Improved resource allocation scheme for optimizing the performance of cell edge users in Long Term Evolution-Advanced (LTE-A) system is proposed in this paper. The proposed algorithm optimally assigns and allocates Carrier Components (CCs), Radio Blocks (RBs) and Modulation and Coding Scheme (MCS) index for the users based on the Quality of Service (QoS) provision to meet the on demand service requests. The proposed algorithm divides the cell regions into cell centre and cell edge depending on the traffic load on the network. The available RBs are allocated to meet the on demands requests of the users among these two regions. RB usage ratio for optimizing the performance of the scheduling algorithm is used during resource allocation. As the RB usage ratio reaches more than 70%, throughput and data rates of the cell may not be reached in timely and reliable manner. The proposed algorithm takes RB usage ratio into consideration during scheduling to maintain minimum QoS provisions of the users for resource allocation. Simulation result shows that, performance of the cell edge users optimally met with minimum throughput and data rates for reliable communication in the LTE-A system.

Improved resource allocation scheme for optimizing the performance of cell-edge users in LTE-A system

Several problems faced by the visually impaired people were addressed over the past 3 decades. It includes transportation, text to voice conversion, alarm, and usage of the internet. There exist still several areas, where support and help for the visually impaired people are dependent on others. Among them is accessing the daily essential needs is of prime concern. Design and implementation of the visual system are proposed in this paper to help and support visually impaired people using Artificial Neural Networks (ANNs). Here deep learning technique is used for the identification of the objects and the distance of the object is measured using an ultrasonic sensor. The proposed methodology suits better for the conversion of the visual scenarios into voice messages along with the distinct location of the objects. The accuracy of the proposed visual model depends on the data sets used in the ANN algorithm. As the depth of the training data set increases, the performance of the prototype also increases with reduced processing delay in identifying the objects. OpenCV platform is used along with the python programming language to navigate through the surrounding.

Design and Implementation of Vision Module for Visually Impaired People

The rapid growth of technology in the modern society has raised many questions on the terms like security and privacy. Due to the evolution in the technology and industrialization the terms like security and privacy has become imperative for a common person. Authentication is a key factor which helps for the identification of authorized people and helps in eradicating fraudulent activities, robberies, and many other social crimes. Most of the crimes are due to the vulnerabilities in the door locking systems which can be easily accessible by the outsiders. Though there are solutions like smart doorbells and video streaming, which have limitations like heavy cost, complex and have loopholes in the security issues. To diminish the limitations and to enhance the security Smart door unlock systems using face recognition is proposed. The proposed system consists of a camera sensor popularly known as esp32-cam for storing the pictures of persons and for live streaming. The proposed system recognizes the face of the person standing in front of the door with the help AI-Thinker in the esp32-cam. The face of the person is compared with the faces of the authorized persons which are stored in the SD card of esp32-cam. If the person is an authorized person then the door gets unlocked which can be achieved with the hardware component solenoid lock. If the person is an unauthorized person then the door will be locked. The proposed system helps in adapting from traditional mechanical lock methods to enhanced security methods. It also helps in case of losing keys and helpful for disabled persons with easier access.

Intelligent Secure Smart Locking System using Face Biometrics

Optimal resource scheduling algorithm for cell boundaries users in heterogenous 5G networks

Globally, rapid industrialization and urbanization have resulted in a reduction in agricultural acreage and production. As a result of this, and the growing desire for chemical-free organic veggies among educated urban families, greenhouses are rapidly gaining popularity for their specific benefits, particularly in regions with severe weather. They provide the perfect conditions for longer and more productive growth seasons, as well as lucrative harvests. The current article proposes and shows a full IoT -based Smart Greenhouse system that combines monitoring, alerting, cloud storage, automation, and disease prediction into a single, easily deployed package. It constantly monitors environmental variables like as temperature, humidity, and soil moisture to guarantee a better crop production and quick correction in the event of aberrant circumstances. A built-in automated irrigation management system is also included. Finally, for disease detection using leaf pictures, it uses the most efficient deep learning model. Furthermore, using cloud storage to optimise memory and storage, a city dweller may construct a greenhouse and watch it from his home, allowing him to take corrective action as needed.

IoT and Deep Learning based Smart Greenhouse Disease Prediction

The hugely increasing demand for mobile broadband access to internet based services will soon challenge even the Universal Mobile Telecommunication Systems (UMTS), Long Term Evolution (LTE) cellular technology and its future enhancements. The Quality of Service (QoS) provisioning is an important issue in the mobility management of wireless femtocell networks of LTE technology. According to the scheduler allocates resources, required QoS of the user with use of Resource Blocks (RBs) based on the received channel condition feedback form the User Equipments (UEs) in the form of Channel Quality Indicator (CQI). There are several scheduling algorithms are existing, but fails to meet the QoS requirement simultaneously along with scheduling disciplines for communications for public safety users that are demanded to intervene in case of special events or in post disaster area where extremely high peaks of internet capacity required. The existing study on LTE Advanced (LTE-A) scheduling is mostly limited to unidirectional method of resource allocation in Uplink Link (UL) or Down Link (DL), separately. But, there are some works on the bidirectional UL-DL scheduling in other wireless technologies. There is scope of research in the upcoming years in UL-DL joint scheduling in LTE-A system. This paper deals with Study and understanding of the existing LTE system, mainly about the different resouce allocation methods to employed and proposes a new novel algorithm for both UL and DL scheduling of resources. The proposed work aims to bring the fairness among the end users with reduced delay.

S Hivashankar

Papers

Study of different resource allocation scheduling policy in advanced LTE with carrier aggregation