Integrated Energy-Efficient Distributed Link Stability Algorithm for UAV Networks

Ad hoc networks offer promising applications due to their ease of use,installation,and deployment,as they do not require a centralized control entity.In these networks,nodes function as senders,receivers,and routers.One such network is the Flying Ad hoc Network(FANET),where nodes operate in three dimensions(3D)using Unmanned Aerial Vehicles(UAVs)that are remotely controlled.With the integration of the Internet of Things(IoT),these nodes form an IoT-enabled network called the Internet of UAVs(IoU).However,the airborne nodes in FANET consume high energy due to their payloads and low-power batteries.An optimal routing approach for communication is essential to address the problem of energy consumption and ensure energy-efficient data transmission in FANET.This paper proposes a novel energy-efficient routing protocol named the Integrated Energy-Efficient Distributed Link Stability Algorithm(IEE-DLSA),featuring a relay mechanism to provide optimal routing with energy efficiency in FANET.The energy efficiency of IEE-DLSA is enhanced using the Red-Black(R-B)tree to ensure the fairness of advanced energy-efficient nodes.Maintaining link stability,transmission loss avoidance,delay awareness with defined threshold metrics,and improving the overall performance of the proposed protocol are the core functionalities of IEE-DLSA.The simulations demonstrate that the proposed protocol performs well compared to traditional FANET routing protocols.The evaluation metrics considered in this study include network delay,packet delivery ratio,network throughput,transmission loss,network stability,and energy consumption.

Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost.One of them is a sensor network with embedded sensors working as the primary nodes,termed Wireless Sensor Networks(WSNs),in which numerous sensors are connected to at least one Base Station(BS).These sensors gather information from the environment and transmit it to a BS or gathering location.WSNs have several challenges,including throughput,energy usage,and network lifetime concerns.Different strategies have been applied to get over these restrictions.Clustering may,therefore,be thought of as the best way to solve such issues.Consequently,it is crucial to analyze effective Cluster Head(CH)selection to maximize efficiency throughput,extend the network lifetime,and minimize energy consumption.This paper proposed an Accelerated Particle Swarm Optimization(APSO)algorithm based on the Low Energy Adaptive Clustering Hierarchy(LEACH),Neighboring Based Energy Efficient Routing(NBEER),Cooperative Energy Efficient Routing(CEER),and Cooperative Relay Neighboring Based Energy Efficient Routing(CR-NBEER)techniques.With the help of APSO in the implementation of the WSN,the main methodology of this article has taken place.The simulation findings in this study demonstrated that the suggested approach uses less energy,with respective energy consumption ranges of 0.1441 to 0.013 for 5 CH,1.003 to 0.0521 for 10 CH,and 0.1734 to 0.0911 for 15 CH.The sending packets ratio was also raised for all three CH selection scenarios,increasing from 659 to 1730.The number of dead nodes likewise dropped for the given combination,falling between 71 and 66.The network lifetime was deemed to have risen based on the results found.A hybrid with a few valuable parameters can further improve the suggested APSO-based protocol.Similar to underwater,WSN can make use of the proposed protocol.The overall results have been evaluated and compared with the existing approaches of sensor networks.

Q-Learning Based Routing Protocol for Congestion Avoidance

The end-to-end delay in a wired network is strongly dependent on congestion on intermediate nodes.Among lots of feasible approaches to avoid congestion efficiently,congestion-aware routing protocols tend to search for an uncongested path toward the destination through rule-based approaches in reactive/incident-driven and distributed methods.However,these previous approaches have a problem accommodating the changing network environments in autonomous and self-adaptive operations dynamically.To overcome this drawback,we present a new congestion-aware routing protocol based on a Q-learning algorithm in software-defined networks where logically centralized network operation enables intelligent control and management of network resources.In a proposed routing protocol,either one of uncongested neighboring nodes are randomly selected as next hop to distribute traffic load to multiple paths or Q-learning algorithm is applied to decide the next hop by modeling the state,Q-value,and reward function to set the desired path toward the destination.A new reward function that consists of a buffer occupancy,link reliability and hop count is considered.Moreover,look ahead algorithm is employed to update the Q-value with values within two hops simultaneously.This approach leads to a decision of the optimal next hop by taking congestion status in two hops into account,accordingly.Finally,the simulation results presented approximately 20%higher packet delivery ratio and 15%shorter end-to-end delay,compared to those with the existing scheme by avoiding congestion adaptively.

