Betweenness estimation in OLSR-based multi-hop networks for distributed filtering

In traditional networks special efforts are put to secure the perimeter with firewalls: particular routers that analyze and filter the traffic to separate zones with different levels of trust. In wireless multi-hop networks the perimeter is a concept extremely hard to identify, thus, it is much more effective to enforce control on the nodes that will route more traffic. But traffic filtering and traffic analysis are costly activities for the limited resources of mesh nodes, so a trade-off must be reached limiting the number of nodes that enforce them. This work shows how, using the OLSR protocol, the centrality of groups of nodes with reference to traffic can be estimated with high accuracy independently of the network topology or size. We also show how this approach greatly limits the impact of an attack to the network using a number of firewalls that is only a fraction of the available nodes.     

Fast fingerprint identification for large databases

Fingerprint matching has emerged as an effective tool for human recognition due to the uniqueness, universality and invariability of fingerprints. Many different approaches have been proposed in the literature to determine faithfully if two fingerprint images belong to the same person. Among them, minutiae-based matchers highlight as the most relevant techniques because of their discriminative capabilities, providing precise results. However, performing a fingerprint identification over a large database can be an inefficient task due to the lack of scalability and high computing times of fingerprint matching algorithms.In this paper, we propose a distributed framework for fingerprint matching to tackle large databases in a reasonable time. It provides a general scheme for any kind of matcher, so that its precision is preserved and its time of response can be reduced.To test the proposed system, we conduct an extensive study that involves both synthetic and captured fingerprint databases, which have different characteristics, analyzing the performance of three well-known minutiae-based matchers within the designed framework. With the available hardware resources, our distributed model is able to address up to 400 000 fingerprints in approximately half a second. Additional details are provided at http://sci2s.ugr.es/ParallelMatching.     

Additive manufacturing in the spare parts supply chain

As additive manufacturing (AM) evolves to become a common method of producing final parts, further study of this computer integrated technology is necessary. The purpose of this research is to evaluate the potential impact of additive manufacturing improvements on the configuration of spare parts supply chains. This goal has been accomplished through scenario modeling of a real-life spare parts supply chain in the aeronautics industry. The spare parts supply chain of the F-18 Super Hornet fighter jet was selected as the case study because the air-cooling ducts of the environmental control system are produced using AM technology. In total, four scenarios are investigated that vary the supply chain configurations and additive manufacturing machine specifications. The reference scenario is based on the spare parts supplier's current practice and the possible future decentralization of production and likely improvements in AM technology. Total operating cost, including downtime cost, is used to compare the scenarios. We found that using current AM technology, centralized production is clearly the preferable supply chain configuration in the case example. However, distributed spare parts production becomes practical as AM machines become less capital intensive, more autonomous and offer shorter production cycles. This investigation provides guidance for the development of additive manufacturing machines and their possible deployment in spare parts supply chains. This study contributes to the emerging literature on AM deployment in supply chains with a real-world case setting and scenario model illustrating the cost trade-offs and critical requirements for technology development.     

Caching and optimized request routing in cloud-based content delivery systems

Geographically distributed cloud platforms enable an attractive approach to large-scale content delivery. Storage at various sites can be dynamically acquired from (and released back to) the cloud provider so as to support content caching, according to the current demands for the content from the different geographic regions. When storage is sufficiently expensive that not all content should be cached at all sites, two issues must be addressed: how should requests for content be routed to the cloud provider sites, and what policy should be used for caching content using the elastic storage resources obtained from the cloud provider. Existing approaches are typically designed for non-elastic storage and little is known about the optimal policies when minimizing the delivery costs for distributed elastic storage.In this paper, we propose an approach in which elastic storage resources are exploited using a simple dynamic caching policy, while request routing is updated periodically according to the solution of an optimization model. Use of pull-based dynamic caching, rather than push-based placement, provides robustness to unpredicted changes in request rates. We show that this robustness is provided at low cost. Even with fixed request rates, use of the dynamic caching policy typically yields content delivery cost within 10% of that with the optimal static placement. We compare request routing according to our optimization model to simpler baseline routing policies, and find that the baseline policies can yield greatly increased delivery cost relative to optimized routing. Finally, we present a lower-cost approximate solution algorithm for our routing optimization problem that yields content delivery cost within 2.5% of the optimal solution.     

Distributed predictive control of continuous-time systems

In the last years focus has been put in the development of distributed Model Predictive Control (MPC) algorithms. With a few exceptions, they have been mostly developed in the discrete-time framework. However, discretization of large-scale systems may destroy the sparsity of the original continuous-time models, making distributed control design and implementation more difficult. Also, more in general, discrete-time control of continuous-time systems does not allow to consider the process inter-sampling behavior. In this paper we present a novel non-cooperative distributed predictive control algorithm for continuous-time systems based on robust MPC concepts. The convergence properties of the proposed control scheme are stated, and its realizability is tested through a simulation case study.     

