Trust management for reliable decision making among social objects in the Social Internet of Things

Kowshalya, A. Meena; Valarmathi, M. L.

doi:10.1049/iet-net.2017.0021

Cited by 59 publications

(22 citation statements)

References 17 publications

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“…Community Interest. Community interest is an important factor that empowers trust and interaction between objects of the same community [34]. Community interest and culture could highly affect how individuals make their decisions.…”

Section: Social Influence-related Factorsmentioning

confidence: 99%

Improving IoT Technology Adoption through Improving Consumer Trust

Alhogail

2018

Technologies

113

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Studies have shown that trust plays a crucial role in the consumers' decision to adopt Internet of Things (IoT) technologies and services since it helps them to overcome perceptions of risk and uncertainty related to it and enhances the customers' level of acceptance and adoption intention. Nevertheless, the literature of IoT still lacks studies on the behavioral aspect that explain the customers' perception towards IoT adoption and focuses more on technological aspect. The main goal of this study is to examine the factors that influence consumer trust and their role in the adoption of IoT technology. A conceptual trust model that encompasses the major factors affecting trust towards IoT technology adoption has been presented. The model is composed of three dimensions of factors that we assume will influence the level of trust which are: product related factors, social influence related factors and security related factors. This framework is validated through surveying consumers' opinions, which provide views and feedback regarding factors influencing their trust towards this technology. The model can assist researchers to further investigate the trust issues and create a trustworthy literature to guide IoT products' development and marketing strategies that are focused on the consumer's requirements.

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Section: Social Influence-related Factorsmentioning

confidence: 99%

Improving IoT Technology Adoption through Improving Consumer Trust

Alhogail

2018

Technologies

113

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“…Here, quality of service was considered, including delivery ratio, measurement of packet ratio (forward and delivery), and consumed energy between sensors, which allowed people to compute the trust level among social networks and boost the level of social relationships among owners and devices [24]. Saied et al [35] suggested a scheme that is fully based on security. This scheme works as a shield against attacks.…”

Section: Current State Of the Art In Peer-to-peer And Client/server Nmentioning

confidence: 99%

“…Chen, Bao [24] Nitti, Atzori [14] Ben Saied, OLIVEREAU [35] Chen, Chang [34] Figure 7 describes the classified catalog of trust-and friendliness-based approaches in the social IoT.…”

Section: Relations Between Objectsmentioning

confidence: 99%

“…Therefore, to consider pretrusted nodes in the IoT environment is a question that needs to be addressed appropriately. Saied et al [35] suggested a trust manager-based model. This model is centralized and has the facility to keep and store trust-based information using IoT devices.…”

Section: Centralized Searchmentioning

confidence: 99%

“…Ben Saied, OLIVEREAU [35] suggested a trust manager for centralized environments. This trust manager is capable of keeping a record of trust-based information for IoT devices.…”

Section: Event-driven Approachesmentioning

confidence: 99%

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Towards Trust and Friendliness Approaches in the Social Internet of Things

2019

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The Internet of Things (IoT) is an interconnected network of heterogeneous entities, such as sensors and embedded devices. During the current era, a new field of research has emerged, referred to as the social IoT, which mainly includes social networking features. The social IoT refers to devices that are capable of creating interactions with each other to independently achieve a common goal. Based on the structure, the support of numerous applications, and networking services, the social IoT is preferred over the traditional IoT. However, aspects like the roles of users and network navigability are major challenges that provoke users’ fears of data disclosure and privacy violations. Thus, it is important to provide reliable data analyses by using trust- and friendliness-based properties. This study was designed because of the limited availability of information in this area. It is a classified catalog of trust- and friendliness-based approaches in the social IoT with important highlights of important constraints, such as scalability, adaptability, and suitable network structures (for instance, human-to-human and human-to-object). In addition, typical concerns like communities of interest and social contacts are discussed in detail, with particular emphasis on friendliness- and trust-based properties, such as service composition, social similarity, and integrated cloud services.

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ANGUISH: Security attack in narrowband‐Internet of Things (NB‐IoT) using game theory and hardware analysis

Kumar

Jha

Jain

2020

Trans Emerging Tel Tech

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In recent years, the Internet of Things (IoT) plays a vital role in our daily life.IoT is dominating in many areas like industries, health care, smart home, social network, defense sector, and the education sector. In the last couple of years, users are facing different challenges like security issues related to social network and defense sector. In this article, our prime focus is to secure the social and defense network associated with narrowband-Internet of Things. This article proposes the artificial intelligence (AI)-based defense security attacks using unmanned aerial vehicle (UAV). In the first part of the article, we have proposed attack modeling on smart home scenario and finalize the critical parameter for performance analysis. In the next section, the performance analysis based on game theory (GT) (AI based) approach is attained, using different techniques like nash equilibrium and Prisoner's dilemma. In the last section, detailed hardware analysis has been carried out for analyzing attack in real-time scenario and is illustrated with the help of step-by-step process. Our proposed model proves that UAV-based router can spoof by the intruder with the probability of 0.4 using GT and hardware analysis. INTRODUCTIONThe evolution of IoT started with radio frequency identification (RFID), which is vastly used in various applications like supply chain management, object tracking, and asset tracking. Other than RFID, wireless sensor network (WSN), machine to machine (M2M), software-defined networking (SDN), supervisory control and data acquisition (SCADA), cloud computing, and embedded computing are numerous technologies that also support the IoT. All these technologies are used for the different applications of IoT, such as smart homes, smart cities, smart grid, wearables, connected healthcare, smart agriculture, industrial Internet, and connected car. The advancement in IoT has different challenges like energy efficiency, spectrum utilization, a massive amount of sensors generated data, device heterogeneity, and data security. The usage of energy-efficient, low power wide area network (LPWAN) radio technologies is increasing gradually. These technologies are licensed as well as unlicensed. Unlicensed spectrum technologies such as long range (LoRa), INGENU, and SIGFOX, while licensed spectrum consists of long-term evolution-machine-type communication s(LTE-M), and NB-IoT. NB-IoT is the energy-efficient technology standard defined by 3rd Generation Partnership Project (3GPP) in release 13. It fulfills the critical requirement of IoT like low-device complexity, extended battery lifetime, deep indoor coverage, and a massive number of two-way communication of device support per cell. 1 NB-IoT is used to achieve the outstanding performance of GSM and LTE technologies and entails a minimum of 180 kHz system bandwidth for both downlink and uplink channel. 2 The uplink communication channel uses single-carrier frequency division Trans Emerging Tel Tech.

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Trust management for reliable decision making among social objects in the Social Internet of Things

Cited by 59 publications

References 17 publications

Improving IoT Technology Adoption through Improving Consumer Trust

Improving IoT Technology Adoption through Improving Consumer Trust

Towards Trust and Friendliness Approaches in the Social Internet of Things

ANGUISH: Security attack in narrowband‐Internet of Things (NB‐IoT) using game theory and hardware analysis

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