MTCR-CR Routing Strategy for Connection-Oriented Routing over Satellite Networks

The high-speed movement of satellites makes it not feasible to directly apply the mature routing scheme on the ground to the satellite network.DT-DVTR in the snapshot-based connectionoriented routing strategy is one of the representative solutions,but it still has room for improvement in terms of routing stability.In this paper,we propose an improved scheme for connection-oriented routing strategy named the Minimal Topology Change Routing based on Collaborative Rules(MTCR-CR).The MTCR-CR uses continuous time static topology snapshots based on satellite status to search for intersatellite link(ISL)construction solutions that meet the minimum number of topology changes to avoid route oscillations.The simulation results in Beidou-3 show that compared with DT-DVTR,MTCR-CR reduces the number of routing changes by about 92%,the number of path changes caused by routing changes is about38%,and the rerouting time is reduced by approximately 47%.At the same time,in order to show our algorithm more comprehensively,the same experimental index test was also carried out on the Globalstar satellite constellation.

POWER-AWARE ROUTING BASED ON DSR FOR MOBILE AD HOC NETWORKS

Energy consumption is a crucial design concern in Mobile Ad hoc NETworks (MANETs) since nodes are powered by batteries with limited energy, whereas Dynamic Source Routing (DSR) protocol does not take the energy limitation of MANET nodes into account. This paper proposes an energy-saving routing algorithm based on DSR: Power Aware Routing protocol based on DSR (PAR-DSR). The design objective of PAR-DSR is to select energy-efficient paths. The main features of PAR-DSR are: (1) Nodes use the Signal Attenuation Rate (SAR) to conduct power control operations; (2) Minimum path cost as metric to balance the traffic and energy consumption of wireless nodes. The simulation results show that PAR-DSR can greatly reduce the energy consumption of MANET nodes. The average node lifetime of PAR-DSR is 50%-77% longer than that of DSR.

Imperative Dynamic Routing Between Capsules Network for Malaria Classification

Malaria is a severe epidemic disease caused by Plasmodium falciparum.The parasite causes critical illness if persisted for longer durations and delay in precise treatment can lead to further complications.The automatic diagnostic model provides aid for medical practitioners to avail a fast and efficient diagnosis.Most of the existing work either utilizes a fully connected convolution neural network with successive pooling layers which causes loss of information in pixels.Further,convolutions can capture spatial invariances but,cannot capture rotational invariances.Hence to overcome these limitations,this research,develops an Imperative Dynamic routing mechanism with fully trained capsule networks for malaria classification.This model identifies the presence of malaria parasites by classifying thin blood smears containing samples of parasitized and healthy erythrocytes.The proposed model is compared and evaluated with novel machine vision models evolved over a decade such as VGG,ResNet,DenseNet,MobileNet.The problems in previous research are cautiously addressed and overhauled using the proposed capsule network by attaining the highest Area under the curve(AUC)and Specificity of 99.03%and 99.43%respectively for 20%test samples.To understand the underlying behavior of the proposed network various tests are conducted for variant shuffle patterns.The model is analyzed and assessed in distinct environments to depict its resilience and versatility.To provide greater generalization,the proposed network has been tested on thick blood smear images which surpassed with greater performance.

Dynamic Routing Combined to Forecast the Behavior of Traffic in the City of Sao Paulo Using Neuro Fuzzy Network

The challenge of keeping and getting new customers drives the development of new practices to meet the consumption needs of increasingly tends to micro-segmentation of product and consumer market. The new consumption habits of brazilians brought new prospects for market. The objective of this paper is to develop of a dynamic vehicle routing system supported by the behavior of urban traffic in the city ofSao Paulo using Neuro Fuzzy Network. The methodology of this paper consists in the capture of relevant events that interfere with the flow of traffic of the city of Sao Paulo and implementation of a Fuzzy Neural Network trained with these events in order to foresee the traffic behavior. The system offers three labels of hierarchical routing, thus is possible to consider not only the basic factors of routing, but too external factors that directly influence on the flow of traffic and the disruption which may be avoided in large cities, through alternative routes （dynamic vehicle routing）. Predicting the behavior of traffic represents the strategic level routing, dynamic vehicle routing is the tactical level, and routing algorithms to the operational level. This paper will not be discussed the operational level.