CEER:Cooperative Energy-Efficient Routing Mechanism for Underwater Wireless Sensor Networks Using Clusters

Underwater acoustic sensor networks(UWASNs)aim to find varied offshore ocean monitoring and exploration applications.In most of these applications,the network is composed of several sensor nodes deployed at different depths in the water.Sensor nodes located at depth on the seafloor cannot invariably communicate with nodes close to the surface level;these nodes need multihop communication facilitated by a suitable routing scheme.In this research work,a Cluster-based Cooperative Energy Efficient Routing(CEER)mechanism for UWSNs is proposed to overcome the shortcomings of the Co-UWSN and LEACH mechanisms.The optimal role of clustering and cooperation provides load balancing and improves the network profoundly.The simulation results using MATLAB show better performance of CEER routing protocol in terms of various parameters as compared to Co-UWSN routing protocol,i.e.,the average end-to-end delay of CEER was 17.39,Co-UWSN was 55.819 and LEACH was 70.08.In addition,the average total energy consumption of CEER was 9.273,Co-UWSN was 12.198,and LEACH was 45.33.The packet delivery ratio of CEER was 53.955,CO-UWSN was 42.047,and LEACH was 30.31.The stability period CEER was 130.9,CO-UWSN was 129.3,and LEACH was 119.1.The obtained results maximized the lifetime and improved the overall performance of the CEER routing protocol.

Estimating Fuel-Efficient Air Plane Trajectories Using Machine Lear

Airline industry has witnessed a tremendous growth in the recent past.Percentage of people choosing air travel as first choice to commute is continuously increasing.Highly demanding and congested air routes are resulting in inadvertent delays,additional fuel consumption and high emission of greenhouse gases.Trajectory planning involves creation identification of cost-effective flight plans for optimal utilizationof fuel and time.This situation warrants the need of an intelligent system for dynamic planning of optimized flight trajectories with least human intervention required.In this paper,an algorithm for dynamic planning of optimized flight trajectories has been proposed.The proposed algorithm divides the airspace into four dimensional cubes and calculate a dynamic score for each cube to cumulatively represent estimated weather,aerodynamic drag and air traffic within that virtual cube.There are several constraints like simultaneous flight separation rules,weather conditions like air temperature,pressure,humidity,wind speed and direction that pose a real challenge for calculating optimal flight trajectories.To validate the proposed methodology,a case analysis was undertaken within Indian airspace.The flight routes were simulated for four different air routes within Indian airspace.The experiment results observed a seven percent reduction in drag values on the predicted path,hence indicates reduction in carbon footprint and better fuel economy.

LSTDA: Link Stability and Transmission Delay Aware Routing Mechanism for Flying Ad-Hoc Network (FANET)

The paper presents a new protocol called Link Stability and Transmission Delay Aware(LSTDA)for Flying Adhoc Network(FANET)with a focus on network corridors(NC).FANET consists of Unmanned Aerial Vehicles(UAVs)that face challenges in avoiding transmission loss and delay while ensuring stable communication.The proposed protocol introduces a novel link stability with network corridors priority node selection to check and ensure fair communication in the entire network.The protocol uses a Red-Black(R-B)tree to achieve maximum channel utilization and an advanced relay approach.The paper evaluates LSTDA in terms of End-to-End Delay(E2ED),Packet Delivery Ratio(PDR),Network Lifetime(NLT),and Transmission Loss(TL),and compares it with existing methods such as Link Stability Estimation-based Routing(LEPR),Distributed Priority Tree-based Routing(DPTR),and Delay and Link Stability Aware(DLSA)using MATLAB simulations.The results show that LSTDA outperforms the other protocols,with lower average delay,higher average PDR,longer average NLT,and comparable average TL.

