Showing papers on "Asynchronous communication published in 2014"

5GNOW: non-orthogonal, asynchronous waveforms for future mobile applications

Abstract: This article provides some fundamental indications about wireless communications beyond LTE/LTE-A (5G), representing the key findings of the European research project 5GNOW. We start with identifying the drivers for making the transition to 5G networks. Just to name one, the advent of the Internet of Things and its integration with conventional human-initiated transmissions creates a need for a fundamental system redesign. Then we make clear that the strict paradigm of synchronism and orthogonality as applied in LTE prevents efficiency and scalability. We challenge this paradigm and propose new key PHY layer technology components such as a unified frame structure, multicarrier waveform design including a filtering functionality, sparse signal processing mechanisms, a robustness framework, and transmissions with very short latency. These components enable indeed an efficient and scalable air interface supporting the highly varying set of requirements originating from the 5G drivers.

Asynchronous Distributed ADMM for Consensus Optimization

Abstract: Distributed optimization algorithms are highly attractive for solving big data problems. In particular, many machine learning problems can be formulated as the global consensus optimization problem, which can then be solved in a distributed manner by the alternating direction method of multipliers (ADMM) algorithm. However, this suffers from the straggler problem as its updates have to be synchronized. In this paper, we propose an asynchronous ADMM algorithm by using two conditions to control the asynchrony: partial barrier and bounded delay. The proposed algorithm has a simple structure and good convergence guarantees (its convergence rate can be reduced to that of its synchronous counterpart). Experiments on different distributed ADMM applications show that asynchrony reduces the time on network waiting, and achieves faster convergence than its synchronous counterpart in terms of the wall clock time.

TriAD: a distributed shared-nothing RDF engine based on asynchronous message passing

Abstract: We investigate a new approach to the design of distributed, shared-nothing RDF engines. Our engine, coined "TriAD", combines join-ahead pruning via a novel form of RDF graph summarization with a locality-based, horizontal partitioning of RDF triples into a grid-like, distributed index structure. The multi-threaded and distributed execution of joins in TriAD is facilitated by an asynchronous Message Passing protocol which allows us to run multiple join operators along a query plan in a fully parallel, asynchronous fashion. We believe that our architecture provides a so far unique approach to join-ahead pruning in a distributed environment, as the more classical form of sideways information passing would not permit for executing distributed joins in an asynchronous way. Our experiments over the LUBM, BTC and WSDTS benchmarks demonstrate that TriAD consistently outperforms centralized RDF engines by up to two orders of magnitude, while gaining a factor of more than three compared to the currently fastest, distributed engines. To our knowledge, we are thus able to report the so far fastest query response times for the above benchmarks using a mid-range server and regular Ethernet setup.

Does Full-Duplex Double the Capacity of Wireless Networks?

Abstract: Full-duplex has emerged as a new communication paradigm and is anticipated to double wireless capacity. Existing studies of full-duplex mainly focused on its PHY layer design, which enables bidirectional transmission between a single pair of nodes. In this paper, we establish an analytical framework to quantify the network-level capacity gain of full-duplex over halfduplex. Our analysis reveals that inter-link interference and spatial reuse substantially reduces full-duplex gain, rendering it well below 2 in common cases. More remarkably, the asymptotic gain approaches 1 when interference range approaches transmission range. Through a comparison between optimal halfand fullduplex MAC algorithms, we find that full-duplex’s gain is further reduced when it is applied to CSMA based wireless networks. Our analysis provides important guidelines for designing full-duplex networks. In particular, network-level mechanisms such as spatial reuse and asynchronous contention must be carefully addressed in full-duplex based protocols, in order to translate full-duplex’s PHY layer capacity gain into network throughput improvement.

