Routing Control Research Articles

Research on Network Routing Control Algorithm Based on OpenFlow and IGP

Given the coexistence of heterogeneous hybrid networks such as IP network, 4G network and Software Defined Network (SDN) in the application of digital news media system, this paper designs a routing control algorithm based on SDN network architecture, which can realize the compatibility of internal gateway protocol and OpenFlow communication strategies. Through the analysis of OpenFlow communication protocol in SDN network architecture, the relationship between Interior Gateway Protocol (IGP) and OpenFlow routing rules is found out. Based on the existing hardware structure of the OpenFlow router, the configuration parameters of a port table, port address table and routing table are improved, and the adjacent routing relation table and information transmission port are used to realize the connection between routers are designed. The attention of users is considered It can reduce the burden of system operation and is compatible with the forwarding control of route forwarding and routing filtering. The adjacent routing relation table is designed to control the process of data collection, storage and forwarding, and the input and output ports between adjacent routers are defined. The open router designed in this paper integrates Simple Network Management Protocol (SNMP) agent and SDN agent mechanism organically to achieve the goal of SDN network management; the routing control module focuses on the management of key data flow, realizes the control of routing state and data flow at the same time, and provides the visualization function of routing state and routing state data flow. By constructing the experimental platform of routing transmission and control functions based on routing control and interior gateway protocol, the effect of routing state control of the two methods is compared and analysed from three aspects of packet transmission state, packet processing state and packet receiving delay in routing control process. The experimental results verify the correctness of the design results.

Naming the Coconut and (De)Colonizing the Middle Ages

Etymological dictionaries declare that the origins of the word coconut are found in Spanish and Portuguese, and they trace its earliest attestations to explorer accounts. While potentially true in a literal sense, this common narrative cloaks a much older European history of the fruit. By 1500, the name Indian nut for what we today call coconut was at least 1,000 years old and used in languages across Europe. In some specialist fields, Latinate Europeans also knew Arabic and even Sanskrit names for coconut. As European nations began conquering other parts of the world, however, they renamed the Indian nut, taking control of the name as they began to take control of the trade routes on which coconuts traveled.

Oral and Injected Tamoxifen Alter Adult Hippocampal Neurogenesis in Female and Male Mice.

Inducible Cre recombinase facilitates temporal control of genetic recombination in numerous transgenic model systems, a feature which has made it a popular tool for adult neurogenesis studies. One of the most common forms of inducible Cre, CreERT2, requires activation by the selective estrogen receptor modulator tamoxifen (TAM) to initiate recombination of LoxP-flanked sequences. To date, most studies deliver TAM via intraperitoneal injection. But the introduction of TAM-infused commercial chows has recently expanded the possible modes of TAM delivery. Despite the widespread use of TAM-inducible genetic models in adult neurogenesis research, the comparative efficiency and off-target effects of TAM administration protocols is surprisingly infrequently studied. Here, we compare a standard, 5 d TAM injection regimen with voluntary consumption of TAM-infused chow. First, we used adult NestinCreERT2;Rosa-LoxP-STOP-LoxP-EYFP reporter mice to show that two weeks of TAM chow and 5 d of injections led to LoxP recombination in a similar phenotypic population of neural stem and progenitor cells (NSPCs) in the adult dentate gyrus. However, TAM chow resulted in substantially less overall recombination than injections. TAM administration also altered adult neurogenesis, but in different ways depending on administration route: TAM injection disrupted neural progenitor cell proliferation three weeks after TAM, whereas TAM chow increased neuronal differentiation of cells generated during the diet period. These findings provide guidance for selection of TAM administration route and appropriate controls in adult neurogenesis studies using TAM-inducible Cre mice. They also highlight the need for better understanding of off-target effects of TAM in other neurologic processes and organ systems.

