MMSE Beamforming Design for IoT MIMO SWIPT System

2018 ◽  
Vol 4 (1) ◽  
pp. 28
Author(s):  
Nguyen Duy Nhat Vien
Keyword(s):  
Internet Of Things ◽  
Human Services ◽  
Large Scale ◽  
Device Simulation ◽  
Mean Square ◽  
Wireless Devices ◽  
The Mean ◽  
Simulation Results ◽  
Iot Devices ◽  
Smart Infrastructure

Internet of Things (IoT) is a smart infrastructure of the unique identification device capable of wireless communication with each other, and human services on a large scale through the Internet. The IoT devices themselves must self-aware and harvest the energy they need from ambient sources. Simultaneous wireless information and power transfer (SWIPT) is a promising new solution to provide an opportunity for energy-restrained wireless devices to operate uninterruptedly. In this paper, we propose a beamforming approach for Internet of Things (IoT) multi-input multi-output (MIMO) SWIPT downlink systems, which minimizes the mean square error (MSE) of the information decode (ID) device while satisfying the energy constraint of the energy harvesting (EH) device. Simulation results are provided to evaluate the performance and confirm the efficiency of the proposed algorithm.

scholarly journals What Is the Impact of Additional Tropical Observations on a Modern Data Assimilation System?

2019 ◽  
Vol 147 (7) ◽  
pp. 2433-2449
Author(s):  
Laura C. Slivinski ◽  
Gilbert P. Compo ◽  
Jeffrey S. Whitaker ◽  
Prashant D. Sardeshmukh ◽  
Jih-Wang A. Wang ◽  
...  
Keyword(s):  
El Niño ◽  
Large Scale ◽  
El Nino ◽  
Mean Square ◽  
Observational Network ◽  
In Situ Observations ◽  
The Mean ◽  
The Impact ◽  
Assimilation System

Abstract Given the network of satellite and aircraft observations around the globe, do additional in situ observations impact analyses within a global forecast system? Despite the dense observational network at many levels in the tropical troposphere, assimilating additional sounding observations taken in the eastern tropical Pacific Ocean during the 2016 El Niño Rapid Response (ENRR) locally improves wind, temperature, and humidity 6-h forecasts using a modern assimilation system. Fields from a 50-km reanalysis that assimilates all available observations, including those taken during the ENRR, are compared with those from an otherwise-identical reanalysis that denies all ENRR observations. These observations reveal a bias in the 200-hPa divergence of the assimilating model during a strong El Niño. While the existing observational network partially corrects this bias, the ENRR observations provide a stronger mean correction in the analysis. Significant improvements in the mean-square fit of the first-guess fields to the assimilated ENRR observations demonstrate that they are valuable within the existing network. The effects of the ENRR observations are pronounced in levels of the troposphere that are sparsely observed, particularly 500–800 hPa. Assimilating ENRR observations has mixed effects on the mean-square difference with nearby non-ENRR observations. Using a similar system but with a higher-resolution forecast model yields comparable results to the lower-resolution system. These findings imply a limited improvement in large-scale forecast variability from additional in situ observations, but significant improvements in local 6-h forecasts.

scholarly journals Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World

2019 ◽  
Vol 11 (4) ◽  
pp. 100 ◽  
Author(s):  
Maurizio Capra ◽  
Riccardo Peloso ◽  
Guido Masera ◽  
Massimo Ruo Roch ◽  
Maurizio Martina
Keyword(s):  
Internet Of Things ◽  
Mobile Devices ◽  
Information Exchange ◽  
Large Scale ◽  
Operating Time ◽  
Edge Computing ◽  
The Internet ◽  
Power Load ◽  
Battery Capacity ◽  
Iot Devices

In today’s world, ruled by a great amount of data and mobile devices, cloud-based systems are spreading all over. Such phenomenon increases the number of connected devices, broadcast bandwidth, and information exchange. These fine-grained interconnected systems, which enable the Internet connectivity for an extremely large number of facilities (far beyond the current number of devices) go by the name of Internet of Things (IoT). In this scenario, mobile devices have an operating time which is proportional to the battery capacity, the number of operations performed per cycle and the amount of exchanged data. Since the transmission of data to a central cloud represents a very energy-hungry operation, new computational paradigms have been implemented. The computation is not completely performed in the cloud, distributing the power load among the nodes of the system, and data are compressed to reduce the transmitted power requirements. In the edge-computing paradigm, part of the computational power is moved toward data collection sources, and, only after a first elaboration, collected data are sent to the central cloud server. Indeed, the “edge” term refers to the extremities of systems represented by IoT devices. This survey paper presents the hardware architectures of typical IoT devices and sums up many of the low power techniques which make them appealing for a large scale of applications. An overview of the newest research topics is discussed, besides a final example of a complete functioning system, embedding all the introduced features.

