scholarly journals Efficient Deployment with Throughput Maximization for UAVs Communication Networks

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6680
Author(s):  
Mohd Abuzar Sayeed ◽  
Rajesh Kumar ◽  
Vishal Sharma ◽  
Mohd Asim Sayeed

The article presents a throughput maximization approach for UAV assisted ground networks. Throughput maximization involves minimizing delay and packet loss through UAV trajectory optimization, reinforcing the congested nodes and transmission channels. The aggressive reinforcement policy is achieved by characterizing nodes, links, and overall topology through delay, loss, throughput, and distance. A position-aware graph neural network (GNN) is used for characterization, prediction, and dynamic UAV trajectory enhancement. To establish correctness, the proposed approach is validated against optimized link state routing (OLSR) driven UAV assisted ground networks. The proposed approach considerably outperforms the classical approach by demonstrating significant gains in throughput and packet delivery ratio with notable decrements in delay and packet loss. The performance analysis of the proposed approach against software-defined UAVs (U-S) and UAVs as base stations (U-B) verifies the consistency and gains in average throughput while minimizing delay and packet loss. The scalability test of the proposed approach is performed by varying data rates and the number of UAVs.

2020 ◽  
Vol 17 (9) ◽  
pp. 4683-4687
Author(s):  
Yogesh Chaba ◽  
Mridul Chaba

Now days wireless networks have become popular as the mobile applications are increasing day by day and mobility of nodes has become an important feature. The desirable property which separates mobile network from wireless networks is the mobility of communication devices. Therefore, there is a need to design routing mechanism in such a way that they can easily adopt to the frequent changes in the mobility pattern of the network. In this paper, Optimized Link State Routing protocol has been modified by implementing Q-Learning concept, a reinforcement learning algorithm which guides network to select next node to which it should forward packets by first calculating the reward R and then calculation of Q-value with neighbors. Performance of this modified routing protocol has been evaluated for parameters like delay, throughput and delivery ratio. Two mobility models have been used, Random Waypoint and Walk. It is observed that performance in terms of above parameters improve considerably in both mobility patterns when intelligent Q-Learning algorithm is implemented in Optimized Link State Routing.


2010 ◽  
Vol 6 (3) ◽  
pp. 249-257 ◽  
Author(s):  
Tzu-Hua Lin ◽  
Han-Chieh Chao ◽  
Isaac Woungang

In an Optimized Link State Routing (OLSR)-based mobile wireless network, optimizing the flooding of broadcast messages is a challenging task due to node's mobility and bandwidth resource consumption. To complement existing solutions to this problem, the Multi-Point Relays (MPR) selection has recently been advocated as a promising technique that has an additional feature of reducing the number of redundant re-transmission occurring in the network. This paper continuous on the investigation of an existing MPR-based solution, arguing that by considering a cost factor as an additional decision parameter in selecting the MPR nodes, the enhanced MPR selection algorithm leads to less packet loss in the network. Simulation experiments are presented to validate the stated goal, using the average packet loss ratio as the performance metric.


2009 ◽  
Vol 5 (2) ◽  
pp. 165-176 ◽  
Author(s):  
Makoto Ikeda ◽  
Leonard Barolli ◽  
Giuseppe De Marco ◽  
Tao Yang ◽  
Arjan Durresi ◽  
...  

In this paper, we evaluate the performance of Optimized Link State Routing (OLSR) protocol by experimental and simulation results. The experiments are carried out by using our implemented testbed and the simulations by using ns-2 simulator. We also designed and implemented a new interface for the ad-hoc network testbed in order to make more easier the experiments. The comparison between experimental and simulation results shows that for the same parameters set, in the simulation we did not notice any packet loss. On the other hand, in the experiments we experienced packet loss because of the environment effects and traffic interference.


2017 ◽  
Vol 13 (1) ◽  
pp. 2-13 ◽  
Author(s):  
Masafumi Yamada ◽  
Miralda Cuka ◽  
Yi Liu ◽  
Tetsuya Oda ◽  
Keita Matsuo ◽  
...  

Purpose This paper aims to present the design and implementation of an Internet of Things (IoT)-based e-learning testbed using Raspberry Pi mounted on Raspbian operating system (OS). Design/methodology/approach The testbed is composed of five Raspberry Pi B+ computers. The experiments are carried out in the department floor considering an non line of sight (NLoS) environment. Single constant bit rate (CBR) flows were transmitted over user datagram protocol (UDP), and data were collected for five metrics: throughput, packet delivery ratio (PDR), hop count, delay and jitter using the Iperf. Findings The implemented testbed was evaluated using experiments. The experimental results showed that the nodes in the testbed were communicating smoothly, and by using attention value, the learner concentration is increased. Research limitations/implications The performance of the Optimized Link State Routing (OLSR) protocol was analyzed in a floor environment considering the NLoS scenario. However, this testbed can be implemented to other protocols also. Originality/value Because of the opportunities provided by the internet, people are taking advantage of e-learning courses, and enormous research efforts have been dedicated to the development of e-learning systems. To date, many e-learning systems are proposed and used practically. However, in these systems, the e-learning completion rate is low. To deal with this problem, an IoT-based e-learning system was implemented to increase the e-learning completion ratio by increasing the learner concentration.


