha-

nisms is provided in Table 1.

4 TAXONOMY

One of the initial ﬁndings when exploring the col-

lected governance mechanisms is the clustering of

the mechanisms around two core concepts: trust as-

sessment and trust assurance. While the governance

mechanisms connected to trust assessment can be ex-

plained as answering the question of ”Can I trust?”,

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Figure 1: Relationship between Trust Assessment and Trust Assurance, and their contribution to governance.

the governance mechanisms connected to trust assur-

ance can be essentially reduced to the support of an-

swering the question of ”How to ensure I can trust?”.

The former cluster consists of mechanisms of trust

score computation, collection of trust evidence and

inputs for such computation, trust score propagation,

and management of the trust score over time, and is

referred to as Trust Score Management in our taxon-

omy introduced in section 4.1.

The latter cluster consists of incentive mecha-

nisms to motivate trustworthy behaviour (and punish

its violations), detection of untrustworthy members

and their isolation to promote safety and wellbeing

of the remaining ecosystem members, and corrective

mechanisms in case of trust misjudgement and dis-

crimination that happens in effect of that. We refer

to this cluster as Ecosystem Wellbeing Management,

which is presented in section 4.2.

The symbiotic relationship between these two

clusters is visualized in 1. Trust Score Management

activities center around trust assessment. It serves

as the foundation for the initial evaluations of each

ecosystem member’s trustworthiness. Once an entity

is assessed for trust, it is awarded a trust score, and

trust assurance comes into play. This is the center

of Ecosystem Wellbeing Management mechanisms,

whose role is to gradually increase the trustworthiness

of individual members (via motivating them to better

behaviour and expelling disruptors). Together, Trust

Score Management and Ecosystem Wellbeing Man-

agement contribute to the governance of smart dy-

namic ecosystems by creating a cycle that reinforces

and sustains the concept of trust within the ecosystem

over time.

4.1 Trust Score Management

The focus of Trust Score Management mechanisms

centers around trust assessment, i.e. awarding a trust

score to an entity. In order to do that, there need to

be mechanisms in place responsible for collecting ev-

idence that serves as input data for trust score calcu-

lation, keeping the scores updated, and propagating it

across the network.

We describe the governance mechanisms respon-

sible for trust score management in the following

paragraphs and summarize them in 2.

1. Trust Evidence Collection and Information Gath-

ering

Collecting information is a necessary prerequi-

site for the calculation of trust scores representing

the trustworthiness of individual ecosystem mem-

bers. These trust scores are calculated on the basis

of selected features called trust metrics, that are

monitored and combined in time (Meena Kow-

shalya and Valarmathi, 2017).

Trust metrics capture different qualities of inter-

actions occurring between agents. These can refer

to QoS metrics reﬂecting the ability of an agent

to provide quality services in terms of reliability

or accuracy (Xiao et al., 2015; Bao and Chen,

2012b). Other mechanisms focus on capturing so-

cial relationships among agents in terms of hon-

esty, openness, altruism, or unselﬁshness (Nitti

et al., 2013) by monitoring social metrics.

To address the time dimension, it is neces-

sary to consider mechanisms that monitor past,

present, and future behaviour. Monitoring trust

metrics over time enables the ecosystem to gain

understanding of how trust dynamics changes and

to adapt to evolving trust scenarios by feeding

design-time, runtime, and predictive models, re-

spectively, and allows for the anticipation of po-

tential malicious intentions (Meena Kowshalya

and Valarmathi, 2017).

Evidence collection also serves as a promising

tool for justifying decisions that might be opposed

by certain ecosystem agents, detecting any poten-

tial trust attacks, and proving malicious intentions

of agents before they become fully evident (Buh-

nova, 2023).

Note that in all these cases, various mechanisms

can be in place to promote the exchange of the

metrics between the trustor and the trustee. How-

ever, as trust is essentially a belief of the trustor

Taxonomy of Governance Mechanisms for Trust Management In Smart Dynamic Ecosystems

703

about the trustee’s trustworthiness, the trustor

needs to be given a way to validate the metrics

themselves, which can be supported by the trustee

by sharing an explanation of their actual or in-

tended actions (Iqbal and Buhnova, 2022).

2. Trust Score Computation

Different mechanisms must be applied to calcu-

late trust scores at different levels of the ecosys-

tem. Typically, the literature mentions local and

global trust score computation (Ghafari et al.,

2020).

Local trust scores are calculated on the ecosys-

tem member level. They are derived from agent-

to-agent relationships, which involve the assess-

ment of one agent’s trustworthiness by another,

utilizing local information such as current obser-

vations or past experience. In contrast, global

trust score extends beyond individual interac-

tions, representing an agent’s reputation within

the broader ecosystem. In this context, each

agent’s reputation is linked to the local trust scores

assigned by other agents in the ecosystem, creat-

ing a network of mutual inﬂuence on overall trust-

worthiness. These calculations are made at the

central authority level, e.g. by reputation mod-

els (Asiri and Miri, 2016).

