IoT-based traffic prediction and traffic signal control system for smart city

Neelakandan, S.; Berlin, M.A.; Tripathi, Sachin; Devi, V. Brindha; Bhardwaj, Indu; Arulkumar, N.

doi:10.1007/s00500-021-05896-x

Cited by 96 publications

(32 citation statements)

References 21 publications

(16 reference statements)

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“…ELM is flexible because it employs a hidden activation function, as demonstrated by the universal approximate ability theorem. Almost any nonlinear piecewise continuous function and its linear combination perform well in the ELM algorithm [ 22 ]. The extreme learning machine (ELM) is a fast convergent training method for single hidden layer feedforward neural networks (SLFNs).…”

Section: The Proposed Mr-lsdgm Techniquementioning

confidence: 99%

LSGDM with Biogeography-Based Optimization (BBO) Model for Healthcare Applications

Harshavardhan

Boyapati²,

Neelakandan

et al. 2022

Journal of Healthcare Engineering

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Several studies aimed at improving healthcare management have shown that the importance of healthcare has grown in recent years. In the healthcare industry, effective decision-making requires multicriteria group decision-making. Simultaneously, big data analytics could be used to help with disease detection and healthcare delivery. Only a few previous studies on large-scale group decision-making (LSDGM) in the big data-driven healthcare Industry 4.0 have focused on this topic. The goal of this work is to improve healthcare management decision-making by developing a new MapReduce-based LSDGM model (MR-LSDGM) for the healthcare Industry 4.0 context. Clustering decision-makers (DM), modelling DM preferences, and classification are the three stages of the MR-LSDGM technique. Furthermore, the DMs are subdivided using a novel biogeography-based optimization (BBO) technique combined with fuzzy C-means (FCM). The subgroup preferences are then modelled using the two-tuple fuzzy linguistic representation (2TFLR) technique. The final classification method also includes a feature extractor based on long short-term memory (LSTM) and a classifier based on an ideal extreme learning machine (ELM). MapReduce is a data management platform used to handle massive amounts of data. A thorough set of experimental analyses is carried out, and the results are analysed using a variety of metrics.

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Section: The Proposed Mr-lsdgm Techniquementioning

confidence: 99%

LSGDM with Biogeography-Based Optimization (BBO) Model for Healthcare Applications

Harshavardhan

Boyapati²,

Neelakandan

et al. 2022

Journal of Healthcare Engineering

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Section: The Proposed Modelmentioning

confidence: 99%

“…For each of the independent variables [ 18 ], the variables L pl and L p 2 are calculated using the Lévy stable distribution. There should only be one set of indexes r selected from the pathfinder solution, in which L pl and L p 2 represent independent variable calculated from the Lévy α ‐stable distribution [ 18 ]. The set of indexes r should be only chosen from the pathfinder solution, and position is upgraded by the mutated variable extracted from the sub‐trail vector as follows:

…”

Section: The Proposed Modelmentioning

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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“…Provided a VM which is to be allocated, the EAVMP-CSSA technique selects a PM which offers the capitals (CPU, memory, and system bandwidth) required by the VMs [ 7 , 30 – 32 ]. The EAVMP-CSSA technique chooses the PM with low-resource wastage after allocating to the present VM.…”

Section: Design Of Eavmp-cssa Techniquementioning

confidence: 99%

“…The VMP method attempts to discover optimum allocations of VM over PM to attain their objectives of the design [ 7 ]. Several design objectives are considered in the survey, for example, reducing SLA violation, improving the power utilization, optimizing resource consumption, and so on.…”

Section: Introductionmentioning

confidence: 99%

Chaotic Salp Swarm Optimization-Based Energy-Aware VMP Technique for Cloud Data Centers

Parthiban

Harshavardhan

Neelakandan

et al. 2022

Computational Intelligence and Neuroscience

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The amount of energy required by Cloud Data Centers (CDCs) has increased significantly in this digital age, and as a result, there is a pressing need to reduce CDC energy ingesting. Consolidation of virtual machines (VMs) and effective virtual machine placement (VMP) techniques are commonly employed in large data middles to reduce energy consumption. The VMP is an NP-hard subject with infeasible optimum explanations even for tiny data middles, and it is dealt with using the Metaheuristic Optimization Algorithm, which is an experiential approach to optimization. With this in mind, this study introduces a novel energy-aware VMP technique for CDCs that is founded on the Disordered Salp Swarm Optimization Algorithm (EAVMP-CSSA) and is enhanced for energy efficiency (EAVMP-CSSA). The EAVMP-CSSA technique attempts to reduce CDC energy ingesting by dropping the quantity of active servers supporting virtual machines. The recommended EAVMP-CSSA strategy also aims to balance the resource operation of active servers (i.e., CPU, RAM, and Bandwidth), hence reducing waste and increasing efficiency. Furthermore, by combining the ideas of chaotic maps with the standard Salp Swarm Optimization Algorithm (SSA), the CSSA is intended to improve overall performance and reduce computational costs (SSA). A comprehensive range of experimental analyses are performed to ensure that the EAVMP-CSSA technique performs better, and the findings are compared to current VMP techniques. The EAVMP-CSSA approach achieves an effective outcome with a maximum service rate of 98.12%, whereas the Random, FFD, ACO, and AP-ACO procedures achieve a minimum service rate of 74.40%, 78.80%, 90.70%, and 96.31%, respectively. The experimental results demonstrate that the EAVMP-CSSA approach outperforms other assessment metrics.

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IoT-based traffic prediction and traffic signal control system for smart city

Cited by 96 publications

References 21 publications

LSGDM with Biogeography-Based Optimization (BBO) Model for Healthcare Applications

LSGDM with Biogeography-Based Optimization (BBO) Model for Healthcare Applications

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

Chaotic Salp Swarm Optimization-Based Energy-Aware VMP Technique for Cloud Data Centers

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