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Congestion in data networks by Waqas
Congestionin Data Networks
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In WSN congestion occurs when traffic load exceeds the capacity available at any point in a network. Congestion acts an important role in degrading the performance of the network or failure of the network. So it is essential to detect and control the congestion in the entire WSN. Thus one can improve the performance of the network. Different factors are involved in the congestion; the main factor is buffer over flow, packet loss, lowers network throughput and energy wastage. To address this challenge this is essential for a distributed algorithm that mitigate congestion and allocate appropriate source rate to a sink node for wireless sensor network. This paper gives some ideas how to control and manage the congestion in a wireless sensor network.
Congestion Control in Wireless Sensor Networks- An overview of Current Trends
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Facing burst traffic, TCP congestion control algorithms severely decrease window size neglecting the fact that such burst traffics are temporal. In the increase phase sending window experiences a linear rise which may lead to waste in hefty proportion of available bandwidth. If congestion control mechanisms be able to estimate future state of network traffic they can cope with different circumstances and efficiently use bandwidth. Since data traffic which is running on networks is mostly self-similar, algorithms can take advantage of self-similarity property and repetitive traffic patterns to have accurate estimations and predictions in large time scales. In this research a two-stage controller is presented. In fact the first part is a RED congestion controller which acts in short time scales (200 milliseconds) and the second is a Kalman filter estimator which do RTT and window size estimations in large time scales (every two seconds). If the RED mechanism decides to increase the window size, the magnitude of this increase is controlled by Kalman filter. To be more precise, if the Kalman filter indicates a non-congested situation in the next large time scale, a magnitude factor is calculated and given to RED algorithm to strengthen the amount of increase.
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Sensor network consists of a large number of small nods, strongly interacting with the physical environment, takes environmental data through sensors, and reacts after processing on information. Wireless network technologies are widely used in most applications. As wireless sensor networks have many activities in the field of information transmission, network congestion cannot be thus avoided. So it seems necessary that some new methods can control congestion and use existing resources for providing better traffic demands. Congestion increases packet loss and retransmission of removed packets and also wastes of energy. In this paper, a novel method is presented for congestion control in wireless sensor networks using genetic algorithm. The results of simulation show that the proposed method, in comparison with the algorithm LEACH, can significantly improve congestion control at high speeds.
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In WSN congestion occurs when traffic load exceeds the capacity available at any point in a network. Congestion acts an important role in degrading the performance of the network or failure of the network. So it is essential to detect and control the congestion in the entire WSN. Thus one can improve the performance of the network. Different factors are involved in the congestion; the main factor is buffer over flow, packet loss, lowers network throughput and energy wastage. To address this challenge this is essential for a distributed algorithm that mitigate congestion and allocate appropriate source rate to a sink node for wireless sensor network. This paper gives some ideas how to control and manage the congestion in a wireless sensor network.
Congestion Control in Wireless Sensor Networks- An overview of Current Trends
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Facing burst traffic, TCP congestion control algorithms severely decrease window size neglecting the fact that such burst traffics are temporal. In the increase phase sending window experiences a linear rise which may lead to waste in hefty proportion of available bandwidth. If congestion control mechanisms be able to estimate future state of network traffic they can cope with different circumstances and efficiently use bandwidth. Since data traffic which is running on networks is mostly self-similar, algorithms can take advantage of self-similarity property and repetitive traffic patterns to have accurate estimations and predictions in large time scales. In this research a two-stage controller is presented. In fact the first part is a RED congestion controller which acts in short time scales (200 milliseconds) and the second is a Kalman filter estimator which do RTT and window size estimations in large time scales (every two seconds). If the RED mechanism decides to increase the window size, the magnitude of this increase is controlled by Kalman filter. To be more precise, if the Kalman filter indicates a non-congested situation in the next large time scale, a magnitude factor is calculated and given to RED algorithm to strengthen the amount of increase.
