Variety Of Network Topologies Research Articles

Mobile Ad Hoc Network Routing Protocols: A Detailed Performance Examination of AODV, DSR and DSDV

ABSTARCT The latest technology MANETs is being studied widely and attracting a large variety of applications. Due to varying network topology, The most common challenging factor in MANET is routing [2][3]. In this research paper, we study the performance of reactive routing protocols, Ad hoc on demand Distance Vector (AODV) and Dynamic Source Routing (DSR) and proactive routing protocol Destination Sequenced Distance Vector (DSDV). We study the performance of MANETs routing protocol in high mobility case under low, medium and high density scenario. It becomes important to study the noticeable effect of high mobility on the performance of these routing protocols. We examine in detail the performance of routing protocol with respect to Average End-to-End Delay, Normalized Routing Load (NRL), Packet Delivery Fraction (PDF) and Throughput. Simulation study with NS-2 confirms that AODV provide better performance as compared to DSR and DSDV

Control-based p-persistent adaptive communication protocol

An enhancement to the CSMA p-persistent protocol family is proposed, based on a fully decentralized control that adjusts the message transmission rate of each node to the estimated density of surrounding transmitting nodes. The system does not require enumeration of nodes nor control messages, the only input to the control coming from the physical medium occupation. In addition to communication protocols, this technique can be used as inexpensive and efficient density estimation of physical entities in highly dynamic scenarios. Stability conditions are proposed by means of a rigorous theoretical investigation. We prove that our algorithm ensures stability even with unknown and time--varying network topologies. We show that good channel exploitation levels can be assured as well by suitably tuning the control parameters. A digital algorithm for practical implementation is proposed. The results are supported by simulation experiments.

Artificial Neural Network Modeling for Biosorption of Pb (II) Ions on Nanocellulose Fibers

This study investigates the efficiency of nanocellulose fibers (NCFs) derived from rice straw for the abatement of lead ions from aqueous solution. The maximum biosorption of lead (II) is found to be 94.21 % at pH 6.5 and equilibrium time of 40 min by using NCFs of 0.5 g/200 ml (test volume) and initial concentration of 25 mg/L of lead concentration. The data collected from laboratory-scale experimental setup are used to train a multilayer perceptron network combined with backpropagation and Levenberg–Marquadt algorithm. Different artificial neural network (ANN) architectures were tested by varying network topology as a function of number of neurons in the hidden layer of ANN model. A single-layer ANN model with ten hidden neurons at 1,000 epochs using sigmoidal transfer function has been found to have the best predictive performance. The network is found to be working satisfactorily with minimum mean squared error (5.8472E-05) and mean absolute error (1.38845310688678) during training phase. Comparison between the network results and experimental data gives a high degree of correlation coefficient (R 2 = 0.995) indicating that the model is able to predict the sorption efficiency with reasonable accuracy. The influence of each parameter on the variable studied was assessed, and pH, volume of the test solution, biomass dose, metal concentration, and contact time were found to be the most significant factors. Simulations based on the developed ANN model can estimate the behavior of the biosorption phenomenon process under different conditions.

A dual-channel cross-layer architecture for underwater acoustic networks

The performance of underwater acoustic networks( UAN) is affected by the node device constraints of memory, processing power, battery life time and network topology variation. To improve the performance and to utilize scarce resource can be obtained with a Cross-layer design. Cross-layer design, one of the key technique in underwater communication networks, overcomes the disadvantages of the strictly layered networks such as nonoptimality and inflexibility. It enables the system to utilize the limited resources more sufficiently, especially in no central control, rapid changes topology of underwater networks, and achieves better performance. This paper discusses the benefits of cross-layer underwater acoustic networks and related work, introduces a representative cross-layer architecture, named dual-channel cross-layer architecture, to promote the overall system performance for underwater networks. In addition, three cross-layer solutions, node adaptive modulation and channel coding, joint design of MAC a...

Mean first-passage time calculations: comparison of the deterministic Hill’s algorithm with Monte Carlo simulations

Accurate determination of mean first-passage times (MFPTs) between any two states of a complex network still attracts considerable attention. Appropriate methods should take into account the discrepancy in MFPTs when a random walker moves first from a source to a target and then in the opposite direction. In addition, it is desirable to allow fast evaluation of mean first-passage times when transition probabilities are allowed to vary over time. For our calculations we make use of Hill’s algorithm, which enables the exact calculation of MFPTs. As we show in this work, when given a fixed distance to travel, the calculation of a particular MFPT depends on the choice of source and target points and their relative position on a lattice. We also demonstrate, when the network contains a relatively low number of cycles, that this deterministic technique provides exact results much faster in comparison to the more standard, but computationally demanding, stochastic Monte Carlo simulation method, where only approximate results can be obtained that are highly dependent on the number of walkers. Therefore, our specific implementation of Hill’s algorithm should facilitate efficient and accurate computation of MFPTs on a variety of network topologies of practical interest to the broad scientific community.

