Optimal Stacked Sparse Autoencoder Based Traffic Flow Prediction in Intelligent Transportation Systems

Neelakandan, S.; Mohan, Prakash; Bhargava, Sanjay; Robert, Nismon Rio; Upadhye, Sachin

doi:10.1007/978-3-030-94102-4_6

Cited by 17 publications

(3 citation statements)

References 20 publications

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“…The results implied that the BDL-PPDT technique has attained effective outcomes with the lower NDSN under all rounds. For instance, with 800 rounds, the BDL-PPDT technique has achieved minimal NDSN of 1, whereas the DEEC, PHC, HNS, CHSES, and RDAC-BC techniques have obtained maximum NDSN of 116, 106, 64, 42, and 5 nodes, respectively [ 28 – 31 ]. At the same time, with 3500 rounds, the BDL-PPDT technique has offered a least NDSN of 290, whereas the DEEC, PHC, HNS, CHSES, and RDAC-BC techniques have reached to an increased NDSN of 488, 481, 472, 470, and 362 nodes, respectively.…”

Section: Resultsmentioning

confidence: 99%

Deep Learning-Based Privacy-Preserving Data Transmission Scheme for Clustered IIoT Environment

Lakshmanna

Kavitha

Geetha

et al. 2022

Computational Intelligence and Neuroscience

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The Industrial Internet of Things (IIoT) has received significant attention from several leading industries like agriculture, mining, transport, energy, and healthcare. IIoT acts as a vital part of Industry 4.0 that mainly employs machine learning (ML) to investigate the interconnection and massive quantity of the IIoT data. As the data are generally saved at the cloud server, security and privacy of the collected data from numerous distributed and heterogeneous devices remain a challenging issue. This article develops a novel multi-agent system (MAS) with deep learning-based privacy preserving data transmission (BDL-PPDT) scheme for clustered IIoT environment. The goal of the BDL-PPDT technique is to accomplish secure data transmission in clustered IIoT environment. The BDL-PPDT technique involves a two-stage process. Initially, an enhanced moth swarm algorithm-based clustering (EMSA-C) technique is derived to choose a proper set of clusters in the IIoT system and construct clusters. Besides, multi-agent system is used to enable secure inter-cluster communication. Moreover, multi-head attention with bidirectional long short-term memory (MHA-BLSTM) model is applied for intrusion detection process. Furthermore, the hyperparameter tuning process of the MHA-BLSTM model can be carried out by the stochastic gradient descent with momentum (SGDM) model to improve the detection rate. For examining the promising performance of the BDL-PPDT technique, an extensive comparison study takes place and the results are assessed under varying measures. A significant amount of capital is required. It goes without saying that one of the most obvious industrial IoT concerns is the high cost of adoption. Secure data storage and management connectivity failures are common among IoT devices due to the massive amount of data they create. The simulation results demonstrate the enhanced outcomes of the BDL-PPDT technique over the recent methods. Despite the fact that the offered BDL-PPDT technique has an accuracy of just 98.15 percent, it produces the best feasible outcome. Because of the data analysis conducted as detailed above, it was determined that the BDL-PPDT technique outperformed the other current techniques on a range of different criteria and was thus recommended.

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Section: Resultsmentioning

confidence: 99%

Deep Learning-Based Privacy-Preserving Data Transmission Scheme for Clustered IIoT Environment

Lakshmanna

Kavitha

Geetha

et al. 2022

Computational Intelligence and Neuroscience

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“…Neelakandan et al [19] developed an Optimum-stacked Sparse Auto-encoder-based TFP (OSSAE-TFP) approach for the ITS. The purpose of the OSSAE-TFP approach was to define the traffic flow level in the ITS.…”

Section: Related Workmentioning

confidence: 99%

Improved Artificial Rabbits Optimization with Ensemble Learning-Based Traffic Flow Monitoring on Intelligent Transportation System

