Random Network Topologies Research Articles

Optimized mesh routing with intermediate recovery for error resilient delivery of MD coded image/video content

Multiple Description (MD) source coding is a technique that breaks a media stream into equally important sub-streams which can be sent over different paths for protection against wireless channel errors. In this paper, we explore the possibility of sending these descriptions through different paths that merge at some specific intermediate nodes, where the corrupted descriptions are recovered from the uncorrupted ones, thereby increasing the quality of received image/video at the destination. To quantify the gain with intermediate recovery, we first devise an analytic model with simplifying assumptions on the network system for quantifying end-to-end distortion of MD coded data as a function of path parameters, and demonstrate that on a Lena image transmitted over long multipath routes, one intermediate recovery stage offers up to 9.2% reduction in distortion compared to the traditional multipath transport. Next, accounting the random network topology we formulate mesh route construction as a cross-layer optimization problem to balance between end-to-end packet delivery delay and distortion. Since this problem is highly complex, we propose two alternative delay/distortion minimization heuristics. Further, a jointly delay and distortion optimizing genetic algorithm based meta-heuristic route construction technique is suggested for networks with highly varying link quality. NS2-based simulations of a realistic network scenario demonstrate that, in terms of peak signal-to-noise ratio the intermediate recovery approach results in substantial improvement, close to 15 dB, in quality of video delivery.

An improved distance vector-Hop localization algorithm based on coordinate correction

In order to improve the localization accuracy of distance vector-Hop algorithm under the random topology network scenarios, a novel algorithm named coordinates correction-distance vector-Hop is pro...

Fault tolerant time synchronization using offsets table robust broadcasting protocol for vehicular ad hoc networks

The requirement of time synchronization emerged in distributed systems remains one of the most significant issues that should be addressed to the extent of that systems evolve. As clock synchronization is important for any type of network, Vehicular Ad hoc networks (VANETs) are being considered for their basic communication platforms, but also for providing the ability to detect movement, location, proximity, and other network capabilities. The intrinsic characteristics of VANETs like: the high speed of nodes and the lack of permanent network connectivity generated by an instable environment, which make communication difficult or temporarily impossible, have created new challenges. These challenges make solutions that have been already proposed for classical networks no longer appropriate. Therefore, to overcome this deficiency, new and adaptive clock synchronization mechanisms should be devised and implemented, dealing so with communication and scalability issues. In this paper, we propose “Offsets Table Robust Broadcasting” (OTRB) algorithm. In this algorithm, instead to each node communicates with its vicinity, a set of nodes is selected to spread the time information over the entire network. The proposed time synchronization protocol is well-adapted to random network topology changes, high nodal velocity while offering good precision and robustness against nodal failure and packet loss. The analytical study and protocol simulation for evaluating the system performance, carried out by a combination of VanetMobiSim and NS2 simulators, have yielded convincing results, outperforming those exhibited by the basic referred protocols.

Exploring time factors in measuring the scientific impact of scholars

Taking advantage of the easy access to the rich and massive scholarly data, more and more researchers are focusing on the studies of analyzing and utilizing the scholarly big data. Among them, evaluating the scientific impact of scholars has significant importance. Measuring the scientific impact of scholars can not only provide basis for the applications of academic foundations and awards, but also shed light on the research directions for scholars. Currently, citation based methods and network based metrics are the most commonly used ways to evaluate the scientific impact. However, these approaches ignore several important facts, i.e. the dynamics of citations and the initial qualities of different articles. To alleviate the shortcomings of them, we propose a Time-aware Ranking algorithm (TRank) to evaluate the impact of scholars. Due to scholars' sustainable supreme concerns of academic innovations, the TRank algorithm gives more credits to the newly published scholarly papers as well as their references according to the representative time functions. Our method also combines the merits of random walk algorithms and heterogeneous network topology, i.e. the mutual influences among different scholarly entities in heterogeneous academic networks. To validate the suitable time function for TRank algorithm and explore its performance, we construct the experiments on two real datasets: (1) Digitial Bibliography and Library Project, and (2) American Physical Society. The experimental results demonstrate that our algorithm outperforms other methods in selecting outstanding scholars and the evaluation results on the overall impact of scholars.