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Sensor network consists of a large number of small nods, strongly interacting with the physical environment, takes environmental data through sensors, and reacts after processing on information. Wireless network technologies are widely used in most applications. As wireless sensor networks have many activities in the field of information transmission, network congestion cannot be thus avoided. So it seems necessary that some new methods can control congestion and use existing resources for providing better traffic demands. Congestion increases packet loss and retransmission of removed packets and also wastes of energy. In this paper, a novel method is presented for congestion control in wireless sensor networks using genetic algorithm. The results of simulation show that the proposed method, in comparison with the algorithm LEACH, can significantly improve congestion control at high speeds.
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In Wireless Sensor Networks (WSN) when an event is detected there is an increase in data traffic that might lead to packets being transmitted through the network close to the packet handling capacity of the WSN. The WSN experiences a decrease in network performance due to packet loss, long delays, and reduction in throughput. In this paper we developed an adaptive congestion control algorithm that monitors network utilization and adjust traffic levels and/or increases network resources to improve throughput and conserve energy. The traffic congestion control protocol DelStatic is developed by introducing backpressure mechanism into NOAH. We analyzed various routing protocols and established that DSR has a higher resource congestion control capability. The proposed protocol, ACCP uses a sink switching algorithm to trigger DelStatic or DSR feedback to a congested node based on its Node Rank. From the simulation results, ACCP protocol does not only improve throughput but also conserves energy which is critical to sensor application survivability on the field. Our Adaptive Congestion control achieved reliability, high throughput and energy efficiency.
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Peer-to-peer and mobile networks gained significant attention of both research community and industry. Applying the peer-to-peer paradigm in mobile networks lead to several problems regarding the bandwidth demand of peer-to-peer networks. Time-critical messages are delayed and delivered unacceptably slow. In addition to this, scarce bandwidth is wasted on messages of less priority. Therefore, the focus of this paper is on bandwidth management issues at the overlay layer and how they can be solved. We present HiPNOS.KOM, a priority based scheduling and active queue management system. It guarantees better QoS for higher prioritized messages in upper network layers of peer-to-peer systems. Evaluation using the peer-to-peer simulator PeerfactSim.KOM shows that HiPNOS.KOM brings significant improvement in Kademlia in comparison to FIFO and Drop-Tail, strategies that are used nowadays on each peer. User initiated lookups have in Kademlia 24% smaller operation duration when using HiPNOS.KOM.
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In wireless networks to improve the competence for event account. Due to the in complete transmission size of nodes, a single path often cannot meet the condition of data transmission. Consequently, multipath show is needed. However, not every path originate by multipath routing algorithms maybe appropriate for conveying image, because a extended routing pathway with a long end to end show delay may not satisfied the time constraint. Furthermore, each data stream includes two kinds of material data handover. We have coming a novel explicit rate-based congestion control method, for supportive requests hand over process. Handover Streaming based Congestion Control (HSBCC), a new adaptive media handover streaming congestion organization in which the assembly packet broadcast rate is adjusted rendering to the active bandwidth share of the connection. They prepare not maintain or continually inform their route tables with the newest route in network. If a node requirements to send a pack to another node then this protocol explorations for the route in an on-demand method and begins the joining in order to communicate and receive the packet. The route detection usually happens by flooding the route application packets throughout the network. It makes sense to simply disregard a packet loss due to random frequency errors than to multiplicatively reduction the current transport rate and it is more suitable to periodically investigation the network during interruption period for a prompt retrieval than to slow down and exponentially increase the retransmission timer.
Reduce Handover Delay Using the HSBCC Based Buffer Over Flow In Wimax Network
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In Wireless Sensor Networks (WSN) when an event is detected there is an increase in data traffic that might lead to packets being transmitted through the network close to the packet handling capacity of the WSN. The WSN experiences a decrease in network performance due to packet loss, long delays, and reduction in throughput. In this paper we developed an adaptive congestion control algorithm that monitors network utilization and adjust traffic levels and/or increases network resources to improve throughput and conserve energy. The traffic congestion control protocol DelStatic is developed by introducing backpressure mechanism into NOAH. We analyzed various routing protocols and established that DSR has a higher resource congestion control capability. The proposed protocol, ACCP uses a sink switching algorithm to trigger DelStatic or DSR feedback to a congested node based on its Node Rank. From the simulation results, ACCP protocol does not only improve throughput but also conserves energy which is critical to sensor application survivability on the field. Our Adaptive Congestion control achieved reliability, high throughput and energy efficiency.