Adsorption behavior of dyes from aqueous solution using agricultural waste: modeling approach

The development of efficient and ecofriendly biosorbent for the removal of dyes is a priority in regions where human health is directly affected by elevated dye concentrations. Biosorption of dyes on shelled Moringa oleifera seed powder (SMOS) was investigated for the removal of methylene blue and Congo red from aqueous solution. Sorption studies led to the standardization of the optimum conditions: dye concentration (25 mg/l), contact time (40 min), particle size (105 μM), and volume (200 ml) at pH 6.5 and 2.5 for the removal of methylene blue (90.27 %) and Congo red (98.52 %). A single layer artificial neural network (ANN) model was developed to simulate the process and predict the removal efficiency of SMOS for the removal of dyes. Different ANN architectures were tested by varying network topology, resulting into an excellent agreement between the experimental data and the predicted values. The Levenberg–Marquardt algorithm was found best of BP algorithms with a minimum mean squared error for training and cross validation as 1.89951E−09 and 0.145001313, respectively.

Isolation Schemes of Virtual Network Platform for Cloud Computing

Network virtualization supports future Internet environments and cloud computing. Virtualization can mitigate many hardware restrictions and provide variable network topologies to support variable cloud services. Owing to several advantages such as low cost, high flexibility, and better manageability, virtualization has been widely adopted for use in network virtualization platforms. Among the many issues related to cloud computing, to achieve a suitable cloud service quality we specifically focus on network and performance isolation schemes, which ensure the integrity and QoS of each virtual cloud network. In this study, we suggest a virtual network platform that uses Xen-based virtualization, and implement multiple virtualized networks to provide variable cloud services on a physical network. In addition, we describe the isolation of virtual networks by assigning a different virtualized network ID (VLAN ID) to each network to ensure the integrity of the service contents. We also provide a method for efficiently isolating the performance of each virtual network in terms of network bandwidth. Our performance isolation method supports multiple virtual networks with different levels of service quality.

Anycast Routing for Survivable Optical Grids: Scalable Solution Methods and the Impact of Relocation

In this paper, we address the issue of resiliency against single link network failures in optical grids and show how the anycast routing principle, which is typical of grids, can be exploited in providing efficient shared path protection. We investigate two different integer linear program models for the full anycast routing problem, deciding on the primary and backup server locations as well as on the lightpaths toward them. The first model is a classical integer linear programming (ILP) model, which lacks scalability. The second model is a large-scale optimization model which can be efficiently solved using column generation techniques. We also design two new heuristics: the first one is an improvement of a previously proposed one which, although providing near optimal solutions, lacks scalability, while the second one is highly scalable, at the expense of reduced accuracy. Numerical results are presented for three mesh networks with varying node degrees. They allow an illustration of the scalability of the newly proposed approaches. Apart from highlighting the difference in performance (i.e., scalability and optimality) among the algorithms, our case studies demonstrate the bandwidth savings that can be achieved by exploiting relocation rather than using a backup path to the original (failure-free) destination site. Numerical results for varying network topologies, as well as different numbers of server sites show that relocation allows bandwidth savings in the range of 7–21%.

Bounds and parameter optimization of medium access control coding for wireless ad hoc and sensor networks

The use of codes to schedule transmissions is an attractive technique able to guarantee a non-zero throughput medium access performance for the nodes of a wireless ad hoc or sensor network regardless of network topology variations. Some authors refer to this technique as topology-transparent scheduling. In this paper, we use the term MAC coding in order to emphasize the exclusive use of codes to achieve topology-transparency within the MAC sub-layer. We present a new upper bound expression on the guaranteed throughput achievable by any linear code used in a MAC coding context. This bound proves to be tighter than the one obtained when the minimum distance of the code is equal to its length. Additionally, we derive new and simple closed analytical expressions for the parameters of maximum distance separable codes that maximize the minimum, average, or joint minimum–average throughput of MAC coding. The optimization methods presented here are also applicable to other codes with available analytical expressions for their minimum distance and distance distribution. Finally, we present system-level simulation results of MAC coding on static and dynamic topologies with mobility and including wireless channel errors. Throughput simulation results are compared with their corresponding analytical expressions and to a random scheduling approach. The results show agreement with analysis and confirm the robustness of MAC coding in maintaining minimum levels of performance with good average performance and graceful degradation.

