Trust models of internet of smart things: A survey, open issues, and future directions

An increasingly large number of cars are being equipped with GPS and Wi-Fi devices, forming vehicular ad-hoc networks (VANETs) and enabling vehicle to vehicle communication with the goal of providing increased passenger and road safety. However, dishonest peers (vehicles) in a VANET may send out false information to maximize their own utility. Given the dire consequences of acting on false information in this context, there is a serious need to establish trust among peers. This article first discusses the challenges for trust management caused by the important characteristics of VANET environments, and identifies desired properties that effective trust management should incorporate in order to address the challenges. The author then surveys and evaluates existing trust models in VANETs, and points out that none of the trust models has achieved all the properties. Finally, the author proposes some important future directions for research towards effective trust management for VANETs.

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Sensing as a Service in Cloud-Centric Internet of Things Architecture

Advances in Wireless Technologies and Telecommunication - Enabling Real-Time Mobile Cloud Computing through Emerging Technologies ◽

10.4018/978-1-4666-8662-5.ch003 ◽

2015 ◽

pp. 83-115 ◽

Cited By ~ 4

Author(s):

Burak Kantarci ◽

Hussein T. Mouftah

Keyword(s):

Internet Of Things ◽

Mobile Devices ◽

Service Provider ◽

The Internet ◽

Communication Overhead ◽

Future Directions ◽

Iot Applications ◽

Cloud Servers ◽

The Internet Of Things ◽

Open Issues

Sensing-as-a-Service (S2aaS) is a cloud-inspired service model which enables access to the Internet of Things (IoT) architecture. The IoT denotes virtually interconnected objects that are uniquely identifiable, and are capable of sensing, computing and communicating. Built-in sensors in mobile devices can leverage the performance of IoT applications in terms of energy and communication overhead savings by sending their data to the cloud servers. Sensed data from mobile devices can be accessed by IoT applications on a pay-as-you-go fashion. Efficient sensing service provider search techniques are emerging components of this architecture, and they should be accompanied with effective sensing provider recruitment algorithms. Furthermore, reliability and trustworthiness of participatory sensed data appears as a big challenge. This chapter provides an overview of the state of the art in S2aaS systems, and reports recent proposals to address the most crucial challenges. Furthermore, the chapter points out the open issues and future directions for the researchers in this field.

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Fog Computing: State-of-Art, Open Issues, Challenges and Future Directions

Learning and Analytics in Intelligent Systems - Intelligent Computing Paradigm and Cutting-edge Technologies ◽

10.1007/978-3-030-65407-8_27 ◽

2021 ◽

pp. 317-326

Author(s):

M. Sathish Kumar ◽

M. Iyapparaja

Keyword(s):

Fog Computing ◽

Future Directions ◽

State Of Art ◽

Open Issues

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Internet-of-Things and Information Fusion: Trust Perspective Survey

Sensors ◽

10.3390/s19081929 ◽

2019 ◽

Vol 19 (8) ◽

pp. 1929 ◽

Cited By ~ 15

Author(s):

Farag Azzedin ◽

Mustafa Ghaleb

Keyword(s):

Internet Of Things ◽

Information Fusion ◽

Data Sources ◽

Fusion Process ◽

Future Directions ◽

Trust Modeling ◽

Real Value ◽

Trust Models ◽

Trust Modelling

The advent of Internet-of-Things (IoT) is creating an ecosystem of smart applications and services enabled by a multitude of sensors. The real value of these IoT smart applications comes from analyzing the information provided by these sensors. Information fusion improves information completeness/quality and, hence, enhances estimation about the state of things. Lack of trust and therefore, malicious activities renders the information fusion process and hence, IoT smart applications unreliable. Behavior-related issues associated with the data sources, such as trustworthiness, honesty, and accuracy, must be addressed before fully utilizing these smart applications. In this article, we argue that behavior trust modeling is indispensable to the success of information fusion and, hence, to smart applications. Unfortunately, the area is still in its infancy and needs further research to enhance information fusion. The aim of this article is to raise the awareness and the need of behavior trust modelling and its effect on information fusion. Moreover, this survey describes IoT architectures for modelling trust as well as classification of current IoT trust models. Finally, we discuss future directions towards trustworthy reliable fusion techniques.

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Agent-based systems for human learners

The Knowledge Engineering Review ◽

10.1017/s0269888910000044 ◽

2010 ◽

Vol 25 (2) ◽

pp. 111-135 ◽

Cited By ~ 15

Author(s):

Elizabeth Sklar ◽

Debbie Richards

Keyword(s):

Learning Activities ◽

Pedagogical Agents ◽

Future Directions ◽

Agent Architectures ◽

Agent Based ◽

Learning Agents ◽

Interdisciplinary Field ◽

Science And Education ◽

The Subject ◽

Open Issues

AbstractApplying intelligent agent technologies to support human learning activities has been the subject of recent work that reaches across computer science and education disciplines. This article discusses agent-based approaches that have been designed to address a range of pedagogical and/or curricular tasks. Three types of agents are identified in the literature:pedagogical agents,peer-learning agents, anddemonstrating agents. Features of each type are considered, as well as the systems in which these agents are incorporated, examining common and divergent goals, system and agent architectures, and evaluation methodologies. Open issues are highlighted, and future directions for this burgeoning interdisciplinary field are suggested.

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Automatic Language Identification in Texts: A Survey

Journal of Artificial Intelligence Research ◽

10.1613/jair.1.11675 ◽

2019 ◽

Vol 65 ◽

Cited By ~ 7

Author(s):

Tommi Jauhiainen ◽

Marco Lui ◽

Marcos Zampieri ◽

Timothy Baldwin ◽

Krister Lindén

Keyword(s):

Natural Language ◽

Text Processing ◽

Language Identification ◽

End User ◽

Future Directions ◽

Extensive Survey ◽

Input Text ◽

History Of ◽

Processing Techniques ◽

Open Issues

Language identification (“LI”) is the problem of determining the natural language that a document or part thereof is written in. Automatic LI has been extensively researched for over fifty years. Today, LI is a key part of many text processing pipelines, as text processing techniques generally assume that the language of the input text is known. Research in this area has recently been especially active. This article provides a brief history of LI research, and an extensive survey of the features and methods used in the LI literature. We describe the features and methods using a unified notation, to make the relationships between methods clearer. We discuss evaluation methods, applications of LI, as well as off-the-shelfLI systems that do not require training by the end user. Finally, we identify open issues, survey the work to date on each issue, and propose future directions for research in LI.

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