A Study of Developments in the Routing Protocols in the Wireless Ad Hoc Networks (WANET)

Abstract: A mobile ad hoc network (manet) is a new name for an obsolete system: a network that doesn't require any pre-existing architecture to function. in the early 1970s, the darpa pr net and suran Programmes were predecessors of this system. the new twitch is the application of this approach in non-military communication circumstances. in addition, the research in this field has recently focused on various advanced aspects of this technology, such as multicasting and security. there have also been a number of unique solutions to "traditional" routing and medium access control problems proposed. this study attempts to characterize the state-of-the-art of networks technology in four areas: routing, intermediate identity management, multicasting, and security. when possible, reviews of the proposed techniques are offered. As part of this research, the Fuzzy based Flexible Energy Efficient Routing protocol (FIE2R) will be presented as a routing system for WANET. The decision maker is in charge of the whole FIE2R procedure, which is separated into two parts. It is the WANET graph start phase, which aids in the second phase's determination of each route's reward. That's from a comprehensive computation of FIE2R using MATLAB and NS2 and a comparative analysis of the same with other networking schemes, the recommended protocol improves performance in terms of energy consumption. Keywords: Adhoc networks, Manet, Wanet, MATLAB. Routing schemes

Deep Graph Reinforcement Learning Based Intelligent Traffic Routing Control for Software-Defined Wireless Sensor Networks

Software-defined wireless sensor networks (SDWSN), where the data and control planes are decoupled, are more suited to handling big sensor data and effectively monitoring dynamic environments and events. To overcome the limitations of using static routing tables under high traffic intensity, such as network congestion, high packet loss rate, low throughput, etc., it is critical to design intelligent traffic routing control for the SDWSNs. In this paper we propose a deep graph reinforcement learning (DGRL) model-based intelligent traffic control scheme for SDWSNs, which combines graph convolution with deterministic policy gradient. The model fits well for the task of intelligent routing control for the SDWSN, as the process of data forwarding can be regarded as the sampling of continuous action space and the traffic data has strong graph features. The intelligent control policies are made by the SDWSN controller and implemented at the sensor nodes to optimize the data forwarding process. Simulation experiments performed on the Omnet++ platform show that, compared with the existing traffic routing algorithms for SDWSNs, the proposed intelligent routing control method can effectively reduce packet transmission delay, increase packet delivery ratio, and reduce the probability of network congestion.

Electrogenetic Signal Transmission and Propagation in Coculture to Guide Production of a Small Molecule, Tyrosine.

There are many strategies to actuate and control genetic circuits, including providing stimuli like exogenous chemical inducers, light, magnetic fields, and even applied voltage, that are orthogonal to metabolic activity. Their use enables actuation of gene expression for the production of small molecules and proteins in many contexts. Additionally, there are a growing number of reports wherein cocultures, consortia, or even complex microbiomes are employed for the production of biologics, taking advantage of an expanded array of biological function. Combining stimuli-responsive engineered cell populations enhances design space but increases complexity. In this work, we co-opt nature's redox networks and electrogenetically route control signals into a consortium of microbial cells engineered to produce a model small molecule, tyrosine. In particular, we show how electronically programmed short-lived signals (i.e., hydrogen peroxide) can be transformed by one population and propagated into sustained longer-distance signals that, in turn, guide tyrosine production in a second population building on bacterial quorum sensing that coordinates their collective behavior. Two design methodologies are demonstrated. First, we use electrogenetics to transform redox signals into the quorum sensing autoinducer, AI-1, that, in turn, induces a tyrosine biosynthesis pathway transformed into a second population. Second, we use the electrogenetically stimulated AI-1 to actuate expression of ptsH, boosting the growth rate of tyrosine-producing cells, augmenting both their number and metabolic activity. In both cases, we show how signal propagation within the coculture helps to ensure tyrosine production. We suggest that this work lays a foundation for employing electrochemical stimuli and engineered cocultures for production of molecular products in biomanufacturing environments.