Distributed multi-agent optimization with state-dependent communication

We study distributed algorithms for solving global optimization problems in which the objective function is the sum of local objective functions of agents and the constraint set is given by the intersection of local constraint sets of agents. We assume that each agent knows only his own local objective function and constraint set, and exchanges information with the other agents over a randomly varying network topology to update his information state. We assume a state-dependent communication model over this topology: communication is Markovian with respect to the states of the agents and the probability with which the links are available depends on the states of the agents. We study a projected multi-agent subgradient algorithm under state-dependent communication. The state-dependence of the communication introduces significant challenges and couples the study of information exchange with the analysis of subgradient steps and projection errors. We first show that the multi-agent subgradient algorithm when used with a constant stepsize may result in the agent estimates to diverge with probability one. Under some assumptions on the stepsize sequence, we provide convergence rate bounds on a “disagreement metric” between the agent estimates. Our bounds are time-nonhomogeneous in the sense that they depend on the initial starting time. Despite this, we show that agent estimates reach an almost sure consensus and converge to the same optimal solution of the global optimization problem with probability one under different assumptions on the local constraint sets and the stepsize sequence.

Chimeras in a network of three oscillator populations with varying network topology

We study a network of three populations of coupled phase oscillators with identical frequencies. The populations interact nonlocally, in the sense that all oscillators are coupled to one another, but more weakly to those in neighboring populations than to those in their own population. Using this system as a model system, we discuss for the first time the influence of network topology on the existence of so-called chimera states. In this context, the network with three populations represents an interesting case because the populations may either be connected as a triangle, or as a chain, thereby representing the simplest discrete network of either a ring or a line segment of oscillator populations. We introduce a special parameter that allows us to study the effect of breaking the triangular network structure, and to vary the network symmetry continuously such that it becomes more and more chain-like. By showing that chimera states only exist for a bounded set of parameter values, we demonstrate that their existence depends strongly on the underlying network structures, and conclude that chimeras exist on networks with a chain-like character.

Intelligent transportation spaces: vehicles, traffic, communications, and beyond

Recent years have witnessed numerous technical breakthroughs in electronics, computing, sensing, robotics, control, signal processing, and communications. These have significantly advanced the state of applications of intelligent transportation systems. More recently, as one leading effort toward the cyber-physical-social system, the concept of intelligent transportation spaces was proposed to further improve the vehicles, traffic, and transportation safety, efficiency and sustainability. ITSp integrate not only various ITS modules, but also pedestrians, vehicles, roadside infrastructures, traffic management centers, sensors, and satellites. With distributed and pervasive intelligence, ITSp clearly impose some stringent requirements on the information exchange among all entities within the ITSp, in terms of the information availability, reliability, fidelity, and timeliness. These requirements, together with the high mobility of vehicles and the highly variable network topology, make the communications and networking for ITSp very challenging. This article will introduce the concept of ITSp and analyze possible communication technology candidates for ITSp. Further discussions will also be provided at the end of this article.

Consensus of synchronization-preferential scale-free networks

In this paper, the relations of the scale-free network topology and the moving consensus of multi-agent systems are studied. A synchronization-preferential BA (SPBA) network with the combinatorial preferential attachment is presented for the prestissimo consensus. The effects of the SPBA network on the algebraic connectivity of the topology graph are compared with the standard BA network. The robustness gain to delay is analyzed for variable network topologies with the same size. The characteristics of dramatic increase for the algebraic connectivity and slightly increase for the robustness gain to delay are unfolded in SPBA network. By comparing the convergence time of the different network structures, the consensuses are studied for multiagent systems with and without communication delays.

Interactive 3D visualization for wireless sensor networks

Wireless sensor networks open up a new realm of ubiquitous computing applications based on distributed large-scale data collection by embedded sensor nodes that are wirelessly connected and seamlessly integrated within the environment. 3D visualization of sensory data is a challenging issue, however, due to the large number of sensors used in typical deployments, continuous data streams, and constantly varying network topology. This paper describes a practical approach for interactive 3D visualization of wireless sensor network data. A regular 3D grid is reconstructed using scattered sensor data points and used to generate view-dependent 2D slices that are consequently rendered with commodity graphics hardware leading to smooth visualization over space and time. Furthermore, the use of efficient space partitioning data structures and the independent processing of sensor data points facilitates interactive rendering for large number of sensors while accommodating constantly changing network topology. The practical value of the proposed method is demonstrated and results obtained for visualizing time-varying temperature distributions in an urban area are presented.

Consensus of Multi-Agent Systems with Prestissimo Scale-Free Networks

In this paper, the relations of the network topology and the moving consensus of multi-agent systems are studied. A consensus-prestissimo scale-free network model with the static preferential-consensus attachment is presented on the rewired link of the regular network. The effects of the static preferential-consensus BA network on the algebraic connectivity of the topology graph are compared with the regular network. The robustness gain to delay is analyzed for variable network topology with the same scale. The time to reach the consensus is studied for the dynamic network with and without communication delays. By applying the computer simulations, it is validated that the speed of the convergence of multi-agent systems can be greatly improved in the preferential-consensus BA network model with different configuration.

