Adaptive Topology Research Articles

Hydraulically informed graph theoretic measure of link criticality for the resilience analysis of water distribution networks

Water Distribution Networks (WDN) are complex and highly interconnected systems. To maintain operation under failure conditions, WDNs should have built-in resilience based on topological and energy redundancy. There are various methods for analysing the resilience of WDNs based on either hydraulic models or surrogate network measures; however, not a single universally accepted method exists. Hydraulic modeling of disruptive operational scenarios suffer from combinatorial restrictions and uncertainties. Methods that rely on surrogate network measures do not take into account the complex interactions between topological and energy redundancy. To bridge this gap, the presented work introduces a hydraulically informed surrogate measure of pipe criticality for the resilience analysis of WDNs, called Water Flow Edge Betweenness Centrality (WFEBC). The WFEBC combines the random walk betweenness centrality with hydraulic (energy) loss principles in pipes. The proposed network resilience estimation method is applied to a case study network and an operational network. Furthermore, a network decomposition approach is proposed to complement the network estimation method and facilitate its scalability to large operational networks. The described resilience analysis method is benchmarked against a hydraulic model-based analysis of WDN reserve capacity. WFEBC is also applied to assess the improvement in resilience allowed by the implementation of a dynamically adaptive topology in an operational network. The benefits and limitations of the proposed method are discussed.

Adaptive topologies against jamming attacks in wireless networks: A game-theoretic approach

Towards securing wireless networks and ensuring their dependability under jamming attacks, this paper presents and analyzes game-theoretic formulations between an adversary and a defender. The adversary jams a subset of nodes to increase the level of interference in the network, while the defender makes judicious adjustments of the transmission power level of the nodes, thereby continuously adapting the underlying network topology to reduce the impact of the attack. The defender's strategy is based on playing Nash equilibria (NE) strategies securing a worst-case network utility. First, a discrete control set is considered in which the space of strategies of the defender grows exponentially with the network size. Scalable decomposition-based approaches are developed yielding a defense strategy whose performance closely approaches that of the non-decomposed game. Second, we develop a marginal strategy to assign the power levels while satisfying a coverage constraint thereby evading the combinatorial complexity associated with enumerating all pure actions. Third, we generalize the marginal-based strategy by considering a continuous action space for both players. For this setting, we prove the existence of a unique pure Nash equilibrium. The presented numerical results show the effectiveness of the proposed defense approach against various attack policies and demonstrate the assignment strategies on a real wireless network deployed in a three-story building.

Temporal Centrality-Balanced Traffic Management for Space Satellite Networks

Structural centrality metrics of the networks are important features to identify central nodes or links, to assess network vulnerability against removal of nodes or links, and to manage or control the network traffic to reduce the network congestion. Classic graph-based centrality metrics are not effective in dynamic space satellite networks due to the changing connectivity and they cannot effectively capture important temporal properties. In this paper, we employ a new technique called time-varying graphs (TVGs) to express temporal concepts and definitions of space satellite networks and propose several temporal centrality metrics in order to account for the temporal features of the networks. To calculate these temporal centrality metrics, we adopt a modified matrix multiplication algorithm to find the shortest journeys between nodes in the TVG. A temporal centrality-balanced traffic management scheme is further developed to enhance the network performance, which is integrated into a temporal centrality-balanced routing algorithm. To show the performance of the proposed centrality metrics and centrality balanced routing algorithm, we conduct some experiments performed on a scenario of the LEO satellite network. The simulation results show that the proposed centrality metrics are more powerful to analyze the evolution of time-varying centrality and vulnerability of space satellite networks than topological adaptation of vanilla centrality methods. Besides, the proposed centrality-balanced routing algorithm reaches the superior quality values of the network performance (in terms of several factors) in comparison with those attained by the traditional shortest journeys routing algorithm and proves to be more powerful to support the high traffic load.