Parents’ Willingness to Allow Their Unaccompanied Children to Use Emerging and Future Travel Modes

This study investigated parents’ willingness to allow their unaccompanied child(ren) to use emerging and future travel modes (e.g., rideshare vehicles and automated vehicles). An online survey was completed by 631 Australian respondents (M = 39.2 years, SD = 10.5 years, Male: 36.6%) who reported that they currently lived with one or more children (17 or below). Approximately one-third (37.9%) of the respondents reported a willingness to allow their child to use a rideshare vehicle alone and more than half of the respondents (57.2%) reported a willingness to allow their child to use an automated vehicle alone. Respondents who expressed willingness to allow their child to use a rideshare vehicle alone were more likely to express a willingness to use an automated vehicle alone (79.1%) compared to respondents who were unwilling to use a rideshare vehicle (43.9%), χ2(1) = 75.158, p < 0.001, Phi = 0.345. Two separate logistic regression models revealed key similarities and differences related to respondents’ willingness to allow their unaccompanied child to use both transport modes. Respondents’ willingness to allow their unaccompanied child to use a rideshare vehicle was significantly related to their previous use of a rideshare vehicle with their child, having an optimistic view of technology, annual mileage, their aberrant driving behaviours, and their desire for route-control and assurance features within the rideshare vehicle, χ2(7) = 159.594, p < 0.001. Respondents’ willingness to allow their child to use an automated vehicle alone was significantly related to awareness of automated vehicles, education level, positive views towards technology, seeing technology to be innovative, and requirements for route control features within the automated vehicle, χ2(6) = 113.325, p < 0.001. Despite the potential for emerging or future travel modes to provide additional personal transportation options, these results suggest that Australian parents are unwilling to allow their unaccompanied child to use these modes of transport. These findings will have significant implications for transport planning, particularly in growing communities where pressures on parents to transport their child(ren) to activities and events with minimal adult supervision is increasing.

Optimized LOADng Routing Protocol Parameters Using Black Widow Optimization Algorithm for IoT

With the dawn of Internet of Things (IoT), interconnected things and smart applications have significantly reduced human intervention in accomplishing tasks. Yet, due to its openness, heterogeneity, limited resources and extensiveness, it suffers from several issues such as lesser capacity links, energy consumption, resource optimization etc. Routing of packets in such constrained environment is yet challenging. Lightweight On-demand Ad hoc Distance-vector Routing Protocol—Next Generation (LOADng) protocol is an extended version of AODV protocol. Unlike AODV, LOADng is lighter version which forbids the intermediate nodes on the route to send a route reply (RREP) for the route request (RREQ) which is originated from the source. A resource constrained IoT network demands minimal routing control overhead with no packet loss. The optimal selection of routing protocol parameters can improve quality of service (QoS) of such constrained environments. So, in this paper we present optimization of the parameters of LOAD grouting protocol using black widow optimization (BWO) algorithm for IoT. Simulation results depict that the LOADng-BWO outperforms the conventional LOADng protocol in terms of delay, overhead and delivery ratio.

Analysing the impact of taxi violence on commuters in South Africa

Taxi violence in South Africa was the tool that the apartheid regime used to pursue their interest by dividing the people so that they could acquire what they needed (Sekhonyane and Dugard, 2004:15). Taxi violence is one of the most burning issues faced by Department of Transport, Police, commuters and taxi industry in South Africa. This industry plays a vital role in commuting people to and from work or different destinations, and in most cases it is caught in the wrong doing in terms of fighting for routes where many innocent lives are lost due to infighting. The inability by government to regulate taxi industry is a serious problem for taxi operators, taxi associations, commuters and society. The deregulation of taxi industry brought many problems such as conflict, violence and mafia type assassinations in South Africa. This article focus on preventive measures to curb taxi violence in South Africa. This article utilised qualitative, case study based approach and the approach is supported by analysis of primary and secondary sources. The article further draws on the findings from the world on taxi violence and formulate steps to counteract taxi violence in South Africa. The research based key steps leading to the prevention of taxi violence in South Africa. The first process related to regulation of taxi industry, awareness campaigns on taxi operations, the control of routes and multidisciplinary investigation team should be established to overcome taxi violence in South Africa.

