Abstract: We propose EnclaveDB, a database engine that guarantees confidentiality, integrity, and freshness for data and queries. EnclaveDB guarantees these properties even when the database administrator is malicious, when an attacker has compromised the operating system or the hypervisor, and when the database runs in an untrusted host in the cloud. EnclaveDB achieves this by placing sensitive data (tables, indexes and other metadata) in enclaves protected by trusted hardware (such as Intel SGX). EnclaveDB has a small trusted computing base, which includes an in-memory storage and query engine, a transaction manager and pre-compiled stored procedures. A key component of EnclaveDB is an efficient protocol for checking integrity and freshness of the database log. The protocol supports concurrent, asynchronous appends and truncation, and requires minimal synchronization between threads. Our experiments using standard database benchmarks and a performance model that simulates large enclaves show that EnclaveDB achieves strong security with low overhead (up to 40% for TPC-C) compared to an industry strength in-memory database engine.

Abstract: In this paper, synchronization of an inertial neural network with time-varying delays is investigated. Based on the variable transformation method, we transform the second-order differential equations into the first-order differential equations. Then, using suitable Lyapunov–Krasovskii functionals and Jensen’s inequality, the synchronization criteria are established in terms of linear matrix inequalities. Moreover, a feedback controller is designed to attain synchronization between the master and slave models, and to ensure that the error model is globally asymptotically stable. Numerical examples and simulations are presented to indicate the effectiveness of the proposed method. Besides that, an image encryption algorithm is proposed based on the piecewise linear chaotic map and the chaotic inertial neural network. The chaotic signals obtained from the inertial neural network are utilized for the encryption process. Statistical analyses are provided to evaluate the effectiveness of the proposed encryption algorithm. The results ascertain that the proposed encryption algorithm is efficient and reliable for secure communication applications.

Abstract: A modern introduction to synchronization phenomena, this text presents recent discoveries and the current state of research in the field, from low-dimensional systems to complex networks. The book describes some of the main mechanisms of collective behaviour in dynamical systems, including simple coupled systems, chaotic systems, and systems of infinite-dimension. After introducing the reader to the basic concepts of nonlinear dynamics, the book explores the main synchronized states of coupled systems and describes the influence of noise and the occurrence of synchronous motion in multistable and spatially-extended systems. Finally, the authors discuss the underlying principles of collective dynamics on complex networks, providing an understanding of how networked systems are able to function as a whole in order to process information, perform coordinated tasks, and respond collectively to external perturbations. The demonstrations, numerous illustrations and application examples will help advanced graduate students and researchers gain an organic and complete understanding of the subject.

Abstract: This paper considers nonfragile exponential synchronization for complex dynamical networks (CDNs) with time-varying coupling delay. The sampled-data feedback control, which is assumed to allow norm-bounded uncertainty and involves a constant signal transmission delay, is constructed for the first time in this paper. By constructing a suitable augmented Lyapunov function, and with the help of introduced integral inequalities and employing the convex combination technique, a sufficient condition is developed, such that the nonfragile exponential stability of the error system is guaranteed. As a result, for the case of sampled-data control free of norm-bound uncertainties, some sufficient conditions of sampled-data synchronization criteria for the CDNs with time-varying coupling delay are presented. As the formulations are in the framework of linear matrix inequality, these conditions can be easily solved and implemented. Two illustrative examples are presented to demonstrate the effectiveness and merits of the proposed feedback control.

Abstract: This paper investigates the problem of sampled-data (SD) exponentially synchronization for a class of Markovian neural networks with time-varying delayed signals. Based on the tunable parameter and convex combination computational method, a new approach named flexible terminal approach is proposed to reduce the conservatism of delay-dependent synchronization criteria. The SD subject to stochastic sampling period is introduced to exhibit the general phenomena of reality. Novel exponential synchronization criterion are derived by utilizing uniform Lyapunov–Krasovskii functional and suitable integral inequality. Finally, numerical examples are provided to show the usefulness and advantages of the proposed design procedure.

Abstract: We consider a cache refresh system where a local server is connected to multiple remote sources and maintains local copies of the data items at the sources. The data at each source is updated randomly and independently without notifying the local server, while the local server refreshes the corresponding cached data periodically. The freshness of the local cache is measured by two different freshness metrics, age of synchronization (AoS) and age of information (AoI). We address the following problem: given a constrained total refresh rate, how does the local server allocate the refresh rate for each source to maintain overall data freshness? We derive the AoI optimal policy which depends only on the square root of the source popularity. For a large refresh rate, we propose an AoS near-optimal rate allocation policy that is proportional to the cube root of both the source update rate and the source popularity. For small refresh rates, we also prove that the square root law with respect to the popularity minimizes both AoS and AoI.