Decentralized adaptive search using the noisy 20 questions framework in time-varying networks

This paper considers the problem of adaptively searching for an unknown target using multiple agents connected through a time-varying network topology. Agents are equipped with sensors capable of fast information processing, and we propose a decentralized collaborative algorithm for controlling their search given noisy observations. Specifically, we propose decentralized extensions of the adaptive query-based search strategy that combines elements from the 20 questions approach and social learning. Under standard assumptions on the time-varying network dynamics, we prove convergence to correct consensus on the value of the parameter as the number of iterations go to infinity. The convergence analysis takes a novel approach using martingale-based techniques combined with spectral graph theory. Our results establish that stability and consistency can be maintained even with one-way updating and randomized pairwise averaging, thus providing a scalable low complexity method with performance guarantees. We illustrate the effectiveness of our algorithm for random network topologies.

Analysis of Linear Network Coding in Cooperative Multi-hop Networks

We investigate the performance of two multi-hop network topologies in which two sources have independent information to be transmitted to a far off common destination. Linear network coding technique is used by the intermediate relays to transmit the combined information of two sources. The first topology contains nodes that are placed in a regular pattern and the transmission process is modeled with a quasi-stationary Markov chain. The relay nodes use decode and forward mechanism at each hop. We find the transition probability matrix of the Markov chain assuming that all the nodes have same transmit power and the channel is Rayleigh fading. The second random network topology has fixed number of nodes that are randomly placed in a strip-shaped network. The outage probability of each node is found and the state distribution at each hop is used to analyze the network coverage for a given signal-to-noise ratio margin. Theoretical results have been included that match with the simulation results.

Distributed Shortcut Networks: Low-Latency Low-Degree Non-Random Topologies Targeting the Diameter and Cable Length Trade-Off

Low communication latency becomes a main concern in highly parallel computers and supercomputers that reach millions of processing cores. Random network topologies are better suited to achieve low average shortest path length and low diameter in terms of the hop counts between nodes. However, random topologies lead to two problems: (1) increased aggregate cable length on a machine room floor that would become dominant for communication latency in next-generation custom supercomputers, and (2) high routing complexity that typically requires a routing table at each node (e.g., topology-agnostic deadlock-free routing). In this context, we first propose low-degree non-random topologies that exploit the small-world effect, which has been well modeled by some random network models. Our main idea is to carefully design a set of various-length shortcuts that keep the diameter small while maintaining a short cable length for economical passive electric cables. We also propose custom routing that uses the regularity of the various-length shortcuts. Our experimental graph analyses show that our proposed topology has low diameter and low average shortest path length, which are considerably better than those of the counterpart 3-D torus and are near to those of a random topology with the same average degree. The proposed topology has average cable length drastically shorter than that of the counterpart random topology, which leads to low cost of interconnection networks. Our custom routing takes non-minimal paths to provide lower zero-load latency than the minimal custom routings on different counterpart topologies. Our discrete-event simulation results using SimGrid show that our proposed topology is suitable for applications that have irregular communication patterns or non-nearest neighbor collective communication patterns.

Less Is More: Creating Spectrum Reuse Opportunities via Power Control for OFDMA Femtocell Networks

Femtocell is a promising technology which can significantly improve the spectrum efficiency and system capacity of wireless cellular networks. However, densely and randomly deployed femtocells may experience severe interfemtocell interference due to rough-and-tumble spectrum reuse with unconstrained transmit power. Different from traditional macrocell scenarios, there is no central controller available for femtocell networks. Moreover, a static planning scheme is not workable here due to random network topology. Based on game theory, we propose a sophisticated downlink power control scheme which creates more spectrum reuse opportunities among mutually interfering femtocells and enables a flexible spectrum reuse scheme. The power control problem is modeled as a generalized Nash equilibrium (NE) problem, and we employ variational inequality theory to analyze it. Existence and uniqueness properties of the NE solution are derived. Furthermore, a distributed channel-aware power control algorithm is proposed to create spectrum reuse opportunities for femtocells interfering each other. Numerical results suggest that a significant capacity improvement is acquired, and the NE point can be reached within a few iterations.

Longitudinal changes in functional connectivity of cortico-basal ganglia networks in manifests and premanifest huntington's disease.