Ragab,

Abdushkour,

Maghrabi

et al. 2023

Sustainability

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Traffic flow monitoring plays a crucial role in Intelligent Transportation Systems (ITS) by dealing with real-time data on traffic situations and allowing effectual traffic management and optimization. A typical approach used for traffic flow monitoring frequently depends on collection and analysis of the data through a manual process that is not only resource-intensive, but also a time-consuming process. Recently, Artificial Intelligence (AI) approaches like ensemble learning demonstrate promising outcomes in numerous ITS applications. With this stimulus, the current study proposes an Improved Artificial Rabbits Optimization with Ensemble Learning-based Traffic Flow Monitoring System (IAROEL-TFMS) for ITS. The primary intention of the proposed IAROEL-TFMS technique is to employ the feature subset selection process with optimal ensemble learning so as to predict the traffic flow. In order to accomplish this, the IAROEL-TFMS technique initially designs the IARO-based feature selection approach to elect a set of features. In addition, the traffic flow is predicted using the ensemble model that comprises a Gated Recurrent Unit (GRU), Long Short-term Memory (LSTM), and Bidirectional Gated Recurrent Unit (BiGRU). Finally, the Grasshopper Optimization Algorithm (GOA) is applied for the adjustment of the optimum hyperparameters of all three DL models. In order to highlight the improved prediction results of the proposed IAROEL-TFMS algorithm, an extensive range of simulations was conducted. The simulation outcomes imply the supremacy of the IAROEL-TFMS methodology over other existing approaches with a minimum RMSE of 16.4539.

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“…Table 2 and Figure 3 report a brief network lifetime (NLT) examination of the IMD-EACBR model compared to other approaches. The results indicate that the IMD-EACBR model accomplished a maximum NLT under all SNs [58][59][60][61][62][63][64][65][66][67]. For instance, with 100 SNs, the IMD-EACBR model attained an maximum NLT of 1719 rounds, whereas the sunflower optimisation (SFO), gray wolf optimisation (GWO), genetic algorithm (GA), ant line optimisation (ALO), and particle swarm optimisation (PSO) models obtained reduced NLTs of 1593, 1448, 1415, 1349, and 1300 rounds, respectively.…”

Section: Experimental Validationmentioning

confidence: 99%

Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks

Neelakandan²,

et al. 2022

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The Internet of Things (IoT) is a network of numerous devices that are consistent with one another via the internet. Wireless sensor networks (WSN) play an integral part in the IoT, which helps to produce seamless data that highly influence the network’s lifetime. Despite the significant applications of the IoT, several challenging issues such as security, energy, load balancing, and storage exist. Energy efficiency is considered to be a vital part of the design of IoT-assisted WSN; this is accomplished by clustering and multi-hop routing techniques. In view of this, we introduce an improved metaheuristic-driven energy-aware cluster-based routing (IMD-EACBR) scheme for IoT-assisted WSN. The proposed IMD-EACBR model intends to achieve maximum energy utilization and lifetime in the network. In order to attain this, the IMD-EACBR model primarily designs an improved Archimedes optimization algorithm-based clustering (IAOAC) technique for cluster head (CH) election and cluster organization. In addition, the IAOAC algorithm computes a suitability purpose that connects multiple structures specifically for energy efficiency, detachment, node degree, and inter-cluster distance. Moreover, teaching–learning-based optimization (TLBO) algorithm-based multi-hop routing (TLBO-MHR) technique is applied for optimum selection of routes to destinations. Furthermore, the TLBO-MHR method originates a suitability purpose using energy and distance metrics. The performance of the IMD-EACBR model has been examined in several aspects. Simulation outcomes demonstrated enhancements of the IMD-EACBR model over recent state-of-the-art approaches. IMD-EACBR is a model that has been proposed for the transmission of emergency data, and the TLBO-MHR technique is one that is based on the requirements for hop count and distance. In the end, the proposed network is subjected to rigorous testing using NS-3.26’s full simulation capabilities. The results of the simulation reveal improvements in performance in terms of the proportion of dead nodes, the lifetime of the network, the amount of energy consumed, the packet delivery ratio (PDR), and the latency.

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Optimal Stacked Sparse Autoencoder Based Traffic Flow Prediction in Intelligent Transportation Systems

Cited by 17 publications

References 20 publications

Deep Learning-Based Privacy-Preserving Data Transmission Scheme for Clustered IIoT Environment

Deep Learning-Based Privacy-Preserving Data Transmission Scheme for Clustered IIoT Environment

Improved Artificial Rabbits Optimization with Ensemble Learning-Based Traffic Flow Monitoring on Intelligent Transportation System

Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks

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