Formation reconfiguration control of quadrotor UAVs based on adaptive communication topology

Formation reconfiguration control of quadrotor UAVs based on adaptive communication topology

Investigation of reconfigurable antennas by foldable, e-textile tessellations: Modeling and experimentation

Physical deformation mechanisms are emerging as compelling and simple ways to adapt wave propagation properties of antenna arrays in contrast to digital steering approaches acting on topologically fixed antennas. Concepts of physical reconfigurability also enable exceptional capabilities such as deployable and morphing antenna arrays that serve multiple functions and permit compact transport with ease. Yet, the emergent concepts lack broad understanding of effective approaches to integrate conformal, electrically conductive architectures with high-compliance foldable frameworks. To explore this essential interface where electrical demands and mechanical requirements may conflict, this research studies e-textile-based reconfigurable antenna arrays that conform to adaptable topologies by origami-inspired tessellations. The e-textiles leverage embroidered conductive threads along frameworks established on origami tessellations to permit large compliance at folding edges as needed while retaining the desired electromagnetic wave propagation characteristics. Computational modeling is used to guide experimental fabrications and validations. It is found that e-textile origami antenna arrays permit significant adaptation of wave radiation while maintaining the required mechanical robustness under folding sequences. These findings may motivate future concepts for reconfigurable antennas established upon physical deformation processes.

Differential evolution with individual-dependent topology adaptation

Differential evolution (DE) is an efficient and robust evolutionary algorithm (EA), that has been widely and successfully applied to solve global optimization problems in diverse real-world applications. As the population structure has a major influence on the behavior of an EA, effectively incorporating population topology into DE has recently attracted increasing attention. Previous works have shown the effectiveness of different topologies in improving the performance of DE and revealed that different topologies can have different effects on the population’s ability to solve optimization problems. However, the synergy of different topologies for the problems being solved has not been systematically investigated in most DE variants. Moreover, individuals with different fitness values play different roles in guiding the search during the evolutionary process. Nevertheless, the individual-dependent roles are not considered in most DE variants that consider the population topology. To overcome these drawbacks and utilize the information that is derived from the differences between the fitness values of individuals for topology adaption, we propose a multi-topology-based DE (MTDE) algorithm that includes an ensemble of multiple population topologies (MPT), an individual-dependent adaptive topology selection (ITS) scheme, and a topology-dependent mutation (TDM) strategy. In the ensemble of MPT, multiple population topologies with different degrees of connectivity are employed. In the ITS scheme, each individual adaptively selects the topology that is most compatible its role in guiding the search based on its fitness value. In the TDM strategy, the parents for mutation are chosen from the neighborhood of the current individual based on the corresponding topology to generate offspring. The effectiveness of the proposed algorithm is extensively evaluated on a suite of benchmark functions. Experimental results demonstrate the competitive performance of MTDE when compared with other state-of-the-art DE variants and EAs.

Distributed decision fusion under nonideal communication channels with adaptive topology

Multi-sensor decision fusion has attracted some attention in information fusion field, meanwhile, the distributed target detection has been a well-studied topic in the multi-sensor detection theory. This paper investigates the increase in detection reliability that an adaptive network (with adaptive topologies and nonideal channels and decision fusion rules) can provide, compared with a fixed topology network. We consider a network, consisting of K-local uncertainty sensors and a Fusion Center (FC) tasked with detecting the presence or absence of a target in the Region of Interest (ROI). Sensors transmit binary modulated local decisions over nonideal channels modeled as Gaussian noise or fading channels. Assuming that the signal intensity emitted by a target follows the isotropic attenuation power model, we consider three classes of network topology architectures: (1) serial topology; (2) tree topology, and (3) parallel topology. Under the Neyman–Pearson (NP) criterion, we derive the optimal threshold fusion rule with adaptive topology to minimize the error probability. Extensive simulations are conducted to validate the correctness and effectiveness of the proposed algorithms.

Architecture of homeostatic security control for digital manufacture systems based on software-defined networks

The authors have offered the homeostatic control system architecture for digital manufacture security based on the software-defined network technology. We have highlighted the development features of digital manufacture systems and defined the technology advantages of software- defined networks, which allow these networks to be successfully applied with flexible and adaptive topology for a highly efficient new generation of cyber-physical systems. We have also described the main operating principles of software-defined networks in digital manufacture systems. A description of homeostatic security control technology, which includes a combination of engines to provide stability of the system’s internal environment and structural and functional resistance to external disturbances, is provided. We have proposed and described in detail the homeostatic security control system architecture including three generalized components: monitoring unit, decision-making unit, protection and control unit.