A dynamic analysis of the interplay between asynchronous and synchronous communication in online learning: The impact of motivation

Abstract: With the increased affordances of synchronous communication tools, more opportunities for online learning to resemble face-to-face settings have recently become available. However, synchronous communication does not afford as much time for reflection as asynchronous communication. Therefore, a combination of synchronous and asynchronous communication in e-learning would seem desirable to optimally support learner engagement and the quality of student learning. It is still an open question though, how to best design online learning with a blend of synchronous and asynchronous communication opportunities over time. Few studies have investigated the relationship between learners' actual use of synchronous and asynchronous communication over time. Therefore, this study addressed that relationship in an online course (N = 110), taking into account student motivation, and employing a dynamic inter-temporal perspective. In line with our assumptions, we found some support for the expected association between autonomous motivation and engagement in asynchronous and synchronous communication, be it restricted primarily to the first course period. Also, positive relations between engagement in synchronous and asynchronous communication were found, with the strongest influence from using asynchronous to synchronous communication. This study adds to the knowledge base needed to develop guidelines on how synchronous communication can be combined with asynchronous learning.

NOMAD: non-locking, stochastic multi-machine algorithm for asynchronous and decentralized matrix completion

Abstract: We develop an efficient parallel distributed algorithm for matrix completion, named NOMAD (Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion). NOMAD is a decentralized algorithm with non-blocking communication between processors. One of the key features of NOMAD is that the ownership of a variable is asynchronously transferred between processors in a decentralized fashion. As a consequence it is a lock-free parallel algorithm. In spite of being asynchronous, the variable updates of NOMAD are serializable, that is, there is an equivalent update ordering in a serial implementation. NOMAD outperforms synchronous algorithms which require explicit bulk synchronization after every iteration: our extensive empirical evaluation shows that not only does our algorithm perform well in distributed setting on commodity hardware, but also outperforms state-of-the-art algorithms on a HPC cluster both in multi-core and distributed memory settings.

Bonsai: An event-based framework for processing and controlling data streams

Abstract: The design of modern scientific experiments requires the control and monitoring of many parallel data streams. However, the serial execution of programming instructions in a computer makes it a challenge to develop software that can deal with the asynchronous, parallel nature of scientific data. Here we present Bonsai, a modular, high-performance, open-source visual programming framework for the acquisition and online processing of data streams. We describe Bonsai's core principles and architecture and demonstrate how it allows for flexible and rapid prototyping of integrated experimental designs in neuroscience. We specifically highlight different possible applications which require the combination of many different hardware and software components, including behaviour video tracking, electrophysiology and closed-loop control of stimulation parameters.

NOVA: QoE-driven optimization of DASH-based video delivery in networks

Abstract: We consider the problem of optimizing video de- livery for a network supporting video clients streaming stored video. Specifically, we consider the joint optimization of network resource allocation and video quality adaptation. Our objective is to fairly maximize video clients' Quality of Experience (QoE) re- alizing tradeoffs among the mean quality, temporal variability in quality, and fairness, incorporating user preferences on rebuffer- ing and cost of video delivery. We present a simple asymptotically optimal online algorithm, NOVA, to solve the problem. NOVA is asynchronous, and using minimal communication, distributes the tasks of resource allocation to network controller, and quality adaptation to respective video clients. Video quality adaptation in NOVA is also optimal for standalone video clients, and is well suited for use in the DASH framework. Further, NOVA can be extended for use with more general QoE models, networks shared with other traffic loads and networks using fixed/legacy resource allocation.

Consensus of linear multi-agent systems via event-triggered control

Abstract: This paper proposes an event-triggered control for multi-agent systems in which all agents have an identical linear dynamic mode. The asynchronous event-triggered control algorithms are proposed based on the triggering time sequences of all agents. The main contribution of this paper is to extend the event-triggered control method to investigate general linear multi-agent systems. First, by applying variable substitution method, we give the asynchronous triggering conditions. Based on the conditions, the consensus can be achieved both under fixed and switching topologies. Meanwhile, all the proposed event-triggered algorithms can exclude Zeno behaviours of the closed-loop systems. Then, the asynchronous results are applied to cope with formation control problem. Finally, numerical simulations are presented to illustrate the effectiveness of the part event-triggered protocol designs.

