Asynchronous Networks Research Articles

Multi-stage data synchronization for public blockchain in complex network environment

The various node connecting ways make the public blockchain live in a complex network environment. Single network model, no matter whether synchronized network, partial asynchronous network, or pure asynchronous network, are failed to accurately describe the practical public network environment. This results in the data synchronization and consensus achievement in public blockchain facing a dilemma, either relies on a weak timing assumption or adopts the strict asynchronous assumption but accepts a complex method.To address this dilemma, in this paper, we propose a multi-stage data synchronization (MSDS) mechanism for the public blockchain. First, we define the complex network for public blockchain in which nodes can join or leave freely. Then, data synchronization is been proven as a sufficient but not necessary condition for consensus. Second, a new kind of block structure called the block abstraction (BA) is proposed to quickly determine the relevance between different blocks. A new epoch-based DAG is proposed in which synchronizations of unrelated per-order blocks are reduced within an epoch. Then, a novel base-address PoW protocol is proposed to screen out nodes who have finished the synchronization and block verification of the previous epoch. Finally, a dual-phase consensus is conducted to achieve consensus that does not rely on timing assumptions on block generation and data synchronization while preventing the adoption of complex asynchronous consensus mechanisms. Extensive experiments are conducted on real data from Bitcoin and Ethereum and the results demonstrate the effectiveness and efficiency of our proposal.

Asynchronous network firmware update over the air designed for wireless sensor and actuator networks

This paper improvies the traditional FOTA system to a more robust one named ANFUOTA. This technique is only applicable to systems without embedded operating systems and to microcontrollers without multiple flash memory bank. This novel system is capable of updating the firmware in an asynchronous mode via networks WSN/WSAN kind and furthermore, it guarantees that the system never be without a valid firmware or rather, it is immune of communication failure or loss of packets and even feeding loss, independently of the updating phase. Even during the memory rewriting, in case it happens a feeding failure, the system will begin all over again the recording in the next rebooting. The used technique foresses an updating via networks WSN kind in an asynchronous mode and the firmware can be received in a distributed way in time and in many pieces or capable of being continuous. During this phase, the system remains online and doesn't enter in recording mode, keeping its activity on network. The system guarantees immunity in face of feeding loss and loss of packets by the network in all phases of updating, enabling updating all devices in network, simultaneously.

Joint Localization and Synchronization for Moving Agents Using One-Way TOAs in Asynchronous Networks

The joint localization and synchronization (JLAS) of moving agents with clock offsets is critical to facilitating location services for Internet of Things (IoT). Existing methods using one-way time-of-arrival measurements require anchors to be synchronized and the agent’s motion to be modeled with a constant velocity. However, the requirement of synchronization between anchors limits the flexibility and scalability of IoT networks. Moreover, existing methods are inapplicable for agents that perform arbitrary motions. In this study, we developed a set of methods to solve the JLAS problem in asynchronous networks. First, we modelled the motion of the moving agent and classified it into two categories, acceleration and time-varying motion. We showed that the existing modeling motion is a special case of acceleration motion. Second, for the case of acceleration motion with priori information, we proposed the optimal JLAS method, namely, JLAS-KAM, to compensate for the movement-caused estimation error. Next, we developed a maximum likelihood estimator, namely, JLAS-UAM, to jointly estimate the position, velocity, and acceleration of the agent in the absence of priori information. Third, for the case of time-varying motion, we developed the optimal JLAS method, namely, JLAS-TVM, to jointly estimate the agent position and velocity at each time instant. Moreover, iterative algorithms were proposed to solve optimization problems. We derived the Cramer-Rao lower bound for three proposed methods and analyzed their performance. Simulation results verified the theoretical analysis of the estimation performance and revealed the characteristics and advantages of the proposed methods.