3. Trust Score Propagation

Trust score propagation describes how trust infor-

mation spreads throughout the network. There are

various kinds of trust score propagation schemes

found in the literature – (1) centralized schemes

depending on a central node that is responsible

for gathering trust-related data and propagating it

across the network (Nitti et al., 2013), (2) decen-

tralized schemes where each ecosystem member

is responsible for trust computation and propaga-

tion on its own (Chen et al., 2014), and (3) hybrid

schemes combining centralized and decentralized

principles (Nitti et al., 2013).

While centralized schemes are vulnerable to

a single point of failure and are not suitable

for large-scale networks (Karthik and Anantha-

narayana, 2017), decentralized schemes face chal-

lenges associated with limited computational ca-

pacity of individual nodes and unbiased propa-

gation of trust scores across the network (Jøsang

et al., 2007). Since hybrid schemes are able

to mitigate the challenges of both aforemen-

tioned propagation schemes (Karthik and Anan-

thanarayana, 2017), they are frequently em-

ployed throughout the research works (Karthik

and Ananthanarayana, 2017; Mahmood et al.,

2019). It is, therefore, necessary to ensure that ap-

propriate trust score propagation mechanisms are

employed in the ecosystem. These include mech-

anisms capable of propagating individual trust

scores not only between members of the ecosys-

tem and the central authority (in both directions)

but also among the members themselves.

4. Trust Score Lifecycle

Besides the evidence collection, computation and

propagation of trust scores throughout the ecosys-

tem, governance mechanisms dealing with trust

score lifecycle need to be established, too. Trust

score lifecycle covers multiple phases, namely

the trust score (1) initialization, (2) update and

(3) erosion, and shall be implemented at both

the local levels (i.e., ecosystem members storing

the trust scores of their peers) and global levels

(i.e., trust scores managed by the global reputa-

tion model). They are responsible for ensuring the

integrity and reliability of the scoring system.

The mechanisms for trust score initialization

(sometimes referred to as bootstrapping) are re-

sponsible for assigning a trust score value to

agents newly entering the ecosystem without any

previous records (Atzori et al., 2012). Determin-

ing the appropriate initial trust score value is a

challenging task (Chen et al., 2015). If the initial

trust score is too low, new agents might experi-

ence difﬁculties in engaging in meaningful inter-

actions with other agents within the ecosystem, as

they are not trusted. On the other hand, setting the

initial trust score value too high may pose a risk

that malicious agents could exploit this initial trust

to inﬂict harm before being identiﬁed as untrust-

worthy, or abuse it to whitewash their reputation

via leaving and re-entering the ecosystem with a

clean trust score.

The update phase demands dynamic mecha-

nisms that facilitate real-time adjustments, con-

sidering evolving circumstances and agents’ be-

haviour. The updates are typically managed

through event-driven, time-driven, or hybrid ap-

proaches. In the event-driven scenario, trust

scores are updated upon the completion of an

interaction with other agents, or after a speciﬁc

event has occurred (Chen et al., 2014). How-

ever, this approach introduces the drawback of in-

creased network trafﬁc overhead. Alternatively,

in the time-driven approach, trust is updated reg-

ularly at speciﬁc time intervals, ensuring a peri-

odic assessment of an agent’s trust score (Namal

et al., 2015). Lastly, the hybrid approach com-

bines both aforementioned approaches, enabling

trust updates at set intervals and/or in response to

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704

Figure 2: Taxonomy of Governance Mechanisms for Trust Score Management.

speciﬁc events or interactions (Xiao et al., 2015).

In addition to the dynamic nature of an agent’s

trust score within the ecosystem, a high trust score

or reputation shall deteriorate towards a neutral

value when an agent experiences a lack of inter-

actions or engages in too few interactions (Truong

et al., 2017). It is therefore necessary to estab-

lish mechanisms taking into account the lifespan

of trust values, whereby the trust score of inactive

agents undergoes the erosion process after a spec-

iﬁed duration of inactivity (Sagar et al., 2022) in

order to keep the trust scores up to date.

4.2 Ecosystem Wellbeing Management

Trust represents a valuable resource inﬂuencing the

overall health of the ecosystem. It elevates various as-

pects of the ecosystem wellbeing, such as the ability

for the ecosystem members to depend on each other

and feel safe, fairness by promoting equitable interac-

tions and decision-making, or solidarity through en-

couraging collaboration and mutual support within

the ecosystem.

In the following paragraphs, we list the gov-

ernance mechanisms responsible for the ecosystem

wellbeing identiﬁed in our study. The mechanisms

are also summarized in 3.

