Providing Secured Data Aggregation in Mobile Wireless Sensor Network

Preethiya, T.; Muthukumar, A.; Durairaj, S.

doi:10.1109/roma46407.2018.8986735

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…The fusion factor is estimated based on the temporal and spatial variation of the nodes as described 59,60 …”

Section: Secured Data For Mobile Clustering Hierarchymentioning

confidence: 99%

“…The fusion factor is estimated based on the temporal and spatial variation of the nodes as described. 59,60 From the Figure 2, we understand that the node n 1 located at L 1 and n 2 located at L 2 in a network and event E happens to take place at location L. Now, both nodes n 1 and n 2 senses the information and forwards to MCH node M with a timestamp. The D m1 and D m2 are the distance between nodes 1 and 2 towards MCH.…”

Section: Data Collection and Aggregation Phasementioning

confidence: 99%

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An approach to data redundancy reduction and secured data delivery using spatial‐temporal correlation factors in heterogeneous Mobile Wireless Sensor Network

Thandapani¹,

Arunachalam

Durairaj

2022

Int J Communication

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As the state-of-the-art technology in the areas of IoT, battlefield surveillance, medical, military applications, and so on, the Mobile Wireless Sensor Network (MWSN) has attracted the attention of vendors and academics in recent years.In MWSN, energy optimization is the major issue because of its dynamicity property. The energy optimization can be achieved by avoiding node compromise attacks and ensuring secured data transmission in the network. In this paper, an algorithm SDCH-M (Secured Data for Mobile Clustering Hierarchy) is proposed to ensure secure data delivery in a double cluster head hybrid topology, which optimizes the network energy consumption. Data redundancy is reduced by estimating spatial-temporal correlation factors, and the variantround voting mechanism is used to verify the accuracy of delivered data and to identify compromised nodes through voting by the base station. Results from the simulation show that the proposed SDCH-M algorithm optimizes energy than M-LEACH and C-LEACH algorithm. The average delivery ratio of SDCH-M (DCH), SDCH-M (SCH), E 2 RP, M-LEACH, and C-LEACH are 0.9175, 0.9037, 0.828, 0.799, and 0.681, respectively. The average delay of M-LEACH, C-LEACH, E 2 RP, SDCH-M (DCH), and SDCH-M (SCH) algorithms is 6.851, 8.655, 6.3, 5.585, and 5.97 s, respectively.

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“…The fusion factor is estimated based on the temporal and spatial variation of the nodes as described 59,60 …”

Section: Secured Data For Mobile Clustering Hierarchymentioning

confidence: 99%

Section: Data Collection and Aggregation Phasementioning

confidence: 99%

An approach to data redundancy reduction and secured data delivery using spatial‐temporal correlation factors in heterogeneous Mobile Wireless Sensor Network

Thandapani¹,

Arunachalam

Durairaj

2022

Int J Communication

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Web-based cost-effective indoor air pollutants monitoring in real-time

Preethiya¹

2023

AIP Conference Proceedings

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Autonomous System and AI

Preethiya,

Subbiah,

Pandiarajan

et al. 2024

Modeling, Simulation, and Control of AI Robotics and Autonomous Systems

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Autonomous systems and AI have revolutionized industry automation and innovation. This abstract shows how this synergy changes our lives and work. AI has rapidly advanced autonomous systems like self-driving cars, drones, and robots. These systems can sense, make smart decisions, and adapt to changing environments with AI. They can navigate complex settings and interact with humans thanks to this superior sense Autonomous systems can make real-time judgments using AI methods like machine learning and deep learning. AI-driven decision-making assures adaptability and efficiency, whether a self-driving car chooses the safest route, or a drone optimizes its flying path. AI-assisted autonomous systems prioritize safety and reliability. These systems can detect anomalies and respond proactively to prevent accidents and failures through self-monitoring and diagnostics.

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Providing Secured Data Aggregation in Mobile Wireless Sensor Network

Cited by 3 publications

References 9 publications

An approach to data redundancy reduction and secured data delivery using spatial‐temporal correlation factors in heterogeneous Mobile Wireless Sensor Network

An approach to data redundancy reduction and secured data delivery using spatial‐temporal correlation factors in heterogeneous Mobile Wireless Sensor Network

Web-based cost-effective indoor air pollutants monitoring in real-time

Autonomous System and AI

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