Secure Cluster-Based Data Integrity Routing for Wireless Sensor Networks

Shashikala,; Kavitha, C.

doi:10.1007/978-981-10-6614-6_16

Advances in Intelligent Systems and Computing

2017

DOI: 10.1007/978-981-10-6614-6_16

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Secure Cluster-Based Data Integrity Routing for Wireless Sensor Networks

Shashikala Shashikala¹,

C. Kavitha²

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Cited by 2 publications

References 7 publications

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Energy and throughput efficient mobile wireless sensor networks: A deep reinforcement learning approach

Alsalmi,

Navaie,

Rahmani

2024

IET Networks

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The efficient development of Mobile Wireless Sensor Networks (MWSNs) relies heavily on optimizing two key parameters: Throughput and Energy Consumption. The proposed work investigates network connectivity issues with MWSN and proposes two routing algorithms, namely Self‐Organising Maps based‐Optimised Link State Routing (SOM‐OLSR) and Deep Reinforcement Learning based‐Optimised Link State Routing (DRL‐OLSR) for MWSNs. The primary objective of the proposed algorithms is to achieve energy‐efficient routing while maximizing throughput. The proposed algorithms are evaluated through simulations by considering various performance metrics, including connection probability (CP), end‐to‐end delay, overhead, network throughput, and energy consumption. The simulation analysis is discussed under three scenarios. The first scenario undertakes ‘no optimisation’, the second considers SOM‐OLSR, and the third undertakes DRL‐OLSR. A comparison between DRL‐OLSR and SOM‐OLSR reveals that the former surpasses the latter in terms of low latency and prolonged network lifetime. Specifically, DRL‐OLSR demonstrates a 47% increase in throughput, a 67% reduction in energy consumption, and a CP three times higher than SOM‐OLSR. Furthermore, when contrasted with the ‘no optimisation’ scenario, DRL‐OLSR achieves a remarkable 69.7% higher throughput and nearly 89% lower energy consumption. These findings highlight the effectiveness of the DRL‐OLSR approach in wireless sensor networks.

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Energy and throughput efficient mobile wireless sensor networks: A deep reinforcement learning approach

Alsalmi,

Navaie,

Rahmani

2024

IET Networks

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An Intelligent Opportunistic Routing Protocol for Big Data in WSNs

Bangotra¹,

Singh

Selwal

2020

International Journal of Multimedia Data Engineering and Management

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Opportunistic routing (OR) is an emerging and promising data communication protocol in wireless sensor networks (WSNs). The OR becomes more important when the routing of data is Big Data (BD) generated from multidimensional distributed sensors nodes. The central idea behind OR is overhearing and coordination between relay nodes in the forwarders list and management of multidimensional BD. It uses the salient broadcast feature of the wireless medium for achieving advanced reliability and maximizing the communication range. This article presents the basic concepts of WSN, reviews different OR protocols, and describes the use of different machine learning (ML) techniques in routing BD. Current issues and challenges associated with WSN in general and OR in particular are also presented in this article.

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Secure Cluster-Based Data Integrity Routing for Wireless Sensor Networks

Cited by 2 publications

References 7 publications

Energy and throughput efficient mobile wireless sensor networks: A deep reinforcement learning approach

Energy and throughput efficient mobile wireless sensor networks: A deep reinforcement learning approach

An Intelligent Opportunistic Routing Protocol for Big Data in WSNs

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