Author(s):  
Chih-Lin I

The fifth-generation (5G) mobile communication networks, which are anticipated to be soft, green, and super-fast, may possibly be deployed in 2020s to satisfy the challenging demands of mobile communication in various scenarios. Characterized by a mixed set of key performance indicators like data rates, latency, mobility, energy efficiency, and traffic density, 5G services demand a fundamental revolution on the end to end network architecture and key technologies design. Toward a “soft, green, and super-fast” 5G, this paper presents seven innovative 5G R&D themes of China Mobile, including: (1) rethinking Shannon to start a green journey on wireless systems; (2) rethinking Ring and Young for no more “cells”; (3) rethinking signaling and control to make network applications aware and load aware; (4) rethinking antennas to make base stations invisible via SmarTiles; (5) rethinking spectrum and air interface to enable wireless signals to “dress for the occasion”; (6) rethinking fronthaul (FH) to enable Soft RAN via next-generation FH interface; and (7) rethinking the protocol stack for flexible configurations of diversified access points and optimal baseband function split between the base band unit pool and the Remote Radio Systems.


2021 ◽  
Vol 2021 ◽  
pp. 1-32
Author(s):  
Kiran Afzal ◽  
Rehan Tariq ◽  
Farhan Aadil ◽  
Zeshan Iqbal ◽  
Nouman Ali ◽  
...  

IoV is the latest application of VANET and is the alliance of Internet and IoT. With the rapid progress in technology, people are searching for a traffic environment where they would have maximum collaboration with their surroundings which comprise other vehicles. It has become a necessity to find such a traffic environment where we have less traffic congestion, minimum chances of a vehicular collision, minimum communication delay, fewer communication errors, and a greater message delivery ratio. For this purpose, a vehicular ad hoc network (VANET) was devised where vehicles were communicating with each other in an infrastructureless environment. In VANET, vehicles communicate in an ad hoc manner and communicate with each other to deliver messages, for infotainment purposes or for warning other vehicles about emergency scenarios. Unmanned aerial vehicle- (UAV-) assisted VANET is one of the emerging fields nowadays. For VANET’s routing efficiency, several routing protocols are being used like optimized link state routing (OLSR) protocol, ad hoc on-demand distance vector (AODV) routing protocol, and destination-sequenced distance vector (DSDV) protocol. To meet the need of the upcoming era of artificial intelligence, researchers are working to improve the route optimization problems in VANETs by employing UAVs. The proposed system is based on a model of VANET involving interaction with aerial nodes (UAVs) for efficient data delivery and better performance. Comparisons of traditional routing protocols with UAV-based protocols have been made in the scenario of vehicle-to-vehicle (V2V) communication. Later on, communication of vehicles via aerial nodes has been studied for the same purpose. The results have been generated through various simulations. After performing extensive simulations by varying different parameters over grid sizes of 300 × 1500 m to 300 × 6000 m, it is evident that although the traditional DSDV routing protocol performs 14% better than drone-assisted destination-sequenced distance vector (DA-DSDV) when we have number of sinks equal to 25, the performance of drone-assisted optimized link state routing (DA-OLSR) protocol is 0.5% better than that of traditional OLSR, whereas drone-assisted ad hoc on-demand distance vector (DA-AODV) performs 22% better than traditional AODV. Moreover, if we increase the number of sinks up to 50, it can be clearly seen that the DA-AODV outperforms the rest of the routing protocols by up to 60% (either traditional routing protocol or drone-assisted routing protocol). In addition, for parameters like MAC/PHY overhead and packet delivery ratio, the performance of our proposed drone-assisted variants of protocols is also better than that of the traditional routing protocols. These results show that our proposed strategy performs better than the traditional VANET protocols and plays important role in minimizing the MAC/PHY and enhancing the average throughput along with average packet delivery ratio.


Author(s):  
Waqas Khan

Mobile Ad-Hoc Networks (MANETs) are a collection of mobile nodes which are free to move from one place to another place without a central control entity. In MANETs the nodes are dependent on each other and the communication among mobile nodes is multi-hop due to which there are security issues in the MANETs protocols. Optimized Link State Routing (OLSR) and Dynamic Source Routing (DSR) protocols are mostly used as proactive and reactive routing protocols in MANETs. This research work analyzed the performance of the OLSR and DSR protocols in the presence and absence of black hole (BH) attack in terms of throughput, end-to-end delay, packet delivery ratio (PDR), and network load in various scenarios using OPNET Modeler 14.5 simulator. The results obtained in this research show that BH attack significantly degrades the performance of both DSR and OLSR protocols but due to the reactive nature of DSR routing protocol the performance is more degraded in DSR routing protocol as compared to OLSR routing protocol in the presence of BH attack.


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