Cluster Reconfiguration Research Articles

Ion Clusters‐Driven Strong and Acid/Alkali/ Freezing‐Tolerant Conductive Hydrogels for Flexible Sensors in Extreme Environments

AbstractSupramolecular polymer hydrogels, exhibiting wide applications in flexible sensing, wearable electronics, artificial skin, etc., often require complex molecule and component design to adapt extreme environments. Most supramolecular polymer hydrogels currently, however, are unable to satisfy the harsh demands ascribed to their poor mechanical properties and chemical stability. Herein, this study demonstrates a novel strategy to fabricate superstrong polyvinyl alcohol (PVA) hydrogels with densely supramolecular polymer networks (SPNs) and complexation, induced by simple deprotonation and reconfiguration of ion clusters (ICs). Such strategy enables the PVA hydrogels with high strength (9.64 ± 0.5 MPa), compressibility, and recoverability (load objects over ≈10000 times more than its weight), resistance to various solvents, freezing tolerance (keep flexible and conductive even at −50 °C) and many other performances. Notably, the properties of resultant hydrogels surpass majority reported, tackling a long‐standing dilemma both mechanical properties and stability for PVA hydrogels. A proof‐of‐concept spider robot is assembled by 3D printed model, which achieves successfully signal acquisition in different extreme conditions. In this study the multifunctional PVA hydrogels are envisioned as a simple and universal strategy for sensors in various conditions, which is significant for the exploration in extreme environments that are not easily accessible to humans, such as the North and South Poles and solvent leakage area.

Energy- and time-optimal reconfiguration of spacecraft clusters with collision avoidance

Spacecraft cluster reconfiguration is one of the enabling technologies to ensure non-traditional attributes of distributed space systems. This work treats energy- and time-optimal reconfiguration problems for both circular and eccentric reference orbits. Furthermore, typical local and coupling constraints have been taken into account. Particularly, focus is given to two coupling constraints: final configuration constraints and collision avoidance constraints. For final configuration constraints, a parameterization method is applied to ensure that the reconfiguration problem can be solved as only one optimization problem, rather than a large number of optimization problems resulting from the traditional discretization method. A generalized formulation is proposed for non-convex collision avoidance constraints, which are then convexified via linearization and convex restriction technology. This method provides the affine approximation as a special case. After incorporating above constraints, the reconfiguration problem is formulated as an open-loop optimal control problem, which is solved via the Gauss pseudospectral method (GPM). By virtue of elegant features of GPM, those solutions can serve as a counterpart and stepping stone for a distributed implementation of reconfiguration algorithms. Various simulations demonstrate that minimum-energy/time cluster reconfiguration problems with collision avoidance for circular and eccentric reference orbits can be solved effectively and efficiently using GPM.

Continuous Coverage Control of UAVs Based on Cluster Reconfiguration in the Context of Counterattack

Continuous Coverage Control of UAVs Based on Cluster Reconfiguration in the Context of Counterattack

Robust distributed model predictive control for satellite cluster reconfiguration with collision avoidance

This article investigates a reconfiguration problem of the satellite cluster with collision avoidance. Focusing on the uncertain disturbances with upper bound, a tube-based auxiliary controller is designed, which can improve the robustness of actual satellite cluster system. Then, a distributed model predictive controller (DMPC) is proposed for the nominal system with thrust constraints. In DMPC, the terminal constraints and terminal control law based on velocity obstacle are designed to satisfy the collision avoidance. Furthermore, the stability and feasibility can be ensured by using optimized slack variable. In addition, to reduce fuel consumption and the actions of collision, an optimal allocation strategy for target points based on Hungarian algorithm is adopted. Finally, the numerical simulations are presented to verify the effectiveness and advantage of the proposed scheme.

Lexical tectonics: Mapping structural change in patterns of lexification

AbstractWhether it is based on philological data or on reconstruction, historical linguistics formulates etymological hypotheses that entail changes both in form and in meaning. Semantic change can be understood as a change in “patterns of lexification”, i. e., correspondences between forms and senses. Thus a polysemous word, which once lexified senses s1–s2–s3, evolves so it later encodes s2–s3–s4. Meanings that used to be colexified are now dislexified, and vice versa. Leaning on empirical data from Romance and from Oceanic, this study outlines a general model of historical lexicology, and identifies five types of structural innovations in the lexicon: split, merger, competition, shift, and relexification.The theoretical discussion is made easier by using a visual approach to structural change, in the form of diachronic maps. Semantic maps have already proven useful to represent synchronic patterns of lexification, outlining each language’s emic categories against a grid of etic senses. The same principle can be profitably used when analysing lexification patterns in diachrony: lexical change is then viewed as the reconfiguration of sense clusters in a semantic space. Maps help us visualize the “lexical tectonics” at play as words evolve over time, gradually shifting their meaning, gaining or losing semantic territory, colliding with each other, or disappearing forever.

