On the Recovery of Terrestrial Wireless Network using Cognitive UAVs in the Disaster Area

Abstract

Natural disasters such as earthquakes, floods and fires may cause the existing wireless network infrastructure to collapse, leaving behind several disconnected network parts. UAVs could help to establish communication between these disconnected parts using their ability to hover and fly across the affected region. However, UAV deployment faces several problems, including how many UAVs would be sufficient and where they could be placed. Such problems can be addressed centrally in a situation with verified information about the segmented network, such as the number of disconnected parts, the number of nodes in each part and the location of each node. However, a damaged network with unknown information (which is mostly the case) requires a distributed networking establishment mechanism. Therefore, this paper proposes an algorithm to restore connectivity among the disconnected parts of the damaged network. Cognitive radiobased UAVs (CR-UAVs) fly into the affected area and try to connect the various parts of the damaged network using the proposed algorithm. The main objective of the proposed algorithm is to connect the different disconnected parts of the broken network with the fewest possible UAVs in the least possible time. The results of the MATLAB simulation illustrate the significance of the proposed algorithm in terms of the number of UAVs used and the total distance they fly.

Retracted: Intrusion detection in cloud environment using hybrid genetic algorithm and back propagation neural network

“…It has the network data with and without attacks, which was approximately the real data of the network. [22][23][24][25][26][27][28][29][30][31][32][33] This dataset was uneven, hence this dataset with the duplicating method as it basically affects the training of the proposed model, and then the testing was performed. This research was experimented using Keras on the Tensorflow package on 64-bit Intel Core-i5 processor with 8 GB RAM in Windows 8 system.…”

Social Public Opinion Communication and Network Legal Management Based on Artificial Intelligence Cognitive Wireless Network

“…Cognitive wireless networks are effective techniques proposed for traditional static spectrum allocation strategies. Cognitive networks have autonomous and intelligent cognitive functions to obtain useful information in the historical and current environment to achieve an effective role for future mechanisms [16]. With the continuous development of wireless communication technology, various new regionalized communication network models such as femtocell networks and wireless access points applied to indoor environments have gradually emerged, but the diversity and disorder of wireless service types and methods make the network not well suited to meet users' requirements for quality of service.…”

“…e whole process is carried out without human intervention and eventually forms intelligent decisions to achieve the goals of wireless resource management, heterogeneous networks, security, and quality of service (QoS) [16]. e key to the intelligent cognitive process is the cognitive feedback loop, which adjusts the next step by understanding the impact of the current behavior on the network.…”

Crisis Communication with Cognitive Radio Networks

“…In [4], the authors talk about ANN and CRAHN that can be used to provide precise results and help in pattern recognition during disasters. Paper [5] introduces the use of UAVs for disaster response as they have advantage of covering remote and inaccessible locations. Paper [6] The other approaches are not cost effective as use a transmission medium for signal/data transfer.…”