Emvlight: A Multi-Agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System

Emergency vehicles (EMVs) play a crucial role in responding to time-critical calls such as medical emergencies and fire outbreaks in urban areas. Existing methods for EMV dispatch typically optimize routes based on historical traffic-flow data and design traffic signal pre-emption accordingly; however, we still lack a systematic methodology to address the coupling between EMV routing and traffic signal control. In this paper, we propose EMVLight, a decentralized reinforcement learning (RL) framework for joint dynamic EMV routing and traffic signal pre-emption. We adopt the multi-agent advantage actor-critic method with policy sharing and spatial discounted factor. This framework addresses the coupling between EMV navigation and traffic signal control via an innovative design of multi-class RL agents and a novel pressure-based reward function. The proposed methodology enables EMVLight to learn network-level cooperative traffic signal phasing strategies that not only reduce EMV travel time but also shortens the travel time of non-EMVs. Simulation-based experiments indicate that EMVLight enables up to a $42.6\%$ reduction in EMV travel time as well as an $23.5\%$ shorter average travel time compared with existing approaches.

Emvlight: A Multi-Agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System

Emvlight: A Multi-Agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System

Scalability Enhancement on Software Defined Industrial Wireless Sensor Networks Over TSCH

Industry 4.0 digitization requirements have led to widespread adoption of industrial wireless sensor networks (IWSNs) in manufacturing environments. These require strict control of the traffic flowing over the network to ensure the quality of service. Software-defined networks (SDNs) can address this challenge, as their centralized operation allows routing and media access control (MAC) protocols to be optimized. In addition, in many cases a massive deployment of metering devices is necessary, therefore the network must be scalable, to allow an increase in the number and density of nodes in the network. This implies an increase in the control traffic required by SDNs, generating a significant reduction in the throughput available for data. This is because the length of the timeslot limits the throughput of the network in protocols that uses time division, such as the Time Slotted Channel Hopping (TSCH), conditioning the number of nodes that the network can have and its data sending rate. In this paper, we propose to exploit the integration and reconfiguration capabilities of SDN to extend the number of nodes in the network through the use of multi-radio sinks called virtual sinks, orchestrated from the SDN controller, and extending their use to multiple SD-IWSNs to take full advantage of the radio spectrum. To optimize bandwidth usage, the SDN controller will allocate network slices, allowing nodes to send data with a high frequency using dedicated channels. The results obtained show that the use of multi-radio sinks provides for increasing the maximum number of nodes that can be deployed while preserving the quality of service (QoS) requirements. A linear increase in nodes at least 0.7 times for each radio interface is obtained compared to an SD-IWSN with a traditional sink. In addition, the use of virtual sinks increases the packet frequency of the network by a factor equal to the number of radio interfaces. Moreover, the total number of nodes is improved through the coexistence of multiple SD-IWSNs, where up to 13.7 times more nodes can be scheduled using all the available spectrum orchestrated from the same SDN controller.

Decentralized Link Failure Prevention Routing (DLFPR) Algorithm for Efficient Internet of Things

This work implements a Decentralized Links Failure Prevention (DLFP) routing algorithm to promote enhanced and efficient Internet of Things (IoT). The work increases the mobility as well as an opportunity for loss of IoT node meeting links due to both mobility and blockers/interferers. The proposed algorithm overcomes loss issues as well as works in dynamically allocating alternate route from other IoT nodes available in near and selecting for efficient route in the network. When the link fails, bandwidth is reduced and coverage area problems for packets sending from source to destination is managed. The proposed algorithm works with light-weight wireless mesh based route handling techniques. In such case, reduced link failure, removal of route discovery latency, communication cost and control overheads, energy consumptions and network life time are to be handled with care. To address these issues, the proposed algorithm enhances the Packet Delivery Ratio (PDR) as well as transmission packet efficiently received through destination node. The ability of Dynamic Alternate Routing (DAR) to relay node handling capabilities and to improve the communication link availability of IoT nodes has ultimately increased the PDR. Simulation work was done using Network Simulator 3 (NS3) and the results show that the performance is better when compared with existing Dynamic Source Routing (DSR) and Energy Efficient Delay Tolerant Routing (EEDTR) algorithm.

