Optimal Deep Learning Enabled Communication System for Unmanned Aerial Vehicles

Recently,unmanned aerial vehicles(UAV)or drones are widely employed for several application areas such as surveillance,disaster management,etc.Since UAVs are limited to energy,efficient coordination between them becomes essential to optimally utilize the resources and effective communication among them and base station(BS).Therefore,clustering can be employed as an effective way of accomplishing smart communication systems among multiple UAVs.In this aspect,this paper presents a group teaching optimization algorithm with deep learning enabled smart communication system(GTOADL-SCS)technique for UAV networks.The proposed GTOADL-SCS model encompasses a two stage process namely clustering and classification.At the initial stage,the GTOADL-SCS model includes a GTOA based clustering scheme to elect cluster heads(CHs)and organize clusters.Besides,the GTOADL-SCS model develops a fitness function containing three input parameters as residual energy of UAVs,average neighoring distance,and UAV degree.For classification process,the GTOADLSCS model applies pre-trained densely connected network(DenseNet201)feature extractor with gated recurrent unit(GRU)classifier.For ensuring the enhanced performance of the GTOADL-SCS model,a widespread simulation analysis is performed and the comparative study reported the significant outcomes over the existing approaches with maximum packet delivery ratio(PDR)of 92.60%.

Internet of Things (IoT)

the world is experiencing a strong rush towards modern technology, while specialized companies are living a terrible rush in the information technology towards the so-called Internet of things IoT or Internet of objects, which is the integration of things with the world of Internet, by adding hardware or/and software to be smart and so be able to communicate with each other and participate effectively in all aspects of daily life, so enabling new forms of communication between people and things, and between things themselves, that’s will change the traditional life into a high style of living. But it won’t be easy, because there are still many challenges and issues that need to be addressed and have to be viewed from various aspects to realize their full potential. The main objective of this review paper will provide the reader with a detailed discussion from a technological and social perspective. The various IoT challenges and issues, definition and architecture were discussed. Furthermore, a description of several sensors and actuators and their smart communication. Also, the most important application areas of IoT were presented. This work will help readers and researchers understand the IoT and its potential application in the real world.

Fully Distributed State Estimation for Power System with Information Propagation Algorithm

In this paper,a new fully distributed state estimation(DSE)based on weighted least square(WLS)method and graph theory is proposed for power system.The proposed method is fully distributed so that the centralized facilities,e.g.,supervisory control and data acquisition(SCADA)and centralized estimators,are not required.Also,different from the existing DSE methods,the proposed method is a bus-level DSE method,in which the power system is not required to be partitioned into several areas.In order to realize the proposed fully distributed DSE method,a novel information propagation algorithm is developed in this paper.This algorithm has great potential in future applications since it is useful to broadcast the local information of the nodes to the entire system in a fully distributed network.The proposed DSE method is compared with the conventional centralized state estimation method and existing multi-area DSE method in different models in this paper.The results show that the proposed method has better performance than the traditional methods.