1. Incentive Mechanisms

Encouraging behaviours aligned with ecosystem’s

rules and values belongs to the key mechanism for

enhancing the well-being of the ecosystem that

need to be established. This is being achieved

through incentives, i.e. a system of rewards and

punishments. The decision to reward or punish

an agent can be determined by various factors,

e.g. its current trust score or based on the re-

cent relative changes in it, such as an increase

or decrease (Bangui et al., 2023a; Bangui et al.,

2023b).

2. Safety Assurance

Given that trust in smart dynamic ecosystems is

understood as ”the attitude or belief of an agent

(trustor) to achieve a speciﬁc goal in interaction

with another agent (trustee) under uncertainty

and vulnerability” (Buhnova, 2023), trust man-

agement is only meaningful in the environments

where the members feel vulnerable in some way.

This lies behind the importance of safety assur-

ance on the ecosystem level, which needs to be in

place to protect vulnerable members in the pres-

ence of members with questionable trustability.

Ensuring safety within the ecosystem is closely

tied to the ability to expel or isolate untrusted

agents, which might be dangerous, or disruptors,

which are assumed to disrupt the wellbeing of the

ecosystem. In situations where trust is used to

navigate the sharing of information or provision

of services, the trustor can easily choose not to

use the knowledge or services provided by un-

trustworthy agents. However, in complex scenar-

ios involving physical safety and human lives, e.g.

avoiding collisions with malicious autonomous

Taxonomy of Governance Mechanisms for Trust Management In Smart Dynamic Ecosystems

705

Figure 3: Taxonomy of Governance Mechanisms for Ecosystem Wellbeing Management.

vehicles, ensuring safety becomes a challenging

task. In these cases, it becomes essential to avoid

the collision by employing mechanisms of adap-

tive function restriction in order to regulate the

ability of untrusted members to cause harm (Ha-

lasz and Buhnova, 2022).

3. Detection of Trust Management Disruptions

To be able to deal with misbehaving agents, it is

ﬁrst crucial to have mechanisms in place capa-

ble of identifying ongoing disruptions within the

ecosystem (Sagar et al., 2022). Detecting these

disruptions involves not only identifying mali-

cious agents as such, but also encompasses the

recognition of various trust attacks that substan-

tially undermine the fundamental pillars of the

ecosystem.

4. Corrective Mechanisms

It is essential to implement mechanisms that en-

able corrections of past trust decisions or elimi-

nations of unfairness observed in the ecosystem

in order to maintain a just and fair environment.

These mechanisms do not only include identiﬁca-

tion and elimination of injustice such as discrim-

ination or unfairness occurring within the ecosys-

tem (e.g. newly joining agents facing issues with

earning the required trust for establishing mean-

ingful interactions), but also allow to correct trust

misjudgements (Bangui et al., 2023a) made in the

past, all by reassessing trust scores, correcting

trust score miscomputations, and providing com-

pensation to the affected agents.

5. Trade-off Analysis in Decision Making

A smart ecosystem represents a place where of-

ten the collective objectives of individual systems,

their goals, and the goals of human members in-

tersect (Tofangchi et al., 2021). Within this dy-

namic setting and all ongoing interactions, con-

ﬂicts may arise. For instance, while the ecosystem

as a whole may prioritize efﬁciency, agents may

seek full control over their actions. Simultane-

ously, people may require privacy and ethical con-

siderations in their interactions with the ecosys-

tem. Effectively managing these conﬂicting val-

ues and ﬁnding common ground requires gover-

nance mechanisms that achieve a balance between

pursuing the goals of all involved parties. For in-

stance, trust-based trade-off analysis using incen-

tives could serve as a tool for resolving conﬂict-

ing values, interests, and goals within a smart dy-

namic ecosystem. Members striving toward the

ecosystem’s shared goals could be rewarded with

special tokens, which could then be replaced as a

form of currency in case a member wants to prior-

itize its own goals even if they may not align with

the goals of the ecosystem as a whole.

5 DISCUSSION

While this paper only takes the initial steps towards a

comprehensive taxonomy of governance mechanisms

for trust management in smart dynamic ecosystems,

we believe it lays a solid foundation covering the

breadth of the governance mechanisms for this chal-

lenging context, which can serve as a starting point for

the research community ﬁlling the necessary details.

5.1 Opportunities for Further Research

Building upon the initial work presented in this pa-

per, further research can focus on studying the gover-

nance of smart dynamic ecosystems in more depth,

classifying the individual mechanisms according to

more parameters and reﬁning them to deeper levels of

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706

categorization. Then, a possible research path is the

development of a comprehensive governance model,

systematically organizing the identiﬁed governance

mechanisms within a structured framework. Such a

model would provide a holistic understanding of the

relationships and dependencies among various gover-

nance components.