A Novel Method for Routing Optimization in Software-Defined Networks

Software-defined network (SDN) is a new form of network architecture that has programmability, ease of use, centralized control, and protocol independence. It has received high attention since its birth. With SDN network architecture, network management becomes more efficient, and programmable interfaces make network operations more flexible and can meet the different needs of various users. The mainstream communication protocol of SDN is OpenFlow, which contains a Match Field in the flow table structure of the protocol, which matches the content of the packet header of the data received by the switch, and completes the corresponding actions according to the matching results, getting rid of the dependence on the protocol to avoid designing a new protocol. In order to effectively optimize the routing for SDN, this paper proposes a novel algorithm based on reinforcement learning. The proposed technique can maximize numerous objectives to dynamically update the routing strategy, and it has great generality and is not reliant on any specific network state. The control of routing strategy is more complicated than many Q-learning-based algorithms due to the employment of reinforcement learning. The performance of the method is tested by experiments using the OMNe++ simulator. The experimental results reveal that our PPO-based SDN routing control method has superior performance and stability than existing algorithms.

Performance Analysis of Efficient Spectrum Utilization in Cognitive Radio Networks by Dynamic Spectrum Access and Artificial Neuron Network Algorithms

Efficient spectrum utilization is a prominent issue in cognitive radio networks. Owing to this, power allocation policies are proposed which underlay cognitive radio networks together among all prime nodes, secondary nodes, eavesdropper and secondary sender powered by renewable energy that is harvested from primary sender to acquire improved energy efficiency to enhance transmission rate, throughput, and Spectrum Utilization (SU). As a result, there is a need for combination of Dynamic Spectrum Access (DSA) algorithm, Artificial Neuron Network (ANN) algorithm which will make an allotment of obtainable network assets for various elements challenging for their resources. The prime objective of this paper is to intend a route control based multi-path Quality Of Service (QoS) and to find substitute paths between Secondary User (SU) source and SU destination fulfilling QoS metrics, specifically providing maximal throughput and minimal delay. In order that primary substitute channels along the paths are used completely to reduce data packets loss by using Network Simulator 2 (NS2) software tool.

Performance Analysis of Machine Learning-based Detection of Sinkhole Network Layer Attack in MANET

This paper proposes an Intrusion Detection System (IDS) against Sinkhole attacks in Mobile Adhoc Networks (MANET) with mobile sinks. A sinkhole attack is where a hacked node advertises a false routing update to draw network traffic. One effect of a sinkhole attack is that it may be used to launch further attacks, such as drops or changed routing information. Sinkhole nodes attempt to forge the source–destination routes to attract the surrounding network traffic. For this purpose, they modify routing control packets to publish fake routing information that makes sinkhole nodes appear as the best path to some destinations. Several machine learning techniques, including Decision Tree (DT), K-Nearest Neighbor (KNN), Convolution neural network (CNN), and Support Vector Machine (SVM), are used to do the categorization. Furthermore, the MANET’s node’s characteristics, particularly speed, are used for feature extraction. Totally 3997 unique samples, including 256 malicious samples and 3604 normal samples are collected. The categorization results demonstrate the accuracy of DT, KNN, CNN, and SVM at 98.4%, 96.7%, 98.6%, and 97.8%, respectively. The CNN approach is more accurate than other methods, at 98.6%, based on the data. After that, Priority, SVM, KNN, and CNN, in that order, each denotes excellent accuracy.