A distributed solution to the network reconstruction problem

It has been recently shown in Ren et al. (2010) that by collecting noise-contaminated time series generated by a coupled-oscillator system at each node of a network, it is possible to robustly reconstruct its topology, i.e. determine the graph Laplacian. Restricting ourselves to linear consensus dynamics over undirected communication networks, in this paper we introduce a new dynamic average consensus least-squares algorithm to locally estimate these time series at each node, thus making the reconstruction process fully distributed and more easily applicable in the real world. We also propose a novel efficient method for separating the off-diagonal entries of the reconstructed Laplacian, and examine several concepts related to the trace of the dynamic correlation matrix of the coupled single integrators, which is a distinctive element of our network reconstruction method. The theory is illustrated with examples from computer, power and transportation systems.     

Analysing interactive devices based on information resource constraints

Analysis of the usability of an interactive system requires both an understanding of how the system is to be used and a means of assessing the system against that understanding. Such analytic assessments are particularly important in safety-critical systems as latent vulnerabilities may exist which have negative consequences only in certain circumstances. Many existing approaches to assessment use tasks or scenarios to provide explicit representation of their understanding of use. These normative user behaviours have the advantage that they clarify assumptions about how the system will be used but have the disadvantage that they may exclude many plausible deviations from these norms. Assessments of how a design fails to support these user behaviours can be a matter of judgement based on individual experience rather than evidence. We present a systematic formal method for analysing interactive systems that is based on constraints rather than prescribed behaviour. These constraints capture precise assumptions about what information resources are used to perform action. These resources may either reside in the system itself or be external to the system. The approach is applied to two different medical device designs, comparing two infusion pumps currently in common use in hospitals. Comparison of the two devices is based on these resource assumptions to assess consistency of interaction within the design of each device.     

A comprehensive approach to evaluating watershed models for predicting river flow regimes critical to downstream ecosystem services

Selection of strategies that help reduce riverine inputs requires numerical models that accurately quantify hydrologic processes. While numerous models exist, information on how to evaluate and select the most robust models is limited. Toward this end, we developed a comprehensive approach that helps evaluate watershed models in their ability to simulate flow regimes critical to downstream ecosystem services. We demonstrated the method using the Soil and Water Assessment Tool (SWAT), the Hydrological Simulation Program–FORTRAN (HSPF) model, and Distributed Large Basin Runoff Model (DLBRM) applied to the Maumee River Basin (USA). The approach helped in identifying that each model simulated flows within acceptable ranges. However, each was limited in its ability to simulate flows triggered by extreme weather events, owing to algorithms not being optimized for such events and mismatched physiographic watershed conditions. Ultimately, we found HSPF to best predict river flow, whereas SWAT offered the most flexibility for evaluating agricultural management practices.     

Comparative analysis of two different middleware approaches for reconfiguration of distributed real-time systems

Software-based reconfiguration of distributed real-time systems is a complex problem with many sides to it ranging from system-wide concerns down to the intrinsic non-robust nature of the specific middleware layer and the used programming techniques. In a completely open distributed system, mixing reconfiguration and real-time is not possible; the set of possible target states can be very large threatening the temporal predictability of the reconfiguration process. Over the last years, middle ware solutions have appeared mainly for general purpose systems where efficient state transitions are sought for, but real-time properties are not considered. One of the few contributions to run-time software reconfiguration in distributed real-time environments has been the iLAND middleware, where the germ of a solution with high potential has been conceived and delivered in practice.¹ The key idea has been the fact that a set of bounds and limitations to the structure of systems and to their open nature needs to be imposed in order to come up with practical solutions. In this paper, the authors present the different sides of the problem of software reconfiguration from two complementary middleware perspectives comparing two strategies built inside distribution middleware. We highlight the lessons learned in the iLAND project aimed at service-based reconfiguration and compare it to our experience in the development of distributed real-time Java reconfiguration based on distributed tasks rescheduling. Authors also provide a language view of both solutions. Lastly, empirical results are shown that validate these solutions and compare them on the basis of different programming language realizations.     

Context-aware adaptive IP mobility anchoring

The novel distributed mobility management trend is a promising direction to cope with the increasing mobile data traffic and flatten network architectures. Most of the novel mobility approaches distribute the mobility anchors through the access level, as opposed to the centralized mobility anchoring model. Other recent approaches argue that mobility anchors closer to the content servers may be the solution to optimize the mobility performance. However, none of the mobility anchoring models is ideal for all scenarios, since it depends on the user, the session and the network. Hence, we propose an IP mobility approach driven by the context of the user, sessions and the network, where the mobility anchors for IP address allocation and for routing/forwarding are distributed through the network nodes, while the mobility context is managed by the mobile devices. Although each session is properly anchored in the establishment phase, the routing/forwarding is adapted over time, according to the user, the session and the network context: the proposed approach is able to signal different mobility anchors to optimize the routing path to new and ongoing sessions of the user. The outcome of the evaluation shows that the proposed approach overall reduces the data cost, the data delay, the tunneled packets and the tunnel length, when compared with other anchoring models.