Information Transmission Delay Research Articles

Random Access Performance of Distributed Sensors Attacked by Unknown Jammers.

In this paper, we model and investigate the random access (RA) performance of sensor nodes (SN) in a wireless sensor network (WSN). In the WSN, a central head sensor (HS) collects the information from distributed SNs, and jammers disturb the information transmission primarily by generating interference. In this paper, two jamming attacks are considered: power and code jamming. Power jammers (if they are friendly jammers) generate noises and, as a result, degrade the quality of the signal from SNs. Power jamming is equally harmful to all the SNs that are accessing HS and simply induces denial of service (DoS) without any need to hack HS or SNs. On the other hand, code jammers mimic legitimate SNs by sending fake signals and thus need to know certain system parameters that are used by the legitimate SNs. As a result of code jamming, HS falsely allocates radio resources to SNs. The code jamming hence increases the failure probability in sending the information messages, as well as misleads the usage of radio resources. In this paper, we present the probabilities of successful preamble transmission with power ramping according to the jammer types and provide the resulting throughput and delay of information transmission by SNs, respectively. The effect of two jamming attacks on the RA performances is compared with numerical investigation. The results show that, compared to RA without jammers, power and code jamming degrade the throughput by up to 30.3% and 40.5%, respectively, while the delay performance by up to 40.1% and 65.6%, respectively.

Cross Border Organ Exchange Among EU Members States. Preliminary Results of the Foedus-EOEO it Portal

Introduction FOEDUS-EOEO portal for the exchange of surplus organs among European Union (EU) Member States (MS) (available at www.foedus-eoeo.eu) is the result of a 3-year EU-funded Joint Action, aimed at facilitating organ exchange between the MS in compliance with Directives 2010/53/EU and 2012/25/EU. Methods FOEDUS-EOEO platform became fully operational as of June 1st 2015, it is accessible through user-id and password and allows sending offers or requests of surplus available organs simultaneously to registered EU countries avoiding any delay in information transmission. Out of 24 registered countries 10 (BG, CZ, FR, GR, IT, LT, SK, ES, PL, CH) have been actively using it to present. The portal allows managing multilateral and bilateral organ exchange agreements, clinical information are transmitted using an agreed-upon donor form. Organs are allocated on the basis of the first-come first-served principle. Each registered member has access to a report-generating function, through which activity data files in either pdf, excel and xml format can be downloaded, analysing portal activity by several variables. Results From June 1st 2015 to March 31st 2017, 380 organs have been offered through the portal, 53 of these have been transplanted, including 23 paediatric transplantations. The most offered organ were lungs (89) whereas the most requested organ was heart (140) and the most transplanted was liver (30). Among the EU countries using the portal France is the most offering country (21) and Italy is the country where most organs have been transplanted (16). Distribution among age groups was rather homogenous, interestingly 11 out of 53 organs were retrieved from really small children (0-3 year-old), therefore allowing to meet the needs of an important group of patients for which it is often difficult to find a correct size match in national programs. Conclusions The development and use of FOEDUS-EOEO platform has allowed to speed up communication processes between the involved EOEOs but also to ensure the transparency and traceability of the exchanged organs. The adoption of a common clinical information form has also contributed to smooth the assessment procedures by coordinators. As a result, whenever suitable matches between donors and recipient are found, available surplus organs can be effectively allocated. In particular an increased number of pediatric patients benefit from this exchange. After the expiration of funding support by EU Commission, ten organ Competent Authorities (CZ, FR, IT, LT, PL, RO, SK, ES, CH, UK) have signed a cooperation agreement, thus committing to fund the maintenance of the portal and encouraging other EU countries to join. These encouraging preliminary figures show that this new IT tool has already become a key instrument for offering additional life-saving opportunities to EU citizens.

Chimera states in a multilayer network of coupled and uncoupled neurons.

We study the emergence of chimera states in a multilayer neuronal network, where one layer is composed of coupled and the other layer of uncoupled neurons. Through the multilayer structure, the layer with coupled neurons acts as the medium by means of which neurons in the uncoupled layer share information in spite of the absence of physical connections among them. Neurons in the coupled layer are connected with electrical synapses, while across the two layers, neurons are connected through chemical synapses. In both layers, the dynamics of each neuron is described by the Hindmarsh-Rose square wave bursting dynamics. We show that the presence of two different types of connecting synapses within and between the two layers, together with the multilayer network structure, plays a key role in the emergence of between-layer synchronous chimera states and patterns of synchronous clusters. In particular, we find that these chimera states can emerge in the coupled layer regardless of the range of electrical synapses. Even in all-to-all and nearest-neighbor coupling within the coupled layer, we observe qualitatively identical between-layer chimera states. Moreover, we show that the role of information transmission delay between the two layers must not be neglected, and we obtain precise parameter bounds at which chimera states can be observed. The expansion of the chimera region and annihilation of cluster and fully coherent states in the parameter plane for increasing values of inter-layer chemical synaptic time delay are illustrated using effective range measurements. These results are discussed in the light of neuronal evolution, where the coexistence of coherent and incoherent dynamics during the developmental stage is particularly likely.