Relaxed Stability and Stabilization Conditions of Networked Fuzzy Control Systems Subject to Asynchronous Grades of Membership

Abstract: This paper presents relaxed stability and stabilization conditions for Takagi-Sugeno (T-S) fuzzy systems under network environments subject to asynchronous grades of membership. Because of the introduction of a communication network, the favorable property in the point-to-point connection, that is, sharing the identical premises in the fuzzy plant and the fuzzy controllers cannot be arbitrarily employed. To widen the applicability of the fuzzy control method under network environments, a novel method is provided to reconstruct the synchronous time scale grades of membership at the controller as those in the plant. As a result, the aforementioned favorable property can be conditionally used in the derivation of the stability and stabilization criteria for the system under consideration controlled over a communication network. Numerical examples have been given to illustrate the effectiveness of the proposed approach.

Stability Analysis of A Class of Hybrid Stochastic Retarded Systems Under Asynchronous Switching

Abstract: The stability of a class of hybrid stochastic retarded systems (HSRSs) with an asynchronous switching controller is investigated. In this model, the controller design relies on the observed jumping parameters, which are however delayed and thus can not be measured in real-time precisely. This delayed time interval, referred to as the “asynchronous switching interval”, is Markovian and dependent on the actual switching signal. The sufficient conditions under which the system is either stochastically asymptotic stable or input-to-state stable are obtained by applying the extended Razumikhin-type theorem to the asynchronous switching interval. These results are less conservative as it is only required that the designed Lyapunov function is non-decreasing. It is shown that the stability of the considered system can be guaranteed by a sufficiently small mode transition rate of the underlying Markov process, which is a conclusion similar to that in asynchronous deterministic switched systems. The effectiveness and correctness of the obtained results are finally verified by a numerical example.

The flipped classroom: now or never?

Abstract: Pedagogical changes and new models of delivering educational content should be considered in the effort to address the recommendations of the 2007 Institute of Medicine report and Benner's recommendations on the radical transformation of nursing. Transition to the nurse anesthesia practice doctorate addresses the importance of these recommendations, but educational models and specific strategies on how to implement changes in educational models and systems are still emerging. The flipped classroom (FC) is generating a considerable amount of buzz in academic circles. The FC is a pedagogical model that employs asynchronous video lectures, reading assignments, practice problems, and other digital, technology-based resources outside the classroom, and interactive, group-based, problem-solving activities in the classroom. This FC represents a unique combination of constructivist ideology and behaviorist principles, which can be used to address the gap between didactic education and clinical practice performance. This article reviews recent evidence supporting use of the FC in health profession education and suggests ways to implement the FC in nurse anesthesia educational programs.

The Effects on Health Behavior and Health Outcomes of Internet-Based Asynchronous Communication Between Health Providers and Patients With a Chronic Condition: A Systematic Review

Abstract: Background: In support of professional practice, asynchronous communication between the patient and the provider is implemented separately or in combination with Internet-based self-management interventions. This interaction occurs primarily through electronic messaging or discussion boards. There is little evidence as to whether it is a useful tool for chronically ill patients to support their self-management and increase the effectiveness of interventions. Objective: The aim of our study was to review the use and usability of patient-provider asynchronous communication for chronically ill patients and the effects of such communication on health behavior, health outcomes, and patient satisfaction. Methods: A literature search was performed using PubMed and Embase. The quality of the articles was appraised according to the National Institute for Health and Clinical Excellence (NICE) criteria. The use and usability of the asynchronous communication was analyzed by examining the frequency of use and the number of users of the interventions with asynchronous communication, as well as of separate electronic messaging. The effectiveness of asynchronous communication was analyzed by examining effects on health behavior, health outcomes, and patient satisfaction. Results: Patients’ knowledge concerning their chronic condition increased and they seemed to appreciate being able to communicate asynchronously with their providers. They not only had specific questions but also wanted to communicate about feeling ill. A decrease in visits to the physician was shown in two studies ( P =.07, P =.07). Increases in self-management/self-efficacy for patients with back pain, dyspnea, and heart failure were found. Positive health outcomes were shown in 12 studies, where the clinical outcomes for diabetic patients (HbA1c level) and for asthmatic patients (forced expiratory volume [FEV]) improved. Physical symptoms improved in five studies. Five studies generated a variety of positive psychosocial outcomes. Conclusions: The effect of asynchronous communication is not shown unequivocally in these studies. Patients seem to be interested in using email. Patients are willing to participate and are taking the initiative to discuss health issues with their providers. Additional testing of the effects of asynchronous communication on self-management in chronically ill patients is needed. [J Med Internet Res 2014;16(1):e19]