Traffic Dataset and Dynamic Routing Algorithm in Traffic Simulation

The purpose of this research is to create a simulated environment for teaching algorithms,big data processing,and machine learning.The environment is similar to Google Maps,with the capacity of finding the fastest path between two points in dynamic traffic situations.However,the system is significantly simplified for educational purposes.Students can choose different traffic patterns and program a car to navigate through the traffic dynamically based on the changing traffic.The environments used in the project are Visual IoT/Robotics Programming Language Environment(VIPLE)and a traffic simulator developed in the Unity game engine.This paper focuses on creating realistic traffic data for the traffic simulator and implementing dynamic routing algorithms in VIPLE.The traffic data are generated from the recorded real traffic data published on the Arizona Maricopa County website.Based on the generated traffic data,VIPLE programs are developed to implement the traffic simulation with support for dynamic changing data.

OTP-Based Software-Defined Cloud Architecture for Secure Dynamic Routing

In the current era,anyone can freely access the Internet thanks to the development of information and communication technology.The cloud is attracting attention due to its ability to meet continuous user demands for resources.Additionally,Cloud is effective for systems with large data flow such as the Internet of Things(IoT)systems and Smart Cities.Nonetheless,the use of traditional networking technology in the cloud causes network traffic overload and network security problems.Therefore,the cloud requires efficient networking technology to solve the existing challenges.In this paper,we propose one-time password-based software-defined cloud architecture for secure dynamic routing to mitigating the above-mention issues.The proposed cloud architecture provides a secure data path through dynamic routing using One-Time Internet Protocol(OTIP)algorithm between each layer.On the network side,we use software-defined technology to provide efficient network management and data security.We introduce a software-defined cloud architecture that applies OTIP algorithms for secure dynamic routing.We conduct a comparative analysis between general IP communication and proposed OTIP communication architecture.It evaluates the performance of OTIP algorithms.Finally,we examine the proposed software-defined cloud architecture,including how to apply OTIP in secure dynamic routing according to the results of the comparative analysis.

Dynamic Routing Optimization Algorithm for Software Defined Networking

Time and space complexity is themost critical problemof the current routing optimization algorithms for Software Defined Networking(SDN).To overcome this complexity,researchers use meta-heuristic techniques inside the routing optimization algorithms in the OpenFlow(OF)based large scale SDNs.This paper proposes a hybrid meta-heuristic algorithm to optimize the dynamic routing problem for the large scale SDNs.Due to the dynamic nature of SDNs,the proposed algorithm uses amutation operator to overcome the memory-based problem of the ant colony algorithm.Besides,it uses the box-covering method and the k-means clustering method to divide the SDN network to overcome the problemof time and space complexity.The results of the proposed algorithm compared with the results of other similar algorithms and it shows that the proposed algorithm can handle the dynamic network changing,reduce the network congestion,the delay and running times and the packet loss rates.

An Improved Multi-Objective Particle Swarm Optimization Routing on MANET

A Mobile Ad hoc Network(MANET)is a group of low-power con-sumption of wireless mobile nodes that configure a wireless network without the assistance of any existing infrastructure/centralized organization.The primary aim of MANETs is to extendflexibility into the self-directed,mobile,and wireless domain,in which a cluster of autonomous nodes forms a MANET routing system.An Intrusion Detection System(IDS)is a tool that examines a network for mal-icious behavior/policy violations.A network monitoring system is often used to report/gather any suspicious attacks/violations.An IDS is a software program or hardware system that monitors network/security traffic for malicious attacks,sending out alerts whenever it detects malicious nodes.The impact of Dynamic Source Routing(DSR)in MANETs challenging blackhole attack is investigated in this research article.The Cluster Trust Adaptive Acknowledgement(CTAA)method is used to identify unauthorised and malfunctioning nodes in a MANET environment.MANET system is active and provides successful delivery of a data packet,which implements Kalman Filters(KF)to anticipate node trustworthiness.Furthermore,KF is used to eliminate synchronisation errors that arise during the sending and receiving data.In order to provide an energy-efficient solution and to minimize network traffic,route optimization in MANET by using Multi-Objective Particle Swarm Optimization(MOPSO)technique to determine the optimal num-ber of clustered MANET along with energy dissipation in nodes.According to the researchfindings,the proposed CTAA-MPSO achieves a Packet Delivery Ratio(PDR)of 3.3%.In MANET,the PDR of CTAA-MPSO improves CTAA-PSO by 3.5%at 30%malware.