Multi constraint shortest path optimization using metaheuristic algorithms in asynchronous network on chip (2020)

Multi constraint shortest path optimization using metaheuristic algorithms in asynchronous network on chip (2020)

Resource saving based dwell time allocation and detection threshold optimization in an asynchronous distributed phased array radar network

The resource optimization plays an important role in an asynchronous Phased Array Radar Network (PARN) tracking multiple targets with Measurement Origin Uncertainty (MOU), i.e., considering the false alarms and missed detections. A Joint Dwell Time Allocation and Detection Threshold Optimization (JDTADTO) strategy is proposed for resource saving in this case. The Predicted Conditional Cramér-Rao Lower Bound (PC-CRLB) with Bayesian Detector and Amplitude Information (BD-AI) is derived and adopted as the tracking performance metric. The optimization model is formulated as minimizing the difference between the PC-CRLBs and the tracking precision thresholds under the constraints of upper and lower bounds of dwell time and false alarm ratio. It is shown that the objective function is nonconvex due to the Information Reduction Factor (IRF) brought by the MOU. A cyclic minimizer-based solution is proposed for problem solving. Simulation results confirm the flexibility and robustness of the JDTADTO strategy in both sufficient and insufficient resource scenarios. The results also reveal the effectiveness of the proposed strategy compared with the strategies adopting the BD without detection threshold optimization and amplitude information.

Research on PoW Protocol Security under Optimized Long Delay Attack

In the blockchain network, the communication delay between different nodes is a great threat to the distributed ledger consistency of each miner. Blockchain is the core technology of Bitcoin. At present, some research has proven the security of the PoW protocol when the number of delay rounds is small, but in complex asynchronous networks, the research is insufficient on the security of the PoW protocol when the number of delay rounds is large. This paper improves the proposed blockchain main chain record model under the PoW protocol and then proposes the TOD model, which makes the main chain record in the model more close to the actual situation and reduces the errors caused by the establishment of the model in the analysis process. By comparing the differences between the TOD model and the original model, it is verified that the improved model has a higher success rate of attack when the probability of mining the delayable block increases. Then, the long delay attack is improved on the balance attack in this paper, which makes the adversary control part of the computing power and improves the success rate of the adversary attack within a certain limit.

The polychronous model of computation and Kahn process networks

In 1974, Gilles Khan defined a seminal semantic model for asynchronous dataflow programming that would then be called as the eponymous Kahn process networks (KPN) and instantiated in as many models of the so-called DPN hierarchy as domain-specific fields of information processing from digital signal processing to hybrid cyber-physical systems. Among these, synchronous programming models have had an important impact in the specific domain of embedded software design. In this paper, we consider an instance of what seems to be one of the many synchronous models of computation: polychrony, initiated by the dataflow language Signal and its multi-clock (i.e. polychronous) model of computation and, later on, CCSL (clock constraints specification language). We provide an in-depth study of its semantic relationship with respect to the original definition of KPNs and hint toward the idea of polychrony as a methodology to locally synchronize (abstractions of) globally asynchronous processes. In particular, we formally define the property, referred to as “polyendochrony”, that allows one to consider a given desynchronized network of synchronous Signal processes (a GALS architecture) as the implementation of a corresponding KPN model (an asynchronous network of Khan-deterministic functions). For this class of networks, we formalize the Signal program analysis and transformations that define synchronous clusters of Signal processes of guaranteed deterministic behavior in an asynchronous network, that is, without synchronizing communications in the entire network. This definition yields a new strategy of multi-threaded code generation that is available in the open-source Polychrony toolset of the Signal language and blurs the limits between the asynchronous and polychronous models of computation.

DR-ALOHA-Q: A Q-Learning-Based Adaptive MAC Protocol for Underwater Acoustic Sensor Networks.

Underwater acoustic sensor networks (UASNs) are challenged by the dynamic nature of the underwater environment, large propagation delays, and global positioning system (GPS) signal unavailability, which make traditional medium access control (MAC) protocols less effective. These factors limit the channel utilization and performance of UASNs, making it difficult to achieve high data rates and handle congestion. To address these challenges, we propose a reinforcement learning (RL) MAC protocol that supports asynchronous network operation and leverages large propagation delays to improve the network throughput.he protocol is based on framed ALOHA and enables nodes to learn an optimal transmission strategy in a fully distributed manner without requiring detailed information about the external environment. The transmission strategy of sensor nodes is defined as a combination of time-slot and transmission-offset selection. By relying on the concept of learning through interaction with the environment, the proposed protocol enhances network resilience and adaptability. In both static and mobile network scenarios, it has been compared with the state-of-the-art framed ALOHA for the underwater environment (UW-ALOHA-Q), carrier-sensing ALOHA (CS-ALOHA), and delay-aware opportunistic transmission scheduling (DOTS) protocols. The simulation results show that the proposed solution leads to significant channel utilization gains, ranging from 13% to 106% in static network scenarios and from 23% to 126% in mobile network scenarios.oreover, using a more efficient learning strategy, it significantly reduces convergence time compared to UW-ALOHA-Q in larger networks, despite the increased action space.