Synchronization transitions induced by partial time delay in a excitatory–inhibitory coupled neuronal network

Information transmission delays are an inherent factor of neuronal systems as a consequence of the finite propagation speeds and time lapses occurring by both dendritic and synaptic processes. In real neuronal systems, some delay between two neurons is too small and can be ignored, which results in partial time delay. In this paper, we focus on investigating influences of partial time delay on synchronization transitions in a excitatory–inhibitory (E–I) coupled neuronal networks. Here, we suppose time delay between two neurons equals to $$\tau $$ with probability $$p_{\mathrm{delay}}$$ and investigate effect of partial time delay on synchronization transitions of the neuronal networks by controlling $$\tau $$ and $$p_{\mathrm{delay}}$$ under three cases. In these three cases, excitatory synapses are always considered to delayed with probability $$p_{\mathrm{delay}}$$ , while inhibitory synapses are considered to be without delays (case I), delayed with probability $$p_{\mathrm{delay}}$$ (case II), and always delayed (case III), respectively. It is revealed that, in the first two cases, partial time delay has little influences on synchronization of the neuronal network for small $$p_{\mathrm{delay}}$$ , while it could induce synchronization transitions at $$\tau $$ around integer multiples of the period of individual neuron T when $$p_{\mathrm{delay}}$$ is large enough, while in the case III, partial time delay could induce synchronization transitions at $$\tau $$ being around odd integer multiples of T / 2 for small $$p_{\mathrm{delay}}$$ and at $$\tau $$ being around integer multiples of T for large $$p_{\mathrm{delay}}$$ . Most interesting observation is that partial time delay could induce frequent synchronization transitions at $$\tau $$ being around integer multiples of T / 2 for intermediate $$p_{\mathrm{delay}}$$ . Moreover, effect of rewiring probability on synchronization transitions induced by partial time delay has been discussed. It is found that synchronization transitions induced by partial time delay are robust to rewiring probability for large $$p_{\mathrm{delay}}$$ under the three cases.

Identifying Topologies of Complex Dynamical Networks With Stochastic Perturbations

Systems taking the form of networks abound in the world and attract extensive attention from the multidisciplinary nonlinear science community. As is known, network topology plays an important role in determining a network's intrinsic dynamics and function. In the past decade, many researchers have investigated the geometric features, control, and synchronization of complex networks with given or known topological structures. However, in many practical situations, the exact structure of a network is usually unknown. Therefore, inferring the intrinsic topology of complex networks is a prerequisite to understanding and explaining the evolutionary mechanisms and functional behaviors of systems built upon those networks. Furthermore, noise is ubiquitous in nature and in man-made networks. The goal of this paper is to present a simple and efficient technique to recover the underlying topologies of noise-contaminated complex dynamical networks with or without information transmission delay. The effectiveness of the approach is illustrated with a complex network composed of FHN systems. In addition, the impact of some network parameters on identification performance is further probed into.

Chimera states in uncoupled neurons induced by a multilayer structure.

Spatial coexistence of coherent and incoherent dynamics in network of coupled oscillators is called a chimera state. We study such chimera states in a network of neurons without any direct interactions but connected through another medium of neurons, forming a multilayer structure. The upper layer is thus made up of uncoupled neurons and the lower layer plays the role of a medium through which the neurons in the upper layer share information among each other. Hindmarsh-Rose neurons with square wave bursting dynamics are considered as nodes in both layers. In addition, we also discuss the existence of chimera states in presence of inter layer heterogeneity. The neurons in the bottom layer are globally connected through electrical synapses, while across the two layers chemical synapses are formed. According to our research, the competing effects of these two types of synapses can lead to chimera states in the upper layer of uncoupled neurons. Remarkably, we find a density-dependent threshold for the emergence of chimera states in uncoupled neurons, similar to the quorum sensing transition to a synchronized state. Finally, we examine the impact of both homogeneous and heterogeneous inter-layer information transmission delays on the observed chimera states over a wide parameter space.