Huntington's disease (HD) is a genetic neurological disorder resulting in cognitive and motor impairments. We evaluated the longitudinal changes of functional connectivity in sensorimotor, associative and limbic cortico-basal ganglia networks. We acquired structural MRI and resting-state fMRI in three visits one year apart, in 18 adult HD patients, 24 asymptomatic mutation carriers (preHD) and 18 gender- and age-matched healthy volunteers from the TRACK-HD study. We inferred topological changes in functional connectivity between 182 regions within cortico-basal ganglia networks using graph theory measures. We found significant differences for global graph theory measures in HD but not in preHD. The average shortest path length (L) decreased, which indicated a change toward the random network topology. HD patients also demonstrated increases in degree k, reduced betweeness centrality bc and reduced clustering C. Changes predominated in the sensorimotor network for bc and C and were observed in all circuits for k. Hubs were reduced in preHD and no longer detectable in HD in the sensorimotor and associative networks. Changes in graph theory metrics (L, k, C and bc) correlated with four clinical and cognitive measures (symbol digit modalities test, Stroop, Burden and UHDRS). There were no changes in graph theory metrics across sessions, which suggests that these measures are not reliable biomarkers of longitudinal changes in HD. preHD is characterized by progressive decreasing hub organization, and these changes aggravate in HD patients with changes in local metrics. HD is characterized by progressive changes in global network interconnectivity, whose network topology becomes more random over time. Hum Brain Mapp 37:4112-4128, 2016. © 2016 Wiley Periodicals, Inc.

Simulation modeling and analysis of the hop count distribution in cognitive radio ad-hoc networks with shadow fading

The number of hops between source node and destination node is a key parameter in studying multi-hop wireless networks. Although hop count in wireless ad-hoc networks (AHNs) has been studied in the literature, no works on investigating the hop count characteristics in cognitive environments have been carried out. In this paper, we model cognitive radio ad-hoc networks (CRAHNs) as geometric random graphs and then propose a framework for studying the hop count distribution and correlated connectivity of communication path between two arbitrary nodes in CRAHNs with shadow fading. The framework consists of an algorithm and a methodology. Specifically, from the perspective of geometric random graph, the algorithm finds all possible paths between two arbitrary nodes and returns the hop count of the shortest path between them by using the global location information of nodes, i.e. primary users – PUs and secondary users – SUs, and the active states of PUs as input data. Meanwhile, through huge number of random network topology trials, the methodology returns the hop count distribution and connection status of communication path between two arbitrary nodes in CRAHNs with shadow fading. From the evaluating scenarios in this paper, important features of hop count distribution and connectivity and their correlating relationship in CRAHNs with shadow fading are revealed and compared with those in AHNs and in CRAHNs without shadow fading.

Challenging issues of average consensus algorithms in wireless sensor networks

This study tackles common challenges of the average consensus algorithms in real-world distributed wireless networks. The drawbacks of asynchronous transmissions, topology changes and communication delays on the distributed decision making of the network members to terminate a consensus algorithm are mainly studied. Two distributed stopping policies are proposed that help the network members to gradually stop message transmissions when they reach an acceptable accuracy of the final consensus value. Simulation results show that the proposed algorithms help previous consensus algorithms to decrease total packet transmissions in a random topology network. Simulation results also confirm that the proposed can perform in the presence of delayed members and non-ideal transmissions.

Synchronous slowing down in coupled logistic maps via random network topology.

The speed and paths of synchronization play a key role in the function of a system, which has not received enough attention up to now. In this work, we study the synchronization process of coupled logistic maps that reveals the common features of low-dimensional dissipative systems. A slowing down of synchronization process is observed, which is a novel phenomenon. The result shows that there are two typical kinds of transient process before the system reaches complete synchronization, which is demonstrated by both the coupled multiple-period maps and the coupled multiple-band chaotic maps. When the coupling is weak, the evolution of the system is governed mainly by the local dynamic, i.e., the node states are attracted by the stable orbits or chaotic attractors of the single map and evolve toward the synchronized orbit in a less coherent way. When the coupling is strong, the node states evolve in a high coherent way toward the stable orbit on the synchronized manifold, where the collective dynamics dominates the evolution. In a mediate coupling strength, the interplay between the two paths is responsible for the slowing down. The existence of different synchronization paths is also proven by the finite-time Lyapunov exponent and its distribution.