FedAGCN: A traffic flow prediction framework based on federated learning and Asynchronous Graph Convolutional Network

FedAGCN: A traffic flow prediction framework based on federated learning and Asynchronous Graph Convolutional Network

Chronos: An Efficient Asynchronous Byzantine Ordered Consensus

Abstract Byzantine ordered consensus, introduced by Zhang et al. (OSDI 2020), is a new consensus primitive that additionally guarantees a correctness specification of transaction order, allowing nodes to assign fairly an ordering indicator to the committed transaction. Zhang et al. also presented a concrete Byzantine ordered consensus protocol called Pompē in the partially synchronous network model. However, Pompē cannot prevent an adversary from manipulating message delivery time. In this paper, we present Chronos, the first Byzantine ordered consensus protocol in the asynchronous network model, where an adversary can arbitrarily manipulate message delivery time. To construct Chronos, we propose a variant of asynchronous common subset called signal asynchronous common subset protocol, which guarantees the liveness of Chronos. We implement both Chronos and its baseline HoneyBadgerBFT using Go language and deploy them on 100 Amazon t3.medium instances distributed throughout 10 regions across the world. The experimental results show that Chronos is more efficient than HoneyBadgerBFT for small network, achieving peak throughput of 59 368 tx/s when the batch size is 100 000 and the number of nodes is 4, while the peak of HoneyBadgerBFT is 57 077 tx/s.

Cross-chain payment protocols with success guarantees

In this paper, we consider the problem of cross-chain payment whereby customers of different escrows—implemented by a bank or a blockchain smart contract—successfully transfer digital assets without trusting each other. Prior to this work, cross-chain payment problems did not require this success, or any form of progress. We introduce a new specification formalism called Asynchronous Networks of Timed Automata to formalise such protocols. We present the first cross-chain payment protocol that ensures termination in a bounded amount of time and works correctly in the presence of clock drift. We then demonstrate that it is impossible to solve this problem without assuming synchrony, in the sense that each message is guaranteed to arrive within a known amount of time. Yet, we solve an eventually terminating weaker variant of this problem, where success is conditional on the patience of the participants, without assuming synchrony, and in the presence of Byzantine failures. We also discuss the relation with the recently defined cross-chain deals.

A Penetration Method for UAV Based on Distributed Reinforcement Learning and Demonstrations

The penetration of unmanned aerial vehicles (UAVs) is an essential and important link in modern warfare. Enhancing UAV’s ability of autonomous penetration through machine learning has become a research hotspot. However, the current generation of autonomous penetration strategies for UAVs faces the problem of excessive sample demand. To reduce the sample demand, this paper proposes a combination policy learning (CPL) algorithm that combines distributed reinforcement learning and demonstrations. Innovatively, the action of the CPL algorithm is jointly determined by the initial policy obtained from demonstrations and the target policy in the asynchronous advantage actor-critic network, thus retaining the guiding role of demonstrations in the initial training. In a complex and unknown dynamic environment, 1000 training experiments and 500 test experiments were conducted for the CPL algorithm and related baseline algorithms. The results show that the CPL algorithm has the smallest sample demand, the highest convergence efficiency, and the highest success rate of penetration among all the algorithms, and has strong robustness in dynamic environments.

