A Multipath Payment Scheme Supporting Proof of Payment

Blockchain technology has always been plagued by performance problems. Given this problem, many scaling schemes have been put forward. A layer 2 network is a technology that solves the performance problem of blockchain. Connected parties in this network can set up channels to send digital currency to each other. Since the interaction with the blockchain is only required when the channel is established and closed, a large number of transactions do not need to be recorded on the blockchain, thus reducing the blockchain capacity. Due to the special structure of the payment channel, the distribution of funds in the channel is often unbalanced, which limits the route payment to a certain extent. This paper improves the original payment method in the second layer network by introducing new scripts. The new payment scheme supports proof of payment which is integral to the nature of the lightning network and divides the payment into several subpayments, so the large payment can be divided into relatively small payments. Due to the capacity limitation of the payment channel, theoretically, the success rate of the micropayment route is higher. This paper tests the new payment scheme on the simulated network and validates the nature of this solution to have a high routing success rate while supporting proof of payment.

PymoNNto: A Flexible Modular Toolbox for Designing Brain-Inspired Neural Networks

The Python Modular Neural Network Toolbox (PymoNNto) provides a versatile and adaptable Python-based framework to develop and investigate brain-inspired neural networks. In contrast to other commonly used simulators such as Brian2 and NEST, PymoNNto imposes only minimal restrictions for implementation and execution. The basic structure of PymoNNto consists of one network class with several neuron- and synapse-groups. The behaviour of each group can be flexibly defined by exchangeable modules. The implementation of these modules is up to the user and only limited by Python itself. Behaviours can be implemented in Python, Numpy, Tensorflow, and other libraries to perform computations on CPUs and GPUs. PymoNNto comes with convenient high level behaviour modules, allowing differential equation-based implementations similar to Brian2, and an adaptable modular Graphical User Interface for real-time observation and modification of the simulated network and its parameters.

On the Importance of Characterizing Virtual PMUs for Hardware-in-the-Loop and Digital Twin Applications

In recent years, the introduction of real-time simulators (RTS) has changed the way of researching the power network. In particular, researchers and system operators (SOs) are now capable of simulating the complete network and of making it interact with the real world thanks to the hardware-in-the-loop (HIL) and digital twin (DT) concepts. Such tools create infinite scenarios in which the network can be tested and virtually monitored to, for example, predict and avoid faults or energy shortages. Furthermore, the real-time monitoring of the network allows estimating the status of the electrical assets and consequently undertake their predictive maintenance. The success of the HIL and DT application relies on the fact that the simulated network elements (cables, generation, accessories, converters, etc.) are correctly modeled and characterized. This is particularly true if the RTS acquisition capabilities are used to enable the HIL and the DT. To this purpose, this work aims at emphasizing the role of a preliminary characterization of the virtual elements inside the RTS system, experimentally verifying how the overall performance is significantly affected by them. To this purpose, a virtual phasor measurement unit (PMU) is tested and characterized to understand its uncertainty contribution. To achieve that, firstly, the characterization of a virtual PMU calibrator is described. Afterward, the virtual PMU calibration is performed, and the results clearly highlight its key role in the overall uncertainty. It is then possible to conclude that the characterization of the virtual elements, or models, inside RTS systems (omitted most of the time) is fundamental to avoid wrong results. The same concepts can be extended to all those fields that exploit HIL and DT capabilities.

Using the RFC 7575 and Models at Runtime for Enabling Autonomic Networking in SDN

The programmable network architectures that emerged in the last decade have allowed new ways to enable Autonomic Networks. However, there are several open issues to address before making such a possibility into a feasible reality. For instance, defining network goals, translating them into network rules, and granting the correct functioning of the network control loop in a self-adaptive manner are examples of complex tasks required to enable an autonomic networking environment. Fortunately, architectures based on the concept of Models at Runtime (MART) provide ways to overcome such complexity. This paper proposes a MART-based framework – using the RFC 7575 as reference (i.e., definitions and design goals for autonomic networking) – to implement autonomic management into a programmable network. The evaluation shows the proposed framework is suitable for satisfying the functional and performance requirements of a simulated network.

Research on gray correlation analysis and situation prediction of network information security

In order to further improve the accuracy and efficiency of network information security situation prediction, this study used the dynamic equal-dimensional method based on gray correlation analysis to improve the GM (1, N) model and carried out an experiment on the designed network security situation prediction (NSSP) model in a simulated network environment. It was found that the predicted result of the improved GM (1, N) model was closer to the actual value. Taking the 11th hour as an example, the predicted value of the improved GM (1, N) model was 28.1524, which was only 0.8983 larger than the actual value; compared with neural network and Markov models, the error of the improved GM (1, N) model was smaller: the average error was only 2.3811, which was 67.88% and 70.31% smaller than the other two models. The improved GM (1, N) model had a time complexity that was 49.99% and 39.53% lower than neural network and Markov models; thus, it had high computational efficiency. The experimental results verify the effectiveness of the improved GM (1, N) model in solving the NSSP problem. The improved GM (1, N) model can be further promoted and applied in practice and deployed in the network of schools and enterprises to achieve network information security.