A Cloud-Based Secure Emergency Message Dissemination Scheme in Vehicular Adhoc Networks

The Internet of vehicles and vehicular ad-hoc networks (VANET) offers numerous opportunities for managing the transportation problems effectively. The high mobility and wireless communication in VANET lead to adequate network topology modifications, resulting in network instability and insecure data communication. With an unsteady flow of traffic, vehicles are unevenly distributed in the geographical areas in practice. A new type 2 fuzzy logic-based secure clustering (T2FLSC) with cloud-based data dissemination scheme called the T2FLSC-CDD model for the VANET has been introduced for resolving this issue. The vehicles are dynamically clustered by the use of the T2FLSC technique, which elects the CHs (Cluster Head) by the use of different parameters, namely, travelling speed (TS), link quality (LQ), trust factor (TF), inter-vehicle distance (IVD) and neighboring node count (NCC). The inclusion of the trust factor helps to select the proper CHs for the secure data dissemination process. Once the CHs are selected, a cloud connection can broadcast the Emergency messages to every vehicle. To save the location details of the vehicles in the cloud securely, the Blowfish technique has also been implemented in this work. For validating the effectiveness of the T2FLSC-CDD model, an extensive results analysis has been performed with respect to various measures. The attained simulation outcomes have pointed out that the proposed model has achieved maximum packet delivery ratio (PDR) and throughput with minimum key computation time (KCT), routing control overhead (RCO), time delay (TD), and key recovery time (KRT).

Design of a Two-tier Supply Chain Based on Integration, Pricing, Routing, and Inventory Control

This paper integrates decisions about the three key components of the supply chain including pricing, routing and inventory control, and multi-product multi-cycle in a two-tier supply chain; includes a manufacturer and a collection of dedicated retailers. In addition to pricing, inventory management and distribution planning, production planning is also considered in the study. The goal is to maximize profits in the supply chain, where products are delivered to retailers by a fleet of limited-capacity identical vehicles under a multiple-delivery strategy. Also, limited production and storage capacity and unauthorized shortages are assumed. The proposed model will then be solved using CPLEX software package in GAMS environment and genetic algorithm in Matlab software to determine the optimal supply chain policy.

Research of functioning processes of wireless sensor networks

The article is devoted to the research of problems of optimal control of data flow routing in heterogeneous sensor networks of variable topology under conditions of constraints and uncertainties. To solve the problem of synthesis of optimal data transmission routes in sensor networks, it is necessary to synthesize a graph model, formally define the optimization problem, investigate the processes of functioning of network elements and obtain formalized descriptions of the dependence of network elements. The mathematical model of functioning of the distributed system of intelligent data flow control in sensor networks of variable topology is considered based on the formal model of the functioning of the distributed system of intelligent network management, the properties of the processes of optimal load management of nodes and the data network as a whole are investigated. The results of the research allowed to obtain formal descriptions of the dependence of the load of the computing system of the sensor network on the time of the data transmission process, waiting time, service time on the load of the computing system of the sensor network, to determine quantitative indicators of delay and loss of data packets depending on the functioning modes.

Trade, Violence and Diplomacy on the Coast of Ikorodu: The Resistance of Balogun Mabadeje Jaiyesimi

From the 1850s, British influence in Lagos and coastal Nigeria expanded, leading to the annexation of Lagos in December 1861 and the establishment of Lagos Colony in 1862. This period also witnessed the British quest for the control of coastal and inland trade routes. Ikorodu’s location north of Lagos and on the lagoon, and its control of trade from the coast to Sagamu, the main city of Remo, involved the town in larger struggles between the Ijebu kingdom and the Egba settlers at Abeokuta, and in the expansionist plans for Lagos under Governor Henry Stanhope Freeman (1862–4) and his successor, Captain John Hawley Glover (1864–6).
 This article explores how Ikorodu successfully manoeuvred between these differen interests under the leadership of Balogun Mabadeje Jaiyesimi to defeat its external aggressors and to increase its independence. It not only addresses the dearth of published work on Ikorodu but also provides a response to Earl Phillips’ discussion of the unsuccessful 1864–5 Egba attack on Ikorodu. Unlike Philips, who suggests that the Egba defeat was primarily engineered by John Glover, this article emphasises the importance of Balogun Jaiyesimi’s strategic and negotiating skills, which led to the formation of a local coalition between Ikorodu and its neighbouring towns, especially Igbogbo, to ensure Ikorodu’s military victory.