Page 81 - Trust in ICT 2017

Trust in ICT 1

8.4.1 Trust in the Internet of Things

The IoT is considered as the network of devices such as household appliances, office appliances, and vehicles
which are embedded with computing system, sensors, connectivity with self-configuring capability. These
electronic devices, which are billions in number and varied in size and computing capabilities, are ranging
from Radio Frequency Identification tags (RFIDs) to vehicles with Onboard Units (OBUs). IoT is expected to
enable advanced services and applications like smart home, smart grid or smart city by integrating a variety
of technologies in many research areas from embedded systems, wireless sensor networks, service
platforms, and automation to privacy, security and trust. Recently, the convergence of two emerging network
paradigms Social Networks and IoT as social IoT has attracted many researchers as a prospective approach
for dealing with challenges in IoT. The benefit of social IoT is the separation in terms of the two levels of
humans and devices; allowing devices to have their own social networks; offering humans to impose rules
on their devices to protect their privacy, security and maximize trust during the interaction among objects.
Indeed, some social IoT systems are currently taking advantages of social relationship models to offer secure
and reliable services by using the reputation and trust such as eBay, Amazon and Google’s Web Page
Rankings.
There are various kinds of trust definitions leading to difficulties in establishing a common, general notation
that holds, regardless of personal dispositions or differing situations. Generally, trust is considered as a
computational value depicted by a relationship between trustor and trustee, described in a specific context
and measured by trust metrics and evaluated by a mechanism. Some important properties of trust are stated
and discussed in this report. Previous research has shown that trust is the interplay among human, social
sciences and computer science, affected by several subjective factors such as social status and physical
properties; and objective factors such as competence and reputation. The competence is measurement of
abilities of the trustee to perform a given task which is derived from trustee’s diplomas, certifications and
experience. Reputation is formed by the opinion of other entities, deriving from third parties' opinions of
previous interactions with the trustee.

A trust system covers a large number of trust-related research aspects ranging from Trust Relationship and
Decision, Data Perception Trust to Identity Trust [14]. Several works focus on trust evaluation and trust
assessment in IoT and in social IoT. The authors assume that entities in the systems are human-related or
human-carried which are capable of establishing relations depending and cooperatively working together in
accordance with their owners’ relationships. They proposed distributed, encounter-based, and activity-based
trust management protocols in which entities compute and update trustworthiness of the partners once
mutual interactions occur. The entities also share trust evaluations to their friends as recommendations to
help friends in their trust-related processes. Thus, a reputation-based mechanism is needed to incorporate
with the trust systems.
However, some malicious entities, which is dishonest and socially uncooperative in nature, could exploit the
principal reputation-based properties to break the functionalities of the system by means of trust-related
attacks such as self-promoting, bad-mouthing, good-mouthing, ballot-stuffing, discriminatory and
whitewashing. Several solutions were proposed to try to deal with these kinds of attack by validating the
identity as well as recommendation information through some trust compositions such as honesty,
cooperativeness, community-interest, relationship factor and centrality. However, these solutions are mostly
built for P2P network, ad-hoc networks or WSNs.
Other works proposed fuzzy approaches to calculate trust score from some TMs such as Experience,
Recommendation, and Knowledge, or based on technical properties extracted from physical layer, core layer,
and application layer in IoT system as a mechanism for access control. The trust scores are then mapped to
permission; and the access requests are accompanied accordingly. This approach of trust calculation is,
however, impossible to deal with the scenarios that TMs are crossed-domain. Several TMs are derived from
both physical layer and core layer and other TMs could only be extracted from both core layer and application
layer. For instance, to reckon the Knowledge TM, it is needed to extract valuable information from data of
both physical layer and application layer, which describes the trustee.
The catalyst for figuring out trust features is that when judging whether a trustee (a person, a device or a
service) is trustable or not, the trustor “thinks” like human by taking its knowledge, recommendations from

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