Next, there is an opportunity to explore the cre-

ation of a logical architecture that aligns with the pre-

viously mentioned governance model. Such an ar-

chitecture would facilitate the implementation of ef-

fective governance mechanisms in diverse smart dy-

namic ecosystems. The steps towards composing the

logical architecture involve the identiﬁcation of the

ecosystem’s actors, deﬁning their roles, and investi-

gating the network of the relationships among them.

The contribution of such an architecture lies in its

ability to provide the underlying structure of smart dy-

namic ecosystems, and thereby provide guidance for

the development of future governance mechanisms

tailored to these ecosystems.

Last, each of the identiﬁed governance mecha-

nisms would deserve a proper examination and re-

search of its underlying principles, especially in the

context of the governance mechanisms it shall be in-

tegrated with. Understanding these deeper levels of

detail is necessary for leading the discussion about

implementing trust management governance in terms

of both technology and policy making.

5.2 Threats to Validity

To promote the external validity of the taxonomy,

which is threatened by the possibility of overlooking

papers that could substantially impact the ﬁndings,

a proactive approach was taken to mitigate the risk.

We employed a backward snowball analysis, which

allowed us to extend our reach beyond the initially

identiﬁed papers and ensured a more comprehensive

inclusion of relevant sources. Besides, we iteratively

re-examined the identiﬁed keywords to ensure that

variations of trust governance terminology are cov-

ered.

To maximize the internal validity, which is inﬂu-

enced by our expertise in taxonomy creation, the cor-

respondence analysis was employed, drawing on in-

sights from ﬁve reference papers (Sagar et al., 2022;

Buhnova et al., 2023; Berger et al., 2021; Ahmed

et al., 2019; Chahal et al., 2020) published in the last

four years. This methodological choice served to en-

hance the credibility of the taxonomy by aligning it

with established literature and ensuring that the dis-

tinctions made were well-founded.

6 CONCLUSION

The aim of this paper was to propose a taxonomy of

governance mechanisms desgined for trust manage-

ment in smart dynamic ecosystems. To achieve this,

we reviewed the existing literature, identiﬁed the key

governance principles and organized them in a co-

hesive structure. The proposed taxonomy serves as

a starting point for further discussion and research

within this ﬁeld. Our intention is to stimulate the ex-

ploration of governance mechanisms, and fostering a

deeper understanding of the necessities and complex-

ities involved in governing trust within smart dynamic

ecosystems.

ACKNOWLEDGEMENTS

The work was supported by Grant Agency of

Masaryk University (GAMU), Interdisciplinary Re-

search Projects sub-programme, project ”Forensic

Support for Building Trust in Smart Software Ecosys-

tems” (no. MUNI/G/1142/2022).

REFERENCES

Abdelghani, W., Zayani, C. A., Amous, I., and S

edes, F.

(2019). Trust evaluation model for attack detection in

social internet of things. In Risks and Security of Inter-

net and Systems: 13th International Conference, CRi-

SIS 2018, Arcachon, France, October 16–18, 2018,

Revised Selected Papers 13, pages 48–64. Springer.

Ahmed, A., Abu Bakar, K., Channa, M. I., Haseeb, K., and

Khan, A. W. (2015). A survey on trust based detection

and isolation of malicious nodes in ad-hoc and sensor

networks. Frontiers of Computer Science, 9:280–296.

Ahmed, A. I. A., Ab Hamid, S. H., Gani, A., Khan, M. K.,

et al. (2019). Trust and reputation for internet of

things: Fundamentals, taxonomy, and open research

challenges. Journal of Network and Computer Appli-

cations, 145:102409.

Ahmed, K. I., Tahir, M., and Lau, S. L. (2020). Trust man-

agement for iot security: Taxonomy and future re-

search directions. In 2020 IEEE Conference on Ap-

plication, Information and Network Security (AINS),

pages 26–31. IEEE, IEEE.

Alkhabbas, F., Spalazzese, R., and Davidsson, P. (2019).

Characterizing internet of things systems through tax-

onomies: A systematic mapping study. Internet of

Things, 7:100084.

Almeida, V. A., Goh, B., and Doneda, D. (2017). A

principles-based approach to govern the iot ecosys-

tem. IEEE Internet Computing, 21(4):78–81.

Alsumayt, A., Haggerty, J., and Lotﬁ, A. (2017). Using

trust to detect denial of service attacks in the internet

Taxonomy of Governance Mechanisms for Trust Management In Smart Dynamic Ecosystems

707

of things over manets. International Journal of Space-

Based and Situated Computing, 7(1):43–56.