Scalable and fair forwarding of elephant and mice traffic in software defined networks

A software defined network decouples the control and data planes of the networking devices and places the control plane of all the switches in a central server. These flow based networks do not scale well because of the increased number of switch to controller communications, limited size of flow tables and increased size of flow table entries in the switches. In our work we use labels to convey control information of path and policy in the packet. This makes the core of the network simple and all routing and policy decisions are taken at the edge. The routing algorithm splits the elephant traffic into mice and distributes them across multiple paths, thus ensuring latency sensitive mice traffic is not adversely affected by elephant traffic. We observed that label based forwarding and traffic splitting work well together to enable scalable and fair forwarding. Our approach is topology independent. We present here a few preliminary simulation results obtained by running our routing algorithm on random network topologies.

Motifs in the assembly of food web networks

The assembly of local communities from regional pools is a multifaceted process that involves the confluence of interactions and environmental conditions at the local scale and biogeographic and evolutionary history at the regional scale. Understanding the relative influence of these factors on community structure has remained a challenge and mechanisms driving community assembly are often inferred from patterns of taxonomic, functional, and phylogenetic diversity. Moreover, community assembly is often viewed through the lens of competition and rarely includes trophic interactions or entire food webs. Here, we use motifs – subgraphs of nodes (e.g. species) and links (e.g. predation) whose abundance within a network deviates significantly as compared to a random network topology – to explore the assembly of food web networks found in the leaves of the northern pitcher plantSarracenia purpurea. We compared counts of three‐node motifs across a hierarchy of scales to a suite of null models to determine if motifs are over‐, under‐, or randomly represented. We then assessed if the pattern of representation of a motif in a given network matched that of the network it was assembled from. We found that motif representation in over 70% of site networks matched the continental network they were assembled from and over 75% of local networks matched the site networks they were assembled from for the majority of null models. This suggests that the same processes are shaping networks across scales. To generalize our results and effectively use a motif perspective to study community assembly, a theoretical framework detailing potential mechanisms for all possible combinations of motif representation is necessary.

Swap-And-Randomize: A Method for Building Low-Latency HPC Interconnects

Random network topologies have been proposed to create low-diameter, low-latency interconnection networks in large-scale computing systems. However, these topologies are difficult to deploy in practice, especially when re-designing existing systems, because they lead to increased total cable length and cable packaging complexity. In this work we propose a new method for creating random topologies without increasing cable length: randomly swap link endpoints in a non-random topology that is already deployed across several cabinets in a machine room. We quantitatively evaluate topologies created in this manner using both graph analysis and cycle-accurate network simulation, including comparisons with non-random topologies and previously-proposed random topologies.

Network topology and functional connectivity disturbances precede the onset of Huntington's disease.

Cognitive, motor and psychiatric changes in prodromal Huntington's disease have nurtured the emergent need for early interventions. Preventive clinical trials for Huntington's disease, however, are limited by a shortage of suitable measures that could serve as surrogate outcomes. Measures of intrinsic functional connectivity from resting-state functional magnetic resonance imaging are of keen interest. Yet recent studies suggest circumscribed abnormalities in resting-state functional magnetic resonance imaging connectivity in prodromal Huntington's disease, despite the spectrum of behavioural changes preceding a manifest diagnosis. The present study used two complementary analytical approaches to examine whole-brain resting-state functional magnetic resonance imaging connectivity in prodromal Huntington's disease. Network topology was studied using graph theory and simple functional connectivity amongst brain regions was explored using the network-based statistic. Participants consisted of gene-negative controls (n = 16) and prodromal Huntington's disease individuals (n = 48) with various stages of disease progression to examine the influence of disease burden on intrinsic connectivity. Graph theory analyses showed that global network interconnectivity approximated a random network topology as proximity to diagnosis neared and this was associated with decreased connectivity amongst highly-connected rich-club network hubs, which integrate processing from diverse brain regions. However, functional segregation within the global network (average clustering) was preserved. Functional segregation was also largely maintained at the local level, except for the notable decrease in the diversity of anterior insula intermodular-interconnections (participation coefficient), irrespective of disease burden. In contrast, network-based statistic analyses revealed patterns of weakened frontostriatal connections and strengthened frontal-posterior connections that evolved as disease burden increased. These disturbances were often related to long-range connections involving peripheral nodes and interhemispheric connections. A strong association was found between weaker connectivity and decreased rich-club organization, indicating that whole-brain simple connectivity partially expressed disturbances in the communication of highly-connected hubs. However, network topology and network-based statistic connectivity metrics did not correlate with key markers of executive dysfunction (Stroop Test, Trail Making Test) in prodromal Huntington's disease, which instead were related to whole-brain connectivity disturbances in nodes (right inferior parietal, right thalamus, left anterior cingulate) that exhibited multiple aberrant connections and that mediate executive control. Altogether, our results show for the first time a largely disease burden-dependent functional reorganization of whole-brain networks in prodromal Huntington's disease. Both analytic approaches provided a unique window into brain reorganization that was not related to brain atrophy or motor symptoms. Longitudinal studies currently in progress will chart the course of functional changes to determine the most sensitive markers of disease progression.

