6.8: Presentation session: Neuroanatomy, neuroregeneration, and modeling: “GPGPU implementation of a synaptically optimized, anatomically accurate spiking network simulator”

Now a days in Wireless Local Area Networks (WLANs) used in different fields because its well-suited simulator and higher flexibility. The concept of WLAN with advanced 5th Generation technologies, related to a Internet-of-Thing (IOT). In this project, representing the Network Simulator (NS-2) used linked-level simulators for Wireless Local Area Networks and still utilized IEEE 802.11g/n/ac with advanced IEEE 802.11ah/af technology. Realization of the whole Wireless Local Area Networking linked-level simulators inspired by the recognized Vienna Long Term Evolution- simulators. As a outcome, this is achieved to link together that simulator to detailed performances of Wireless Local Area Networking with Long Term Evolution, operated in the similar RF bands. From the advanced 5th Generation support cellular networking, such explore is main because different coexistences scenario can arise linking wireless communicating system to the ISM and UHF bands.

Download Full-text

Virtualized Fog Network with Load Balancing for IoT based Fog-to-Cloud

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.4.3.374 ◽

2020 ◽

Vol 4 (3) ◽

Author(s):

Istabraq M. Al-Joboury ◽

Emad H. Al-Hemiary

Keyword(s):

Load Balancing ◽

Packet Loss ◽

Fog Computing ◽

Transport Protocol ◽

Network Simulator ◽

Huge Number ◽

Message Queue ◽

Limited Bandwidth ◽

Cloud Architecture ◽

Fog Network

Fog Computing is a new concept made by Cisco to provide same functionalities of Cloud Computing but near to Things to enhance performance such as reduce delay and response time. Packet loss may occur on single Fog server over a huge number of messages from Things because of several factors like limited bandwidth and capacity of queues in server. In this paper, Internet of Things based Fog-to-Cloud architecture is proposed to solve the problem of packet loss on Fog server using Load Balancing and virtualization. The architecture consists of 5 layers, namely: Things, gateway, Fog, Cloud, and application. Fog layer is virtualized to specified number of Fog servers using Graphical Network Simulator-3 and VirtualBox on local physical server. Server Load Balancing router is configured to distribute the huge traffic in Weighted Round Robin technique using Message Queue Telemetry Transport protocol. Then, maximum message from Fog layer are selected and sent to Cloud layer and the rest of messages are deleted within 1 hour using our proposed Data-in-Motion technique for storage, processing, and monitoring of messages. Thus, improving the performance of the Fog layer for storage and processing of messages, as well as reducing the packet loss to half and increasing throughput to 4 times than using single Fog server.

Download Full-text

SPREAD - A Routing Protocol to Meet End-to-end Adjusted Deadline in Real Time Wireless Sensor Networks

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327909666190207164153 ◽

2020 ◽

Vol 10 (1) ◽

pp. 63-78

Author(s):

Neetika Jain ◽

Sangeeta Mittal

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Real Time ◽

Routing Protocol ◽

Resource Constraints ◽

Wireless Sensor ◽

Network Simulator ◽

Packet Delivery ◽

High Data ◽

Hard Real Time

Background: Real Time Wireless Sensor Networks (RT-WSN) have hard real time packet delivery requirements. Due to resource constraints of sensors, these networks need to trade-off energy and latency. Objective: In this paper, a routing protocol for RT-WSN named “SPREAD” has been proposed. The underlying idea is to reserve laxity by assuming tighter packet deadline than actual. This reserved laxity is used when no deadline-meeting next hop is available. Objective: As a result, if due to repeated transmissions, energy of nodes on shortest path is drained out, then time is still left to route the packet dynamically through other path without missing the deadline. Results: Congestion scenarios have been addressed by dynamically assessing 1-hop delays and avoiding traffic on congested paths. Conclusion: Through extensive simulations in Network Simulator NS2, it has been observed that SPREAD algorithm not only significantly reduces miss ratio as compared to other similar protocols but also keeps energy consumption under control. It also shows more resilience towards high data rate and tight deadlines than existing popular protocols.

Download Full-text

Erratum: Empirical prediction of radiowave propagation by neural network simulator

Electronics Letters ◽

10.1049/el:19920743 ◽

1992 ◽

Vol 28 (12) ◽

pp. 1176

Author(s):

K.E. Stocker ◽

F.M. Landstorfer

Keyword(s):

Neural Network ◽

Radiowave Propagation ◽

Network Simulator ◽

Empirical Prediction

Download Full-text

A Novel Rail-Network Hardware Simulator for Embedded System Programming

Electronics ◽

10.3390/electronics10010013 ◽

2020 ◽

Vol 10 (1) ◽

pp. 13

Author(s):

Balaji M ◽

Chandrasekaran M ◽

Vaithiyanathan Dhandapani

Keyword(s):

Embedded Systems ◽

Embedded System ◽

Real Time ◽

Learning Outcomes ◽

Real World ◽

Time Constraints ◽

Network Simulator ◽

Practical Applications ◽

Rail Network ◽

Knowledge Enhancement

A Novel Rail-Network Hardware with simulation facilities is presented in this paper. The hardware is designed to facilitate the learning of application-oriented, logical, real-time programming in an embedded system environment. The platform enables the creation of multiple unique programming scenarios with variability in complexity without any hardware changes. Prior experimental hardware comes with static programming facilities that focus the students’ learning on hardware features and programming basics, leaving them ill-equipped to take up practical applications with more real-time constraints. This hardware complements and completes their learning to help them program real-world embedded systems. The hardware uses LEDs to simulate the movement of trains in a network. The network has train stations, intersections and parking slots where the train movements can be controlled by using a 16-bit Renesas RL78/G13 microcontroller. Additionally, simulating facilities are provided to enable the students to navigate the trains by manual controls using switches and indicators. This helps them get an easy understanding of train navigation functions before taking up programming. The students start with simple tasks and gradually progress to more complicated ones with real-time constraints, on their own. During training, students’ learning outcomes are evaluated by obtaining their feedback and conducting a test at the end to measure their knowledge acquisition during the training. Students’ Knowledge Enhancement Index is originated to measure the knowledge acquired by the students. It is observed that 87% of students have successfully enhanced their knowledge undergoing training with this rail-network simulator.

Download Full-text

A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses

Computational Intelligence and Neuroscience ◽

10.1155/2011/869348 ◽

2011 ◽

Vol 2011 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Karim El-Laithy ◽

Martin Bogdan

Keyword(s):

Reinforcement Learning ◽

Spike Timing ◽

Neural Representation ◽

Model Parameters ◽

Learning Framework ◽

Reference Target ◽

Wide Range ◽

Spiking Network ◽

Dynamic Synapses ◽

Exclusive Or

An integration of both the Hebbian-based and reinforcement learning (RL) rules is presented for dynamic synapses. The proposed framework permits the Hebbian rule to update the hidden synaptic model parameters regulating the synaptic response rather than the synaptic weights. This is performed using both the value and the sign of the temporal difference in the reward signal after each trial. Applying this framework, a spiking network with spike-timing-dependent synapses is tested to learn the exclusive-OR computation on a temporally coded basis. Reward values are calculated with the distance between the output spike train of the network and a reference target one. Results show that the network is able to capture the required dynamics and that the proposed framework can reveal indeed an integrated version of Hebbian and RL. The proposed framework is tractable and less computationally expensive. The framework is applicable to a wide class of synaptic models and is not restricted to the used neural representation. This generality, along with the reported results, supports adopting the introduced approach to benefit from the biologically plausible synaptic models in a wide range of intuitive signal processing.

Download Full-text