Rasefiberry: Secure and efficient Raspberry-Pi based gateway for smarthome IoT architecture

Internet-of-Things or IoT technology becomes essential in everyday lives. The risk of security and privacy towards IoT devices, especially smarthomes IoT gateway device, becoming apparent as IoT technology progressed. The need for affordable, secure smarthome gateway device or router that smarthome user prefer. The problem of low-performance smarthome gateways was running security programs on top of smarthome gateway programs. This problem motivates the researcher designing a secure and efficient smarthome gateway using Raspberry Pi hardware as an affordable smarthome gateway device and able to run both smarthome gateways and security programs. In this research, researchers implemented snort as intrusion detection system (IDS), openHab as IoT gateway applications, and well-known encryption algorithms for file encryption in Raspberry PI 3B+ model. The researcher evaluated Snort capability on network attacks and compared each of the well-known encryption algorithm efficiency. From the result, we found Rasefiberry customized snort configuration for Raspberry pi 60 percent of the simulated network attacks. Twofish encryption algorithms were found to have best encryption time, throughput, and power consumption compared to other encryption algorithms in the research. Rasefiberry architecture successfully processes both lightweight security programs and Openhab smarthome gateway programs with a lowperformance computing device such as Raspberry Pi.

SACT: A New Model of Covert Communication Based on SDN

Anonymous tracking technology of network watermarking is limited by the deployment of tracking devices in traditional network structure, resulting in poor scalability and reusability. Software Defined Network (SDN) boasts more freedom thanks to its separation of the control plane from the data plane. In this paper, a new anonymous communication tracking model SDN-based Anonymous Communication Tracking (SACT) is proposed, which introduces network watermarking into SDN and combines IP time hidden channel and symbol expansion technology. In addition, we introduce a hopping protection mechanism to improve the anti detection ability of the watermark as well. The experimental results show that in a variety of simulated network environments, SACT achieves excellent detection rate and bit error rate, thus it is sufficient to determine the communication relationship between the two parties. Meanwhile, SACT solves the deployment problem of anonymous tracking and improves the availability and scalability of covert communication.

On the Origin of Ultraslow Spontaneous Na+ Fluctuations in Neurons of the Neonatal Forebrain

Spontaneous neuronal and astrocytic activity in the neonate forebrain is believed to drive the maturation of individual cells and their integration into complex brain-region-specific networks. The previously reported forms include bursts of electrical activity and oscillations in intracellular Ca2+ concentration. Here, we use ratiometric Na+ imaging to demonstrate spontaneous fluctuations in the intracellular Na+ concentration of CA1 pyramidal neurons and astrocytes in tissue slices obtained from the hippocampus of mice at postnatal days 2-4 (P2-4). These occur at very low frequency (~2/h), can last minutes with amplitudes up to several mM, and mostly disappear after the first postnatal week. To further investigate their mechanisms, we model a network consisting of pyramidal neurons and interneurons. Experimentally observed Na+ fluctuations are mimicked when GABAergic inhibition in the simulated network is made depolarizing. Our experiments and computational model show that blocking voltage-gated Na+ channels or GABAergic signaling significantly diminish the neuronal Na+ fluctuations. On the other hand, blocking a variety of other ion channels, receptors, or transporters including glutamatergic pathways, does not have significant effects. Our model also shows that the amplitude and duration of Na+ fluctuations decrease as we increase the strength of glial K+ uptake. Furthermore, neurons with smaller somatic volumes exhibit fluctuations with higher frequency and amplitude. As opposed to this, larger extracellular to intracellular volume ratio observed in neonatal brain exerts a dampening effect. Finally, our model predicts that these periods of spontaneous Na+ influx leave neonatal neuronal networks more vulnerable to seizure-like states when compared to mature brain.

Simulated poaching affects connectedness and efficiency in social networks of African savanna elephants

Poaching of mature and socially influential African savanna elephants for their prominent tusks alters the structure of their social networks. To learn if targeted poaching affects the functioning of elephant associations, we simulated network formation and disturbance via ‘poaching’ experiments in one wild and 100 virtual populations. To simulate virtual networks, we built an individual-based model guided by empirical association trends. After poaching of 1) the most mature or socially central individuals or 2) individuals selected at random, we evaluated network connectedness and efficiency. The networks never broke down, suggesting structural robustness. Unlike in age-specific deletions, eliminating individuals with the highest topological centrality decreased network connectedness and efficiency. The simulated networks, although structurally stable, became less functionally resilient when subject to poaching-like stress. Our work may offer new insights into elephant behavior vis-à-vis anthropogenic pressure, and inform conservation efforts focused on translocation of social species or trophy hunting practices.