Study about influence of doors’ opening degree on crowd evacuation based on simulation

2019 ◽  
Vol 30 (11) ◽  
pp. 1950096
Author(s):  
Yuanchun Ding ◽  
Falu Weng ◽  
Lizhong Yang
Keyword(s):  
Linear Relationship ◽  
Flow Rate ◽  
Saving Rate ◽  
Mean Square ◽  
Mean Flow ◽  
Time Saving ◽  
Evacuation Time ◽  
Crowd Evacuation ◽  
The Mean ◽  
Simulation Results

Based on simulation, the influence of the doors’ opening degree (DOD) on crowd evacuation is investigated in this paper. First of all, an evacuation model, which has one exit with two doors, is established by utilizing the software Pathfinder. Then, based on the obtained model, some evacuation scenarios are considered. The simulation results indicate, when the DOD is within 115∘–135∘, the time saving rate is more than 13%, and the maximum time saving rate is achieved when the DOD is 125∘. Furthermore, there is a linear relationship between the mean square error and the number of the evacuees. For a small number of evacuees, the total evacuation time is mainly influenced by the distributions of the evacuees, however, as the number of the evacuees increases, it is mainly influenced by the number of the evacuees. Moreover, when the DOD is 125∘, the mean flow rate per unit width (MFRPUW) decreases along with the increasing of exit’s width, however, it increases along with the increasing of exit’s width while the DOD is 180∘. Compared with the 180∘ DOD, the 125∘ DOD can always achieve a higher MFRPUW, and the narrower the exit is, the higher MFRPUW the 125∘ DOD achieves.

Parameters Selection and Application of LS-SVM Based on Chaotic Ant Swarm Algorithm

2011 ◽  
Vol 204-210 ◽  
pp. 423-426
Author(s):  
Chun Li Xie ◽  
Dan Dan Zhao ◽  
Juan Wang ◽  
Cheng Shao
Keyword(s):  
Support Vector Machines ◽  
Nonlinear Systems ◽  
Fitness Function ◽  
Selection Method ◽  
Support Vector ◽  
Mean Square ◽  
Parameters Selection ◽  
Vector Machines ◽  
The Mean ◽  
Simulation Results

Parameters selection plays an important role for the performance of least squares support vector machines (LS-SVM). In this paper, a novel parameters selection method for LS-SVM is presented based on chaotic ant swarm (CAS) algorithm. Using this method, the optimization model is established, within which the fitness function is the mean square error (MSE) index, and the constraints are the ranges of the designing parameters. The proposed method is used in the identification for inverse model of the nonlinear systems, and simulation results are given to show the efficiency.

scholarly journals Developing a real time navigation for the mobile robots at unknown environments

2020 ◽  
Vol 20 (1) ◽  
pp. 500
Author(s):  
Sarah Haider Abdulredah ◽  
Dheyaa Jasim Kadhim
Keyword(s):  
Mobile Robot ◽  
Mean Square ◽  
Matlab Simulation ◽  
Robot Operating System ◽  
Unknown Environments ◽  
Localization And Mapping ◽  
Software Platforms ◽  
The Mean ◽  
Simulation Results ◽  
Robot Platform

<p><span>This research deals with the feasibility of a mobile robot to navigate and discover its location at unknown environments, and then constructing maps of these navigated environments for future usage. In this work, we proposed a modified Extended Kalman Filter- Simultaneous Localization and Mapping (EKF-SLAM) technique which was implemented for different unknown environments containing a different number of landmarks. Then, the detectable landmarks will play an important role in controlling the overall navigation process and EKF-SLAM technique’s performance. MATLAB simulation results of the EKF-SLAM technique come with better performance as compared with an odometry approach performance in terms of measuring the mean square error, especially when increasing the number of landmarks. After that, we simulate and evaluate a mobile robot platform named TurtleBot2e in Gazebo simulator software to achieve the using of the SLAM technique for a different environment using the Rviz library which was built on Robot Operating System in Linux. The main conclusion comes with this work is the simulation and implementation of the SLAM technique using two software platforms separately (MATLAB and ROS) in different unknown environments containing a different number of landmarks so a few number of landmark will make the mobile robot loses its path.</span></p>

scholarly journals An Efficient Data Stream Analytics Model for Real Time Internet of Things (Iot) Applications

2020 ◽  
Vol 8 (6) ◽  
pp. 495-500
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Real Time ◽  
Private Information ◽  
Large Scale ◽  
Layer System ◽  
Collection Data ◽  
Efficient Data ◽  
Multiple Environments ◽  
Iot Devices