Traffic-Aware Virtual Machine Migration in Topology-Adaptive DCN

Virtual machine (VM) migration is a key technique for network resource optimization in modern data center networks. Previous work generally focuses on how to place the VMs efficiently in a static network topology by migrating the VMs with large traffic demands to close servers. As the flow demands between VMs change, however, a great cost will be paid for the VM migration. In this paper, we propose a new paradigm for VM migration by dynamically constructing adaptive topologies based on the VM demands to lower the cost of both VM migration and communication. We formulate the traffic-aware VM migration problem in an adaptive topology and show its NP-hardness. For periodic traffic, we develop a novel progressive-decompose-rounding algorithm to schedule VM migration in polynomial time with a proved approximation ratio. For highly dynamic flows, we design an online decision-maker (ODM) algorithm with proved performance bound. Extensive trace-based simulations show that PDR and ODM can achieve about four times flow throughput among VMs with less than a quarter of the migration cost compared to other state-of-art VM migration solutions. We finally implement an OpenvSwitch-based testbed and demonstrate the efficiency of our solutions.

On-Line Arabic Characters Recognition Using Enhanced Time Delay Neural Networks

This paper concerns with the online recognition of isolated hand writing Arabic characters, through, the interpretation of a script presented by a pen trajectory. This technique was generally used in the electronic organizers of Personal Digital Assistant type. First of all, we have built a data base with several scripters using a graphic tablet which will be used in our application. In order to have a precise recognition of the isolated characters, it is important to model their structure the most correctly possible. In this work we present the study, the implementation and the result of the test of a particular neural network which is the Time Delay Neural Networks. We have followed a two steps approach, in the first one, the character characteristics are extracted, and in the second one, a temporal multi-layered perception is developed for a future classification. Our temporal approach with the adaptive topology, responds to the nature of the Arabic script during the acquisition phase while the use of different learning algorithms can minimize the cost function and improve recognition rates. The parameterization of these two parts will allow us to analyze the impact of the neural network topology on the results of character recognition rates.

Delay Management in Mesh-Based P2P Live Streaming Using a Three-Stage Peer Selection Strategy

Peer-to-peer (P2P) live streaming systems have gained popularity due to the self-scalability property of the P2P overlay networks. In P2P live streaming, peers retrieve stream content from other peers in the system. Therefore, peer selection strategy is a fundamental element to build an overlay which manages the playback delay and startup delay experienced by the peers. In this paper, we propose a peer selection strategy which manages to build a minimum delay overlay using three different stages of overlay construction. In the first stage, the tracker suggests some peers as prospective partners to a new peer. In the second stage, the peer selects its partners out of these peers such that delay is minimized. The third stage is the topology adaptation phase of peers, where peers reposition themselves in the overlay to maintain minimum delay during peer churn. In the proposed peer selection strategy, peers are selected in all the stages based on parameters such as propagation delay, upload capacity, buffering duration and buffering level. The proposed strategy is compared with two existing strategies in the literature: Fast-Mesh (Ren et al. in IEEE Trans Multimed 11: 1446, 2009) and Hybrid live p2p streaming protocol (Hammami et al., 2014) using simulations. Our results show that playback delay and startup delay are reduced significantly with the help of proposed strategy. We demonstrate that the stability of the system also improves during peer churn.

Genetic mechanism-enhanced standard particle swarm optimization 2011

Standard particle swarm optimization 2011 (SPSO2011) is a major improvement of the original particle swarm optimization (PSO) with its adaptive random topology and rotational invariance. Its overall performance has also been improved considerably from the original PSO algorithm, but further improvement is still possible. This study attempts to enhance the exploration ability of SPSO2011 further. The enhancement method conditionally introduces a new genetic mechanism to improve the personal best condition of each particle. This conditional event is called an event-triggered mechanism. Moreover, the new genetic mechanism is utilized to crossover, mutate, and select an improved offspring to improve the condition of the cognitive component and indirectly enhance the exploration ability. The proposed algorithm is called genetic mechanism-enhanced SPSO2011 (SPSO2011_GM). SPSO2011_GM is empirically analyzed with 42 benchmark functions. Results confirm the efficiency of the proposed enhancement method and verify the convergence, exploration, reliability, and scalability of the method.