A Two-Tier Framework Based on GoogLeNet and YOLOv3 Models for Tumor Detection in MRI

Medical Image Analysis(MIA)is one of the active research areas in computer vision,where brain tumor detection is the most investigated domain among researchers due to its deadly nature.Brain tumor detection in magnetic resonance imaging(MRI)assists radiologists for better analysis about the exact size and location of the tumor.However,the existing systems may not efficiently classify the human brain tumors with significantly higher accuracies.In addition,smart and easily implementable approaches are unavailable in 2D and 3D medical images,which is the main problem in detecting the tumor.In this paper,we investigate various deep learning models for the detection and localization of the tumor in MRI.A novel twotier framework is proposed where the first tire classifies normal and tumor MRI followed by tumor regions localization in the second tire.Furthermore,in this paper,we introduce a well-annotated dataset comprised of tumor and normal images.The experimental results demonstrate the effectiveness of the proposed framework by achieving 97%accuracy using GoogLeNet on the proposed dataset for classification and 83%for localization tasks after finetuning the pre-trained you only look once(YOLO)v3 model.

Hybrid Deep Learning Architecture to Forecast Maximum Load Duration Using Time-of-Use Pricing Plans

Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models.Especially,we need the adequate model to forecast the maximum load duration based on time-of-use,which is the electricity usage fare policy in order to achieve the goals such as peak load reduction in a power grid.However,the existing single machine learning or deep learning forecasting cannot easily avoid overfitting.Moreover,a majority of the ensemble or hybrid models do not achieve optimal results for forecasting the maximum load duration based on time-of-use.To overcome these limitations,we propose a hybrid deep learning architecture to forecast maximum load duration based on time-of-use.Experimental results indicate that this architecture could achieve the highest average of recall and accuracy(83.43%)compared to benchmark models.To verify the effectiveness of the architecture,another experimental result shows that energy storage system(ESS)scheme in accordance with the forecast results of the proposed model(LSTM-MATO)in the architecture could provide peak load cost savings of 17,535,700 KRW each year comparing with original peak load costs without the method.Therefore,the proposed architecture could be utilized for practical applications such as peak load reduction in the grid.

An Efficient Scheme for Interference Mitigation in 6G-IoT Wireless Networks

The Internet of Things(IoT)is the fourth technological revolution in the global information industry after computers,the Internet,and mobile communication networks.It combines radio-frequency identification devices,infrared sensors,global positioning systems,and various other technologies.Information sensing equipment is connected via the Internet,thus forming a vast network.When these physical devices are connected to the Internet,the user terminal can be extended and expanded to exchange information,communicate with anything,and carry out identification,positioning,tracking,monitoring,and triggering of corresponding events on each device in the network.In real life,the IoT has a wide range of applications,covering many fields,such as smart homes,smart logistics,fine agriculture and animal husbandry,national defense,and military.One of the most significant factors in wireless channels is interference,which degrades the system performance.Although the existing QR decomposition-based signal detection method is an emerging topic because of its low complexity,it does not solve the problem of poor detection performance.Therefore,this study proposes a maximumlikelihood-based QR decomposition algorithm.The main idea is to estimate the initial level of detection using the maximum likelihood principle,and then the other layer is detected using a reliable decision.The optimal candidate is selected from the feedback by deploying the candidate points in an unreliable scenario.Simulation results show that the proposed algorithm effectively reduces the interference and propagation error compared with the algorithms reported in the literature.

The Decade of International Engagement in Afghanistan

As a result of the Bonn and Tokyo Conferences when extensive .intemational engagement began in Afghanistan in early 2002, hopes rose high that now peace and stability would be restored in this war ravaged country. Given the scale of the commitment and excitement demonstrated by the international community to combat terrorism and help Afghanistan in its overall reconstruction process, it appeared that extensive political and economic activities would soon revive that would help in ending the decades-long miseries of the poor Afghan nation. In the immediate aftermath a significant performance was also recorded in some spheres, such as：