Mobile node aware opportunistic routing in dynamic wireless sensor network

Opportunistic routing (OR) could adapt to dynamic wireless sensor networks (WSNs) because of its inherent broadcast nature. Most of the existing OR protocols focus on the variations of propagation environment which are caused by channel fading. However, a few works deal with the dynamic scenario with mobile nodes. In this paper, a mobile node (MN) aware OR (MN-OR) is proposed and applied to a WSN in the high-speed railway scenario where the destination node is deployed inside a high speed moving train, and the MN-OR not only considers the mobility of node but also utilizes the candidate waiting time induced by the timer-based coordination scheme. Specifically, to reduce the number of duplicate transmissions and mitigate the delay of information transmission, a new selection strategy of the candidate forwarders is presented. In addition, two priority assignment methods of the candidate forwarders are proposed for the general relay nodes (GRNs) and the rail-side nodes (RSNs) according to their different routing requirements. Extensive simulation results demonstrate that the proposed MN-OR protocol can achieve better network performances compared with some existing routing schemes such as the well-known Ad-hoc on-demand distance vector routing (AODV) and the extremely opportunistic routing (ExOR) protocols.

Impairments of Social Motor Synchrony Evident in Autism Spectrum Disorder.

Social interactions typically involve movements of the body that become synchronized over time and both intentional and spontaneous interactional synchrony have been found to be an essential part of successful human interaction. However, our understanding of the importance of temporal dimensions of social motor synchrony in social dysfunction is limited. Here, we used a pendulum coordination paradigm to assess dynamic, process-oriented measures of social motor synchrony in adolescents with and without autism spectrum disorder (ASD). Our data indicate that adolescents with ASD demonstrate less synchronization in both spontaneous and intentional interpersonal coordination. Coupled oscillator modeling suggests that ASD participants assembled a synchronization dynamic with a weaker coupling strength, which corresponds to a lower sensitivity and decreased attention to the movements of the other person, but do not demonstrate evidence of a delay in information transmission. The implication of these findings for isolating an ASD-specific social synchronization deficit that could serve as an objective, bio-behavioral marker is discussed.

Stochastic resonance enhancement of small-world neural networks by hybrid synapses and time delay

The synergistic effect of hybrid electrical-chemical synapses and information transmission delay on the stochastic response behavior in small-world neuronal networks is investigated. Numerical results show that, the stochastic response behavior can be regulated by moderate noise intensity to track the rhythm of subthreshold pacemaker, indicating the occurrence of stochastic resonance (SR) in the considered neural system. Inheriting the characteristics of two types of synapses—electrical and chemical ones, neural networks with hybrid electrical-chemical synapses are of great improvement in neuron communication. Particularly, chemical synapses are conducive to increase the network detectability by lowering the resonance noise intensity, while the information is better transmitted through the networks via electrical coupling. Moreover, time delay is able to enhance or destroy the periodic stochastic response behavior intermittently. In the time-delayed small-world neuronal networks, the introduction of electrical synapses can significantly improve the signal detection capability by widening the range of optimal noise intensity for the subthreshold signal, and the efficiency of SR is largely amplified in the case of pure chemical couplings. In addition, the stochastic response behavior is also profoundly influenced by the network topology. Increasing the rewiring probability in pure chemically coupled networks can always enhance the effect of SR, which is slightly influenced by information transmission delay. On the other hand, the capacity of information communication is robust to the network topology within the time-delayed neuronal systems including electrical couplings.

Effects of distance-dependent delay on small-world neuronal networks.

We study firing behaviors and the transitions among them in small-world noisy neuronal networks with electrical synapses and information transmission delay. Each neuron is modeled by a two-dimensional Rulkov map neuron. The distance between neurons, which is a main source of the time delay, is taken into consideration. Through spatiotemporal patterns and interspike intervals as well as the interburst intervals, the collective behaviors are revealed. It is found that the networks switch from resting state into intermittent firing state under Gaussian noise excitation. Initially, noise-induced firing behaviors are disturbed by small time delays. Periodic firing behaviors with irregular zigzag patterns emerge with an increase of the delay and become progressively regular after a critical value is exceeded. More interestingly, in accordance with regular patterns, the spiking frequency doubles compared with the former stage for the spiking neuronal network. A growth of frequency persists for a larger delay and a transition to antiphase synchronization is observed. Furthermore, it is proved that these transitions are generic also for the bursting neuronal network and the FitzHugh-Nagumo neuronal network. We show these transitions due to the increase of time delay are robust to the noise strength, coupling strength, network size, and rewiring probability.