Abstract: This paper proposes attack-resilient distributed control for synchronization of islanded, networked, inverter-based microgrids. Existing cooperative control techniques are susceptible to attacks and cannot guarantee synchronization. The effect of attacks on sensor/actuator, communication links, and hijacking controllers is studied. A resilient synchronization protocol is presented to address sensors/actuators attacks. Attacks on communication links and adverse effects of hijacking controllers are also mitigated by designing a trust-based control protocol. The efficacy of the proposed solutions is evaluated for a modified IEEE 34-bus feeder system under different types of attack.

Abstract: The development of automated and coordinated driving systems (platooning) is an hot topic today for vehicles and it represents a challenging scenario that heavily relies on distributed control in the presence of wireless communication network. To actuate platooning in a safe way it is necessary to design controllers able to effectively operate on informations exchanged via Inter-Vehicular Communication (IVC) systems despite the presence of unavoidable communication impairments, such as multiple time-varying delays that affect communication links. To this aim in this paper we propose a novel distributed adaptive collaborative control strategy that exploits information coming from connected vehicles to achieve leader synchronization and we analytically demonstrate its stability with a Lyapunov-Krasovskii approach. The effectiveness of the proposed strategy is shown via numerical simulations in P lexe , a state of the art IVC and mobility simulator that includes basic building blocks for platooning.

Abstract: We study synchronization in a two-node network built out of the smallest possible self-sustained oscillator: a spin-1 oscillator. We first demonstrate that phase locking between the quantum oscillators can be achieved, even for limit cycles that cannot be synchronized to an external semiclassical signal. Building upon the analytical description of the system, we then clarify the relation between quantum synchronization and the generation of entanglement. These findings establish the spin-based architecture as a promising platform for understanding synchronization in complex quantum networks.

Abstract: Energy-efficient and robust-time synchronization is crucial for industrial Internet of things (IIoT). Some energy-efficient time synchronization schemes that achieve high accuracy have been proposed recently. However, some unsynchronized nodes namely isolated nodes exist in the schemes. To deal with the problem, this paper presents R-Sync, a robust time synchronization scheme for IIoT. We use a pulling timer to pull isolated nodes into synchronized networks whose initial value is set according to level of spanning tree. Then, another timer is set up to select backbone node and its initial value is related to the distance to parent node. Moreover, we do experiments based on simulation tool NS-2 and testbed based on wireless hardware nodes. The experimental results show that our approach makes all the nodes get synchronized and gets the better performance in terms of accuracy and energy consumption, compared with three existing time synchronization algorithms TPSN, GPA, STETS.

Abstract: This paper considers a complex dynamical network model, in which the input and output vectors have different dimensions. We, respectively, investigate the passivity and the relationship between output strict passivity and output synchronization of the complex dynamical network with fixed and adaptive coupling strength. First, two new passivity definitions are proposed, which generalize some existing concepts of passivity. By constructing appropriate Lyapunov functional, some sufficient conditions ensuring the passivity, input strict passivity and output strict passivity are derived for the complex dynamical network with fixed coupling strength. In addition, we also reveal the relationship between output strict passivity and output synchronization of the complex dynamical network with fixed coupling strength. By employing the relationship between output strict passivity and output synchronization, a sufficient condition for output synchronization of the complex dynamical network with fixed coupling strength is established. Then, we extend these results to the case when the coupling strength is adaptively adjusted. Finally, two examples with numerical simulations are provided to demonstrate the effectiveness of the proposed criteria.

Abstract: This paper is concerned with the finite-time and fixed-time synchronization of complex networks with discontinuous nodes dynamics. Firstly, under the framework of Filippov solution, a new theorem of finite-time and fixed-time stability is established for nonlinear systems with discontinuous right-hand sides by using mainly reduction to absurdity. Furthermore, for a class of discontinuous complex networks, a general control law is firstly designed. Under the unified control framework and the same conditions, the considered networks are ensured to achieve finite-time or fixed-time synchronization by only adjusting the value of a key control parameter. Based on the similar discussion, a unified control strategy is also provided to realize respectively asymptotical, exponential and finite-time synchronization of the addressed networks. Finally, the derived theoretical results are supported by an example with numerical simulations.