Alves, C., de Oliveira, J. A. P., and Jansen, S. (2017). Soft-

ware ecosystems governance-a systematic literature

review and research agenda. ICEIS (3), pages 215–

226.

Asiri, S. and Miri, A. (2016). An iot trust and reputation

model based on recommender systems. In 2016 14th

Annual Conference on Privacy, Security and Trust

(PST), pages 561–568. IEEE.

Atzori, L., Iera, A., Morabito, G., and Nitti, M. (2012).

The social internet of things (siot)–when social net-

works meet the internet of things: Concept, archi-

tecture and network characterization. Computer net-

works, 56(16):3594–3608.

Banerjee, M., Lee, J., Chen, Q., and Choo, K.-K. R. (2018).

Blockchain-based security layer for identiﬁcation and

isolation of malicious things in iot: A conceptual de-

sign. In 2018 27th International Conference on Com-

puter Communication and Networks (ICCCN), pages

1–6. IEEE.

Bangui, H., Cioroaica, E., Ge, M., and Buhnova, B.

(2023a). Deep-learning based trust management with

self-adaptation in the internet of behavior. In Proceed-

ings of the 38th ACM/SIGAPP Symposium on Applied

Computing, pages 874–881.

Bangui, H., Ge, M., and Buhnova, B. (2023b). Deep-

learning based reputation model for indirect trust

management. Procedia Computer Science, 220:405–

412.

Bao, F. and Chen, I.-R. (2012a). Dynamic trust manage-

ment for internet of things applications. In Proceed-

ings of the 2012 international workshop on Self-aware

internet of things, pages 1–6.

Bao, F. and Chen, R. (2012b). Trust management for the

internet of things and its application to service com-

position. In 2012 IEEE international symposium on

a world of wireless, mobile and multimedia networks

(WoWMoM), pages 1–6. IEEE.

Beer, J. M., Fisk, A. D., and Rogers, W. A. (2014). Toward

a framework for levels of robot autonomy in human-

robot interaction. Journal of human-robot interaction,

3(2):74.

Berger, C., Eichhammer, P., Reiser, H. P., Domaschka, J.,

Hauck, F. J., and Habiger, G. (2021). A survey on re-

silience in the iot: Taxonomy, classiﬁcation, and dis-

cussion of resilience mechanisms. ACM Computing

Surveys (CSUR), 54(7):1–39.

Bo, Z., Huan, Z., Meizi, L., Qin, Z., and Jifeng, H. (2017).

Trust traversal: a trust link detection scheme in social

network. Computer Networks, 120:105–125.

Buhnova, B. (2023). Trust management in the inter-

net of everything. In Software Architecture. ECSA

2022 Tracks and Workshops, pages 123–137, Cham.

Springer International Publishing.

Buhnova, B., Halasz, D., Iqbal, D., and Bangui, H.

(2023). Survey on trust in software engineering for

autonomous dynamic ecosystems. In Proceedings of

the 38th ACM/SIGAPP Symposium on Applied Com-

puting, pages 1490–1497.

Caminha, J., Perkusich, A., and Perkusich, M. (2018). A

smart trust management method to detect on-off at-

tacks in the internet of things. Security and Commu-

nication Networks, 2018.

Capilla, R., Cioroaica, E., Buhnova, B., and Bosch, J.

(2021). On autonomous dynamic software ecosys-

tems. IEEE Transactions on Engineering Manage-

ment, 69(6):3633–3647.

Chahal, R. K., Kumar, N., and Batra, S. (2020). Trust

management in social internet of things: A taxonomy,

open issues, and challenges. Computer Communica-

tions, 150:13–46.

Chen, R., Bao, F., and Guo, J. (2015). Trust-based ser-

vice management for social internet of things systems.

IEEE transactions on dependable and secure comput-

ing, 13(6):684–696.

Chen, R., Guo, J., and Bao, F. (2014). Trust management

for soa-based iot and its application to service com-

position. IEEE Transactions on Services Computing,

9(3):482–495.

Clarke, S., Christianson, B., and Xiao, H. (2013). Trust*:

Using local guarantees to extend the reach of trust.

In Security Protocols XVII: 17th International Work-

shop, Cambridge, UK, April 1-3, 2009. Revised Se-

lected Papers 17, pages 171–178. Springer.

Etalle, S., den Hartog, J., and Marsh, S. (2007). Trust and

punishment. In 1st International ICST Conference on

Autonomic Computing and Communication Systems.

Gerber, A. and Kansal, S. (2020). Simplify the de-

velopment of your iot solutions with iot architec-

tures. IBM. Accessed online: 6 December 2023.

Available from: https://developer.ibm.com/articles/

iot-lp201-iot-architectures/.