Motorsport Valley revisited: Cluster evolution, strategic cluster coupling and resilience

Over 20 years ago a series of papers identified a strikingly dominant economic cluster – the UK’s Motorsport Valley (MSV) – which led to MSV becoming an international exemplar of concepts such as agglomeration, clusters and knowledge-driven systems of regional development. Utilising an evolutionary perspective on cluster development, this paper asks ‘whatever happened to MSV?’. Drawing on the framework of strategic cluster coupling, four cluster development episodes are conceptualised that each depict the dynamic evolution of the cluster’s multi-scalar institutional environment, strategic coupling trajectories and economic development outcomes. Reflecting the emerging synthesis between evolutionary economic geography and geographical political economy, the paper describes an extended case study of cluster development, an evolutionary process of strategic cluster coupling and, ultimately, an example of cluster resilience. Through a focus on strategic cluster coupling, the paper provides further understanding of cluster evolution and path development mechanisms at key moments of cluster reconfiguration – and an empirical update and continuation of the economic story and cluster lifecycle of MSV.

Acoustic Target Tracking Through a Cluster of Mobile Agents.

This paper discusses the problem of tracking a moving target by means of a cluster of mobile agents that is able to sense the acoustic emissions of the target, with the aim of improving the target localization and tracking performance with respect to conventional fixed-array acoustic localization. We handle the acoustic part of the problem by modeling the cluster as a sensor network, and we propose a centralized control strategy for the agents that exploits the spatial sensitivity pattern of the sensor network to estimate the best possible cluster configuration with respect to the expected target position. In order to take into account the position estimation delay due to the frame-based nature of the processing, the possible positions of the acoustic target in a given future time interval are represented in terms of a compatible set, that is, the set of all possible future positions of the target, given its dynamics and its present state. A frame-by-frame cluster reconfiguration algorithm is presented, which adapts the position of each sensing agent with the goal of pursuing the maximum overlap between the region of high acoustic sensitivity of the entire cluster and the compatible set of the sound-emitting target. The tracking scheme iterates, at each observation frame, the computation of the target compatible set, the reconfiguration of the cluster, and the target acoustic localization. The reconfiguration step makes use of an opportune cost function proportional to the difference of the compatibility set and the acoustic sensitivity spatial pattern determined by the mobile agent positions. Simulations under different geometric configurations and positioning constraints demonstrate the ability of the proposed approach to effectively localize and track a moving target based on its acoustic emission. The Doppler effect related to moving sources and sensors is taken into account, and its impact on performance is analyzed. We compare the localization results with conventional static-array localization and positioning of acoustic sensors through genetic algorithm optimization, and results demonstrate the sensible improvements in terms of localization and tracking performance. Although the method is discussed here with respect to acoustic target tracking, it can be effectively adapted to video-based localization and tracking, or to multimodal information settings (e.g., audio and video).

Multi UAV Cluster Control Method Based on Virtual Core in Improved Artificial Potential Field

In this paper, a method of multi UAV cluster control based on improved artificial potential field (APF) is proposed. The k-means method is used to integrate and optimize the attractive force between UAVs, and the concept of virtual core is introduced to realize the cluster control and adaptive formation flight of multiple UAVs. The attractive disturbance component of the target point is introduced and the backtracking-filling method is proposed to solve the local minimum problem in the APF. The repulsion force in the APF can realize obstacle avoidance and collision avoidance, and the virtual core can control the UAV cluster to fly to the target point under the attractive force of potential field, so as to realize the track planning and multi aircraft cooperative task. In the process of cluster flight when the UAV fails, merges or dispatches, the method can realize cluster reconfiguration and the cluster control effect and task execution success rate can be improved. The simulation experiments in virtual APF and urban environment APF show the effectiveness of this method.

