On computing the reliability of opportunistic multihop networks with Mobile relays

Opportunistic multihop networks with mobile relays recently have drawn much attention from researchers across the globe due to their wide applications in various challenging environments. However, because of their peculiar intrinsic features like lack of continuous connectivity, network partitioning, highly dynamic behavior, and long delays, it is very arduous to model and effectively capture the temporal variations of such networks with the help of classical graph models. In this work, we utilize an evolving graph to model the dynamic network and propose a matrix‐based algorithm to generate all minimal path sets between every node pair of such network. We show that these time‐stamped‐minimal‐path sets (TS‐MPS) between each given source‐destination node pair can be used, by utilizing the well‐known Sum‐of‐Disjoint Products technique, to generate various reliability metrics of dynamic networks, ie, two‐terminal reliability of dynamic network and its related metrics, ie, two‐terminal reliabilities of the foremost, shortest, and fastest TS‐MPS, and Expected Hop Count. We also introduce and compute a new network performance metric?Expected Slot Count. We use two illustrative examples of dynamic networks, one of four nodes, and the other of five nodes, to show the salient features of our technique to generate TS‐MPS and reliability metrics.     

A DNA-based molecular motor that can navigate a network of tracks

Synthetic molecular motors can be fuelled by the hydrolysis or hybridization of DNA. Such motors can move autonomously and programmably, and long-range transport has been observed on linear tracks. It has also been shown that DNA systems can compute. Here, we report a synthetic DNA-based system that integrates long-range transport and information processing. We show that the path of a motor through a network of tracks containing four possible routes can be programmed using instructions that are added externally or carried by the motor itself. When external control is used we find that 87% of the motors follow the correct path, and when internal control is used 71% of the motors follow the correct path. Programmable motion will allow the development of computing networks, molecular systems that can sort and process cargoes according to instructions that they carry, and assembly lines that can be reconfigured dynamically in response to changing demands.     

Current-reinforced random walks for constructing transport networks

Biological systems that build transport networks, such as trail-laying ants and the slime mould Physarum, can be described in terms of reinforced random walks. In a reinforced random walk, the route taken by ‘walking’ particles depends on the previous routes of other particles. Here, we present a novel form of random walk in which the flow of particles provides this reinforcement. Starting from an analogy between electrical networks and random walks, we show how to include current reinforcement. We demonstrate that current-reinforcement results in particles converging on the optimal solution of shortest path transport problems, and avoids the self-reinforcing loops seen in standard density-based reinforcement models. We further develop a variant of the model that is biologically realistic, in the sense that the particles can be identified as ants and their measured density corresponds to those observed in maze-solving experiments on Argentine ants. For network formation, we identify the importance of nonlinear current reinforcement in producing networks that optimize both network maintenance and travel times. Other than ant trail formation, these random walks are also closely related to other biological systems, such as blood vessels and neuronal networks, which involve the transport of materials or information. We argue that current reinforcement is likely to be a common mechanism in a range of systems where network construction is observed.     

Linked title mentions: a new automated link search candidate

Many webometric studies have used hyperlinks to investigate links to or between specific collections of websites to estimate their impact or identify connectivity patterns. Whilst major commercial search engines have previously been used to identify hyperlinks for these purposes, their hyperlink search facilities have now been shut down. In response, a range of alternative sources of link data have been suggested, but all have limitations. This article introduces a new type of link that can be identified from commercial search engines, linked title mentions. These can be found by querying title mentions in a search engine and then removing those not associated with a relevant hyperlink. Results of a proof of concept test on 51 U.S. library and information science schools and four other sets of schools suggest that linked title mentions may tend to give better results than title mentions in some cases when used for site inlinks but may not always be an improvement on URL citations. For links between or co-inlinks to specified pairs of academic websites, linked title mentions do not generally provide an improvement over title mentions, but they do over URL citations in some cases. Linked title mentions may also be useful for sets of non-academic websites when the alternatives give too few or misleading results.     

Safety models incorporating graph theory based transit indicators

There is a considerable need for tools to enable the evaluation of the safety of transit networks at the planning stage. One interesting approach for the planning of public transportation systems is the study of networks. Network techniques involve the analysis of systems by viewing them as a graph composed of a set of vertices (nodes) and edges (links). Once the transport system is visualized as a graph, various network properties can be evaluated based on the relationships between the network elements. Several indicators can be calculated including connectivity, coverage, directness and complexity, among others. The main objective of this study is to investigate the relationship between network-based transit indicators and safety. The study develops macro-level collision prediction models that explicitly incorporate transit physical and operational elements and transit network indicators as explanatory variables. Several macro-level (zonal) collision prediction models were developed using a generalized linear regression technique, assuming a negative binomial error structure. The models were grouped into four main themes: transit infrastructure, transit network topology, transit route design, and transit performance and operations. The safety models showed that collisions were significantly associated with transit network properties such as: connectivity, coverage, overlapping degree and the Local Index of Transit Availability. As well, the models showed a significant relationship between collisions and some transit physical and operational attributes such as the number of routes, frequency of routes, bus density, length of bus and 3+ priority lanes.     