Ghafari, S. M., Beheshti, A., Joshi, A., Paris, C., Mahmood,

A., Yakhchi, S., and Orgun, M. A. (2020). A survey

on trust prediction in online social networks. IEEE

Access, 8:144292–144309.

Guo, L., Yang, H., Luan, K., Luo, Y., Sun, L., and Zheng,

X. (2021). A trust management model based on mu-

tual trust and a reward-with-punishment mechanism

for cloud environments. Concurrency and Computa-

tion: Practice and Experience, 33(16):e6283.

Gwak, B., Son, H., Kang, J., and Lee, D. (2017). Iot

trust estimation in an unknown place using the opin-

ions of i-sharing friends. In 2017 IEEE Trust-

com/BigDataSE/ICESS, pages 602–609. IEEE.

Halasz, D. and Buhnova, B. (2022). Rethinking safety in

autonomous ecosystems. In IEEE 17th Conference

on Computer Science and Intelligence Systems, pages

81–87.

Hategekimana, F., Whitaker, T. J., Pantho, M. J. H., and

Bobda, C. (2020). Iot device security through dy-

namic hardware isolation with cloud-based update.

Journal of Systems Architecture, 109:101827.

He, Y., Han, G., Jiang, J., Wang, H., and Martinez-Garcia,

M. (2020). A trust update mechanism based on re-

inforcement learning in underwater acoustic sensor

networks. IEEE Transactions on Mobile Computing,

21(3):811–821.

ENASE 2024 - 19th International Conference on Evaluation of Novel Approaches to Software Engineering

708

Hou, F. and Jansen, S. (2023). A systematic literature re-

view on trust in the software ecosystem. Empirical

Software Engineering, 28(1):8.

Illi, E., Qaraqe, M., Althunibat, S., Alhasanat, A., Alsafas-

feh, M., de Ree, M., Mantas, G., Rodriguez, J., Aman,

W., and Al-Kuwari, S. (2023). Physical layer security

for authentication, conﬁdentiality, and malicious node

detection: a paradigm shift in securing iot networks.

IEEE Communications Surveys & Tutorials.

Iqbal, D. and Buhnova, B. (2022). Model-based approach

for building trust in autonomous drones through digi-

tal twins. In 2022 IEEE International Conference on

Systems, Man, and Cybernetics (SMC), pages 656–

662. IEEE.

Jafarian, B., Yazdani, N., and Haghighi, M. S. (2020).

Discrimination-aware trust management for social in-

ternet of things. Computer Networks, 178:107254.

Jøsang, A., Ismail, R., and Boyd, C. (2007). A survey of

trust and reputation systems for online service provi-

sion. Decision support systems, 43(2):618–644.

Kamvar, S. D., Schlosser, M. T., and Garcia-Molina, H.

(2003). The eigentrust algorithm for reputation man-

agement in p2p networks. In Proceedings of the 12th

international conference on World Wide Web, pages

640–651.

Karthik, N. and Ananthanarayana, V. (2017). A hybrid trust

management scheme for wireless sensor networks.

Wireless Personal Communications, 97:5137–5170.

Katina, P. F. and Keating, C. B. (2018). Cyber-physical sys-

tems governance: a framework for (meta) cybersecu-

rity design. Security by Design: Innovative Perspec-

tives on Complex Problems, pages 137–169.

Katina, P. F., Keating, C. B., Gheorghe, A. V., and Masera,

M. (2017). Complex system governance for critical

cyber-physical systems. International Journal of Crit-

ical Infrastructures, 13(2-3):168–183.

Khan, M. S., Midi, D., Khan, M. I., and Bertino, E. (2015).

Adaptive trust update frequency in manets. In 2015

IEEE 21st International Conference on Parallel and

Distributed Systems (ICPADS), pages 132–139. IEEE.

Khan, W. Z., Hakak, S., Khan, M. K., et al. (2020). Trust

management in social internet of things: Architec-

tures, recent advancements, and future challenges.

IEEE Internet of Things Journal, 8(10):7768–7788.

Khatun, M. A., Chowdhury, N., and Uddin, M. N. (2019).

Malicious nodes detection based on artiﬁcial neural

network in iot environments. In 2019 22nd Inter-

national Conference on Computer and Information

Technology (ICCIT), pages 1–6. IEEE.

okciyan, N. and Yolum, P. (2020). Turp: Managing trust

for regulating privacy in internet of things. IEEE In-

ternet Computing, 24(6):9–16.

Li, B., Ye, R., Gu, G., Liang, R., Liu, W., and Cai, K.

(2020). A detection mechanism on malicious nodes

in iot. Computer Communications, 151:51–59.

Liu, L., Ma, Z., and Meng, W. (2019). Detec-

tion of multiple-mix-attack malicious nodes using

perceptron-based trust in iot networks. Future gen-

eration computer systems, 101:865–879.

