On Sidechain-Assisted Transaction Service Management for Internet of Things: A Random Contract Approach

Li, Jing; Liu, Tingting; Niyato, Dusit; Li, Jun; Han, Zhu

doi:10.1109/tnse.2022.3181114

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…The rapid proliferation of the Internet of Medical Things (IoMT) has revolutionized the healthcare industry, providing unprecedented opportunities for real-time patient monitoring, data analytics, and improved clinical decision-making process [1,2,3]. The dense networks of interconnected devices characteristic of IoMT environments demand an efficient and effective routing mechanism to ensure Quality of Service (QoS) while managing the voluminous data generated.…”

Section: Introductionmentioning

confidence: 99%

Design of an Incremental Graph-based Routing Model for enhancing QoS of dense IoMT Networks using Truncated Side chains

Fuke,

Ranawat

2024

Preprint

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The exponential growth of Internet of Medical Things (IoMT) has necessitated the need for an efficient and secure routing mechanism capable of handling dense network traffic while also ensuring quality of service (QoS) and privacy. Existing solutions in the domain are often limited by their inability to effectively fuse temporal and spatial information for route optimization and lack comprehensive methods to secure route data samples. In this paper, I present the Incremental Graph-based Routing Model for QoS enhancement in dense IoMT Networks using Truncated Sidechains (IGRMQITS). Our model leverages a graph-based Q Learning (GQL) algorithm for route optimization, which is further enhanced using a novel Teacher Learner, based Firefly Optimizer (TLFFO). This ensures the identification of optimal routes by efficiently fusing temporal and spatial information of the nodes. To secure the data on these routes, we introduce an innovative truncated sidechaining model, which employs Grey Wolf Whale Optimizer (GWWO) for selecting optimal sidechain lengths. The chains are then truncated using a Fuzzy Analytic Hierarchy Process (AHP) that prioritizes different privacy levels, thereby ensuring a robust security mechanism for real-time networks. The proposed model has proven to significantly enhance the network performance, with a 10.5% reduction in communication delay, 8.5% increase in energy efficiency, 3.9% improvement in throughput, 1.5% increase in packet delivery ratio, and an 8.3% reduction in jitter. Furthermore, our model also excels in reducing the mining delay by 12.5%, cutting down energy consumption during mining by 5.9%, and decreasing the storage requirements for mining by 4.9%. Thus, IGRMQITS represents a significant step forward in the field of IoMT, addressing the critical challenges of route optimization, data security, and privacy in dense network environments. This work not only enhances the QoS in IoMT networks but also sets a new benchmark for future research in the domain, pushing the boundaries of what is possible in the field of IoMT network optimization and security levels.

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Section: Introductionmentioning

confidence: 99%