Genetic algorithm for optimizing service distributions

This paper deals with optimizing the distribution of services performed by agents in a mobile agent network (MAN) using a genetic algorithm. The MAN is a model which consists of agents and various network elements and nodes. Each agent is capable of performing remote software management services. In order to verify the efficiency of various service distributions on different network topologies, we developed a mobile agent network simulator. This simulator allows the user to define the services which need to be performed by agents and describe the physical network used in the process. In this paper, an approach using a genetic algorithm is proposed. This algorithm is used to generate a suboptimal distribution of services among agents which optimizes the total execution time of services on a variety of network topologies with different network parameters.

Greedy Gossip With Eavesdropping

This paper presents greedy gossip with eavesdropping (GGE), a novel randomized gossip algorithm for distributed computation of the average consensus problem. In gossip algorithms, nodes in the network randomly communicate with their neighbors and exchange information iteratively. The algorithms are simple and decentralized, making them attractive for wireless network applications. In general, gossip algorithms are robust to unreliable wireless conditions and time varying network topologies. In this paper we introduce GGE and demonstrate that greedy updates lead to rapid convergence. We do not require nodes to have any location information. Instead, greedy updates are made possible by exploiting the broadcast nature of wireless communications. During the operation of GGE, when a node decides to gossip, instead of choosing one of its neighbors at random, it makes a greedy selection, choosing the node which has the value most different from its own. In order to make this selection, nodes need to know their neighbors' values. Therefore, we assume that all transmissions are wireless broadcasts and nodes keep track of their neighbors' values by eavesdropping on their communications. We show that the convergence of GGE is guaranteed for connected network topologies. We also study the rates of convergence and illustrate, through theoretical bounds and numerical simulations, that GGE consistently outperforms randomized gossip and performs comparably to geographic gossip on moderate-sized random geometric graph topologies.

How network structure shapes pairwise correlations between integrate-and-fire neurons

Correlations between neurons can be caused by various mechanisms and can be pronounced on different levels – on the level of spikes or membrane potentials – and both in time and space. Here, we analyze quantitatively the role of network structure on pairwise correlations in a model of local recurrent networks of excitatory and inhibitory leaky integrate-and-fire neurons. Especially correlations between the input currents of neurons prove to be highly sensitive to the statistical features of network structure like topology and weight distribution. We studied a variety of network topologies, ranging from random to small-world and regular [1,2]. If the external input spike trains are uncorrelated and Poissonian, input correlations between neurons are predominantly due to direct and indirect common input that is inherent to the recurrence of the local network. We speak of direct common input if two neurons share a common presynaptic neuron, and of indirect common input if two neurons receive inputs from two different presynaptic neurons, which in turn share a common presynaptic neuron.

Random Sensory Networks: A Delay Analysis

A fundamental function performed by a sensory network is the retrieval of data gathered collectively by sensor nodes. The metrics that measure the efficiency of this data collection process are time and energy. In this paper, we study via simple discrete mathematical models, the statistics of the data collection time in sensory networks. Specifically, we analyze the average minimum delay in collecting randomly located/distributed sensors data for networks of various topologies when the number of nodes becomes large. Furthermore, we analyze the impact of various parameters such as size of packet, transmission range, and channel erasure probability on the optimal time performance. Our analysis applies to directional antenna systems as well as omnidirectional ones. This paper focuses on directional antenna systems and briefly presents results on omnidirectional antenna systems. Finally, a simple comparative analysis shows the respective advantages of the two systems.

Delay-based TCP congestion avoidance: A network calculus interpretation and performance improvements

In delay-based TCP congestion avoidance mechanisms, a source adjusts its window size to adapt to changes in network conditions as measured through changing queueing delays. Although network calculus (NC) has been used to study window flow control and determine performance bounds, there is a lack of a bridge between NC theory and the practical issues of delay-based TCP congestion avoidance. In this paper, we use an NC-based approach to derive ideal congestion controllers for representative delay-based window flow control models with time-variant feedback delays. We show that the basic delay-based TCP congestion avoidance mechanisms in TCP Vegas, Enhanced TCP Vegas, and FAST TCP can be viewed as different approaches to approximating a certain NC controller. Moreover, we derive another NC controller that is explicitly constructed to address the throughput degradation of the current delay-based methods due to delayed acknowledgement (ACK) packets caused by network traffic in the reverse path (ACK packet) direction. The approximation of this F-model NC-based controller is shown to provide better throughput and fairness over the existing delay-based methods for a variety of network topologies in ns-2 simulations.