A Secure and Pragmatic Routing Protocol for Mobile Ad hoc Networks

An ad hoc network is a group of wireless mobile computers(or nodes),in which individual nodes cooperate by forwarding packets for each other to allow nodes to communicate beyond direct wireless transmission range.Because of node mobility and power limitations,the network topology changes frequently.Routing protocol plays an important role in the ad hoc network.A recent trend in ad hoc network routing is the reactive on-demand philosophy where routes are established only when required.As an optimization for the current Dynamic Source Routing Protocol,a secure and pragmatic routes selection scheme based on Reputation Systems was proposed.We design the Secure and Pragmatic Routing protocol and implement simulation models using GloMoSim.Simulation results show that the Secure and Pragmatic Routing protocol provides better experimental results on packet delivery ratio,power consumption and system throughput than Dynamic Source Routing Protocol.

High-efficiency calculation method for watershed rainfallrunoff routing using one-dimensional dynamic wave equations

Efficiency in solving the Saint-Venant equations for watershed rainfall-runoff routing is important in flood hydrology. This paper presents a high-efficiency numerical solution of one-dimensional dynamic wave equations(HEDWE) for watershed rainfall-runoff routing, in which the full momentum equation is written as a quadratic equation with only one unknown variable Q, water depth is derived from the continuity equation using the two-step predictorcorrector method, and the discrete scheme is the explicit upwind scheme. The results of numerical tests showed the HEDWE approach has several major advantages. 1) It is a stable numerical method, even for an initially dry area. 2) Its computational efficiency is higher than 4.76E+05 times/s. 3) It can be used for overland flow, river flow, and combinations thereof. The primary disadvantages of the HEDWE approach are its unsuitability for rapidly varying flow, such as dam-break floods.

Dynamic routing and wavelength assignment algorithm of optical satellite networks based on cross-layer design

In order to overcome the adverse effects of Doppler wavelength shift on data transmission in the optical satellite networks,a dynamic routing and wavelength assignment algorithm based on crosslayer design( CL-DRWA) is introduced which can improve robustness of the network. Above all,a cross-layer optimization model is designed,which considers transmission delay and wavelength-continuity constraint,as well as Doppler wavelength shift. Then CL-DRWA is applied to solve this model,resulting in finding an optimal light path satisfying the above constraints for every connection request. In CL-DRWA,Bellman-Ford method is used to find an optimal route and a distributed relative capacity loss method is implemented to get an optimal wavelength assignment result on the optimal route. Moreover,compared with the dynamic routing and wavelength assignment algorithm based on minimum delay strategy( MD-DRWA),CL-DRWA can make an improvement of 5. 3% on the communication success probability. Meanwhile,CL-DRWA can meet the requirement of transmission delay for real-time services.

Efficient Route Planning for Real-Time Demand-Responsive Transit

Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of dynamicvehicle scheduling and real-time route planning in urban public transportation systems, with a focus on busservices. It addresses the limitations of current shared mobility routing algorithms, which are primarily designedfor simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. Theresearch introduces an route planning algorithm designed to dynamically accommodate passenger travel needsand enable real-time route modifications. Unlike traditional methods, this algorithm leverages a queue-based,multi-objective heuristic A∗ approach, offering a solution to the inflexibility and limited coverage of suburbanbus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based onGenetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, routelength, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposedalgorithmsignificantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existingmodels, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. Thestudy concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalablesolution for improving urban mobility.

