Presenting a New Method, Using Topology Virtualization for Stabilizing Flow Tables in SDWN
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Software Defined Networks split the data plane from the control plane. They can be used in wireless networks and will bring flexibility, less interference, simple management, less energy consumption and load balancing. They can also improve service quality, handover and mobility between different service providers. In previous methods, when link states were changed the controller deleted the stored topology and the tables in switches and updated them. Therefore, the controller should not only build the topology, but also execute the routing algorithms and install the routes in switch tables again. This may not cause any problems in wired networks, since link failure may not happen too often, but in wireless networks this happens frequently. In this paper the problems of (1) dynamic topology, (2) removing the whole flow table due to topology changes, and (3) time consuming routing algorithms are addressed. In the proposed method, after building a virtual topology with virtual node coordinates, the controller executes a heuristic routing algorithm on this topology. When the topology changes, the action of omitting rules from tables is applied only on routes including omitted or modified links and the rules related to missing routes should be deleted. Finally, the proposed method is compared with the L2_multi method (method used in POX controller). Results show that the proposed method decreases the average delay.Keywords:
Topology Control
Logical topology
This Webopedia Study Guide describes five of the most common network topologies. In computer networking, topology refers to the layout of connected devices. In communication networks, a topology is a usually schematic description of the arrangement of a network, including its nodes and connecting lines. There are two ways of defining network geometry: the physical topology and the logical (or signal) topology.
Schematic
Logical topology
Comparison of topologies
Geometric topology
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The objective of the paper is to implement topology control by maintain minimum degree and connectivity between nodes from the Virtual-Backbone (VB) updates. VB construction is very familiar, to reduce flooding and broadcast storm problem in wireless networks. Nodes in MANET, move anywhere at any time, it requires strong topology and connectivity maintenance between VB and other nodes. To overcome this problem we provide the topology maintenance procedure and connectivity maintenance using 2-connected graph approximation. Stable topology is achieved by connection maintenance between virtual backbone nodes by providing minimum rerouting and connection balanced routing between nodes. We proposed a localized algorithm (Backbone based Topology Control Algorithm, BTCA) to support minimum degree nodes, by reducing the number of connections in one VB and maintain network topology. Our results are witnessed by simulation.
Topology Control
Logical topology
Backbone network
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Wireless sensor networks (WSNs) are at present a standout amongst the most, guaranteeing areas in the field of information and communication technologies (ICT). This new technology has boundless potential for various applications in distinctive regions, including environmental research, medical application, military, transportation, stimulation, emergency administration, security, and smart spaces. However, several constraints of the sensor nodes are the principal obstacles in planning efficient protocols for WSNs. The major challenges of WSNs include energy dissipation; prolong the network lifetime and throughput. This thesis explores logical topologies in WSN. Logical topologies play the most significant role in the overall performance of the network, including its lifetime, routing efficiency, energy dissipation and overheads. A number of logical topologies have been proposed for WSNs including flat topology, cluster-distributed topology, cluster-centralized topology, and chain topology along with their corresponding routing protocols. In addition, the outcome of the study should definitely performed an important of those parameters of concerned. The simulation experiments are done by using NS-2.34 program for the logical topologies considered are cluster–distributed, chain-based, cluster–centralized and flat with the corresponding protocols of LEACH, PEGASIS, LEACH-C and MTE respectively, while MATLAB is used to plot the graphs. The performance metrics studied are the network lifetime, energy dissipation and aggregate data received at the base station. From the results it can be deduced, that the chain topology (PEGASIS) gives a better performance (network lifetime, energy dissipation and throughput at the base station) overall topologies (LEACH, LEACH-C and MTE).
Logical topology
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Wireless sensor networks (WSNs) have several constraints of the sensor nodes such as limited energy source, low
memory size and low processing speed, which are the principal obstacles to design efficient protocols for WSNs. Major
challenges of WSNs are to prolong the network lifetime and throughput. This paper explores performance of WSNs in
different logical topologies. Logical topologies play very significant role in the overall performances of the network, such
as network lifetime, throughput, , energy consumption and end-to-end delay. A number of logical topologies was
proposed for WSNs, including flat topology, cluster-distributed topology, cluster-centralized topology and chain
topology, along with their corresponding routing protocols. Simulation experiments were done by using NS-2.34 program
for the logical topologies. The topologies were cluster–distributed, chain-based, cluster–centralized and flat with its
corresponding protocols of LEACH, PEGASIS, LEACH-C and MTE respectively. MATLAB is used to plot the graphs.
Performance metrics measured are the network lifetime, energy consumption and total amount of aggregate data received
at the base station.
Logical topology
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Distributed computing through topology control involves making changes to the underlying network (modeled as a graph) to decrease the cost of distributed algorithms when executed across the modified networks. Topology construction builds reduced topology and topology maintenance adopts the reduced topology when the current topology is no longer optimal. This study makes major contributions to topology control by constructing new topologies named sparkle topologies using a ring topology, structured web topology, sun topology, and star topology. The efficiency of the sparkle topologies is checked by calculating the reliability using Wiener Index and data transfer using Cisco packet simulation. Data transmission times, the number of hops required, and the number of potential failure points are all reduced in sparkle topologies compared to ring topologies.
Logical topology
Comparison of topologies
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In recent days for computing, distributed computer systems have become very important and popular issue. It delivers high end performance at a low cost. Autonomous computers are connected by means of a communication network in a distributed computing environment which is arranged in a geometrical shape called network topology. In the present paper a detailed study and analysis on network topologies is presented. Definitions of Physical and Logical Topologies are also provided.
Logical topology
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Wireless Sensor Networks (WSNs) are formed by a large collection of power-conscious wireless-capable sensors without the support of pre-existing infrastructure, possibly by unplanned deployment. With a sheer number of sensor nodes, their unattended deployment and hostile environment very often preclude reliance on physical configuration or physical topology. It is, therefore, often necessary to depend on the logical topology. Logical topologies govern how a sensor node communicates with other nodes in the network. In this way, logical topologies play a vital role for resource-constraint sensor networks. It is thus more intuitive to approach the constraint minimizing problems from (logical) topological point of view. Hence, this paper aims to study the logical topologies of WSNs. In doing so, a set of performance metrics is identified first. We identify various logical topologies from different application protocols of WSNs, and then compare the topologies using the set of performance metrics.
Logical topology
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Topology Control
Logical topology
Hierarchical network model
Backbone network
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Abstrcat This paper put forward a planning algorithm, which aims to design topology of survivable regional integrated communications network. By hierarchical means, known subnetworks were abstracted to nodes, hence network topologies were computed by considering network topology survivability matrix as constraints. The network topology acquired by this algorithm satisfies not only survivability requirements, but also costs less by computer simulation. The elements in the matrix of network topology survivability metric are short disjoint paths between node pares in topologies, and the integrated network topologies designed by the algorithm mentioned are more practical and reliable based on the constraints matrix.
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