On Layout Optimization of Wireless Sensor Network Using Meta-Heuristic Approach

One of the important research issues in wireless sensor networks(WSNs)is the optimal layout designing for the deployment of sensor nodes.It directly affects the quality of monitoring,cost,and detection capability of WSNs.Layout optimization is an NP-hard combinatorial problem,which requires optimization of multiple competing objectives like cost,coverage,connectivity,lifetime,load balancing,and energy consumption of sensor nodes.In the last decade,several meta-heuristic optimization techniques have been proposed to solve this problem,such as genetic algorithms(GA)and particle swarm optimization(PSO).However,these approaches either provided computationally expensive solutions or covered a limited number of objectives,which are combinations of area coverage,the number of sensor nodes,energy consumption,and lifetime.In this study,a meta-heuristic multi-objective firefly algorithm(MOFA)is presented to solve the layout optimization problem.Here,the main goal is to cover a number of objectives related to optimal layouts of homogeneous WSNs,which includes coverage,connectivity,lifetime,energy consumption and the number of sensor nodes.Simulation results showed that MOFA created optimal Pareto front of non-dominated solutions with better hyper-volumes and spread of solutions,in comparison to multi-objective genetic algorithms(IBEA,NSGA-II)and particle swarm optimizers(OMOPSO,SMOPSO).Therefore,MOFA can be used in real-time deployment applications of large-scale WSNs to enhance their detection capability and quality of monitoring.

Energy efficient indoor localisation for narrowband internet of things

There are an increasing number of Narrow Band IoT devices being manufactured as the technology behind them develops quickly.The high co‐channel interference and signal attenuation seen in edge Narrow Band IoT devices make it challenging to guarantee the service quality of these devices.To maximise the data rate fairness of Narrow Band IoT devices,a multi‐dimensional indoor localisation model is devised,consisting of transmission power,data scheduling,and time slot scheduling,based on a network model that employs non‐orthogonal multiple access via a relay.Based on this network model,the optimisation goal of Narrow Band IoT device data rate ratio fairness is first established by the authors,while taking into account the Narrow Band IoT network:The multidimensional indoor localisation optimisation model of equipment tends to minimize data rate,energy constraints and EH relay energy and data buffer constraints,data scheduling and time slot scheduling.As a result,each Narrow Band IoT device's data rate needs are met while the network's overall performance is optimised.We investigate the model's potential for convex optimisation and offer an algorithm for optimising the distribution of multiple resources using the KKT criterion.The current work primarily considers the NOMA Narrow Band IoT network under a single EH relay.However,the growth of Narrow Band IoT devices also leads to a rise in co‐channel interference,which impacts NOMA's performance enhancement.Through simulation,the proposed approach is successfully shown.These improvements have boosted the network's energy efficiency by 44.1%,data rate proportional fairness by 11.9%,and spectrum efficiency by 55.4%.

Hypo-Driver: A Multiview Driver Fatigue and Distraction Level Detection System

Traffic accidents are caused by driver fatigue or distraction in many cases.To prevent accidents,several low-cost hypovigilance(hypo-V)systems were developed in the past based on a multimodal-hybrid(physiological and behavioral)feature set.Similarly in this paper,real-time driver inattention and fatigue(Hypo-Driver)detection system is proposed through multi-view cameras and biosignal sensors to extract hybrid features.The considered features are derived from non-intrusive sensors that are related to the changes in driving behavior and visual facial expressions.To get enhanced visual facial features in uncontrolled environment,three cameras are deployed on multiview points(0◦,45◦,and 90◦)of the drivers.To develop a Hypo-Driver system,the physiological signals(electroencephalography(EEG),electrocardiography(ECG),electro-myography(sEMG),and electrooculography(EOG))and behavioral information(PERCLOS70-80-90%,mouth aspect ratio(MAR),eye aspect ratio(EAR),blinking frequency(BF),head-titled ratio(HT-R))are collected and pre-processed,then followed by feature selection and fusion techniques.The driver behaviors are classified into five stages such as normal,fatigue,visual inattention,cognitive inattention,and drowsy.This improved hypo-Driver system utilized trained behavioral features by a convolutional neural network(CNNs),recurrent neural network and long short-term memory(RNN-LSTM)model is used to extract physiological features.After fusion of these features,the Hypo-Driver system is classified hypo-V into five stages based on trained layers and dropout-layer in the deep-residual neural network(DRNN)model.To test the performance of a hypo-Driver system,data from 20 drivers are acquired.The results of Hypo-Driver compared to state-of-theart methods are presented.Compared to the state-of-the-art Hypo-V system,on average,the Hypo-Driver system achieved a detection accuracy(AC)of 96.5%.The obtained results indicate that the Hypo-Driver system based on multimodal and multiview features outperforms other state-of-the-art driver Hypo-V systems by handling many anomalies.

Deep Learning Enabled Disease Diagnosis for Secure Internet of Medical Things

In recent times,Internet of Medical Things(IoMT)gained much attention in medical services and healthcare management domain.Since healthcare sector generates massive volumes of data like personal details,historical medical data,hospitalization records,and discharging records,IoMT devices too evolved with potentials to handle such high quantities of data.Privacy and security of the data,gathered by IoMT gadgets,are major issues while transmitting or saving it in cloud.The advancements made in Artificial Intelligence(AI)and encryption techniques find a way to handle massive quantities of medical data and achieve security.In this view,the current study presents a new Optimal Privacy Preserving and Deep Learning(DL)-based Disease Diagnosis(OPPDL-DD)in IoMT environment.Initially,the proposed model enables IoMT devices to collect patient data which is then preprocessed to optimize quality.In order to decrease the computational difficulty during diagnosis,Radix Tree structure is employed.In addition,ElGamal public key cryptosystem with Rat Swarm Optimizer(EIG-RSO)is applied to encrypt the data.Upon the transmission of encrypted data to cloud,respective decryption process occurs and the actual data gets reconstructed.Finally,a hybridized methodology combining Gated Recurrent Unit(GRU)with Convolution Neural Network(CNN)is exploited as a classification model to diagnose the disease.Extensive sets of simulations were conducted to highlight the performance of the proposed model on benchmark dataset.The experimental outcomes ensure that the proposed model is superior to existing methods under different measures.

Implications of SSO Solutions on Cloud Applications

The trend in businesses is moving towards a single browser tool on portable devices to access cloud applications which would increase portability but at the same time would introduce security vulnerabilities. This resulted in the need for several layers of password authentications for cloud applications access. Single Sign-On (SSO) is a tool of access control of multiple software systems. This research explores the effects and implications of SSO solutions on cloud applications. We utilize a new framework of different attributes developed by acquiring IT experts’ opinions through extensive interviews to expand significant strategic parameters at the workplace. The framework was further tested using data collected from a sample of 400+ users in the UAE.