通信网中最重要节点的确定方法

提出了一种确定通信网中最重要节点的方法——节点删除法,并给出了归一化的表达式。最重要的节点是去掉该节点以及相关联的链路后,使得图的生成树数目最小。节点删除法反映了某个节点失效时,对整个通信网可靠性的破坏程度。该方法可以评价全网范围内的节点重要性,通过比较生成树的数目,可以判断通信网中任意两个节点的相对重要性。实验结果证明了节点删除法的有效性。     

Two-terminal reliability analyses for a mobile ad hoc wireless network

Reliability is one of the most important performance measures for emerging technologies. For these systems, shortcomings are often overlooked in early releases as the cutting edge technology overshadows a fragile design. Currently, the proliferation of the mobile ad hoc wireless networks (MAWN) is moving from cutting edge to commodity and thus, reliable performance will be expected. Generally, ad hoc networking is applied for the flexibility and mobility it provides. As a result, military and first responders employ this network scheme and the reliability of the network becomes paramount. To ensure reliability is achieved, one must first be able to analyze and calculate the reliability of the MAWN. This work describes the unique attributes of the MAWN and how the classical analysis of network reliability, where the network configuration is known a priori, can be adjusted to model and analyze this type of network. The methods developed acknowledge the dynamic and scalable nature of the MAWN along with its absence of infrastructure. Thus, the methods rely on a modeling approach that considers the probabilistic formation of different network configurations in a MAWN. Hence, this paper proposes reliability analysis methods that consider the effect of node mobility and the continuous changes in the network's connectivity.     

Managing product reliability in business processes ‘under pressure’

Product reliability is often seen as a product attribute. Models with different degree of sophistication analyze and predict the reliability of a product as a function of the internal structure (such as components and their relation). The practical relevance of these models, in relation with the (business) processes in which the related products are actually used, is not often addressed. Different types of reliability issues, however, can be relevant for products in different industrial contexts. This paper will present a classification model to describe different business processes, based on the degree of product innovation. It will also propose a taxonomy that can be used to classify different types of reliability problems. As this paper will demonstrate, only certain combinations of reliability problems are relevant for certain business processes. It will also show that, given certain technology trends, some combinations will become more relevant in the future. The final part of this paper will demonstrate that especially for these combinations many of the existing reliability analysis and prediction methods can be considered inadequate.     

The network of sheep movements within Great Britain: Network properties and their implications for infectious disease spread.

During the 2001 foot and mouth disease epidemic in the UK, initial dissemination of the disease to widespread geographical regions was attributed to livestock movement, especially of sheep. In response, recording schemes to provide accurate data describing the movement of large livestock in Great Britain (GB) were introduced. Using these data, we reconstruct directed contact networks within the sheep industry and identify key epidemiological properties of these networks. There is clear seasonality in sheep movements, with a peak of intense activity in August and September and an associated high risk of a large epidemic. The high correlation between the in and out degree of nodes favours disease transmission. However, the contact networks were largely dissasortative: highly connected nodes mostly connect to nodes with few contacts, effectively slowing the spread of disease. This is a result of bipartite-like network properties, with most links occurring between highly active markets and less active farms. When comparing sheep movement networks (SMNs) to randomly generated networks with the same number of nodes and node degrees, despite structural differences (such as disassortativity and higher frequency of even path lengths in the SMNs), the characteristic path lengths within the SMNs are close to values computed from the corresponding random networks, showing that SMNs have 'small-world'-like properties. Using the network properties, we show that targeted biosecurity or surveillance at highly connected nodes would be highly effective in preventing a large and widespread epidemic.     

Link stability and mobility in ad hoc wireless networks

The fact that ad hoc networks are required to support mobility of individual network nodes results in problems arising when routing data. These problems include route loss, poor longevity of established routes and asymmetric communications links. Mobility of nodes also increases the control traffic overhead and affects the performance of the protocol. Mobility can, however, be exploited to improve route longevity when establishing the route. In some situations, the source of information is not available (e.g. GPS information in the underground), hence protocols relying on this information will fail to operate correctly. In such situations, alternative `self-content' information should be available to perform the needed task of routing. Three novel schemes that make use of such information, the heading direction angle, to provide a mechanism for establishing and maintaining robust and long-lived routes are presented. The results show that these schemes reduce the overhead and increase the route longevity when compared with the AODV protocol. The schemes described can operate as a standalone mechanism or can be adopted by other routing protocols in order to improve their performance.     

