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Table 4 An overview of trust strategies for a recommendation in the network layer of IoT

From: Trust-based recommendation systems in Internet of Things: a systematic literature review

Reference

Advantages

Disadvantages

Evaluation technique

Evaluation environment

Wang et al. [74]

Overcomes the DoS threats by selfish nodes

Avoids DDoS threats by crowdsourcing

Non-cooperative parties reduce efficiency

Non-malicious members received Lower

may wrong results provide by honest participants

No simulation

 

Fortino et al. [75]

Mutual benefit of single agent engagement in a group

By group formation, the untrusted agents, gradually substituted with trusted ones

Local reputation avoids global mechanism’s overhead

Good performance achieved with agents’ small portion of involvement

Few statistical parameters were concerned

With a minimum number of examined group, a few untrusted agents exit which lessened not totally removed with rising numbers

Poisson distribution

Not mentioned

Garcia-de-Prado et al. [78]

Facilitates communications and data delivery between C-IoT layers

Avoids edge nodes waste resource

Save expense in cloud

real-time data processing

Not process many events per second in fog nodes

Deter user profile and experience

Esper CEP

Mule open source ESB

MQTT Mosquitto broker—Eclipse

Case study: ir4HealthAdmin

Sfar et al. [81]

Adapted to any real environment to Improve productivity

intensify issues of security and privacy

Theoretical rigor limitation

Do not consider: auto-immunity, safety, reliability, and responsibility

 

Case study

Ouaddah et al. [82]

OM-AM access control solutions

Considers centralized and decentralized approaches

Usability aspects are not extended

No implementation on privacy-preserving access control framework

No simulation

 

Roman et al. [83]

Cooperation diverse IoT entities despite lack of central systems

Probability to pinpoint the problem origins

Implements privacy and scalability

Push/pulls data when needed

Key management between the limited device

Overhead caused by incoming connections

Concerns about internet protocols adaptability with context

No simulation

 

Mahalle et al. [84]

Guarantees scalability, flexibility, and energy efficiency

Devices proliferation does not degrade performance

Avoids communication in low trust nodes

More power conservation and high residual source

A mathematical model not implemented in real time RFID

Not organized with capability-based access control

Mamdani logic

MATLAB 7.0. 


NS2

Sicari et al. [86]

Validated by real-time open data feeds

NOS detects:

 Data confidentiality, source privacy, and integrity violations;

 Unauthorized access

 Robustness of key management

 Replay or routing attacks

Without restarting the whole system add new modules, duplicate or remove available ones

Independent of the data model and application domain

Low reliability regarding confidentiality, integrity, and privacy, weak accuracy and precision

 

Open-source Mosquitto

Visualization service not mentioned

Hellaoui et al. [87]

Energy conservation and yet secure

Vast participation by tracking entities’ attributes

Deterred adversaries to alternate between forwarding a (un)authenticated messages

Not consider untrustworthy recommendations

Not evaluated in lossy networks

HMAC MD5

Cooja, the Contiki OS simulator

Azad et al. [88]

Assess nodes’ reputation:

(1) Independent of third-person and stay away of:

  Single point of failure

  Be in middle of threat

  Privacy destruction

(2) Use aggregated behavior-based weights

(3) Encryption/decryption promise privacy preservation

(4) Avoid fraudulent response by NIZK proof

Security is ensured by Decisional Diffie Hellman

Trust value is concealed and could not abused to derive relations

Bandwidth and storage capacity of RSU is sensible

Free infrastructure of physical layer and performed in session layer

Delay occurs for database exploration and caused by wireless connection among objects

NIZK proof

CPU 3.6 GHz core i7, 8 GB memory

Java program

Yu et al. [90]

Lowers the energy usage due to trust exchange among adjacent members

Ensures accuracy and minimizes recommended trust

Obtains more robustness and adaptability

Security of packets forwarding and defeating attacks

Assumptions:

Densely deployed sensors are prohibited to move freely

Communicate under maximum power

Attacker intercept any nodes has communication capabilities as normal nodes

The communication channel is symmetric

 

Simulation in MATLAB

Khan et al. [93]

Explored trust management on SIoT

Define SCIoT

Propose an attack model

Classifies probable threats

Highlighted relevant challenges

The proposed issue remains unresolved

Not implemented

 

Ing-Ray Chen [94]

Dynamic trust management

Accurate trust assessment or minimizing trust bias

Maximize application performance

totally distributed without the need for centralized entities

Not consider the thing-to-thing autonomous Social relation

Disregard the caching usage to relieve constraint storage

After status changes recommendations do not improve trust convergence

 

ns3

Dwarakanath et al. [95]

In decentralized sources, privacy-aware collaboration consumes less than 0.5% battery for execution

On–off adversaries lengthen delay in gaining trust values

Combats collusion and on–off threats of a mischievous minority

Not practicable for single device data

 

Google Nexus 5 smartphones

CEPsim

Wang et al. [97]

Screens dishonest nodes

Desirable convergence, accuracy, and resiliency

Compared with P2P gives more accurate trust prediction and resists against collusion

Resilient to ballot-stuffing, bad-mouthing

Applicability in a hostile and noisy environments

The overhead for SOANET with constraint memory capacity

Trust prediction takes minutes rather than seconds for less powerful node

Random Waypoint mobility (RWM)

MATLAB