A developed distributed ledger technology architectural layer framework for decentralized governance implementation in virtual enterprise

Distributed Ledger technology (DLT) has recently emerged as a disruptive system with a wide range of applicability, with prospect to improve societal interactions at large. In virtual enterprise (VE) context researchers and practitioners have started to investigate the deployment of DLT to automate the processing of data and implementation of decisions to support the provision of digital services. Although academic interest in this domain is growing, a practical analysis of DLT from a governance perspective is still lacking to date. Accordingly, this study aims to fill this gap and provide implications related to decentralized governance of DLT. This article develops an architectural governance-by-design framework that defines the governance of DLT as a combination of architectural layers and governance of DLT dimensions. Design science is employed, and IOTA tangle an open-source DLT which employs a decentralized asynchronous network is deployed to evaluate the applicability of the developed architectural governance-by-design framework through qualitative interviews and literature inquiry. The findings confirm the developed architectural governance-by-design framework and offer a shared discussion and insight surrounding the topic of governance of DLT. The findings also identify limitations associated with governance of DLT solutions and proposes policy recommendations to be used as guidelines for practitioners to improve the adoption of DLT to accelerate VE digitalization.

COFlood: Concurrent Opportunistic Flooding in Asynchronous Duty Cycle Networks

For energy constraint wireless IoT nodes, their radios usually operate in duty cycle mode. With low maintenance and negotiation cost, asynchronous duty cycle radio management is widely adopted. To achieve fast network flooding is challenging in asynchronous duty cycle networks. Recently, concurrent flooding, which allows a set of nodes (called concurrent senders ) to immediately broadcast the received packet without any backoff, is a promising approach to improve the flooding speed. We observe that selecting either large or small number of concurrent senders cannot achieve the optimal flooding speed in different deployments. There is a tradeoff between the degradation of concurrent broadcast efficiency and the missing of early receiving chance. In this article, we propose COFlood (Concurrent Opportunistic Flooding), a practical and efficient concurrent flooding protocol in asynchronous duty cycle networks. First, COFlood constructs a concurrent flooding tree in distributed manner. The non-leaf nodes are selected as concurrent senders and they can cover the entire network while reserving the most capacity of concurrent broadcast for later added opportunistic concurrent senders. Moreover, we find that exploiting both early wake-up nodes and long lossy links can speed up the concurrent flooding tree-based network flooding by increasing the early receiving chances. Then, COFlood develops a lightweight method to select the nodes that meet the conditions of these two opportunities as opportunistic concurrent senders. We implement COFlood in TinyOS and evaluate it on two real testbeds. In comparison with state-of-the-art concurrent flooding protocol, completion time and energy consumption can be reduced by up to 35.3% and 26.6%.

Distributed Bayesian Inference Over Sensor Networks.

In this article, two novel distributed variational Bayesian (VB) algorithms for a general class of conjugate-exponential models are proposed over synchronous and asynchronous sensor networks. First, we design a penalty-based distributed VB (PB-DVB) algorithm for synchronous networks, where a penalty function based on the Kullback-Leibler (KL) divergence is introduced to penalize the difference of posterior distributions between nodes. Then, a token-passing-based distributed VB (TPB-DVB) algorithm is developed for asynchronous networks by borrowing the token-passing approach and the stochastic variational inference. Finally, applications of the proposed algorithm on the Gaussian mixture model (GMM) are exhibited. Simulation results show that the PB-DVB algorithm has good performance in the aspects of estimation/inference ability, robustness against initialization, and convergence speed, and the TPB-DVB algorithm is superior to existing token-passing-based distributed clustering algorithms.

High-speed CMOS-free purely spintronic asynchronous recurrent neural network

The exceptional capabilities of the human brain provide inspiration for artificially intelligent hardware that mimics both the function and the structure of neurobiology. In particular, the recent development of nanodevices with biomimetic characteristics promises to enable the development of neuromorphic architectures with exceptional computational efficiency. In this work, we propose biomimetic neurons comprised of domain wall-magnetic tunnel junctions that can be integrated into the first trainable CMOS-free recurrent neural network with biomimetic components. This paper demonstrates the computational effectiveness of this system for benchmark tasks and its superior computational efficiency relative to alternative approaches for recurrent neural networks.

Lipschitz continuity under toric equivalence for asynchronous Boolean networks.

