Table 5 An overview of trust strategies for a recommendation in the Application layer of IoT

Al-Hamadi and Chen [99]

Flexible to noisy data captured either intentionally or not

Trust evaluation regarding location rating, rater and witness trust value

For trustworthy decision takes data and source trust into account

Customized information concerning user trust measures

There is a centralized cloud for trust rating and lacks a distributed cloud of IoT devices for storage and processing

Poor decision accuracy due to disregarding SIoT attributes for P2P trust evaluation

NS3 simulation

Kounelis et al. [103]

Promoting trust in human–IoT relationship: enhancing agency through “Rights in Design"

No metric evaluation

Usability (user expertise and previous knowledge) not implemented

SecKit: model-based security toolkit

(MQTT) message broker

SecKit GUI

Yan et al. [104]

Find open issues:

Trust assessment disregards context awareness and trustor’s subjective approach

Lacks a comprehensive trust management framework

DPT for capability-constrained WSN

Power efficiency makes trust management less energy-consuming

Not considered:

Demands for trust in heterogeneous IoT

Challenges on SMC and homomorphic encryption improvement

Human privacy and processes confidentiality

Hard to control cloud

Difficult to achieve trustworthy data fusion

Incomplete privacy preservation

DTCT was not associated with other TM

Immature SMC research

HCTI is almost ignored

No simulation

Wang and Zhang [105]

Address IoT challenges:

Lack of fully distributed, applicable security solution

Few studies on privacy and anonymity

Scalable and secure mobile trust

Lack of empirical evidence

No implementation

Suryani et al. [106]

Categorize trust metrics, types, methods, related attacks

No comparison to demonstrate methods applicability

Lack of optimal resource utilization

No practical result on direct/indirect trust formulas

Mendoza and Kleinschmidt [107]

Despite 30% of malicious nodes, trust model’s performance is well

Besides bad mouthing attack, may detect other attack types

Higher interval of trust table update, lowers anomaly detection time

Frequent update results in higher traffic and more resources consumption

Average time for assigning nodes a distrust is lower than trust

Unit Disk Graph Medium (UDGM) as radio model,

ContikiMAC as radio duty cycle (RDC) protocol

CSMA/CA (Carrier Sensor Multiple Access with Collision Avoidance)

Cooja simulator of the Contiki operation system

Tmote sky nodes

Chen et al. [108]

Assessing organizations’ reputation does not cost heavy load due to smaller number than nodes

Avoid modification, replay and message dropping attacks and protect the integrity, authenticity originality and non-repudiation

ORES well detect attacks in both scattered or dense dispersion of nodes

No investigation on badly behaved user and organization

Skip over other types of attacks

Software-defined networking (SDN) technology

Not mentioned

Margaris and Vassilakis [30]

Stop too cold or hot venues for the users’ likings or marginal arrival times

Improved satisfaction and recommendation accuracy

Incorporates any IoT-sourced species to suit domain needs

Not consider recommendation with a lower score than 5 out of 10 or didn’t pass with the highest rating

No representative demographics

A limited number of participants

Not consider keywords and tags

Data extracted by Facebook Graph API and Tripadvisor

Guo et al. [109]

Aggregates trust according to belief theory or regression

Combine social trust metrics

validate the defense mechanism

Applies scalability, mobility, the social interaction for trust evaluation

Combines centralized cloud with trust propagation

Real-world IoT applications

Deals between accuracy and energy consumption

No simulation

Bernabe et al. [28]

Considering security evidence

copes with information vagueness

Multidimensional approach


Uses resilient and lightweight mechanisms

Combined with DCapBAC access control

Rise in memory requirement by the number of devices to handle trust management

Lack of a fully distributed approach

Miss well-defined interoperable negotiation language

Android SDK

Android Platform 2.3.3 (API level 10)

Kowshalya and Valarmathi [21]

Defys on–off selective forwarding threats

Inspects vulnerabilities to identify and isolate untrustworthy nodes

Lack of participation opportunity for low trust nodes undermines all types of attack identification

Dataset from CRAWDAD

NS3

SocNetV 1.9

Mashal
et al. [114]

Recommend third-party services

SMHSR combination of SR, MPSO, and OBCF algorithms

Servrank (SR):

Solve sparsity with high accuracy and low assessment duration

Independence of contextual information

No publicly available popular big database

not depict sensor localization and mobility

TagRec

Lightweight RESTful platform

Mashal
et al. [113]

A formal model for the service recommendation in IoT

Still in beginning and in the data collection phase

Hard to find a large-scale dataset

No simulation

Atzori et al. [49]

Guarantees the network navigability

Associates things and social network

Trustworthiness leverages degree of friends’ interaction

Social networks models reused to address IoT

requiring continuous communication

detects CLOR, CWOL, and SOR

Reduce efficiency in resource discovery

Simulation in SWIM mobility simulator

Chen et al. [115]

Identifies that inherent limitations affect security and stability

Timeliness tackles dynamic behavior in a distributed scenario

Recommendation based on reputation or past performance, social transaction and energy

Lack of actual unstable secure network

Not achieved mutual boosting in social relationship and access service recommendation

CRAWDAD data set

Lin and Dong [116]

Bilateral trust evaluation

Infers Trust from historical task.