Modeling Delay and Packet Drop in Networked Control Systems Using Network Simulator NS2

Wireless Networked Control Systems (WNCS) are used to implement a control mechanism over a wireless network that is capable of carrying real-time traffic. This field has drawn enormous attention from current researchers because of its flexibility and robustness. However, designing efficient WNCS over Mobile Ad Hoc Networks (MANET) is still a challenging topic because of its less-predictable aspects, such as inconsistent delay, packet drop probability, and dynamic topology. This paper presents design guidelines for WNCS over MANET using the Network Simulator version 2, NS2 software. It investigates the impact of packet delay and packet drop under the AODV and DSR routing protocols. The simulation results have been compared to MATLAB results for validation. Keywords Adhoc On-Demand Distance Vector (AODV) routing - Dynamic Source routing (DSR) - Mobile Adhoc Networks (MANET) - Wireless Networked Control Systems (WNCS) Mohammad Shahidul Hasan received his BSc and first MSc in Computer Science from the University of Dhaka, Bangladesh. He obtained his 2nd MSc in Computer & Network Engineering from Sheffield Hallam University, Sheffield, UK. Currently he is pursuing his PhD under the Faculty of Computing, Engineering and Technology, Staffordshire University, Stafford, UK in Networked Control Systems over MANET.Chris Harding received his BSc in Computing Science and Masters by Research from Staffordshire University, UK. Currently he is pursuing his PhD in Wireless Networked Control Systems, specifically looking at NCS over MANETs, with research interests in this area concentrating on the network routing and effect of routing protocols on the NCS system.Hongnian Yu is Professor of Computer Science at Staffordshire University. He was a lecturer in Control and Systems Engineering at Yanshan University, China in 1985–1990, did his PhD in Robotics at King’s College London (1990–1994), was a research fellow in Manufacturing Systems at Sussex University (1994–1996), a lecturer in Artificial Intelligence at Liver-pool John Moore’s University (1996–1999), a lecturer in Control and Systems Engineering at the University of Exeter (1999–2002), and a Senior Lecturer in Computing at the University of Bradford (2002–2004). He now leads the Mobile Computing and Distributed Systems Research Group at Staffordshire University. He was a founding member of the Modeling Optimisation Scheduling and Intelligent Control research group at the University of Bradford. He has extensive research experience in neural networks, mobile computing, modeling, control of robot manipulators, and modeling, scheduling, planning, and simulations of large discrete event dynamic systems with applications to manufacturing systems, supply chains, transportation networks, and computer networks. He has published over 100 research papers focusing on the following: neural networks, computer networks, adaptive and robust control of robot manipulators, analysis and control of hybrid machines, control of timed delay systems, predictive control, manufacturing system modeling and scheduling, planning, and supply chains. He has held several research grants from EPSRC, the Royal Society, and the EU, as well as from industry. He was awarded the F.C. William Premium for his paper on adaptive and robust control of robot manipulators by the IEE Council in 1997. Professor Yu is an EPSRC college member, a member of IEEE, and a committee member of several conferences and journal editorial boards.Alison Griffiths has been a Senior Lecturer in Telecommunications at Staffordshire University since 2003. She was a lecturer in Computing at Staffordshire University in 2002–2003. She was a Research Associate on an EPSRC funded project whilst doing her PhD on the convergence of Mobile Computing and Telecommunications at Staffordshire University (1999–2003). The investigation consisted of the communication of different types of media (voice, video conferencing, web browsing, and downloading) over a common network, using a mobile device. Problems considered were the complications that occurred when a user moves, and consequently changes their end-point in the network during communication, with respect to the type of service the user is provided with (delays and losses). She obtained both her MEng and 1st Class BEng (Hons) from Staffordshire University in 1999 and 1998 respectively. She is now part of the Mobile Computing and Distributed Systems Research Group at Staffordshire University. She has published 8 research papers focusing on quality of service and access between cellular and IP packet switched networks. Future directions include mobile agents and control of mobile wireless ad-hoc networks. Her current research interests have extended to Wireless Networked Control Systems, specifically looking at NCS over MANETs, with research interests in this area concentrating on the network routing and effect of routing protocols on the NCS system.