Mathematical models rooted in network representations are becoming increasingly more common for capturing a broad range of phenomena. Boolean networks (BNs) represent a mathematical abstraction suited for establishing general theory applicable to such systems. A key thread in BN research is developing theory that connects the structure of the network and the local rules to phase space properties or so-called structure-to-function theory. While most theory for BNs has been developed for the synchronous case, the focus of this work is on asynchronously updated BNs (ABNs) which are natural to consider from the point of view of applications to real systems where perfect synchrony is uncommon. A central question in this regard is sensitivity of dynamics of ABNs with respect to perturbations to the asynchronous update scheme. Macauley & Mortveit [Nonlinearity 22, 421-436 (2009)] showed that the periodic orbits are structurally invariant under toric equivalence of the update sequences. In this paper and under the same equivalence of the update scheme, the authors (i) extend that result to the entire phase space, (ii) establish a Lipschitz continuity result for sequences of maximal transient paths, and (iii) establish that within a toric equivalence class the maximal transient length may at most take on two distinct values. In addition, the proofs offer insight into the general asynchronous phase space of Boolean networks.

Sensor Fusion with Asynchronous Decentralized Processing for 3D Target Tracking with a Wireless Camera Network.

We present a method to acquire 3D position measurements for decentralized target tracking with an asynchronous camera network. Cameras with known poses have fields of view with overlapping projections on the ground and 3D volumes above a reference ground plane. The purpose is to track targets in 3D space without constraining motion to a reference ground plane. Cameras exchange line-of-sight vectors and respective time tags asynchronously. From stereoscopy, we obtain the fused 3D measurement at the local frame capture instant. We use local decentralized Kalman information filtering and particle filtering for target state estimation to test our approach with only local estimation. Monte Carlo simulation includes communication losses due to frame processing delays. We measure performance with the average root mean square error of 3D position estimates projected on the image planes of the cameras. We then compare only local estimation to exchanging additional asynchronous communications using the Batch Asynchronous Filter and the Sequential Asynchronous Particle Filter for further fusion of information pairs' estimates and fused 3D position measurements, respectively. Similar performance occurs in spite of the additional communication load relative to our local estimation approach, which exchanges just line-of-sight vectors.

Attractor separation and signed cycles in asynchronous Boolean networks

The structure of the graph defined by the interactions in a Boolean network can determine properties of the asymptotic dynamics. For instance, considering the asynchronous dynamics, the absence of positive cycles guarantees the existence of a unique attractor, and the absence of negative cycles ensures that all attractors are fixed points. In presence of multiple attractors, one might be interested in properties that ensure that attractors are sufficiently “isolated”, that is, they can be found in separate subspaces or even trap spaces, subspaces that are closed with respect to the dynamics. Here we introduce notions of separability for attractors and identify corresponding necessary conditions on the interaction graph. In particular, we show that if the interaction graph has at most one positive cycle, or at most one negative cycle, or if no positive cycle intersects a negative cycle, then the attractors can be separated by subspaces. If the interaction graph has no path from a negative to a positive cycle, then the attractors can be separated by trap spaces. Furthermore, we study networks with interaction graphs admitting two vertices that intersect all cycles, and show that if their attractors cannot be separated by subspaces, then their interaction graph must contain a copy of the complete signed digraph on two vertices, deprived of a negative loop. We thus establish a connection between a dynamical property and a complex network motif. The topic is far from exhausted and we conclude by stating some open questions.

Asynchronous photonic time-delay reservoir computing.

Time-delay reservoir computing uses a nonlinear node associated with a feedback loop to construct a large number of virtual neurons in the neural network. The clock cycle of the computing network is usually synchronous with the delay time of the feedback loop, which substantially constrains the flexibility of hardware implementations. This work shows an asynchronous reservoir computing network based on a semiconductor laser with an optical feedback loop, where the clock cycle (20 ns) is considerably different to the delay time (77 ns). The performance of this asynchronous network is experimentally investigated under various operation conditions. It is proved that the asynchronous reservoir computing shows highly competitive performance on the prediction task of Santa Fe chaotic time series, in comparison with the synchronous counterparts.