A coverage-aware and energy-efficient protocol for the distributed wireless sensor networks

Wireless sensor network (WSNs) are composed of a large number of battery-powered wireless sensors, which acquire and monitor physical data from their surroundings through self-organization. The sensors are deployed randomly in a target area where maintenance and battery replacement are difficult or even impossible. To achieve better coverage and prolong network lifetime, networks typically adopt clustering protocols with hierarchical inter-cluster topology for network management and data acquisition in WSNs. However, typical solutions require cluster re-configuration due to early death of cluster heads (CHs) and cause energy inefficiency. This paper proposes a coverage- and energy-aware protocol with intra- and inter-cluster methods called CEMST that considers the sensor node density and coverage overlapping. In addition, to adapt network dynamics while improving energy efficiency, self-stabilizing algorithm and Borůvka algorithm are applied to construct the minimum spanning trees (MST) for intra- and inter-cluster routes, respectively. Simulation results indicate that CEMST produces the balanced clustering structures and provides better coverage and longer network lifetime than previous methods.

Hierarchical Clustering-Task Scheduling Policy in Cluster-Based Wireless Sensor Networks

Organizing sensor nodes into a clustered architecture is an effective method for load balancing and prolonging the network lifetime. However, a serious drawback of the clustering approach is the imposed energy overhead caused by the “global” clustering operations in every round of the global round-based policy (GRBP). To mitigate this problem, this paper proposes a hierarchical clustering-task scheduling policy (HCSP), which triggers node-driven clustering as opposed to GRBP's time-driven clustering. Based on HCSP, each cluster is reconfigured only once at each local super round. Therefore, the cluster reconfiguration frequency varies on-demand and may differ from one cluster to another throughout the network lifetime. However, in order to refresh the entire network structure, global clustering is performed at the end of every global hyper round. Accordingly, HCSP aims to achieve a more flexible, energy-efficient, and scalable clustering-task scheduling than that of GRBP. This policy mitigates the clustering overhead, which is the worst disadvantage of clustering approaches. Energy consumption calculations and extensive simulations show the effectiveness of HCSP in saving energy and in prolonging the network lifetime.

On the Performance of Reconfigurable Distributed MIMO in Mobile Networks

We propose a new distributed Multi-Input Multi-Output (MIMO) architecture for mobile networks, which we refer to as reconfigurable distributed MIMO (RD-MIMO), where the communicating mobile nodes temporarily recruit adjacent nodes to operate as distributed antenna arrays. To best serve the communicating nodes, the node clusters are continuously reconfigured due to the node mobility and varying channel conditions. The frequency of reconfiguration depends on the required system performance, exhibiting a tradeoff between performance and complexity. We propose a practical node selection scheme, which activates only a small subset of transmitters. We evaluate the asymptotic performance of the scheme as a function of the number of recruited nodes, demonstrating that there is an optimal number of such nodes. Compared with the system that blindly activates all available transmitting nodes, our results show that the proposed RD-MIMO architecture with node selection achieves superior performance, especially as evident at low SNR. Furthermore, assuming the Brownian motion model, an analytical expression for the reconfiguration time to select a new cluster of transmitting nodes is obtained. Numerical results show that the obtained expression serves as a good estimation for the order of magnitude of the cluster reconfiguration time for other mobility patterns, such as the random walk model.

Decentralized autonomous planning of cluster reconfiguration for fractionated spacecraft

Autonomous cluster operation such as cluster reconfiguration is one of the enabling technologies for fractionated spacecraft. By virtue of the multi-agent system theory, this paper presents an organizational architecture for fractionated spacecraft, which not only enables autonomous cluster operations but also facilitates its non-traditional attributes. Within this organizational architecture, a decentralized framework is proposed to solve cluster reconfiguration problems based on primal and dual decomposition, where subgradient methods are adopted to include reconfiguration cases with non-differentiable objectives. Two typical constraints are considered: final configuration constraints representing coupling variables and collision avoidance constraints representing coupling constraints, both of which are non-convex. General schemes are proposed to convexify those constraints via the linearization and convex restriction technology. Then final configuration constraints are tackled by primal decomposition, while collision avoidance constraints by dual decomposition. To the end, multi-level primal and dual decompositions are employed to solve reconfiguration problems with both coupling variables and coupling constraints. For illustration an example of in-plane cluster reconfiguration is solved and compared with the centralized approach the solution is optimal.