Spatial distribution of water supply reliability and critical links of water supply to crucial water consumers under an earthquake

This paper describes a process to characterize spatial distribution of water supply reliability among various consumers in a water system and proposes methods to identify critical links of water supply to crucial water consumers under an earthquake. Probabilistic performance of water supply is reflected by the probability of satisfying consumers’ water demand, Damage Consequence Index (DCI) and Upgrade Benefit Index (UBI). The process is illustrated using a hypothetical water supply system, where direct Monte Carlo simulation is used for estimating the performance indices. The reliability of water supply to consumers varies spatially, depending on their respective locations in the system and system configuration. The UBI is adopted as a primary index in the identification of critical links for crucial water consumers. A pipe with a relatively large damage probability is likely to have a relatively large UBI, and hence, to be a critical link. The concept of efficient frontier is employed to identify critical links of water supply to crucial water consumers. It is found that a group of links that have the largest UBI individually do not necessarily have the largest group UBI, or be the group of critical links.     

Human reliability analysis: a human point of view

Present human reliability analysis (HRA) is like a Centaur in that it is only half human. Error psychology is expected to provide a path leading us in the right direction. AI technology can be utilized to better understand errors. It is pointed out that the next generation HRA will require two additional components in which an analysis is made to identify two sets of error prone situations (EPSs): one in which human operators are likely to initiate critical events, and another in which they are likely to override engineered safeguard features. Carefully designed empirical studies are believed to be useful in obtaining general knowledge about critical EPSs. It is suggested that any future effort should be made based on sound psychological concepts.     

Modelling hierarchical structure in functional brain networks

In this work, we focus on a complex-network approach for the study of the brain. In particular, we consider functional brain networks, where the vertices represent different anatomical regions and the links their functional connectivity. First, we build these networks using data obtained with functional magnetic resonance imaging. Then, we analyse the main characteristics of these complex networks, including degree distribution, the presence of modules and hierarchical structure. Finally, we present a network model with dynamical nodes and adaptive links. We show that the model allows for the emergence of complex networks with characteristics similar to those observed in functional brain networks.     

Maximum Data Generation Rate Routing Protocol Based on Data Flow Controlling Technology for Rechargeable Wireless Sensor Networks

For rechargeable wireless sensor networks, limited energy storage capacity, dynamic energy supply, low and dynamic duty cycles cause that it is unpractical to maintain a fixed routing path for packets delivery permanently from a source to destination in a distributed scenario. Therefore, before data delivery, a sensor has to update its waking schedule continuously and share them to its neighbors, which lead to high energy expenditure for reestablishing path links frequently and low efficiency of energy utilization for collecting packets. In this work, we propose the maximum data generation rate routing protocol based on data flow controlling technology. For a sensor, it does not share its waking schedule to its neighbors and cache any waking schedules of other sensors. Hence, the energy consumption for time synchronization, location information and waking schedule shared will be reduced significantly. The saving energy can be used for improving data collection rate. Simulation shows our scheme is efficient to improve packets generation rate in rechargeable wireless sensor networks.     

Quantitative analyses of the plant cytoskeleton reveal underlying organizational principles

The actin and microtubule (MT) cytoskeletons are vital structures for cell growth and development across all species. While individual molecular mechanisms underpinning actin and MT dynamics have been intensively studied, principles that govern the cytoskeleton organization remain largely unexplored. Here, we captured biologically relevant characteristics of the plant cytoskeleton through a network-driven imaging-based approach allowing us to quantitatively assess dynamic features of the cytoskeleton. By introducing suitable null models, we demonstrate that the plant cytoskeletal networks exhibit properties required for efficient transport, namely, short average path lengths and high robustness. We further show that these advantageous features are maintained during temporal cytoskeletal rearrangements. Interestingly, man-made transportation networks exhibit similar properties, suggesting general laws of network organization supporting diverse transport processes. The proposed network-driven analysis can be readily used to identify organizational principles of cytoskeletons in other organisms.     