Design Considerations for Robust Noise Rejection in Otoacoustic Emissions Measured In-Field Using Adaptive Filtering

Otoacoustic emission (OAE) measurement is a sensitive and effective technique to monitor changes in the inner ear potentially induced by noise exposure. However, outside a controlled testing environment, measurements are challenging since the level of ambient noise might be higher than the low-level OAE response. Therefore, an OAE system was designed, suitable to measure OAEs repeatedly on an individual worker in noisy test conditions. This system features a left and right earpiece, each equipped with a pair of miniature loudspeakers, an external and an internal microphone. In addition to the passive attenuation of the earpiece, adaptive filtering on the distortion product OAE (DPOAE) signals is used to further enhance the ratio between the measured OAE signal and interfering noise. The adaptive filtering technique uses the sound captured by the ipsilateral external and internal microphones as well as from the contralateral internal and external microphones. In this paper, the accuracy of DPOAE signals are studied using different combinations of the four microphones in single and dual adaptive filter topologies, as well as the benefits of adding a fixed transfer function in the adaptive filtering algorithm topology to estimate the acoustic path. Side-by-side comparison shows that a dual-stage adaptive filtering algorithm, using a combination of the contralateral internal microphone with the ipsilateral external microphone, is the most promising approach to denoise the DPOAE signal.

Adaptive Topology Optimization of Heat Transfer Structure by Optimality Criteria

Adaptive Topology Optimization of Heat Transfer Structure by Optimality Criteria

Development of an adaptive routing mechanism in software-defined networks

The purpose of this paper is to develop of a single mechanism of the adaptive routing of different types of traffic based on the current quality of service requirements. Software-defined networking is a technology of the future. The current development trend of communication systems constantly confirms this fact. However, to date, the use of this technology in its current form is only justified in large networks of major technology companies and service providers. Currently, a large number of dynamic routing protocols have been developed in communication networks. Our task is to create a solution that can make it possible to use the ability of each node to make a decision on the transmission of information by every possible means for each type of traffic. This task can be accomplished by solving the problem of the development of generalized metric that characterizes the communication channels between devices in the network in detail and the problem of the development of a mechanism of adaptive network logical topology reconfiguration (route control) in order to ensure the high quality of service of the whole network that meets current quality requirements for a particular type of service.

Multi-scale and Multi-orientation Face Recognition using Voting based Extreme Learning Machine

our daily life human can remember many faces and can recognize them irrespective of illumination, aging, obstructions, variation in views. Most of researchers have worked on the problem of face recognition to develop an automatic face recognition system with capabilities to recognize faces as human beings can do. However, in unconstrained situations where a face may be captured in outdoor environmental conditions, while under changing illumination and pose variations Face Recog-nition Techniques fails to work. Here, a new face recognition method is implemented based on Gabor filter and Voting based extreme learning machine, it presents an effective algorithm to pose invariant face recognition called as Multi- scale and Multi-orientation face classification using voting based extreme learning machine. In proposed approach, facial features are extracted by applying set of Gabor filters and Local directional Pattern (LDP), then histogram pattern of result is obtained which is subjected to generate distinctive feature vectors and further classified using V-ELM classifier.The application area of Wireless sensor network(WSN) in real time environment are unreliable and inaccessible , leads to degradation of network performance. The major issues of WSN are QoS ,power and it is impossible to access the WSN to change its power capacity. Long -hops transmission i.e. high range communication which provides the QoS with more energy consumption leads to reduction in network lifetime. The paper concentrates on adjustment of power , range and bit rates to attain adaptive topology control(ATC) at physical layer to maintain equivalent QoS. The simulation are carries out by using MIXIM 2.3 framework Omnet++ 4.6.The comparison of QoS for non- ATC and ATC is presented and an improvement of 29 percentage was resulted.