Learning Faster or More Precisely? Strategic Experimentation in Networks

The paper analyzes a dynamic model of rational strategic learning in a network. It complements existing literature by providing a detailed picture of short-run dynamics in a game of strategic experimentation where agents are located in a social network. The delay in information transmission caused by incomplete network structures induces players to increase own experimentation efforts. As a consequence a complete network can fail to be optimal even if there are no costs for links. This means that in the design of networks there exists a trade-off between the speed of learning and accuracy. For an incomplete network to generate strictly higher payoffs than the complete network a combination of delay and specialization, where only some agents exert effort, is necessary. Further, splitting a network is only strictly optimal in case of specialization.

Adaptive stochastic resonance in self-organized small-world neuronal networks with time delay

In this paper, adaptive stochastic resonance in time-delayed Newman–Watts small-world neuronal networks is studied, where the strength of synaptic connections between neurons is adaptively modulated by spike-timing-dependent plasticity (STDP). Numerical results show that, in the absence of information transmission delay, the phenomenon of stochastic resonance occurs and the efficiency of networked stochastic resonance can be slightly depressed by STDP. Due to the reduction of strong couplings induced by STDP, for a larger adjusting rate of STDP, a smaller peak value of the resonance response is obtained. In addition, the effect of stochastic resonance can be either promoted or destroyed by time delay, and multiple stochastic resonances appear intermittently at the integer multiples of periods of the subthreshold forcing. Furthermore, it is demonstrated that the networked stochastic resonance can also be dramatically affected by the small-world topology. For small and moderate adjusting rate of STDP, fine-tuning of the probability of adding links can significantly enhance the effect of stochastic resonance in adaptive neural network. Additionally, there is an optimal probability of adding links by which the noise-induced transmission of weak periodic signal peaks and the location of this span depends largely on the time delay and adjusting rate.

Efficient Scheduling of Packets in Wireless Sensor Networks Using Priority Based Scheduling Approach

The packet scheduling is essential and plays an important role to sense the element nodes with resource constraints in Wireless Networks. The packet scheduling scheme also scale the energy consumption and end-to-end information transmission delay in time period and non-real-time information packets during wireless transmission. The proposed Priority based Packet Scheduling (PPS) technique has a tendency to overcome the issues based on priority queues in a wireless network. The priority queue has three levels at each node, except those at the last virtual hierarchy level in the zone-based topology of wireless network. In the meantime packet square are measured and placed in the highest-priority queue and might pre-empt information packets in alternative queues. In the non-real-time period packet square are measured and placed into two alternative queues supported at a particular threshold of their calculable interval. The time period and non-real time information has two queues in a leaf node, since they did not receive information from alternative nodes which causes scale back end-to-end delay. The proposed PPS technique has affinity to evaluate performance through simulations for the time period and non-real-time information. Simulation results illustrate that the PPS Scheme outperforms typical schemes in terms of average information waiting time and end-to-end delay.

Modeling and Simulation of AWACS Supporting Fighter Plane Air Blockade Combat

This paper analyses the process of AWACS aircraft supporting fighter plane with the implementation of air blockade. On the basis of it, model fighter plane blockade combat, establish the hit probability model for solving fighter air regional blockade, do experiments by using this model, adopt orthogonal experiment for design to reduce complexity, get the key factors influencing fighter blockade combat by further combination of analytic method and simulation analysis, carry on importance analysis, and finally put forward that the first selection of combat forces configuration index are external probe and target designation information transmission delay.

Local and global synchronization transitions induced by time delays in small-world neuronal networks with chemical synapses.

Effects of time delay on the local and global synchronization in small-world neuronal networks with chemical synapses are investigated in this paper. Numerical results show that, for both excitatory and inhibitory coupling types, the information transmission delay can always induce synchronization transitions of spiking neurons in small-world networks. In particular, regions of in-phase and out-of-phase synchronization of connected neurons emerge intermittently as the synaptic delay increases. For excitatory coupling, all transitions to spiking synchronization occur approximately at integer multiples of the firing period of individual neurons; while for inhibitory coupling, these transitions appear at the odd multiples of the half of the firing period of neurons. More importantly, the local synchronization transition is more profound than the global synchronization transition, depending on the type of coupling synapse. For excitatory synapses, the local in-phase synchronization observed for some values of the delay also occur at a global scale; while for inhibitory ones, this synchronization, observed at the local scale, disappears at a global scale. Furthermore, the small-world structure can also affect the phase synchronization of neuronal networks. It is demonstrated that increasing the rewiring probability can always improve the global synchronization of neuronal activity, but has little effect on the local synchronization of neighboring neurons.