Signature-free asynchronous byzantine consensus with t

Abstract: This paper presents a new round-based asynchronous consensus algorithm that copes with up to t

Asynchronous email interview as a qualitative research method in the humanities

Abstract: The article focuses on a method for collecting qualitative data. The method is the asynchronous email interview. The authors assess the advantages, challenges and best practices of the asynchronous email interview method. They base their assessment on the academic literature and their own experiences using this data collection method in qualitative research on women who had experienced perinatal loss. The asynchronous email interview will never fully replace traditional face-to-face interviews, but it could gain a solid position as a qualitative research method thanks to its unique benefits.

A Distributed, Asynchronous and Incremental Algorithm for Nonconvex Optimization: An ADMM Based Approach

Abstract: The alternating direction method of multipliers (ADMM) has been popular for solving many signal processing problems, convex or nonconvex. In this paper, we study an asynchronous implementation of the ADMM for solving a nonconvex nonsmooth optimization problem, whose objective is the sum of a number of component functions. The proposed algorithm allows the problem to be solved in a distributed, asynchronous and incremental manner. First, the component functions can be distributed to different computing nodes, who perform the updates asynchronously without coordinating with each other. Two sources of asynchrony are covered by our algorithm: one is caused by the heterogeneity of the computational nodes, and the other arises from unreliable communication links. Second, the algorithm can be viewed as implementing an incremental algorithm where at each step the (possibly delayed) gradients of only a subset of component functions are update d. We show that when certain bounds are put on the level of asynchrony, the proposed algorithm converges to the set of stationary solutions (resp. optimal solutions) for the nonconvex (resp. convex) problem. To the best of our knowledge, the proposed ADMM implementation can tolerate the highest degree of asynchrony, among all known asynchronous variants of the ADMM. Moreover, it is the first ADMM implementation that can deal with nonconvexity and asynchrony at the same time.

A 65k-neuron 73-Mevents/s 22-pJ/event asynchronous micro-pipelined integrate-and-fire array transceiver

Abstract: We present a 65k-neuron integrate-and-fire array transceiver (IFAT) for spike-based neural computation with low-power, high-throughput connectivity. The internally analog, externally digital chip is fabricated on a 4×4 mm2 die in 90 nm CMOS and arranged in 4 quadrants of 16k parallel addressable neurons. Each neuron circuit serves input spike events by dynamically instantiating conductance-based synapses onto four local synapse circuits over two membrane compartments, and produces output spike events upon reaching a threshold in integration over one of the membrane compartments. Fully asynchronous input and output spike event data streams are mediated over the standard address event representation (AER) protocol. To support full event throughput at large synaptic fan-in, a two-tier micro-pipelining scheme parallelizes input events along neural array cores, and along rows of each core. Measured results show sustained peak synaptic event throughput of 18.2 Mevents/s per quadrant, at 22 pJ average energy per synaptic input event and 25 μW standby power.

Asynchronous stochastic optimization for sequence training of deep neural networks

Abstract: This paper explores asynchronous stochastic optimization for sequence training of deep neural networks. Sequence training requires more computation than frame-level training using pre-computed frame data. This leads to several complications for stochastic optimization, arising from significant asynchrony in model updates under massive parallelization, and limited data shuffling due to utterance-chunked processing. We analyze the impact of these two issues on the efficiency and performance of sequence training. In particular, we suggest a framework to formalize the reasoning about the asynchrony and present experimental results on both small and large scale Voice Search tasks to validate the effectiveness and efficiency of asynchronous stochastic optimization.

