People-Centric Internet of Things

Silva, Jorge Sá; Zhang, Pei; Pering, Trevor; Boavida, Fernando; Hara, Takahiro; Liebau, Nicolas

doi:10.1109/mcom.2017.7841465

Cited by 24 publications

(16 citation statements)

References 10 publications

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“…In IoT ecosystems data from various sources such as actuations, sensors, and smart devices are gathered, analyzed and processed to provide ubiquitous and intelligent services [8], [9]. In this environment, users could contribute to the process through sharing not only data sensed from their own devices sensors but also their incidents and knowledge over social networks without the need to pre-allocate sensing devices in [10], hence saving deployment costs [11], [12]. This prospect coined the term MCS that has since gained popularity as a promising data acquisition approach for the IoT because of the increasing usage of mobile smart devices.…”

Section: A Background On Mobile Crowd-sensing In the Iotmentioning

confidence: 99%

“…The output at the beginning state is set to 0 (line #2). For each request R(i) from a user U (i) and for each sensing task ST R(i) (j), the algorithm uses out ← out + QoS(R(i)); 10 end 11 Return out the polyf it and polyval functions for finding the coefficients and predicting the next QoD scores for each user (line #5, line #6); then, it recruits users with highest predicted QoD scores (line #7). When the sensing task has been accomplished, the algorithm calculates the QoD score for the sensing data collected from the recruited users (line #8) and updates the QoDScore matrix accordingly (line #9).…”

Section: Trust-based Average and Polynomial Regression User Recrmentioning

confidence: 99%

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Trust Evaluation Mechanism for User Recruitment in Mobile Crowd-Sensing in the Internet of Things

Truong

Lee

et al. 2019

IEEE Trans.Inform.Forensic Secur.

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Mobile Crowd-Sensing (MCS) has appeared as a prospective solution for large-scale data collection, leveraging built-in sensors and social applications in mobile devices that enables a variety of Internet of Things (IoT) services. However, the human involvement in MCS results in a high possibility for unintentionally contributing corrupted and falsified data or intentionally spreading disinformation for malevolent purposes, consequently undermining IoT services. Therefore, recruiting trustworthy contributors plays a crucial role in collecting highquality data and providing better quality of services while minimizing the vulnerabilities and risks to MCS systems. In this article, a novel trust model called Experience-Reputation (E-R) is proposed for evaluating trust relationships between any two mobile device users in a MCS platform. To enable the E-R model, virtual interactions among the users are manipulated by considering an assessment of the quality of contributed data from such users. Based on these interactions, two indicators of trust called Experience and Reputation are calculated accordingly. By incorporating the Experience and Reputation trust indicators (TIs), trust relationships between the users are established, evaluated and maintained. Based on these trust relationships, a novel trust-based recruitment scheme is carried out for selecting the most trustworthy MCS users to contribute to data sensing tasks. In order to evaluate the performance and effectiveness of the proposed trust-based mechanism as well as the E-R trust model, we deploy several recruitment schemes in a MCS testbed which consists of both normal and malicious users. The results highlight the strength of the trust-based scheme as it delivers better quality for MCS services while being able to detect malicious users. We believe that the trust-based user recruitment offers an effective capability for selecting trustworthy users for various MCS systems and, importantly, the proposed mechanism is practical to deploy in the real world.

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Section: A Background On Mobile Crowd-sensing In the Iotmentioning

confidence: 99%

Section: Trust-based Average and Polynomial Regression User Recrmentioning

confidence: 99%

Trust Evaluation Mechanism for User Recruitment in Mobile Crowd-Sensing in the Internet of Things

Truong

Lee

et al. 2019

IEEE Trans.Inform.Forensic Secur.

View full text Add to dashboard Cite

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“…The concept of internet of things (IoT) as an association of smart‐interconnected devices came across years back in the past . Because of the gradual increase in requirement of communication and computation, every device is equipped with sensors, processor, memory, and wireless interfaces that enable device‐to‐device communication, shaping the IoT .…”

Section: Introductionmentioning

confidence: 99%

A lightweight IoT‐based security framework for inventory automation using wireless sensor network

Batra

Verma

Kavita

et al. 2019

Int J Communication

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Internet of things (IoT) has evolved as an innovation of next generation in this world of smart devices. IoT tends to provide services for data collection, data management, and data and device security required for application development. Things or devices in IoT communicate and compute to make our lives comfortable and safe. In inventory automation, real-time check on items, their information management, and status management, monitoring can be carried out using IoT. The huge amount of data that flows among the devices in the network demands for a security framework that ensures authentication, authorization, integrity, and confidentiality of data. The existing security solutions like SIMON or SPECK offer lightweight security solutions but are vulnerable to differential attack because of their simplicity. Moreover, existing solutions do not offer inbuilt authentication. Therefore, this research work contributes a secure and lightweight IoT-based framework using wireless sensor network (WSN) as a technology. The existing security solutions SPECK and SIMON are compared with the proposed security approach using COOJA simulator. The results show that proposed approach outstands others by 2% reduction in number of CPU cycles, 10% less execution time, 4% less memory requirements of security approach, and with minimum 10% more security impact.

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“…The IoT is predominantly user-centric, and in this respect, when it comes to the users, the elements by which they mainly communicate and interact with IoT elements such as smart devices or the cloud are smartphones; the latter play the role of an intermediator and interface for users to the IoT by means of mobile applications [4]. By using smartphones in the context of IoT cyber-physical systems, users can not only access information, but also command and control smart devices that can influence the physical world.…”

Section: Introductionmentioning

confidence: 99%

Validation of Authentication Measures Implementation in Iot Mobile Applications

Bascuñana

2019

Smart Cities

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Smartphones are an integral cog of the IoT environment and a fundamental bloc of any related security solution, given that IoT mobile applications allow users not only to get information, but also to influence the environment. This paper presents a methodological instrument that can contribute to implementing and evaluating security measures in mobile applications by means of an automated analysis tool. A clear process for linking policy and high-level security guidelines and measures to concrete source code elements is depicted, as well as an automated way of testing a set of mobile applications against them. In addition, the obtained results highlight the current state of authentication measures’ implementation in IoT mobile applications; at the same time, it is important to note that the proposed approach is generic enough to accommodate other security principles as well.

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People-Centric Internet of Things

Cited by 24 publications

References 10 publications

Trust Evaluation Mechanism for User Recruitment in Mobile Crowd-Sensing in the Internet of Things

Trust Evaluation Mechanism for User Recruitment in Mobile Crowd-Sensing in the Internet of Things

A lightweight IoT‐based security framework for inventory automation using wireless sensor network

Validation of Authentication Measures Implementation in Iot Mobile Applications

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