Routing Algorithms Simulation for Self-Aware SDN

This paper presents a self-aware network approach with cognitive packets, with a routing engine based on random neural networks. The simulation study, performed using a custom simulator extension of OmNeT++, compares RNN routing with other routing methods. The performance results of RNN-based routing, combined with the distributed nature of its operation inaccessible to other presented methods, demonstrate the advantages of introducing neural networks as a decision-making mechanism in selecting network paths. This work also confirms the usefulness of the simulator for SDN networks with cognitive packets and various routing algorithms, including RNN-based routing engines.

Important Milestones in the Study of Neural Networks with Random Weights

Neural networks with partially random weights are currently not really an independent field of research. However, the first works on random neural networks date back to the 1990s and in the last three decades there have been important new works in which random weights have been used and which are promising in that they give surprisingly good results when compared to approaches in which all weights are trained. These works, however, come from very different subareas of neural networks: Random Feedforward Neural Networks, Random Recurrent Neural Networks and Random ConvNets. In this paper, I analyze the most important works from these three areas and thereby follow a chronological order. I also work out the core result of each work. As a result, the reader can get a quick overview of this field of research.<br>

Important Milestones in the Study of Neural Networks with Random Weights

Neural networks with partially random weights are currently not really an independent field of research. However, the first works on random neural networks date back to the 1990s and in the last three decades there have been important new works in which random weights have been used and which are promising in that they give surprisingly good results when compared to approaches in which all weights are trained. These works, however, come from very different subareas of neural networks: Random Feedforward Neural Networks, Random Recurrent Neural Networks and Random ConvNets. In this paper, I analyze the most important works from these three areas and thereby follow a chronological order. I also work out the core result of each work. As a result, the reader can get a quick overview of this field of research.<br>

Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks

Accurate localization for wireless sensor end devices is critical, particularly for Internet of Things (IoT) location-based applications such as remote healthcare, where there is a need for quick response to emergency or maintenance services. Global Positioning Systems (GPS) are widely known for outdoor localization services; however, high-power consumption and hardware cost become a significant hindrance to dense wireless sensor networks in large-scale urban areas. Therefore, wireless technologies such as Long-Range Wide-Area Networks (LoRaWAN) are being investigated in different location-aware IoT applications due to having more advantages with low-cost, long-range, and low-power characteristics. Furthermore, various localization methods, including fingerprint localization techniques, are present in the literature but with different limitations. This study uses LoRaWAN Received Signal Strength Indicator (RSSI) values to predict the unknown X and Y position coordinates on a publicly available LoRaWAN dataset for Antwerp in Belgium using Random Neural Networks (RNN). The proposed localization system achieves an improved high-level accuracy for outdoor dense urban areas and outperforms the present conventional LoRa-based localization systems in other work, with a minimum mean localization error of 0.29 m.

A reservoir of timescales in random neural networks

The temporal activity of many biological systems, including neural circuits, exhibits fluctuations simultaneously varying over a large range of timescales. The mechanisms leading to this temporal heterogeneity are yet unknown. Here we show that random neural networks endowed with a distribution of self-couplings, representing functional neural clusters of different sizes, generate multiple timescales activity spanning several orders of magnitude. When driven by a time-dependent broadband input, slow and fast neural clusters preferentially entrain slow and fast spectral components of the input, respectively, suggesting a potential mechanism for spectral demixing in cortical circuits.