An Event-Based Neural Network Architecture With an Asynchronous Programmable Synaptic Memory

Abstract: We present a hybrid analog/digital very large scale integration (VLSI) implementation of a spiking neural network with programmable synaptic weights. The synaptic weight values are stored in an asynchronous Static Random Access Memory (SRAM) module, which is interfaced to a fast current-mode event-driven DAC for producing synaptic currents with the appropriate amplitude values. These currents are further integrated by current-mode integrator synapses to produce biophysically realistic temporal dynamics. The synapse output currents are then integrated by compact and efficient integrate and fire silicon neuron circuits with spike-frequency adaptation and adjustable refractory period and spike-reset voltage settings. The fabricated chip comprises a total of 32 × 32 SRAM cells, 4 × 32 synapse circuits and 32 × 1 silicon neurons. It acts as a transceiver, receiving asynchronous events in input, performing neural computation with hybrid analog/digital circuits on the input spikes, and eventually producing digital asynchronous events in output. Input, output, and synaptic weight values are transmitted to/from the chip using a common communication protocol based on the Address Event Representation (AER). Using this representation it is possible to interface the device to a workstation or a micro-controller and explore the effect of different types of Spike-Timing Dependent Plasticity (STDP) learning algorithms for updating the synaptic weights values in the SRAM module. We present experimental results demonstrating the correct operation of all the circuits present on the chip.

SMT-based Task- and Network-level Static Schedule Generation for Time-Triggered Networked Systems

Abstract: In Ethernet-based time-triggered networks, like TTEthernet, a global communication scheme, for which the schedule synthesis is known to be an NP-complete problem, establishes contention-free windows for the exchange of messages with guaranteed low latency and minimal jitter. However, in order to achieve end-to-end determinism at the application level, software tasks running on the end-system nodes need to obey a similar execution scheme with tight dependencies towards the network domain. In this paper we address the simultaneous co-synthesis of network as well as application schedules for preemptive time-triggered tasks communicating in a switched multi-speed time-triggered network. We use Satisfiability Modulo Theories (SMT) to formulate the scheduling constraints and solve the resulting problem using a state-of-the-art SMT solver. Furthermore, we introduce a novel incremental scheduling approach, based on the demand bound test for asynchronous constrained-deadline periodic tasks, which significantly improves scalability for the average case without sacrificing schedulability. We demonstrate the performance of our approach using synthetic network topologies and system configurations.

Maiter: An Asynchronous Graph Processing Framework for Delta-based Accumulative Iterative Computation

Abstract: Myriad of graph-based algorithms in machine learning and data mining require parsing relational data iteratively. These algorithms are implemented in a large-scale distributed environment in order to scale to massive data sets. To accelerate these large-scale graph-based iterative computations, we propose delta-based accumulative iterative computation (DAIC). Different from traditional iterative computations, which iteratively update the result based on the result from the previous iteration, DAIC updates the result by accumulating the "changes" between iterations. By DAIC, we can process only the "changes" to avoid the negligible updates. Furthermore, we can perform DAIC asynchronously to bypass the high-cost synchronous barriers in heterogeneous distributed environments. Based on the DAIC model, we design and implement an asynchronous graph processing framework, Maiter. We evaluate Maiter on local cluster as well as on Amazon EC2 Cloud. The results show that Maiter achieves as much as 60x speedup over Hadoop and outperforms other state-of-the-art frameworks.

Asynchronous Contention Resolution Diversity ALOHA: Making CRDSA Truly Asynchronous

Abstract: Following the introduction of contention resolution diversity slotted ALOHA (CRDSA), a number of variants of the scheme have been proposed in literature. A major drawback of these slotted random access (RA) schemes is related to the need to keep slot synchronization among all transmitters. The volume of signaling generated to maintain transmitters' slot synchronization is impractical for large networks. In this paper, we describe in detail asynchronous contention resolution diversity ALOHA (ACRDA), which represents the evolution of the CRDSA RA scheme. ACRDA provides better throughput performance with reduced demodulator complexity and lower transmission latency than its predecessor while allowing truly asynchronous access to the shared medium. The performance of the ACRDA protocol is evaluated via mathematical analysis and computer simulations and is compared with that of CRDSA.

