RraR: Robust Recommendation Aggregation using Retraining in Internet of Things

Sharma, Avani; Pilli, Emmanuel S.; Mazumdar, Arka Prokash

doi:10.1109/wf-iot.2019.8767324

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…Accordingly, the untrustworthy nodes and the trustworthy nodes are divided within the cluster. The edge mobile node is assigned the task of gathering data from the clusters [10].…”

Section: Problem Formulationmentioning

confidence: 99%

Edge Computing through Virtual Force for Detecting Trustworthy Values

Dhaya¹,

Kanthavel²

2020

JSWS

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As the advancement of IoT (Internet of Things) and other emerging mobile application continues, it is an accepted fact that Edge Computing paradigm is considered to be the best fit in terms of fulfilling the resource requirements. Moreover, it is a fact that the data collected by the sensor networks serves as the base for the IoT applications as well as the systems. However, due to advancement in cybercrimes, there is a possibility that the data collected through the sensor networks are vulnerable to attacks which may result in serious consequences. The proposed work focuses on a new model which is used to gather trustworthy data using edge computing in IoT. In order to get the accurately quantified trust values, the sensor nodes are analyzed and found from different dimensions. Moreover, with the help of trust value obtained, it is possible to find the best mobility path which carries the highest value of trust. This data is gathered from the sensors with the help of mobile edge data collector. This analysis shows that for a trustworthy data collection model of IoT, there is noticeable improvement in terms of energy conservation and system security, thereby improving the performance of the system.

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“…Accordingly, the untrustworthy nodes and the trustworthy nodes are divided within the cluster. The edge mobile node is assigned the task of gathering data from the clusters [10].…”

Section: Problem Formulationmentioning

confidence: 99%