Trust transited via intermediate node

Update Trust with delegation effects

Adjust trust with dynamic environments

Despite obtaining more trust than conservative, aggressive transitivity suffers from complexity and communication overhead

Radio Frequency for Consumer Electronics (RF4CE)

IEEE 802.15.4, Zigbee

Facebook, Google+ and Twitter

Texas Instruments’ Z-Stack (version 2.5.0)

CC2530 chip

social networks simulator not mentioned

Nitti et al. [35]

Isolate malicious nodes

Cope with dynamic behaviors

Immunity against malicious nodes mistreating

An increased network traffic due to feedback information swap

Lower credibility and malicious behavior on strongly relation nodes

Theoretical analysis

SWIM

Brightkite dataset

Fernandez-Gago et al. [123]

Consider trust, identity, and privacy Requirements

Taking into account dynamicity and evolution

Lack of extension of a modeling language to represent trust requirements

Disregard functional requirements in architecture

Not implemented

Scenario: Field Service Teams (FST)

Ben Saied et al. [111]

Identifies a group of threats against the trustworthiness

Proposes a proper partnership for cooperativeness

Offers fine-tunes trust for erroneous witnesses

Trust level decreases the first time bad-mouthing threat occurs

Simulation by the TRM

Chabridon et al. [124]

Privacy and QoC: middleware solutions for context managers

Confidentiality and QoC

Choose QoC level is not easy

QoC (change) is sensitive information

Not consider dynamicity and spatio-temporal condition of context-aware management

No simulation

Tang and Meersman [125]

Not limited in types of components (either software modules or physical smart object)

Combine algorithms

Not evaluate usability

Only recommends parts defined by domain ontologies

ORM/ORM2

OWL/RDF(s)

Java J2EE/Eclipse SDK

SDT editor

Collibra studio

Chen et al. [112]

Minimizes trust bias

Optimizes application performance

Minimizes convergence time

Minimum computation in the capacity-limited node for trust update

Only considered persistent attackers

Only considered self-interest incentives

NS-3 network simulator

Ko et al. [29]

Improves recommendation accuracy in average precision by 28.87%

Accuracy does not decrease by increasing data sparsity

Overcomes data-sparsity problem

The tested dataset has lower sparsity than the actual one

MCMLI is not scalable

matrix Completion takes a lot of time

Processing time rises exponentially by increasing the number of user/item

PREA recommendation algorithm toolkit

TripAdvisor and Yahoo! Movies datasets

Eclipse Indigo Java EE Indigo SR2 and JDK 1.7.0_03

Chen et al. [126]

Good performance under a large density of malicious nodes

Early discover nodes attitude alteration, produce desirable result on time-dependent attacks

For faster data transmission will substitute 5G with current IEEE802.11p

Vehicles speed acceleration, cause more packet loss and let to drop precision and recall

Drop in recall and precision due to high proportion of adversaries

Dempster–Shafer (D–S) theory

IEEE 802.11p

NS2

Citymob mobility model

SUMO

Tormo et al. [129]

Quickly chooses proper trust and reputation

The smooth and automatic transition between the reputation computation engines

More accurate reputation values than traditional models of only one reputation computation engine

Reputation engines have weak accuracy for some time after activation

Costly interchange among reputation engines regarding accuracy, without transition time

ROMEO: ReputatiOn Model Enhancing OpenID Simulator

Nguyen et al. [130]

Used for all situations without relying on historical experience or recommendations

No dependency on third entities

Trust values are consistent

Other pertinent factors environment-specific are not considered

Not mentioned

Ali et al. [131]

An automatic recommendation process

Prediction accuracy and a precision rate of recommendation

Lack of irrelevant data filtering mechanism

Deplete information retrieval of social network

T2FSs in MATLAB

Protégé OWL 4.3 package reasoners: Pellet, Fact ++, Hermit “SWRLTab”

Mahmud et al. [132]

Less AECR rate depicts TMM anomaly identification ability

Less energy consumption

In data transmission

With 10 to 50% of adversaries, the throughput dropped due to malicious nodes disassociation in packet forwarding

Adhoc On-demand Distance Vector (AODV) routing protocol

NS2

Asiri and Miri
[22]

guarantees better availability, no SPOF

Conserves energy, adds the life span of battery devices, decreases maintenance expense, immediate response

Lessens computation overhead for information transmission

Protect against bad/good mouthing attacks

No implementation in reality

No simulation

Al-Turjman [33]

CCFF outperforms due to learning elements, searching data, fidelity

increases data publisher by Decreasing loads

puts services/resources close to users in the edge

Replaces cached data according to fog and user obligation

Substitutes unemployed data according utility task

Edge nodes security problems, susceptible to untrusted data

NS3: fog node implemented in Golang