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Router Assisted Congestion Control (RACC) was designed to improve endto-end congestion control performance by using prior knowledge on network condition. However, the traditional Internet does not provide such information, which makes this approach is not feasible to deliver. Our paper addresses this network information deficiency issue by proposing a new congestion control method that works on the Software Defined Network (SDN) framework. We call this proposed method as PACEC (Path Associativity Centralized Congestion Control). In SDN, global view of the network information contains the network topology including link properties (i.e., type, capacity, power consumption, etc.). PACEC uses this information to determine the feedback signal, in order for the source to start sending data at a high rate and to quickly reach fair-share rate. The simulation shows that the efficiency and fairness of PACEC are better than Transmission Control Protocol (TCP) and Rate Control Protocol (RCP).
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Transmission Control Protocol (TCP) is a connection oriented end-end reliable byte stream transport layer protocol. It is widely used in the Internet.TCP is fine tuned to perform well in wired networks. However the performance degrades in mobile ad hoc networks. This is due to the characteristics specific to wireless networks, such as signal fading, mobility, unavailability of routes. This leads to loss of packets which may arise either from congestion or due to other non-congestion events. However TCP assumes every loss as loss due to congestion and invokes the congestion control procedures. TCP reduces congestion window in response, causing unnecessary degradation in throughput. In mobile ad hoc networks multi-hop path forwarding further worsens the packet loss and throughput. To understand the TCP behavior and improve the TCP performance over mobile ad hoc networks considerable research has been carried out. As the research is still active in this area a comprehensive and in-depth study on the TCP throughput and the various parameters that degrade the performance of TCP have been analyzed. The analysis is done using simulations in Qualnet 5.0
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NetWork Design Question 2.) How does TCP prevent Congestion? Dicuss the information identifying congestion in the network as well as the mechanism for reducing congestion? Solution Congestion is a problem that occurs on shared networks when multiple users contend for access to the same resources (bandwidth, buffers, and queues). Transmission Control Protocol (TCP) uses a network congestion-avoidance algorithm that includes various aspects of an additive increase/multiplicative decrease (AIMD) scheme, with other schemes such as slow-start to achieve congestion avoidance. The TCP congestion-avoidance algorithm is the primary basis for congestion control in the Internet. Congestion typically occurs where multiple links feed into a single link, such as where internal LANs are connected to WAN links. Congestion also occurs at routers in core networks where nodes are subjected to more traffic than they are designed to handle. TCP/IP networks such as the Internet are especially susceptible to congestion because of their basic connection- less nature. There are no virtual circuits with guaranteed bandwidth. Packets are injected by any host at any time, and those packets are variable in size, which make predicting traffic patterns and providing guaranteed service impossible. While connectionless networks have advantages, quality of service is not one of them. Shared LANs such as Ethernet have their own congestion control mechanisms in the form of access controls that prevent multiple nodes from transmitting at the same time. Identifying: Congestion is primarily reflected by a conventional user feeling-- slowness. This statement reflects the change in the network effective flow, that is the time required to transmit an entire data from one point to another. The effective flow doenot exist as such, it consists in reality of three seperate indicators: *Latency:the effective flow is inversely proportional to the latency. *Jitter:it is latency variation over time, impacts by influencing the flow latency *Loss Rate:the theoritical bandwidth is inversely proportional to the square root of the loss rate These Congestion symtoms allow us to rely on objective indicators to characterize it. Mechanism to reduce congestion: The standard fare in TCP implementations today has four standard congestion control algorithms that are now in common use. Their usefulness has passed the test of time. The four algorithms, Slow Start, Congestion Avoidance, Fast Retransmit and Fast Recovery are described below. (a) Slow Start Slow Start, a requirement for TCP software implementations is a mechanism used by the sender to control the transmission rate, otherwise known as sender-based flow control. This is accomplished through the return rate of acknowledgements from the receiver. In other words, the rate of acknowledgements returned by the receiver determine the rate at which the sender can transmit data. When a TCP connection first begins, the Slow Start algorithm initializes a .