Abstract: This work addresses a home health care routing and scheduling problem with time window and synchronization constraints. Each patient is associated with a period of availability according to their preferences while some visits may require the presence of two staff members simultaneously, which requires the synchronization of two visits. In this paper, the problem is studied with hard and soft patients time window and synchronization constraints. We developed a mixed integer programming model and a memetic algorithm featuring two original crossover operators. Experiments are conducted on benchmark instances from the literature as well as new instances based on real life data from a home health care provider in France. The results highlight the efficiency of the memetic algorithm since it provides great results while being flexible to the instance type. Indeed, the memetic algorithm is efficient whether the problem is studied with hard or soft time window and synchronization constraints, various caregivers qualification or several home health care offices.

Abstract: Approximate computing has gained research attention recently as a way to increase energy efficiency and/or performance by exploiting some applications’ intrinsic error resiliency. However, little attention has been given to its potential for tackling the communication bottleneck that remains one of the looming challenges to be tackled for efficient parallelism. This article explores the potential benefits of approximate computing for communication reduction by surveying three promising techniques for approximate communication: compression, relaxed synchronization, and value prediction. The techniques are compared based on an evaluation framework composed of communication cost reduction, performance, energy reduction, applicability, overheads, and output degradation. Comparison results demonstrate that lossy link compression and approximate value prediction show great promise for reducing the communication bottleneck in bandwidth-constrained applications. Meanwhile, relaxed synchronization is found to provide large speedups for select error-tolerant applications, but suffers from limited general applicability and unreliable output degradation guarantees. Finally, this article concludes with several suggestions for future research on approximate communication techniques.

Abstract: Optimal output synchronization of multi-agent leader–follower systems with unknown nonlinear dynamics is considered. The agents are assumed heterogeneous so that the dynamics may be nonidentical. A distributed observer is designed to estimate the leader state for each agent. A discounted performance function is defined for each agent, and an augmented Hamilton–Jacobi–Bellman (HJB) equation is derived to find its minimal value. The HJB solution depends on the trajectories of the local state and the distributed observer state. A control protocol based on the HJB solution assures that the synchronization error goes to zero locally asymptotically fast for all agents. The proposed approach has two main advantages compared to standard output synchronization methods. First, it is optimal in the sense that it not only makes the steady-state synchronization error zero, but also minimizes the transient error. Second, it does not require the explicit solution to the output regulator equations, though the HJB solutions implicitly provide optimal solutions to them. Finally, a reinforcement learning technique is used to learn the optimal control protocol for each agent without requiring any knowledge of the agents or the leader dynamics. Simulation studies on a notional multi-agent system validate the proposed approach.

Abstract: The training process of Deep Neural Network (DNN) is compute-intensive, often taking days to weeks to train a DNN model. Therefore, parallel execution of DNN training on GPUs is a widely adopted approach to speed up the process nowadays. Due to the implementation simplicity, data parallelism is currently the most commonly used parallelization method. Nonetheless, data parallelism suffers from excessive inter-GPU communication overhead due to frequent weight synchronization among GPUs. Another approach is pipelined model parallelism, which partitions a DNN model among GPUs, and processes multiple mini-batches concurrently. This approach can significantly reduce inter-GPU communication cost compared to data parallelism. However, pipelined model parallelism faces the weight staleness issue; that is, gradients are computed with stale weights, leading to training instability and accuracy loss. In this paper, we present a pipelined model parallel execution method that enables high GPU utilization while maintaining robust training accuracy via a novel weight prediction technique, SpecTrain. Experimental results show that our proposal achieves up to 8.91x speedup compared to data parallelism on a 4-GPU platform while maintaining comparable model accuracy.

Abstract: Dual-linear-motor-driven gantry systems have been widely used in high-speed or heavy payload precision motion systems. Due to the unique physical properties such as mechanical coupling, the precise synchronization control of such kinds of systems is crucial to achieve good tracking and smooth operation performance. However, the existing synchronization schemes usually focus on the pure motion synchronization only and, thus, have certain inherent performance limitations. In this paper, an accurate multi-input multi-output mathematical model of a dual-driven gantry is presented first, where the complete planar motions of the crossbeam include the traditional linear motion along the guide rails and the previously ignored rotational motion around the mass center. With the better understanding of the mechanical coupling and the internal forces caused by the rotational dynamics, an advanced synchronization control scheme by directly taking into account the additive rotational dynamics is presented, which not only synchronizes the motions of two parallel motors but also regulates the internal forces. In the proposed scheme, the adaptive robust control algorithm is also applied to obtain a guaranteed robust performance in the presence of both parametric uncertainties and uncertain nonlinearities. Comparative experiments with previous control schemes are carried out to show the better synchronization performance and verify the effectiveness of the proposed method.