Liu, Z., Yang, D.-s., Wen, D., Zhang, W.-m., and Mao, W.

(2011). Cyber-physical-social systems for command

and control. IEEE Intelligent Systems, 26(4):92–96.

Lochner, M. and Smilek, D. (2023). The uncertain advi-

sor: trust, accuracy, and self-correction in an auto-

mated decision support system. Cognitive Processing,

24(1):95–106.

Magdich, R., Jemal, H., Nakti, C., and Ayed, M. B.

(2021). An efﬁcient trust related attack detection

model based on machine learning for social internet

of things. In 2021 International Wireless Communica-

tions and Mobile Computing (IWCMC), pages 1465–

1470. IEEE.

Mahmood, A., Butler, B., Zhang, W. E., Sheng, Q. Z.,

and Siddiqui, S. A. (2019). A hybrid trust manage-

ment heuristic for vanets. In 2019 IEEE International

Conference on Pervasive Computing and Communi-

cations Workshops (PerCom Workshops), pages 748–

752. IEEE.

Malik, S., Dedeoglu, V., Kanhere, S. S., and Jurdak, R.

(2019). Trustchain: Trust management in blockchain

and iot supported supply chains. In 2019 IEEE In-

ternational Conference on Blockchain (Blockchain),

pages 184–193. IEEE.

Marche, C. and Nitti, M. (2020). Trust-related attacks and

their detection: A trust management model for the so-

cial iot. IEEE Transactions on Network and Service

Management, 18(3):3297–3308.

Masmoudi, M., Abdelghani, W., Amous, I., and S

edes, F.

(2020). Deep learning for trust-related attacks de-

tection in social internet of things. In Advances in

E-Business Engineering for Ubiquitous Computing:

Proceedings of the 16th International Conference on

e-Business Engineering (ICEBE 2019), pages 389–

404. Springer.

Mechanic, D. (1996). The logic and limits of trust. Con-

temporary Sociology, 25(4):455.

Meena Kowshalya, A. and Valarmathi, M. (2017). Trust

management for reliable decision making among so-

cial objects in the social internet of things. IET Net-

works, 6(4):75–80.

Mehdizadeh, N. and Farzaneh, N. (2022). An evidence

theory based approach in detecting malicious con-

troller in the multi-controller software-deﬁned inter-

net of things network. Adhoc & Sensor Wireless Net-

works, 51(4).

Mendoza, C. V. and Kleinschmidt, J. H. (2015). Mit-

igating on-off attacks in the internet of things us-

ing a distributed trust management scheme. In-

ternational Journal of Distributed Sensor Networks,

11(11):859731.

Muzammal, S. M., Murugesan, R. K., and Jhanjhi, N. Z.

(2020). A comprehensive review on secure routing

in internet of things: Mitigation methods and trust-

based approaches. IEEE Internet of Things Journal,

8(6):4186–4210.

Namal, S., Gamaarachchi, H., MyoungLee, G., and Um,

T.-W. (2015). Autonomic trust management in cloud-

based and highly dynamic iot applications. In 2015

ITU Kaleidoscope: Trust in the Information Society

(K-2015), pages 1–8. IEEE.

Taxonomy of Governance Mechanisms for Trust Management In Smart Dynamic Ecosystems

709

Nandhini, P., Kuppuswami, S., Malliga, S., and DeviPriya,

R. (2022). A lightweight energy-efﬁcient algorithm

for mitigation and isolation of internal rank attackers

in rpl based internet of things. Computer Networks,

218:109391.

Nitti, M., Girau, R., and Atzori, L. (2013). Trustwor-

thiness management in the social internet of things.

IEEE Transactions on knowledge and data engineer-

ing, 26(5):1253–1266.

Nwebonyi, F. N., Martins, R., and Correia, M. E. (2019).

Security and fairness in iot based e-health system:

A case study of mobile edge-clouds. In 2019 Inter-

national Conference on Wireless and Mobile Com-

puting, Networking and Communications (WiMob),

pages 318–323. IEEE.

Peng, S., He, J., and Meng, Y. (2008). Reputation-based

trust update in network environment. In 2008 Interna-

tional Symposium on Electronic Commerce and Secu-

rity, pages 118–123. IEEE.

Rana, K., Singh, A. V., and Vijaya, P. (2022). Recent trust

management models for secure iot ecosystem. Inter-

national Journal of Intelligent Systems and Applica-

tions in Engineering, 10(1s):23–33.

Resnick, P., Kuwabara, K., Zeckhauser, R., and Friedman,

E. (2000). Reputation systems. Communications of

the ACM, 43(12):45–48.