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A state occurring in network layer when the message traffic is so heavy that it slows down network response time. Effects of Congestion As delay increases, performance decreases. If delay increases, retransmission occurs, making situation worse.
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The CNIC Information System is a comprehensive database managed by the National Database and Registration Authority (NADRA) of Pakistan. It serves as the primary source of identification for Pakistani citizens and residents, containing vital information such as name, date of birth, address, and biometric data.
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Dubai, known for its towering skyscrapers, luxurious lifestyle, and relentless pursuit of innovation, often finds itself in the global spotlight. However, amidst the glitz and glamour, the emirate faces its own set of challenges, including the occasional threat of flooding. In recent years, Dubai has experienced sporadic but significant floods, disrupting normalcy and posing unique challenges to its infrastructure. Among the critical nodes in this bustling metropolis is the Dubai International Airport, a vital hub connecting the world. This article delves into the intersection of Dubai flood events and the resilience demonstrated by the Dubai International Airport in the face of such challenges.
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Dubai, often portrayed as a shimmering oasis in the desert, faces its own set of challenges, including the occasional threat of flooding. Despite its reputation for opulence and modernity, the emirate is not immune to the forces of nature. In recent years, Dubai has experienced sporadic but significant floods, testing the resilience of its infrastructure and communities. Among the critical lifelines in this bustling metropolis is the Dubai International Airport, a bustling hub that connects the city to the world. This article explores the intersection of Dubai flood events and the resilience demonstrated by the Dubai International Airport in the face of such challenges.
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Webinar Recording: https://www.panagenda.com/webinars/why-teams-call-analytics-is-critical-to-your-entire-business Nothing is as frustrating and noticeable as being in an important call and being unable to see or hear the other person. Not surprising then, that issues with Teams calls are among the most common problems users call their helpdesk for. Having in depth insight into everything relevant going on at the user’s device, local network, ISP and Microsoft itself during the call is crucial for good Microsoft Teams Call quality support. To ensure a quick and adequate solution and to ensure your users get the most out of their Microsoft 365. But did you know that ‘bad calls’ are also an excellent indicator of other problems arising? Precisely because it is so noticeable!? Like the canary in the mine, bad calls can be early indicators of problems. Problems that might otherwise not have been noticed for a while but can have a big impact on productivity and satisfaction. Join this session by Christoph Adler to learn how true Microsoft Teams call quality analytics helped other organizations troubleshoot bad calls and identify and fix problems that impacted Teams calls or the use of Microsoft365 in general. See what it can do to keep your users happy and productive! In this session we will cover - Why CQD data alone is not enough to troubleshoot call problems - The importance of attributing call problems to the right call participant - What call quality analytics can do to help you quickly find, fix-, and prevent problems - Why having retrospective detailed insights matters - Real life examples of how others have used Microsoft Teams call quality monitoring to problem shoot problems with their ISP, network, device health and more.
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Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows. We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases. This video focuses on the deployment of external web forms using Jotform for Bonterra Impact Management. This solution can be customized to your organization’s needs and deployed to support the common use cases below: - Intake and consent - Assessments - Surveys - Applications - Program registration Interested in deploying web form automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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In this presentation, we delve into leveraging Amazon Q to elevate developer efficiency and craft GenAI applications. Discover the key features and benefits of Amazon Q for streamlined application development. Learn how Amazon Q can revolutionize your development processes and empower you to create cutting-edge GenAI applications.
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Congestionin Data Networks
1.
William Stallings Data
and Computer Communications 7 th Edition Chapter 1 3 Congestion in Data Networks
2.
3.
Queues at a
Node
4.
5.
Interaction of Queues
6.
Ideal Network
Utilization Power = thruput/delay Fig. 13.3
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8.
Effects of
Congestion - No Control Fig. 13.4
9.
Mechanisms for
Congestion Control
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19.
20.
Operation of CIR
21.
Relationship Among
Congestion Parameters
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23.
24.
25.
26.
Configuration of
VCCs and VPCs
27.
28.
29.
30.
31.
Token Bucket
for Traffic Shaping
Baixar agora