For the past few years, the IoT (Internet of Things)-based restricted WSN (Wireless sensor network) has sparked a lot of attention and progress in order to attain improved resource utilisation as well as service delivery. For data transfer between heterogeneous devices, IoT requires a stronger communication network and an ideally placed energy-efficient WSN. This study uses deep learning architectures to provide a unique resource allocation method for wireless sensor IoT networks with energy efficiency as well as data optimization. EE (Energy efficiency) and SE (spectral efficiency) are two competing optimization goals in this case. The network’s energy efficiency has been improved because of a deep neural network based on whale optimization. The heuristic-based multi-objective firefly algorithm was used to optimise the data. This proposed method is applied to optimal power allocation and relay selection. The study is for a cooperative multi-hop network topology. The best resource allocation is achieved by reducing overall transmit power, and the best relay selection is accomplished by meeting Quality of Service (QoS) standards. As a result, an energy-efficient protocol has been created. The simulation results demonstrate the suggested model’s competitive performance when compared to traditional models in terms of throughput of 96%, energy efficiency of 95%, QoS of 75%, spectrum efficiency of 85%, and network lifetime of 91 percent.

Purpose: The purpose of this study is to propose an alternative efficient 3D emotion recognition model for variable-length electroencephalogram (EEG) data.Design/methodology/approach: Classical AMIGOS data set which comprises of multimodal records of varying lengths on mood, personality and other physiological aspects on emotional response is used for empirical assessment of the proposed overlapping sliding window (OSW) modelling framework. Two features are extracted using Fourier and Wavelet transforms: normalised band power (NBP) and normalised wavelet energy (NWE), respectively. The arousal, valence and dominance (AVD) emotions are predicted using one-dimension (1D) and two-dimensional (2D) convolution neural network (CNN) for both single and combined features.Findings: The two-dimensional convolution neural network (2D CNN) outcomes on EEG signals of AMIGOS data set are observed to yield the highest accuracy, that is 96.63%, 95.87% and 96.30% for AVD, respectively, which is evidenced to be at least 6% higher as compared to the other available competitive approaches.Originality/value: The present work is focussed on the less explored, complex AMIGOS (2018) data set which is imbalanced and of variable length. EEG emotion recognition-based work is widely available on simpler data sets. The following are the challenges of the AMIGOS data set addressed in the present work: handling of tensor form data; proposing an efficient method for generating sufficient equal-length samples corresponding to imbalanced and variable-length data.; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.

COVID-19,stress,coping,public health,machine learning