OSCAR: Object Security Architecture for the Internet of Things

Abstract: Billions of smart, but constrained objects wirelessly connected to the global network require novel paradigms in network design. New protocol standards, tailored to constrained devices, have been designed taking into account requirements such as asynchronous application traffic, need for caching, and group communication. The existing connection oriented security architecture is not able to keep up---first, in terms of the supported features, but also in terms of the scale and resulting latency on small constrained devices. In this paper, we propose an architecture that leverages the security concepts both from content-centric and traditional connection-oriented approaches. We rely on secure channels established by means of (D)TLS for key exchange, but we get rid of the notion of the 'state' among communicating entities. We provide a mechanism to protect from replay attacks by coupling our scheme with the CoAP application protocol. Our object-based security architecture (OSCAR) intrinsically supports caching and multicast, and does not affect the radio duty-cycling operation of constrained objects. We evaluate OSCAR in two cases: 802.15.4 Low Power and Lossy Networks (LLN) and Machine-to-Machine (M2M) communication for two different hardware platforms and MAC layers on a real testbed and using the Cooja emulator. We show significant energy savings at constrained servers and reasonable delays. We also discuss the applicability of OSCAR to Smart City deployments.

Caching Eliminates the Wireless Bottleneck in Video Aware Wireless Networks

Abstract: Wireless video is the main driver for rapid growth in cellular data traffic. Traditional methods for network capacity increase are very costly and do not exploit the unique features of video, especially asynchronous content reuse. In this paper we give an overview of our work that proposed and detailed a new transmission paradigm exploiting content reuse and the widespread availability of low-cost storage. Our network structure uses caching in helper stations (femtocaching) and/or devices, combined with highly spectrally efficient short-range communications to deliver video files. For femtocaching, we develop optimum storage schemes and dynamic streaming policies that optimize video quality. For caching on devices, combined with device-to-device (D2D) communications, we show that communications within clusters of mobile stations should be used; the cluster size can be adjusted to optimize the tradeoff between frequency reuse and the probability that a device finds a desired file cached by another device in the same cluster. In many situations the network throughput increases linearly with the number of users, and the tradeoff between throughput and outage is better than in traditional base-station centric systems. Simulation results with realistic numbers of users and channel conditions show that network throughput can be increased by two orders of magnitude compared to conventional schemes.

On-Demand Asynchronous Localization for Underwater Sensor Networks

Abstract: In this paper we consider the issue of localization in the context of underwater sensor networks which contain anchor nodes with perfect knowledge of their position, but asynchronous clocks. By taking advantage of a sequential transmission protocol and the broadcasting nature of the acoustic underwater medium, the entire network can be localized simultaneously with small overhead. Additionally, it can be initiated by any node at any time. Through extensive simulation and derivation of the Cramer-Rao Lower Bound (CRLB), we first verify the utility and performance of the algorithm, demonstrating that both initiator and passive nodes can achieve low-error positioning. We then implement the algorithm on an existing modem, and through tests performed within a pool and lake we have determined the accuracy and effectiveness of the algorithm in a true underwater environment.

Realm: an event-based low-level runtime for distributed memory architectures

Abstract: We present Realm, an event-based runtime system for heterogeneous, distributed memory machines. Realm is fully asynchronous: all runtime actions are non-blocking. Realm supports spawning computations, moving data, and reservations, a novel synchronization primitive. Asynchrony is exposed via a light-weight event system capable of operating without central management. We describe an implementation of Realm that relies on a novel generational event data structure for efficiently handling large numbers of events in a distributed address space. Microbenchmark experiments show our implementation of Realm approaches the underlying hardware performance limits. We measure the performance of three real-world applications on the Keeneland supercomputer. Our results demonstrate that Realm confers considerable latency hiding to clients, attaining significant speedups over traditional bulk-synchronous and independently optimized MPI codes.