GACNet: A Generative Adversarial Capsule Network for Regional Epitaxial Traffic Flow Prediction

With continuous urbanization,cities are undergoing a sharp expansion within the regional space.Due to the high cost,the prediction of regional traffic flow is more difficult to extend to entire urban areas.To address this challenging problem,we present a new deep learning architecture for regional epitaxial traffic flow prediction called GACNet,which predicts traffic flow of surrounding areas based on inflow and outflow information in central area.The method is data-driven,and the spatial relationship of traffic flow is characterized by dynamically transforming traffic information into images through a two-dimensional matrix.We introduce adversarial training to improve performance of prediction and enhance the robustness.The generator mainly consists of two parts:abstract traffic feature extraction in the central region and traffic prediction in the extended region.In particular,the feature extraction part captures nonlinear spatial dependence using gated convolution,and replaces the maximum pooling operation with dynamic routing,finally aggregates multidimensional information in capsule form.The effectiveness of the method is evaluated using traffic flow datasets for two real traffic networks:Beijing and New York.Experiments on highly challenging datasets show that our method performs well for this task.

Packet Drop Battling Mechanism for Energy Aware Detection in Wireless Networks

Network security and energy consumption are deemed to be two important components of wireless and mobile ad hoc networks(WMANets).There are various routing attacks which harm Ad Hoc networks.This is because of the unsecure wireless communication,resource constrained capabilities and dynamic topology.In order to cope with these issues,Ad Hoc On-Demand Distance Vector(AODV)routing protocol can be used to remain the normal networks functionality and to adjust data transmission by defending the networks against black hole attacks.The proposed system,in this work,identifies the optimal route from sender to collector,prioritizing the number of jumps,the battery life,and security,which are fundamental prerequisites.Researches have proposed various plans for detecting the shortest route,as well as ensuring energy conversions and defense against threats and attacks.In this regard,the packet drop attack is one of the most destructive attack against WMANet communication and hence merits special attention.This type of attack may allow the attacker to take control of the attacked hubs,which may lost packets or transmitted information via a wrong route during the packets journey from a source hub to a target one.Hence,a new routing protocol method has been proposed in this study.It applies the concept of energy saving systems to conserve energy that is not required by the system.The proposed method for energy aware detection and prevention of packet drop attacks in mobile ad hoc networks is termed the Ad Hoc On-Demand and Distance Vector–Packet Drop Battling Mechanism(AODV–PDBM).

Average flooding distance for MANETs in random graph models with spatial reuse

The flooding distance is an important parameter in the design and evaluation of a routing protocol, which is related not only to the delay time in the route discovery, but also to the stability and reliability of the route. In this paper, the average flooding distance （AFD） for a mobile ad hoc network （MANET） in a random graph model was given based on the dynamic source routing （DSR） protocol. The influence of spatial reuse on the AFD was also studied. Compared with that in the model without the spatial reuse, the AFD in the model with the spatial reuse has much smaller value, when the connetivity probability between nodes in the network is small and when the number of reused times is large. This means that the route discovery with the spatial reuse is much more effective.

DSR-Based Selective Repeat ARQ Protocol in MANET

The efficient route algorithms involved in mobile ad hoc network(MANET) are studied. An arrangement of a combination of the traditional dynamic source routing(DSR) protocol is put forward and the selective repeat ARQ protocol is put forward by analyzing and studying them in detail and providing the scheme. In networks, especially in wireless networks, the nodes are capable to process data much faster than transmission, the DSR-based selective repeat ARQ protocol has real meanings in MANET.