A link- and hop-constrained clustering for multi-hop wireless sensor networks

The connectivity of radio frequency (RF) devices is usually limited by their predefined bandwidth. Due to stochastic selection of cluster heads, most wireless sensor networks (WSN) require cluster re-configuration protocols without connection and hop constraints. This paper proposes a connection-constrained real-time power-aware (ccWSN) clustering and routing method for multi-hop WSN with a connection bound for each sensor nodes (SN). Each data packet in the network is given a hop-count denoted as the deadline to arrive at base station (BS). ccWSN also controls the diameter of multi-hop clusters so as to derive appropriate clusters size and increase network lifetime. This static clustering method does not require re-configuration which would entail additional energy consumption and time overhead. For each cluster, a set of backup CHs will be selected and serve the cluster as a CH in turn. Simulations result shows that ccWSN increases network lifetime and improves load balance among SNs and CHs.

Polymorphic Ring-Shaped Molecular Clusters Made of Shape-Variable Building Blocks.

Self-assembling molecular building blocks able to dynamically change their shapes, is a concept that would offer a route to reconfigurable systems. Although simulation studies predict novel properties useful for applications in diverse fields, such kinds of building blocks, have not been implemented thus far with molecules. Here, we report shape-variable building blocks fabricated by DNA self-assembly. Blocks are movable enough to undergo shape transitions along geometrical ranges. Blocks connect to each other and assemble into polymorphic ring-shaped clusters via the stacking of DNA blunt-ends. Reconfiguration of the polymorphic clusters is achieved by the surface diffusion on mica substrate in response to a monovalent salt concentration. This work could inspire novel reconfigurable self-assembling systems for applications in molecular robotics.

Improvisation and Entrepreneurial Bricolage versus Rationalisation: A case-based analysis of contrasting responses to economic instability in the UK brass musical instruments industry

In periods of economic crisis and instability, the response of many business organisations is to try and adapt to prevailing market conditions. This typically results in a pattern of retrenchment and rationalisation designed to cut costs. Responses of this kind may be justifiable and, to varying degrees, effective at a firm-level. However, their wider repercussions can include the worsening of a pre-existing economic downturn (e.g. large- scale redundancies affecting local communities and cancelled orders affecting other firms in the supply chain). When faced with an economic crisis, some firms adopt a more entrepreneurial approach, in which the key features are strategic reappraisal and various forms of product, process and organisational innovation (Filippetti and Archibugi, 2011). While large corporations are capable of this kind of transformation, there is an increasing recognition of the important part that smaller entrepreneurial firms can play in innovation (Christensen, 1997) including the reinvigoration of industry sectors through open innovation (Chesbrough, 2003) and contributing to the reconfiguration of geographic clusters (Best, 2001). Studies of long wave cycles have shown that periods of economic crisis and depression can be important for innovation: they can disrupt established industry structures and cause entrepreneurs to see markets and customers in a different light so that they re-think products and services (Barras, 2009). However, comparatively little attention has been directed to considering just how entrepreneurial individuals in smaller firms mobilise the resources necessary for innovation and cope with risk in the unfavourable and demanding conditions that prevail in times of economic crisis. This exploratory study seeks to address this research gap. It does so through an in-depth historical case study of the contrasting responses of two firms, in the same industry sector but operating on different scales with differing modes of production (i.e. artisanal v. mechanised), to the greatest economic crisis of the 20th century, namely, the Great Depression of the 1930s (Crafts and Fearon, 2011). The two firms, which served the same markets and were affected by the same external forces, followed very different paths: the larger one engaged in a series of acquisitions as a means of rationalising production and cutting costs, while the much smaller firm that operated on a very modest scale chose to innovate. This innovation involved developing a product that was new to Britain at the time, namely the sousaphone, an unorthodox musical instrument that hitherto had only been produced in the United States. As well as comparing the activities of the two firms operating on different scales, the study examines why the owners of this small firm decided to innovate in the very difficult trading conditions that prevailed at the time, and exactly how they were able to acquire and mobilise the resources needed to pursue this path. In particular, the study focuses on the use of improvisation (Kamoche, Cunha and Cunha, 2002), that is to say ‘impromptu action’ (Dickson, 1997: p. 37), and the closely related concept of entrepreneurial ‘bricolage’ (Baker and Nelson, 2005; Phillips and Tracey, 2007) or ‘making do’ (Eisenberg, 1990: p. 154), as a means of accessing the resources required. The findings suggest that while large-scale enterprises often concluded that a strategy of retrenchment and rationalisation was the appropriate response to economic crisis, firms operating on a smaller scale viewed the situation differently and responded to the altered trading conditions in more positive, creative and entrepreneurial ways. As a result they were able to identify opportunities associated with new and expanding markets with scope for innovation. The study provides insights into the ways in which these small firms were able to identify and access the necessary resources for their innovations. It also sheds new light on the improvisatory nature of their entrepreneurial response, and its capacity to overcome seemingly insurmountable obstacles to growth in a recessionary environment (e.g. adapting existing resources to new uses, forming unconventional subcontracting arrangements and turning existing skills to new uses). The paper concludes with a summary of the key findings and their implications for future research and practice.