Assessing supply chain robustness to links failure

Supply chain networks need to respond efficiently to operation disruptions, as one of their aims is to guarantee the on time delivery of products. Hence, robustness has become one of the important issues to consider when designing supply networks. There are alternative ways to measure what robustness means in this context. In this paper, we propose a new metric based on the effect on service level of the collapse of active transportation links. Numerical experiments are carried out to understand how different design factors affect robustness. Robustness under a targeted attack is compared with robustness to random failures. Results show that flow complexity (i.e. the number of potential transportation links between supply network nodes) is the most influential factor affecting supply network and its robustness, as well as the service level that can be maintained after disruptions. Thus, diversification both in supply sources and transportation routes seems to be key to robustness.     

Network reliability assessment using a cellular automata approach

Two cellular automata (CA) models that evaluate the s–t connectedness and shortest path in a network are presented. CA based algorithms enhance the performance of classical algorithms, since they allow a more reliable and straightforward parallel implementation resulting in a dynamic network evaluation, where changes in the connectivity and/or link costs can readily be incorporated avoiding recalculation from scratch. The paper also demonstrates how these algorithms can be applied for network reliability evaluation (based on Monte-Carlo approach) and for finding s–t path with maximal reliability.     

相依失效生命线工程系统抗震可靠度估计的统一RDA算法

在网络连通可靠度分析的递推分解算法(Recursive Decomposition Algorithm,简称RDA)的基础上,先整合原RDA算法中分别用于不同网络赋权形式的三部分程序,发展出统一RDA算法。应用顺序复合法(Sequential Compounding Method,简称SCM)和Gumbel Copula函数分别计算RDA算法中不交最小路和不交最小割事件的联合失效概率,解决元件相依失效生命线工程系统的抗震可靠度估计问题,最终发展出相依失效生命线工程系统抗震可靠度估计的统一RDA算法。36节点格栅型网络可靠度案例计算结果表明:此方法可以用于相依失效网络系统的动力抗震可靠度估计,计算精度较高。     

Analytical methods for quantifying environmental connectivity for the control and surveillance of infectious disease spread

The sustained transmission and spread of environmentally mediated infectious diseases is governed in part by the dispersal of parasites, disease vectors and intermediate hosts between sites of transmission. Functional geospatial models can be used to quantify and predict the degree to which environmental features facilitate or limit connectivity between target populations, yet typical models are limited in their geographical and analytical approach, providing simplistic, global measures of connectivity and lacking methods to assess the epidemiological implications of fine-scale heterogeneous landscapes. Here, functional spatial models are applied to problems of surveillance and control of the parasitic blood fluke Schistosoma japonicum and its intermediate snail host Oncomelania haupensis in western China. We advance functional connectivity methods by providing an analytical framework to (i) identify nodes of transmission where the degree of connectedness to other villages, and thus the potential for disease spread, is higher than is estimated using Euclidean distance alone and (ii) (re)organize transmission sites into disease surveillance units based on second-order relationships among nodes using non-Euclidean distance measures, termed effective geographical distance (EGD). Functional environmental models are parametrized using ecological information on the target organisms, and pair-wise distributions of inter-node EGD are estimated. A Monte Carlo rank product analysis is presented to identify nearby nodes under alternative distance models. Nodes are then iteratively embedded into EGD space and clustered using a k-means algorithm to group villages into ecologically meaningful surveillance groups. A consensus clustering approach is taken to derive the most stable cluster structure. The results indicate that novel relationships between nodes are revealed when non-Euclidean, ecologically determined distance measures are used to quantify connectivity in heterogeneous landscapes. These connections are not evident when analysing nodes in Euclidean space, and thus surveillance and control activities planned using Euclidean distance measures may be suboptimal. The methods developed here provide a quantitative framework for assessing the effectiveness of ecologically grounded surveillance systems and of control and prevention strategies for environmentally mediated diseases.     

Information processing by biochemical networks: a dynamic approach

Understanding how information is encoded and transferred by biochemical networks is of fundamental importance in cellular and systems biology. This requires analysis of the relationships between the stochastic trajectories of the constituent molecular (or submolecular) species that comprise the network. We describe how to identify conditional independences between the trajectories or time courses of groups of species. These are robust network properties that provide important insight into how information is processed. An entire network can then be decomposed exactly into modules on informational grounds. In the context of signalling networks with multiple inputs, the approach identifies the routes and species involved in sequential information processing between input and output modules. An algorithm is developed which allows automated identification of decompositions for large networks and visualization using a tree that encodes the conditional independences. Only stoichiometric information is used and neither simulations nor knowledge of rate parameters are required. A bespoke version of the algorithm for signalling networks identifies the routes of sequential encoding between inputs and outputs, visualized as paths in the tree. Application to the toll-like receptor signalling network reveals that inputs can be informative in ways unanticipated by steady-state analyses, that the information processing structure is not well described as a bow tie, and that encoding for the interferon response is unusually sparse compared with other outputs of this innate immune system.