Introduction: The Internet of Things (IoT) and Smart Grids (SGs) growth results from the advancement of computer hardware and ubiquitous computation for energy-efficient and reliable data gathering.
...Background: The limitations of the present cloud computing framework in energy systems remain unresolved, such as fully recognizing the requisites of high data usage with low latency which is discussed for a cloud computing scheme in IoT-based smart power stations.
Problem Statement: IoT growth results from the advancement of computer hardware and ubiquitous computation. Among the various IoT solutions, SGs stand out because they combine several embedded smart techniques to increase the security and dependability of electricity grids.
Methodology: A Particle Swarm Optimization based energy efficient integration of Smart Grid (PSO-SG) is designed using IoT. Modern technology establishes a novel hardware and software architecture and integrates cloud services into the cloud-based electricity network.
Findings: As a result, a sizable amount of data produced by the electricity network will be examined, handled, and saved at the network. IoT-based power systems will enable the interconnection and administration of large endpoint devices, offer real-time evaluation and treatment of vast data, and promote the modernization of the power grid with the help of the cloud computing model.
In infrastructure-less ad-hoc networks, the nodes can converse directly with other nodes without centralized servers. The vehicular networks are derived from the features of mobile ad-hoc networks ...(MANETs) whereas the message transmissions between the nodes are direct with each other or through some fixed terminals. But, a unique trademark is that the vehicle-to-vehicle ad-hoc links are poor because of extreme mobility which leads to frequent route breakage that causes emergency message delivery failure. The routing protocols availed in MANETS does not suit due to quick topology changes. The hop selection is based on the best cumulative communication duration that will be failing in some cases. Also, the worst/average case scenario is that the minimum connectivity to the source vehicle is not described in existing approaches. The core idea about the proposed research is to deliver data through an optimum path and to eliminate rupture link during the packet transmission. The system uses the Heterogeneous Motion Guess (HMG) algorithm for vehicular networks to set up a vested path. The HMG will select the relay vehicle's current position is near to the target vehicle plus the vehicle will opt the relay forwarder that has good radio coverage. The link lifetime of the selected hop should be greater than the minimum connectivity duration with the source vehicle. The selected vehicle will append the link expiry time and minimum connectivity duration in Route Demand Dispatch message in a unicast manner. This approach significantly minimizes the hop count numbers and improves the stability of the path. It radically predicts the link life, so the node can decide an alternative vested route. The simulation result shows how well the reactive demand-based HMG affords optimum results in terms of packet transmission success rate, average minimum hop counts, and communication delay.
Since the end of 2019, the world has faced severe issues over Corona Virus Disease of 2019 (COVID-19). So there is a need for some essential precautionary measures until the development of vaccines ...to battle the COVID-19 pandemic. In addition to that, quarantine and social distancing have become the more significant practice in the world. COVID-19 associated with the virus not only degraded the economy of the world due to the lockdown but also saturated the healthcare system of the people due to its exponential spread. In this case, the Internet of Things (IoT) system offers frequent monitoring facilities to doctors. But, the fight against COVID-19 gets continued until people get vaccinated. Therefore, an IoT-based COVID prediction is designed using deep learning techniques. Firstly, IoT data is collected from online resources. Then, the data is fed to the autoencoder (AE) for attaining the deep features. Further, the deep features are forwarded to the attentive and adaptive ensemble model (AAEM), which includes deep temporal convolution network (DTCN), one-dimensional convolutional neural network (1DCNN), and long short-term memory (LSTM) model for COVID prediction or monitoring. By utilizing the hybrid algorithm fitness position of Eurasian oystercatcher and sewing training (FPEOST), the parameter in the ensemble model is tuned for further improvement in the process. Finally, the COVID-19 disease prediction outcomes are attained on the basis of the high-ranking process. Thus, the developed model achieved an effective prediction rate than conventional approaches over multiple experimental analyses.
In this work, the Peregrine falcon's airfoil is derived and is analysed numerically and experimentally in a non-flapping, non-morphing case. The airfoil is analysed numerically using a Steady State ...RANS k-ω SST model at different Reynolds numbers with ANSYS Fluent solver and is compared with NACA 23015 and NACA 64–210. The numerical method of approach is validated at a high Reynolds number of 1x106 experimentally. The results dictate that FALCON airfoil has a cl, max that is 54 % and 118 % higher than NACA 23015 and NACA 64–210 respectively. Stalling at 12oangle of attack, the airfoil generates an (LD) of 84, and produces positive lift coefficients till -5o angle of attack. The cl of FALCON is higher than the NACA 64–210 and 23015 at all angles of attack. The variation of (LD) with AoA is smooth and of a parabolic nature than an abrupt one.
COVID-19 pandemic scenario makes extensive use of remote temperature sensors, using IoT devices and communications. Earlier, flexible organic-inorganic luminous inks were used to design and print ...univocal smart labels based on QRCs (quick response codes) on medical substrates (protective masks and adhesive). Through simultaneous combination of luminescence thermometry and PUFs (physical unclonable functions), temperature measurements in non-contacts, identifications, and connections with IoTs. PUFs are extremely helpful for hardware security, but are open to attacks from MLTs (machine learning techniques) that mimic the behaviours of CRPs (controvert-response pairs). The study suggests ARPUFs architecture that implements two-round controverts to randomise the mapping of CRPs in order to satisfy the unpredictability requirements for resistances, intriguing examples on the usages of organic/inorganic hybrids luminous inks changed by lanthanide ions for creating smart labels with the ability of sensing temperatures including maximal thermal sensitivities up to 1.46%K and uncertainties of 0.2 K, along with authentications like methodology accuracies (98.4%), precisions (99.6%), and recalls (87.7%). Proposed methodology of univocal identifications and monitoring of mobile optical temperatures in individuals, enable control of accesses to restricted areas and medical information transfers for post-medical evaluation results in opening the door to new generations of mobile-assisted eHealths (mHealth-mobile-assisted eHealths).
