A Brief Survey on Data Aggregation and Data Compression Models using Blockchain Model in Wireless Sensor Network

Sudheer, Bandlamudi Naga; Sujatha, Kamepalli

doi:10.1109/icidca56705.2023.10100009

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…Prevailing contending proposed secure data approaches such as SD, EEHA, HAS, IIF, and RHC protocols focus only on authentication and ignore the authorization process in wireless sensor networks [42]. Since a newly deployed node could be malicious, a WSN protocol must be cautious when a new node deployment occurs in the network.…”

Section: Authorization Processmentioning

confidence: 99%

Secure Data Aggregation Using Authentication and Authorization for Privacy Preservation in Wireless Sensor Networks

Erskine

2024

Sensors

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Existing secure data aggregation protocols are weaker to eliminate data redundancy and protect wireless sensor networks (WSNs). Only some existing approaches have solved this singular issue when aggregating data. However, there is a need for a multi-featured protocol to handle the multiple problems of data aggregation, such as energy efficiency, authentication, authorization, and maintaining the security of the network. Looking at the significant demand for multi-featured data aggregation protocol, we propose secure data aggregation using authentication and authorization (SDAAA) protocol to detect malicious attacks, particularly cyberattacks such as sybil and sinkhole, to extend network performance. These attacks are more complex to address through existing cryptographic protocols. The proposed SDAAA protocol comprises a node authorization algorithm that permits legitimate nodes to communicate within the network. This SDAAA protocol’s methods help improve the quality of service (QoS) parameters. Furthermore, we introduce a mathematical model to improve accuracy, energy efficiency, data freshness, authorization, and authentication. Finally, our protocol is tested in an intelligent healthcare WSN patient-monitoring application scenario and verified using an OMNET++ simulator. Based upon the results, we confirm that our proposed SDAAA protocol attains a throughput of 444 kbs, representing a 98% of data/network channel capacity rate; an energy consumption of 2.6 joules, representing 99% network energy efficiency; an effected network of 2.45, representing 99.5% achieved overall performance of the network; and time complexity of 0.08 s, representing 98.5% efficiency of the proposed SDAAA approach. By contrast, contending protocols such as SD, EEHA, HAS, IIF, and RHC have throughput ranges between 415–443, representing 85–90% of the data rate/channel capacity of the network; energy consumption in the range of 3.0–3.6 joules, representing 88–95% energy efficiency of the network; effected network range of 2.98, representing 72–89% improved overall performance of the network; and time complexity in the range of 0.20 s, representing 72–89% efficiency of the proposed SDAAA approach. Therefore, our proposed SDAAA protocol outperforms other known approaches, such as SD, EEHA, HAS, IIF, and RHC, designed for secure data aggregation in a similar environment.

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Section: Authorization Processmentioning

confidence: 99%

Secure Data Aggregation Using Authentication and Authorization for Privacy Preservation in Wireless Sensor Networks

Erskine

2024

Sensors

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“…Note that this work does not consider the handling of data security issues in data collection data scheduling, and data compression. A brief analysis of data compression security is sues and implementing efficient data compression and encryption schemes in network services can be found in [27][28][29].…”

Section: Data Collectionmentioning

confidence: 99%

Design of Edge-IoMT Network Architecture with Weight-Based Scheduling

Tseng,

Chen,

Wen

2023

Sensors

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Population health monitoring based on the Internet of Medical Things (IoMT) is becoming an important application trend healthcare improvement. This work aims to develop an autonomous network architecture, collecting sensor data with a cluster topology, forwarding information through relay nodes, and applying edge computing and transmission scheduling for network scalability and operational efficiency. The proposed distributed network architecture incorporates data compression technologies and effective scheduling algorithms for handling the transmission scheduling of various physiological signals. Compared to existing scheduling mechanisms, the experimental results depict the network performance and show that in analyzing the delay and jitter, the proposed WFQ-based algorithms have reduced the delay and jitter ratio by about 40% and 19.47% compared to LLQ with priority queueing scheme, respectively. The experimental results also demonstrate that the proposed network topology is more effective than the direct path transmission approach in terms of energy consumption, which suggests that the proposed network architecture may improve the development of medical applications with body area networks such that the goal of self-organizing population health monitoring can be achieved.

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A Brief Survey on Data Aggregation and Data Compression Models using Blockchain Model in Wireless Sensor Network

Cited by 2 publications

References 17 publications

Secure Data Aggregation Using Authentication and Authorization for Privacy Preservation in Wireless Sensor Networks

Secure Data Aggregation Using Authentication and Authorization for Privacy Preservation in Wireless Sensor Networks

Design of Edge-IoMT Network Architecture with Weight-Based Scheduling

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