SecDL: QoS-Aware Secure Deep Learning Approach for Dynamic Cluster-Based Routing in WSN Assisted IoT

Sujanthi, S.; Kalyani, S. Nithya

doi:10.1007/s11277-020-07469-x

Cited by 74 publications

(38 citation statements)

References 53 publications

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“…S. Sujanthi et al [20] developed a Secure-DL (SecDL) aimed at dynamic cluster-centered WSN-IoT networks. The network was planned as Bi-Concentrics Hexagons and also Mobile Sink technology for improving energy efficiency.…”

Section: Related Workmentioning

confidence: 99%

An Energy Efficient and Bandwidth Aware Optimal Routing for IoT in Agriculture

Jain

Chauhan

Jain

2021

Preprint

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The agriculture that is enabled with the Internet of Things (IoT) in addition to Wireless Sensors Networks (WSN) can aid the farmers in monitoring their product along with conditions in real-time. The indispensable issues that one has to confront with WSN are Energy-saving along with effective bandwidth usage. Lately, for the amelioration of network stability together with lifetime, disparate cluster-centered solutions are modeled. Nevertheless, most techniques are merely modeled in an energy-aware manner and utilize merely a distance parameter aimed at the data communication. It is imperative for satisfying the bandwidth use of IoT. Here, a cluster-centered energy as well as bandwidth aware routing model is proposed for agriculture data on IoT-centered WSN. Optimal Clusters Head (CH) selection as well as clustering is performed on the WSN for carrying out cluster-centered Data Aggregation (DA). The novel chaos mapping and Opposition centered Learning Grasshopper Optimization Algorithm (CO2GA) chooses the collection of CH as of agricultural Sensor Nodes (SN). Next, centered upon the distance betwixt the chosen CH and SN, the clusters are generated. The clusters share the data to their CH, and the Chaos key generated Advanced Encryption Standard with Rivest–Shamir–Adleman (CKAES-RSA) algorithm encrypt the aggregated data of CH. Lastly, the encrypted data of IoT data are shared with the Base Stations (BS) or Sink Node (Sn) by means of the optimal routing. Aimed at Optimal Route Selection (ORS), the paper utilizes a Deep Learning (DL) approach, explicitly crossover and mutation-based optimal Multi-Layer Perceptrons (CM-OMLP), which computes the fitness of hidden layer by regarding energy, bandwidth, trust, delay, along with congestion level. The proposed encryption and routing mechanism’s results are weighted against the first-rate techniques. The proposed work achieves the highest level of security aimed at the IoT data and fulfills the QoS requirements regarding packet delivery, throughput, delay, along with Network LifeTimes (NLT).

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Section: Related Workmentioning

confidence: 99%

An Energy Efficient and Bandwidth Aware Optimal Routing for IoT in Agriculture

Jain

Chauhan

Jain

2021

Preprint

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“…The routing protocol chooses the node with the highest residual energy which helps to provide a high packet delivery ratio. Another distinctive clustering strategy was presented in [5], the authors utilized different parameters that are related to the nodes' remaining energy, degrees and centrality factors to select the CH node.…”

Section: Clustering Technology In Wsn Networkmentioning

confidence: 99%

“…First, the DynCH is constructed, then symmetrical block structures are implemented. The performance of the DynCH scheme is compared to constant clustering techniques [5]- [9] called "SteadyCH".…”

Section: Simulation and Environmentsmentioning

confidence: 99%

“…Clustering aims at grouping the nodes in a cluster, where a Cluster Head (CH) node is elected. This node is responsible for gathering the nodes' data, aggregate it, perform some simple operations on it such as compression and de-correlation, and ultimately sending it to a remote node for further processing and analysis [5]- [9]. Clustering is often used in a stationary environment with static nodes.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, having a dynamic clustering algorithm is crucial for MWSNs. Many of the available studies [5]- [9] focused on clustering for static WSN nodes, whereas the available dynamic clustering studies have a high degree of complexity [10]- [13]. Hence, the authors in [14] have proposed new Dynamic Clustering (DynCH) scheme to support dynamic clustering in MWSNs in a simple and effective manner.…”

Section: Introductionmentioning

confidence: 99%

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Analysis the Effect of Dynamic Clustering and Lightweight Symmetric Encryption Approaches on Network Lifetime in WSNs

Abu-Ain

Ahmad

Sundararajan

2021

Preprint

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Energy consumption and security efficiency are still main challenges in Wireless Sensor Networks (WSNs) due to their hardware resource-constrained nature. The 6LoWPAN protocol was developed to improve WSNs communication, security, and node management optimization. Hence the protocol energy efficiency and security can be improved. In this paper, we address the WSN nodes’ power consumption by analyzing the Dynamic Cluster Head (DynCH) technique, which automates the process of selecting WSN Cluster Head (CH) nodes based on the WSN nodes’ energy and nodes’ distances among each other in mobile WSN nodes. Moreover, this analysis covers the complexity of DynCH in different environments to prove its efficiency compared to the steady CH mechanism. In addition, we present the performance analysis of different lightweight systematic block encryption algorithms along with DynCH scheme on WSNs lifetime. In particular, Speck128, FlexenTech, Tiny Encryption Algorithm (TEA), and Advanced Encryption Standard (AES) algorithms are used in order to determine the amount of energy consumed by the sensor nodes and their effect on the network lifetime. Furthermore, the lightweight key management mechanism is used to secure the data and keys exchange between WSN nodes in all different systematic encryption algorithms. The Cooja simulator with Contiki operating system are used to evaluate our analysis. Finally, the outcome of the analysis has showed that DynCH improves the wireless network lifetime by 45% compared to the steady clustering approach. Moreover, the analysis also shows that, Speck128 consumed 26%, FlexenTech consumed 52%, TEA consumed 65%, and AES consumed 78% of wireless network lifetime compared to unsecure wireless networks communication, respectively.

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