Internet of Things (IoT), data analytics is supporting multiple applications. These numerous applications try to gather data from different environments, here the gathered data may be homogeneous or heterogeneous, but most of the data collected from multiple environments were heterogeneous, the task of gathering, processing, storing and the analysis that is being performed on data are still challenging. Providing security to all these things is also a challenging task due to untrusted networks and big data. Big data management in the ever-expanding network may rise several non-trivial concerns on data collection, data-efficient processing, analytics, and security. However, the above said scenarios depends on large scale sensor deployed. Sensors continuously transmit data to clouds for real time use, which can raise the issue of privacy disclosure because IoT devices may gather data including a kind of sensitive private information. In this context, we propose a two-layer system or model for analyzing IoT data, collected from multiple applications. The first layer is mainly used for gathering data from multiple environments and acts as a service-oriented interface to ingest data. The second layer is responsible for storing and analyses data securely. The Proposed solutions are implemented by the use of open source components.

Blockchain-Enabled Decentralized Reliable Smart Industrial Internet of Things (BCIIoT)

2021 ◽  
pp. 192-204
Author(s):  
Chandramohan Dhasarathan ◽  
Shanmugam M. ◽  
Shailesh Pancham Khapre ◽  
Alok Kumar Shukla ◽  
Achyut Shankar
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Wireless Communication ◽  
Information Flow ◽  
Data Transfer ◽  
Base Station ◽  
Transmission Power ◽  
Wireless Devices ◽  
Industrial Internet ◽  
Iot Devices

The development of wireless communication in the information technological era, collecting data, and transfering it from unmanned systems or devices could be monitored by any application while it is online. Direct and aliveness of countless wireless devices in a cluster of the medium could legitimate unwanted users to interrupt easily in an information flow. It would lead to data loss and security breach. Many traditional algorithms are effectively contributed to the support of cryptography-based encryption to ensure the user's data security. IoT devices with limited transmission power constraints have to communicate with the base station, and the data collected from the zones would need optimal transmission power. There is a need for a machine learning-based algorithm or optimization algorithm to maximize data transfer in a secure and safe transmission.

scholarly journals IoTDS: A One-Class Classification Approach to Detect Botnets in Internet of Things Devices

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3188 ◽  
Author(s):  
Vitor Hugo Bezerra ◽  
Victor Guilherme Turrisi da Costa ◽  
Sylvio Barbon Junior ◽  
Rodrigo Sanches Miani ◽  
Bruno Bogaz Zarpelão
Keyword(s):  
Internet Of Things ◽  
Large Scale ◽  
Detection System ◽  
Predictive Performance ◽  
Support Vector ◽  
Local Outlier Factor ◽  
Training Activities ◽  
Iot Devices ◽  
One Class Classification ◽  
Local Outlier

Internet of Things (IoT) devices have become increasingly widespread. Despite their potential of improving multiple application domains, these devices have poor security, which can be explored by attackers to build large-scale botnets. In this work, we propose a host-based approach to detect botnets in IoT devices, named IoTDS (Internet of Things Detection System). It relies on one-class classifiers, which model only the legitimate device behaviour for further detection of deviations, avoiding the manual labelling process. The proposed solution is underpinned by a novel agent-manager architecture based on HTTPS, which prevents the IoT device from being overloaded by the training activities. To analyse the device’s behaviour, the approach extracts features from the device’s CPU utilisation and temperature, memory consumption, and number of running tasks, meaning that it does not make use of network traffic data. To test our approach, we used an experimental IoT setup containing a device compromised by bot malware. Multiple scenarios were made, including three different IoT device profiles and seven botnets. Four one-class algorithms (Elliptic Envelope, Isolation Forest, Local Outlier Factor, and One-class Support Vector Machine) were evaluated. The results show the proposed system has a good predictive performance for different botnets, achieving a mean F1-score of 94% for the best performing algorithm, the Local Outlier Factor. The system also presented a low impact on the device’s energy consumption, and CPU and memory utilisation.

scholarly journals Software Defined Networking Based Protection against DDOS in IoT

2020 ◽  
Vol 9 (5) ◽  
pp. 739-745
Keyword(s):  
Internet Of Things ◽  
Software Defined Networking ◽  
Data Packets ◽  
Ddos Attack ◽  
Neuro Fuzzy ◽  
Simulation Results ◽  
Iot Devices

Safety has become enormously important with the proliferation of internet of Things(IoT) technologies. Most of the IoT devices are linked with the DDoS attack, there are many risk nowadays for IoT because of DDoS attack.The new software-defined everything(SDx) model offers a way to handle IoT devices securely. The proposed S-IOT framework consists of a pool that includes S-IoT controllers, S-IoT switches and IoT devices. A new ENeFS algorithm is proposed to identify and reduce the DDoS attack. The proposed algorithm uses neuro fuzzy instruct rule to identify the DDoS attack and the number of data packets count also considered for the identification. The simulation results shows that the proposed algorithm performs better to improve the reliability of the IoT with different and unsafe gadgets.

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