Trust aggregation authentication protocol using machine learning for IoT wireless sensor networks

Chinnaswamy, Santhanakrishnan; Annapurani, K.

doi:10.1016/j.compeleceng.2021.107130

Cited by 27 publications

(15 citation statements)

References 6 publications

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“…3 The block construction equipment is bound to the Eq-DataModel according to the method of automatic matching of data features or manual matching. Through a series of established rules or processes such as rule expression, heterogeneous data analysis and field mapping processing, the real-time data, attribute information and so on obtained by the model are generalized and parsed into readable JSON data with the same structure and format, which are stored as main parameters [21]. After the equipment binding is completed, the data flows normally.…”

Section: Implementation Methods Of the Equipment Data Modelmentioning

confidence: 99%

Online Monitoring of Ship Block Construction Equipment Based on the Internet of Things and Public Cloud: Take the Intelligent Tire Frame as an Example

2021

KSII TIIS

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In view of the problems of insufficient data collection and processing capability of multisource heterogeneous equipment, and low visibility of equipment status at the ship block construction site. A data collection method for ship block construction equipment based on wireless sensor network (WSN) technology and a data processing method based on edge computing were proposed. Based on the Browser/Server (B/S) architecture and the OneNET platform, an online monitoring system for ship block construction equipment was designed and developed, which realized the visual online monitoring and management of the ship block construction equipment status. Not only that, the feasibility and reliability of the monitoring system were verified by using the intelligent tire frame system as the application object. The research of this project can lay the foundation for the ship block construction equipment management and the ship block intelligent construction, and ultimately improve the quality and efficiency of ship block construction.

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Section: Implementation Methods Of the Equipment Data Modelmentioning

confidence: 99%

Online Monitoring of Ship Block Construction Equipment Based on the Internet of Things and Public Cloud: Take the Intelligent Tire Frame as an Example

2021

KSII TIIS

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“…Authors of [14] have focused on the trust aggregation authentication protocol based on the machine learning technique. The total trust value for the internet gateways is taken for each device using its behavior and data trust value.…”

Section: Related Workmentioning

confidence: 99%

Improve Performances of Wireless Sensor Networks for Data Transfer Based on Fuzzy Clustering and Huffman Compression

et al. 2022

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In today’s world, the main challenge is to save use energy optimally. The IoT devices generate a large amount of data for wide applications. Considering the application perspective in the IoT market, in one instance of the IoT technology, that is a wireless sensor network, factors like energy, storage capacity, computation power, and limitations of communication bandwidth resources are the reason for using data fusion. Data fusion and aggregation in wireless sensor networks (WSNs) such that minimum energy is consumed are an essential issue. In most clustering models, data aggregation is carried out by the cluster-head (CH). In the proposed algorithm, data aggregation in the cluster-head is carried out using the lossless cascode Huffman compression algorithm. Due to the correlation among data of nodes, the data sensed by each node is compared with the data of the cluster-head node; after removing redundancy, the coded data is transmitted to the main node. The CH node is selected by an algorithm based on fuzzy logic according to the residual energy of the node and the distance of the node from the sink node. Various fuzzy type-I systems of Mamdani and Takagi-Sugeno and type-II systems are used. In this paper, the CH selection algorithms are evaluated using three scenarios in terms of the number of live nodes, received packets and CHs, proper distribution rate, and other parameters of the LEACH protocol, network lifetime, and network energy. In the following, to demonstrate the performance of this new algorithm, simulations are performed in MATLAB based on the proposed method. The results show that the proposed compression algorithm in environments with high data correlation improves the compression rate by 8% compared to the conventional Huffman compression, while in environments with low data correlation, these two algorithms perform almost the same. This compression helps reduce the energy consumption of the network.

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“…The belief function, calculated using the recommendation function along with the confidence function (as an add-on function), defines how the cumulative trust value is applied to a node. [12] Furthermore, the function of a selected grouping is determined by the context of the relationship between the nodes. This manuscript employs a fully weighted approach [36].…”

Section: Trust Computation Using An Aggregate Functionmentioning

confidence: 99%

“…Because all nodes in the network inspect it, the node with the highest cumulative value of trust is chosen for data transmission. Since this method points out the most trusted node, it helps in the removal of confusion for selecting nodes for secure transmission [12].…”

Section: Trust Computation Using Cumulated Trust Valuementioning

confidence: 99%

Intelligent Trust based Security Framework for Internet of Things

Rana¹,

Singh²,

Vijaya

2022

IJRITCC

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Trust models have recently been proposed for Internet of Things (IoT) applications as a significant system of protection against external threats. This approach to IoT risk management is viable, trustworthy, and secure. At present, the trust security mechanism for immersion applications has not been specified for IoT systems. Several unfamiliar participants or machines share their resources through distributed systems to carry out a job or provide a service. One can have access to tools, network routes, connections, power processing, and storage space. This puts users of the IoT at much greater risk of, for example, anonymity, data leakage, and other safety violations. Trust measurement for new nodes has become crucial for unknown peer threats to be mitigated. Trust must be evaluated in the application sense using acceptable metrics based on the functional properties of nodes. The multifaceted confidence parameterization cannot be clarified explicitly by current stable models. In most current models, loss of confidence is inadequately modeled. Esteem ratings are frequently mis-weighted when previous confidence is taken into account, increasing the impact of harmful recommendations. In this manuscript, a systematic method called Relationship History along with cumulative trust value (Distributed confidence management scheme model) has been proposed to evaluate interactive peers trust worthiness in a specific context. It includes estimating confidence decline, gathering & weighing trust parameters and calculating the cumulative trust value between nodes. Trust standards can rely on practical contextual resources, determining if a service provider is trustworthy or not and does it deliver effective service? The simulation results suggest that the proposed model outperforms other similar models in terms of security, routing and efficiency and further assesses its performance based on derived utility and trust precision, convergence, and longevity.

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Trust aggregation authentication protocol using machine learning for IoT wireless sensor networks

Cited by 27 publications

References 6 publications

Online Monitoring of Ship Block Construction Equipment Based on the Internet of Things and Public Cloud: Take the Intelligent Tire Frame as an Example

Online Monitoring of Ship Block Construction Equipment Based on the Internet of Things and Public Cloud: Take the Intelligent Tire Frame as an Example

Improve Performances of Wireless Sensor Networks for Data Transfer Based on Fuzzy Clustering and Huffman Compression

Intelligent Trust based Security Framework for Internet of Things

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