Transmission of Image Information in the Network Multimedia Teaching

To solve the problem of image information transmission delay in the network multimedia teaching system, the compression and multicast solutions are put forward on the basis of usual screen blocking skill of data. According to the characteristics of the multimedia teaching system, the screen changes typically little at smaller intervals. It means that only a few screen blocks, which are changing, are needed to be transferred by screen blocking skill. At the same time, the network video data transmission volume is further curtailed through compression and multicast so that the multimedia information network transmission delay is decreased. The minimal buffer size for allocation is determined to ensure the smooth transmission of video data in the multimedia teaching system.

Delay-induced synchronization transitions in modular scale-free neuronal networks with hybrid electrical and chemical synapses

We study the dependence of synchronization transitions in modular networks of bursting neurons with hybrid electrical–chemical synapses on the information transmission delay and the probability of electrical synapses. The modular network is composed of subnetworks (clusters); each of them presents the scale-free property. It is shown that, irrespective of the probability of electrical synapses, the time delay can always induce synchronization transitions in modular neuronal networks. Regions of synchronization and non-synchronization appear intermittently as the delay increases. In particular, all these transitions to burst synchronization occur approximately at integer multiples of oscillatory period of individual neurons. In addition, for larger probability of electrical synapses, the intermittent synchronization transition is more profound, due to the stronger synchronization capability of electrical synapses compared with chemical ones. Furthermore, the transition to synchronous bursting can also be induced by the variation of modular network parameters, that is, the coupling strength between neurons, the interconnection probability between different subnetworks, as well as the number of subnetworks. Particularly, we find that a modular neuronal network is harder to get global synchronization when constituting neurons are dispersed over more clusters. On the other hand, chemical and electrical synapses can perform complementary roles in the synchronization of hybrid modular neuronal networks: the larger the electrical synapse strength is the smaller the chemical synapse strength needed to achieve burst synchronization.

Design of Networked Control System of Vehicle Body Based on Hybrid Scheduling Algorithm

Along with the body control system is becoming more intelligent, making the body network scale and the communication load increase dramatically, transmission delay of information in the body network is increasing obvious, greatly reduces the system's real-time performance and reliability. Based on the analysis of body network communication needs, we design the reasonable body CAN network topology structure. According to the characteristics of information transmission of the body network control system, designed a hybrid scheduling algorithm is applied to the body network control system, and is verified by experiments on the performance. Experimental results show that the design method of body network system can effectively guarantee the real-time and feasibility of information transmission, and optimized the body network control system as a whole.

Multiple synchronization transitions in scale-free neuronal networks with electrical and chemical hybrid synapses

The impacts of information transmission delay on the synchronization transitions in scale-free neuronal networks with electrical and chemical hybrid synapses are investigated. Numerical results show that multiple appearances of synchronization regions transitions can be induced by different information transmission delays. With the time delay increasing, the synchronization of neuronal activities can be enhanced or destroyed, irrespective of the probability of chemical synapses in the whole hybrid neuronal network. In particular, for larger probability of electrical synapses, the regions of synchronous activities appear broader with stronger synchronization ability of electrical synapses compared with chemical ones. Moreover, it can be found that increasing the coupling strength can promote synchronization monotonously, playing the similar role of the increasing the probability of the electrical synapses. Interestingly, the structures and parameters of the scale-free neuronal networks, especially the structural evolvement plays a more subtle role in the synchronization transitions. In the network formation process, it is found that every new vertex is attached to the more old vertices already present in the network, the more synchronous activities will be emerge.

Cyber-physical logistics system-based vehicle routing optimization

Vehicle routing problem is a classic combinational optimization problem, which has been attracting research attentions in logistics and optimization area. Conventional static vehicle routing problem assumes the logistics information is accurate and timely, and does not take into account the uncertainties, which is therefore inadequate during practical applications. In this paper, a vehicle initial routing optimization model considering uncertainties is proposed, the vehicle capacity, customer time-window, and the maximum travelling distance as well as the road capacity are considered. In the cyber-physical logistics system background, a routing adjustment model is proposed to minimize the total distribution cost considering the road congestion, and the static and dynamic models are proposed for traffic information transmission network to quantitatively analyse the impact of the traffic information transmission delay on the vehicle routing optimization. The learnable genetic algorithm is adopted to solve the initial routing optimization model and the routing adjustment model. The simulation results have verified its effectiveness.