Assessing the Structural Complexity of Computer and Communication Networks

In this tutorial, 17 structural complexity indices are presented and compared, each representing one of the following categories: adjacency- and distance-based metrics, Shannon entropy-based metrics, product measures, subgraph-based metrics, and path- and walk-based metrics. The applicability of these indices to computer and communication networks is evaluated with the aid of different elementary, specifically designed, random, and real network topologies. On the grounds of the evaluation study, advantages and disadvantages of particular metrics are identified. In addition, their general properties and runtimes are assessed, and a general view on the structural network complexity is presented.

Stochastic geometry modeling and analysis of cognitive heterogeneous cellular networks

In this paper, we present a cognitive radio (CR)-based statistical framework for a two-tier heterogeneous cellular network (femto-macro network) to model the outage probability at any arbitrary secondary (femto) and primary (macro) user. A system model based on stochastic geometry (utilizing the spatial Poisson point process (PPP) theory) is applied to model the random locations and network topology of both secondary and primary users. A considerable performance improvement can be generally achieved by mitigating interference in result of applying the CR idea over the above model. Novel closed-form expressions are derived for the downlink outage probability of any typical femto and macro user considering the Rayleigh fading for the desired and interfering links. We also study some important design factors which their role and importance in the determination of outage and interference cannot be ignored. We conduct simulations to validate our analytical results and evaluate the proposed schemes in terms of outage probability for different values of signal-to-interference-plus-noise ratio (SINR) target.

End-to-End Communication Delay Analysis in Industrial Wireless Networks

WirelessHART is a new standard specifically designed for real-time and reliable communication between sensor and actuator devices for industrial process monitoring and control applications. End-to-end communication delay analysis for WirelessHART networks is required to determine the schedulability of real-time data flows from sensors to actuators for the purpose of acceptance test or workload adjustment in response to network dynamics. In this paper, we consider a network model based on WirelessHART, and map the scheduling of real-time periodic data flows in the network to real-time multiprocessor scheduling. We then exploit the response time analysis for multiprocessor scheduling and propose a novel method for the delay analysis that establishes an upper bound of the end-to-end communication delay of each real-time flow in the network. Simulation studies based on both random topologies and real network topologies of a <inline-formula><tex-math>$74$</tex-math></inline-formula> -node physical wireless sensor network testbed demonstrate that our analysis provides safe and reasonably tight upper bounds of the end-to-end delays of real-time flows, and hence enables effective schedulability tests for WirelessHART networks.

On Decentralized Estimation With Active Queries

We consider the problem of decentralized 20 questions with noise for multiple players/agents under the minimum entropy criterion in the setting of stochastic search over a parameter space, with application to target localization. We propose decentralized extensions of the active query-based stochastic search strategy that combines elements from the 20 questions approach and social learning. We prove convergence to correct consensus on the value of the parameter. This framework provides a flexible and tractable mathematical model for decentralized parameter estimation systems based on active querying. We illustrate the effectiveness and robustness of the proposed decentralized collaborative 20 questions algorithm for random network topologies with information sharing.