A three-phase energy-saving strategy for cloud storage systems

In the running process of cloud data center, the idle data nodes will generate a large amount of unnecessary energy consumption. Furthermore, the resource misallocation will also cause a great waste of energy. This paper proposes a three-phase energy-saving strategy named TPES in order to save energy and operational costs for cloud suppliers. The three phases are replica management based on variable replication factor, cluster reconfiguration according to the optimal total costs and state transition based on observed and predicted workloads. These three phases save energy for the system at different levels which enhance the adaptability of our strategy. We evaluate our strategy using the expanded CloudSim toolkit and the results show that the proposed strategy achieves better energy reduction under different conditions in comparison with the existing schemes.

Dynamic Cluster Reconfiguration for Energy Conservation in Computation Intensive Service

This paper considers the problem of dynamic cluster reconfiguration for computation intensive services. In order to provide a quality-of-service in terms of overload probability, we formulate the problem of energy consumption as a constrained optimization problem, i.e., minimizing the number of active servers to reduce the energy consumption while keeping the overload probability below a desired threshold. An overload probability estimation model is derived by applying large deviation principle, and an online measurement based algorithm is developed to decide the number of servers to power on/off, which makes decision based on current workload without any prior knowledge of the workload statistics. Moreover, the proposed dynamic cluster reconfiguration algorithm iteratively adjusts the number of the active servers, instead of directly determining the number of active servers that is hard to guarantee optimality for the nonstationary workloads. Since the distribution of the workloads among the servers has an impact on potential active servers to turn off, a server scheduling strategy is proposed to collaborate with the proposed decision algorithm to achieve better energy conservation. In order to provide an integrated solution, we present an event model-based implementation to demonstrate the practical application of the proposed approach. Finally, we evaluate the performance of the scheme by using real workloads. The experimental results show the adaptability of the proposed approach to the variations in the workload and robustness of quality-of-service for nonstationary workloads.

A Mobility Pattern Aware Clustering Mechanism for Mobile Vehicular Networks

Clustering is one of the promising technologies for group mobility in mobile vehicular networks. MPAC (Mobility Pattern Aware Clustering) mechanism is proposed in this paper. MPAC aims at improving the stability of the cluster depending on GR (Group Relation), which is derived from some basic mobility metrics such as speed, direction, acceleration and vehicle location. The cluster formation and cluster maintenance operation based on the above mobility metrics are defined. Simulation results show that MPAC has obvious performance advantages in cluster stability, which is illustrated by the mean number of cluster reconfiguration and the mean life time of cluster heads.

Dynamic energy efficient data placement and cluster reconfiguration algorithm for MapReduce framework

With the recent emergence of cloud computing based services on the Internet, MapReduce and distributed file systems like HDFS have emerged as the paradigm of choice for developing large scale data intensive applications. Given the scale at which these applications are deployed, minimizing power consumption of these clusters can significantly cut down operational costs and reduce their carbon footprint—thereby increasing the utility from a provider’s point of view. This paper addresses energy conservation for clusters of nodes that run MapReduce jobs. The algorithm dynamically reconfigures the cluster based on the current workload and turns cluster nodes on or off when the average cluster utilization rises above or falls below administrator specified thresholds, respectively. We evaluate our algorithm using the GridSim toolkit and our results show that the proposed algorithm achieves an energy reduction of 33% under average workloads and up to 54% under low workloads.

Complex Inductance, Excess Noise, and Surface Magnetism in dc SQUIDs

We have characterized the complex inductance of dc SQUIDs cooled to millikelvin temperatures. The SQUID inductance displays a rich, history-dependent structure as a function of temperature, with fluctuations of order 1 fH. At a fixed temperature, the SQUID inductance fluctuates with a 1/f power spectrum; the inductance noise is highly correlated with the conventional 1/f flux noise. The data are interpreted in terms of the reconfiguration of clusters of surface spins, with correlated fluctuations of effective magnetic moments and relaxation times.