Vehicular Ad Hoc Networks (VANETs), which have the strategic goal of ensuring service delivery on roads and smart cities, are made up of a number of critical components, including the integration and ...communication between vehicles, sensors, and stationary road-side components. VANETs have many distinctive characteristics, including quick movable nodes, self-organization, scattered networks, and often changing topologies. Since attack prevention is still a contentious topic, security, data integrity, and user private information continue to be major concerns despite the recent development of VANETs. In particular, detecting Sybil attacks, in which one node attempts to appear as many, seemingly conflicts with the goal of privacy preservation, and existing schemes fail on either one or both accounts. In order to prevent Sybil attacks, VANETs assert that they have security measures in place. A new framework for VANET security and sybil attack detection was developed in this study. It groups the cars together using Kernel k-harmonic means (KKHM). Kernel functions are here included into the k-means problem's cost function via kernelizing in the feature space. Cluster centres are believed to exist in a higher level Hilbert space called the feature space. Using the Floyd-Warshall algorithm (FWA), cluster heads are chosen. The Deep Neural Network (DNN) is used to identify the malicious CH by extracting the necessary information from the CH. For the purpose of optimizing DNN parameters, a new technique known as gradient-based elephant herding optimization (GBEHO) is presented. To initialize the population in the search space, the gradient-based method GBO is paired with the elephant optimization algorithm (EHO). Second, by including Gaussian chaos mapping, the initialized population is enhanced in order to correct the disparity between the first exploration and utilization of the EHO. Additionally, two operators are programmed to modify the agents' position update strategy: the random wandering and variation operators. Last but not least, the Modified Advanced Encryption Standard (MAES) is used to send the data in the CH safely to the cloud. According on experimental findings, the suggested model increases the security rate of 96% and reduces the encryption time of 19(s) for 100 (kb) data which is better when compared to other existing models.
Nowadays the road transportation system is enlarged much due to the population and fast-moving world. Due to human fault, vehicle accidents are quite unavoidable. The primary cause for accidents is ...the desecration of traffic rules such as over speed, unplanned overtake, etc. To increase the safety of vehicle rider the autonomous cars are coming into the picture. The autonomous vehicles can decide on their own for safe self-driving. But, the autonomous vehicles are still on the bench because lots of realistic considerations and predictions are needed to make the system more intelligence. The vehicular ad-hoc network acts an important role in autonomous vehicle building by considering various facts. The motive of the research is to give assistance message to the drivers for safe overtaking. In the literature, safety assistance is provided through the Road Side Control Units (RSCU). But the construction cost and maintenance for the RSCU is too high. Our methodology will give a sustainable solution for safety assistance when the vehicle tries to overtake. The proposed methodology uses Cooperative Overtaking Sight Assistance (COSA) which predicts the comfort zone and danger zone boundaries for safe overtaking. The methodology greatly reduces infrastructure dependency. The experimental evaluations are carried out in SUMO and NS2.
In many applications, surveillance, robots, and autonomous driving, human detection is an essential role. Deep learning techniques have made significant strides in recent years when it comes to ...quickly and accurately detecting humans. This research proposes a deep learning-based investigation into human detection. This study specifically concentrates on how well YOLO, SSD, and Faster R-CNN perform among other cutting-edge deep learning models for human detection. Using benchmark datasets, these models are assessed and their advantages and disadvantages are considered. Furthermore, by adding contextual information into the detection process, this study suggests a unique method for enhancing human detection accuracy in low-light situations. The proposed tests demonstrate that the proposed strategy outperforms other existing approaches. Aerial picture detection, multi-person detection, and real-time detection are some of the issues and future directions are discussed in the final section. The research findings show how deep learning may be used to perform human detection and offer guidance for further study in this field.
In this technological era automation is playing a vital role in making things faster. Face Recognition is one of the examples of automation. Facial Recognition is categorized as a biometric method ...alongside fingerprint scan and iris scan. Back in the days facial recognition is only limited for safety and security purposes but nowadays it is widely used in various industries like Banking, Healthcare, and many more. One such industry is the educational industry. Various universities around the globe have already implemented biometrics through facial recognition not only for the staff but also for recording the student's attendance. Currently available Facial Recognition systems are able only able to track one person at an instance. The constructed system must track the individual attendance while he/she being recorded in a live video or while they were in a group photo using machine learning techniques. The system must analyze each individual in a group using the image provided and mark the individual's basic information like name, class, section, etc. In general, the system is trained with each individual's 180-degree facial view with the help of convolutional neural networks. And to improve accuracy the system must be trained using deep learning and deploying with a huge and distinct dataset. For achieving this we have conducted a study comparing five well-established algorithms to know which algorithm is reliable
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