Abstract: Optical network security is attracting increasing research attention, as loss of confidentiality of data transferred through an optical network could impact a huge number of users and services. Data encryption is an effective way to enhance optical network security. In particular, QKD is being investigated as a secure mechanism to provide keys for data encryption at the endpoints of an optical network. In a QKD-enabled optical network, apart from TDChs, two additional channels, called QSChs and PIChs, are required to support secure key synchronization. How to allocate network resources to QSChs, PIChs, and TDChs is emerging as a novel problem for the design of a security-guaranteed optical network. This article addresses the resource allocation problem in optical networks secured by QKD. We first discuss a possible architecture for a QKD-enabled optical network, where an SDN controller is in charge of allocating the three types of channels (TDCh, QSCh, and PICh) over different wavelengths exploiting WDM. To save wavelength resources, we propose to adopt OTDM to allocate multiple QSChs and PIChs over the same wavelength. An RWTA algorithm is designed to allocate wavelength and time slot resources for the three types of channels. Different security levels are included in the RWTA algorithm by considering different key updating periods (i.e., the period after which the secure key between two endpoints has to be updated). Illustrative simulation results show the effects of different security-level configuration schemes on resource allocation.

Abstract: This paper proposes a combination of composite nonlinear feedback and integral sliding mode techniques for fast and accurate chaos synchronization of uncertain chaotic systems with Lipschitz nonlinear functions, time-varying delays and disturbances. The composite nonlinear feedback method allows accurate following of the master chaotic system and the integral sliding mode control provides invariance property which rejects the perturbations and preserves the stability of the closed-loop system. Based on the Lyapunov- Krasovskii stability theory and linear matrix inequalities, a novel sufficient condition is offered for the chaos synchronization of uncertain chaotic systems. This method not only guarantees the robustness against perturbations and time-delays, but also eliminates reaching phase and avoids chattering problem. Simulation results demonstrate that the suggested procedure leads to a great control performance.

Abstract: Blockchain is a technology uniquely suited to support massive number of transactions and smart contracts within the Internet of Things (IoT) ecosystem, thanks to the decentralized accounting mechanism. In a blockchain network, the states of the accounts are stored and updated by the validator nodes, interconnected in a peer-to-peer fashion. IoT devices are characterized by relatively low computing capabilities and low power consumption, as well as sporadic and low-bandwidth wireless connectivity. An IoT device connects to one or more validator nodes to observe or modify the state of the accounts. In order to interact with the most recent state of accounts, a device needs to be synchronized with the blockchain copy stored by the validator nodes. In this work, we describe general architectures and synchronization protocols that enable synchronization of the IoT endpoints to the blockchain, with different communication costs and security levels. We model and analytically characterize the traffic generated by the synchronization protocols, and also investigate the power consumption and synchronization trade-off via numerical simulations. To the best of our knowledge, this is the first study that rigorously models the role of wireless connectivity in blockchain-powered IoT systems.

Abstract: This paper is concerned with the synchronization analysis and control problems for a class of nonlinear discrete-time stochastic complex dynamical networks (CDNs) consisting of identical nodes. The discrete-time stochastic dynamical networks under consideration are quite general that account for asymmetric coupling configuration, nonlinear inner coupling structures as well as nonidentical exogenous disturbances. By resorting to both the error bound and the synchronization probability, a notion of quasi-synchronization in probability is first introduced to assess the synchronization performance of the addressed CDNs. An event-triggered pinning feedback control strategy is adopted to control a small fraction of the network nodes with hope to reduce the frequency of updating and communication in the control process while preserving the desired dynamical behaviors of the controlled networks. By using the Lyapunov function method and the stochastic analysis techniques, a general framework is established within which the problems of dynamics analysis and controller synthesis are solved for the closed-loop stochastic dynamical networks. Two numerical examples and their simulations are presented to illustrate the effectiveness and the usefulness of our theoretical results.