Human Signature Identification Using IoT Technology and Gait Recognition

Hnatiuc, Mihaela; Geman, Oana; Avram, Andrei; Gupta, Deepak; Shankar, K.

doi:10.3390/electronics10070852

Cited by 26 publications

(15 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An important problem in IoT is the secured broadcast from source-destination. At this point, it can be presented LB manner for minimized the energy utilization of combined circuit of IoT node and enhance the LB in IoT platform [7]. Kumar et al [8] establish a task processing structure which is the decision making ability for selecting the optimum resource at runtime for processing the application (different and difficult nature) at virtual machine (VM) utilizing modified particle swarm optimization (PSO) technique in end-user deadline.…”

Section: Introductionmentioning

confidence: 99%

Improved DHOA-Fuzzy Based Load Scheduling in IoT Cloud Environment

Raj¹,

Ilango²,

Thomas³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Internet of things (IoT) has been significantly raised owing to the development of broadband access network, machine learning (ML), big data analytics (BDA), cloud computing (CC), and so on. The development of IoT technologies has resulted in a massive quantity of data due to the existence of several people linking through distinct physical components, indicating the status of the CC environment. In the IoT, load scheduling is realistic technique in distinct data center to guarantee the network suitability by falling the computer hardware and software catastrophe and with right utilize of resource. The ideal load balancer improves many factors of Quality of Service (QoS) like resource performance, scalability, response time, error tolerance, and efficiency. The scholar is assumed as load scheduling a vital problem in IoT environment. There are many techniques accessible to load scheduling in IoT environments. With this motivation, this paper presents an improved deer hunting optimization algorithm with Type II fuzzy logic (IDHOA-T2F) model for load scheduling in IoT environment. The goal of the IDHOA-T2F is to diminish the energy utilization of integrated circuit of IoT node and enhance the load scheduling in IoT environments. The IDHOA technique is derived by integrating the concepts of Nelder Mead (NM) with the DHOA. The proposed model also synthesized the T2L based on fuzzy logic (FL) systems to counterbalance the load distribution. The proposed model finds useful to improve the efficiency of IoT system. For validating the enhanced load scheduling performance of the IDHOA-T2F technique, a series of simulations take place to highlight the improved performance. The experimental outcomes demonstrate the capable outcome of the IDHOA-T2F technique over the recent techniques.

show abstract

Section: Introductionmentioning

confidence: 99%

Improved DHOA-Fuzzy Based Load Scheduling in IoT Cloud Environment

Raj¹,

Ilango²,

Thomas³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…At first, network efficiency of 6G is set to upgrade to new high levels, for instance, high data rate (upto Tbps), low latency (sub-ms), 3D ubiquitous coverage (in sea, undersea, and space), high precision localization (up to cm-level), extreme level security, privacy, etc. Next, 6G application areas are set to be highly complex and multi-faceted in nature, resulting in conflicting needs [1].…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic Resource Allocation Strategy for Cluster Based 6G Industrial Applications

Hamida¹,

Mrabet²,

Belguith³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

The emergence of Beyond 5G (B5G) and 6G networks translated personal and industrial operations highly effective, reliable, and gainful by speeding up the growth of next generation Internet of Things (IoT). Industrial equipment in 6G encompasses a huge number of wireless sensors, responsible for collecting massive quantities of data. At the same time, 6G network can take real-world intelligent decisions and implement automated equipment operations. But the inclusion of different technologies into the system increased its energy consumption for which appropriate measures need to be taken. This has become mandatory for optimal resource allocation in 6G-enabled industrial applications. In this scenario, the current research paper introduces a new metaheuristic resource allocation strategy for clusterbased 6G industrial applications, named MRAS-CBIA technique. MRAS-CBIA technique aims at accomplishing energy efficiency and optimal resource allocation in 6G-enabled industrial applications. The proposed MRAS-CBIR technique involves three major processes. Firstly, Weighted Clustering Technique (WCT) is employed to elect the optimal Cluster Heads (CHs) or coordinating agents with the help of three parameters namely, residual energy, distance, and node degree. Secondly, Decision Tree-based Location Prediction (DTLP) mechanism is applied to determine the exact location of Management Agent (MA). Finally, Fuzzy C-means with Tunicate Swarm Algorithm (FCM-TSA) is used for optimal resource allocation in 6G industrial applications. The performance of the proposed MRAS-CBIA technique was validated and the results were examined under different dimensions. The resultant experimental values highlighted the superior performance of MRAS-CBIR technique over existing state-of-the-art methods.

show abstract

“…Zhao et al extracted the information of human motion shape by Canny edge detection to represent the information of motion edge and then achieved the purpose of human motion recognition by matching similar edges [ 6 ]. Hnatiuc et al changed the traditional separate training and combined attitude estimation and action recognition in order and put forward a framework for combining attitude estimation and action recognition [ 7 ]. Luo et al reconstruct the 3D structure of human beings to generate views and 3D models that can be generated in any 2D by projection, but they usually need to fully control and cooperate in multiple camera environments [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Recognition and Analysis of Gait Contour of Dance Movements Based on Generative Adversarial Networks

Ren

Park

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

With the generation of images, videos, and other data, how to identify the gait of the action in the video has gradually become the focus of research. Aiming at the problems of complex and changeable movements, strong coherence, and serious occlusion in dance video images, this paper proposes a dynamic recognition model of gait contour of dance movements based on GAN (generative adversarial networks). GAN method is used to convert the gait diagrams in any state into a group of gait diagrams in normal state with multiple angles, which are arranged in turn. In order to retain as much original feature information as possible, multiple loss strategy is adopted to optimize the network, increase the distance between classes, and reduce the distance within classes. Experimental results show that the average recognition rates of this model at 50°, 90°, and 120°are 93.24, 98.24, and 97.93, respectively, which shows that the recognition accuracy of dance movement recognition method is high. And this method can effectively improve the dynamic recognition of gait contour of dance movements.

show abstract

Human Signature Identification Using IoT Technology and Gait Recognition

Cited by 26 publications

References 27 publications

Improved DHOA-Fuzzy Based Load Scheduling in IoT Cloud Environment

Improved DHOA-Fuzzy Based Load Scheduling in IoT Cloud Environment

Metaheuristic Resource Allocation Strategy for Cluster Based 6G Industrial Applications

Dynamic Recognition and Analysis of Gait Contour of Dance Movements Based on Generative Adversarial Networks

Contact Info

Product

Resources

About