Deep Learning Convolutional Neural Network for ECG Signal Classification Aggregated Using IoT

Much attention has been given to the Internet of Things (IoT) by citizens, industries, governments, and universities for applications like smart buildings, environmental monitoring, health care and so on. With IoT, networkconnectivity is facilitated between smart devices from anyplace and anytime.IoT-based health monitoring systems are gaining popularity and acceptance forcontinuous monitoring and detect health abnormalities from the data collected.Electrocardiographic (ECG) signals are widely used for heart diseases detection.A novel method has been proposed in this work for ECG monitoring using IoTtechniques. In this work, a two-stage approach is employed. In the first stage, arouting protocol based on Dynamic Source Routing (DSR) and Routing byEnergy and Link quality (REL) for IoT healthcare platform is proposed for effi-cient data collection, and in the second stage, classification of ECG for Arrhythmia. Furthermore, this work has evaluated Support Vector Machine (SVM),Artificial Neural Network (ANN), and Convolution Neural Networks (CNNs)-based approach for ECG signals classification. Deep-ECG will use a deep CNNto extract critical features and then compare through evaluation of simple and fastdistance functions in order to obtain an efficient classification of heart abnormalities. For the identification of abnormal data, this work has proposed techniquesfor the classification of ECG data, which has been obtained from mobile watchusers. For experimental verification of the proposed methods, the Beth Israel Hospital (MIT/BIH) Arrhythmia and Massachusetts Institute of Technology (MIT)Database was used for evaluation. Results confirm the presented method’s superior performance with regards to the accuracy of classification. The CNN achievedan accuracy of 91.92% and has a higher accuracy of 4.98% for the SVM and2.68% for the ANN.

Energy Based Random Repeat Trust Computation in Delay Tolerant Network

As the use of mobile devices continues to rise,trust administration will significantly improve security in routing the guaranteed quality of service(QoS)supply in Mobile Ad Hoc Networks(MANET)due to the mobility of the nodes.There is no continuance of network communication between nodes in a delay-tolerant network(DTN).DTN is designed to complete recurring connections between nodes.This approach proposes a dynamic source routing protocol(DSR)based on a feed-forward neural network(FFNN)and energybased random repetition trust calculation in DTN.If another node is looking for a node that swerved off of its path in this situation,routing will fail since it won’t recognize it.However,in the suggested strategy,nodes do not stray from their pathways for routing.It is only likely that the message will reach the destination node if the nodes encounter their destination or an appropriate transitional node on their default mobility route,based on their pattern of mobility.The EBRRTC-DTN algorithm(Energy based random repeat trust computation)is based on the time that has passed since nodes last encountered the destination node.Compared to other existing techniques,simulation results show that this process makes the best decision and expertly determines the best and most appropriate route to send messages to the destination node,which improves routing performance,increases the number of delivered messages,and decreases delivery delay.Therefore,the suggested method is better at providing better QoS(Quality of Service)and increasing network lifetime,tolerating network system latency.

Brain MRI Patient Identification Based on Capsule Network

In the deep learning field,“Capsule”structure aims to overcome the shortcomings of traditional Convolutional Neural Networks(CNN)which are difficult to mine the relationship between sibling features.Capsule Net(CapsNet)is a new type of classification network structure with“Capsule”as network elements.It uses the“Squashing”algorithm as an activation function and Dynamic Routing as a network optimization method to achieve better classification performance.The main problem of the Brain Magnetic Resonance Imaging(Brain MRI)recognition algorithm is that the difference between Alzheimer’s disease(AD)image,the Mild Cognitive Impairment(MCI)image,and the normal image is not significant.It is difficult to achieve excellent results using a multi-layer CNN.However,CapsNet can be in the case of a shallower network,which can accommodate more useful feature information for identifying brain MRI.In this paper,we designed a shallow CapsNet to identify patients with brain MRI by binary classification.Compared with VGG16,Resnet34,DenseNet121 and ResNeXt50.Experimental results illustrate that CapsNet is superior to CNN network in its accuracy and F1-score.The indicators were 86.67%and 83.33%,respectively.Furthermore,we show that the capsule network shows excellent performance in brain MRI recognition compared with those popular networks.