Virtual Network Topology Adaptability Based on Data Analytics for Traffic Prediction

The introduction of new services requiring large and dynamic bitrate connectivity can cause changes in the direction of the traffic in metro and even core network segments throughout the day. This leads to large overprovisioning in statically managed virtual network topologies (VNTs), which are designed to cope with the traffic forecast. To reduce expenses while ensuring the required grade of service, in this paper we propose a VNT reconfiguration approach based on data analytics for traffic prediction (VENTURE). It regularly reconfigures the VNT based on the predicted traffic, thus adapting the topology to both the current and the predicted traffic volume and direction. A machine learning algorithm based on an artificial neural network is used to provide robust and adaptive traffic models. The reconfiguration problem that takes as its input the traffic prediction is modeled mathematically, and a heuristic is proposed to solve it in practical times. To support VENTURE, we propose an architecture that allows collecting and storing data from monitoring at the routers and that is used to train predictive models for every origin-destination pair. Exhaustive simulation results of the algorithm, together with the experimental assessment of the proposed architecture, are finally presented.

Real-time dissection of organs via hybrid coupling of geometric metaballs and physics-centric mesh-free method

This paper systematically describes a real-time dissection approach for digital organs by strong coupling of geometric metaballs and physically correct mesh-free method. For organ geometry, we employ a novel hybrid model comprising both inner metaballs and high-resolution surface mesh with texture information. Through the use of metaballs, the organ interior is geometrically simplified via a set of overlapping spheres with different radii. As for digital organ’s physical representation, we systematically articulate a hybrid framework to interlink metaballs with physics-driven mesh-free method based on moving least squares (MLS) shape functions. MLS approach enables the direct and rapid transition from metaball geometry to local nodal formulations, which afford potential-energy-correct physical modeling and simulation over continuum domain with physical accuracy. For soft tissue dissection, the nature of our MLS-driven mesh-free method also facilitates adaptive topology modification and cutting surface reconstruction. To expedite simulation towards real-time performance, at the numerical level, we resort to position-based dynamics to simplify physical deformation to drive metaballs participating in the mesh-free formulation. Since nodal points participating in the physical process exist temporarily only in localized regions adjacent to the cutting path, our method could warrant accurate cutting surface without sacrificing real-time computational efficiency. This hybrid dissection technique has already been integrated into a VR-based laparoscopic surgery simulator with a haptic interface.

Towards adaptive topology optimization

This paper presents a new fully-automated adaptation strategy for structural topology optimization (TO) methods. In this work, TO is based on the SIMP method on unstructured tetrahedral meshes. The SIMP density gradient is used to locate solid-void interface and h-adaptation is applied for a better definition of this interface and, at the same time, de-refinement is performed to coarsen the mesh in fully solid and void regions. Since the mesh is no longer uniform after such an adaptation, classical filtering techniques have to be revisited to ensure mesh-independency and checkerboard-free designs. Using this adaptive scheme improves the objective function minimization and leads to a higher resolution in the description of the optimal shape boundary (solid-void interface) at a lower computational cost. This paper combines a 3D implementation of the SIMP method for unstructured tetrahedral meshes with an original mesh adaptation strategy. The approach is validated on several examples to illustrate its effectiveness.

Epidemic spreading on weighted networks with adaptive topology based on infective information

Abstract In this paper, we consider epidemic spreading on a weighted adaptive network in which the network topology varies according to the global and local infective information of individuals. We focus on the relationship between network topology and epidemic spreading. Interacting strength is defined to evaluate the level of how individuals’ infective information taking effects on their connections. The model is analyzed by discrete-time Monte-Carlo simulations with an initial BA scale-free network. It is found that greater interacting strength leads to higher epidemic threshold, lower average disease density of steady-state and shorter epidemic prevalent decay time. Besides, the interaction tends to change the initial BA scale-free network to a fat-tail network while the scaling exponent almost keeps unchanged. In addition, individual who reacts with local infective information will significantly restrain the outbreak of disease comparing to the one with global infective information, and this phenomenon becomes more notable if the interacting strength becomes greater.