Rizvi, S., Kurtz, A., Pfeffer, J., and Rizvi, M. (2018). Se-

curing the internet of things (iot): A security taxon-

omy for iot. In 2018 17th IEEE International Con-

ference On Trust, Security And Privacy In Computing

And Communications/12th IEEE International Con-

ference On Big Data Science And Engineering (Trust-

Com/BigDataSE), pages 163–168. IEEE.

Roopa, M., Pattar, S., Buyya, R., Venugopal, K. R., Iyen-

gar, S., and Patnaik, L. (2019). Social internet of

things (siot): Foundations, thrust areas, systematic re-

view and future directions. Computer Communica-

tions, 139:32–57.

Sagar, S., Mahmood, A., Sheng, Q. Z., Pabani, J. K., and

Zhang, W. E. (2022). Understanding the trustworthi-

ness management in the social internet of things: a

survey. arXiv preprint arXiv:2202.03624.

Schreieck, M., Wiesche, M., and Krcmar, H. (2016). Design

and governance of platform ecosystems-key concepts

and issues for future research. In Ecis, volume 16,

pages 12–15.

Seshadri, S. S., Rodriguez, D., Subedi, M., Choo, K.-K. R.,

Ahmed, S., Chen, Q., and Lee, J. (2020). Iotcop:

A blockchain-based monitoring framework for detec-

tion and isolation of malicious devices in internet-

of-things systems. IEEE Internet of Things Journal,

8(5):3346–3359.

She, W., Liu, Q., Tian, Z., Chen, J.-S., Wang, B., and Liu,

W. (2019). Blockchain trust model for malicious node

detection in wireless sensor networks. IEEE Access,

7:38947–38956.

Singh, M. and Kim, S. (2018). Trust bit: Reward-based in-

telligent vehicle commination using blockchain paper.

In 2018 IEEE 4th World Forum on Internet of Things

(WF-IoT), pages 62–67. IEEE.

Tofangchi, S., Hanelt, A., Marz, D., and Kolbe, L. M.

(2021). Handling the efﬁciency–personalization

trade-off in service robotics: A machine-learning ap-

proach. Journal of Management Information Systems,

38(1):246–276.

Truong, N. B., Um, T.-W., Zhou, B., and Lee, G. M. (2017).

From personal experience to global reputation for trust

evaluation in the social internet of things. In GLOBE-

COM 2017-2017 IEEE Global Communications Con-

ference, pages 1–7. IEEE.

Usman, M., Britto, R., B

orstler, J., and Mendes, E. (2017).

Taxonomies in software engineering: A systematic

mapping study and a revised taxonomy develop-

ment method. Information and Software Technology,

85:43–59.

Wang, F. and Wei, Z. (2021). A statistical trust for detecting

malicious nodes in iot sensor networks. IEICE Trans-

actions on Fundamentals of Electronics, Communica-

tions and Computer Sciences, 104(8):1084–1087.

Williams, P., Rojas, P., and Bayoumi, M. (2019). Security

taxonomy in iot–a survey. In 2019 IEEE 62nd Inter-

national Midwest Symposium on Circuits and Systems

(MWSCAS), pages 560–565. IEEE.

Xia, F. and Ma, J. (2011). Building smart communities with

cyber-physical systems. In Proceedings of 1st interna-

tional symposium on From digital footprints to social

and community intelligence, pages 1–6.

Xiao, H., Sidhu, N., and Christianson, B. (2015). Guaran-

tor and reputation based trust model for social internet

of things. In 2015 International wireless communi-

cations and mobile computing conference (IWCMC),

pages 600–605. IEEE.

Xiaoxue, M., Zixian, W., Jing, B., and Fei, L. (2010). Trust

model based on rewards and punishment mechanism.

In 2010 Second International Workshop on Education

Technology and Computer Science, volume 2, pages

182–185. IEEE.

Yan, Z., Ding, W., Niemi, V., and Vasilakos, A. V. (2016).

Two schemes of privacy-preserving trust evaluation.

Future Generation Computer Systems, 62:175–189.

Yu, Y., Jia, Z., Tao, W., Xue, B., and Lee, C. (2017). An efﬁ-

cient trust evaluation scheme for node behavior detec-

tion in the internet of things. Wireless Personal Com-

munications, 93:571–587.

Zavvos, E., Gerding, E. H., Yazdanpanah, V., Maple, C.,

Stein, S., et al. (2021). Privacy and trust in the in-

ternet of vehicles. IEEE Transactions on Intelligent

Transportation Systems, 23(8):10126–10141.

Zhaofeng, M., Lingyun, W., Xiaochang, W., Zhen, W., and

Weizhe, Z. (2019). Blockchain-enabled decentralized

trust management and secure usage control of iot big

data. IEEE Internet of Things Journal, 7(5):4000–

4015.

ENASE 2024 - 19th International Conference on Evaluation of Novel Approaches to Software Engineering

710