Caliper: Precise and Responsive Traffic Generator

2012 ◽  
Author(s):  
Monia Ghobadi ◽  
Geoffrey Salmon ◽  
Yashar Ganjali ◽  
Martin Labrecque ◽  
J. Gregory Steffan
Keyword(s):  
Traffic Generator

scholarly journals IoT Traffic: Modeling and Measurement Experiments

IoT ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 140-162
Author(s):  
Hung Nguyen-An ◽  
Thomas Silverston ◽  
Taku Yamazaki ◽  
Takumi Miyoshi
Keyword(s):  
Smart Home ◽  
Large Scale ◽  
Traffic Modeling ◽  
The Novel ◽  
Network Behavior ◽  
Performance Accuracy ◽  
Traffic Generator ◽  
Multiple Devices ◽  
Iot Devices ◽  
The Internet Of Things

We now use the Internet of things (IoT) in our everyday lives. The novel IoT devices collect cyber–physical data and provide information on the environment. Hence, IoT traffic will count for a major part of Internet traffic; however, its impact on the network is still widely unknown. IoT devices are prone to cyberattacks because of constrained resources or misconfigurations. It is essential to characterize IoT traffic and identify each device to monitor the IoT network and discriminate among legitimate and anomalous IoT traffic. In this study, we deployed a smart-home testbed comprising several IoT devices to study IoT traffic. We performed extensive measurement experiments using a novel IoT traffic generator tool called IoTTGen. This tool can generate traffic from multiple devices, emulating large-scale scenarios with different devices under different network conditions. We analyzed the IoT traffic properties by computing the entropy value of traffic parameters and visually observing the traffic on behavior shape graphs. We propose a new method for identifying traffic entropy-based devices, computing the entropy values of traffic features. The method relies on machine learning to classify the traffic. The proposed method succeeded in identifying devices with a performance accuracy up to 94% and is robust with unpredictable network behavior with traffic anomalies spreading in the network.

A Reconfigurable On-die Traffic Generator in 45nm CMOS for a 48 iA-32 Core Network-on-Chip

2012 ◽  
Author(s):  
Praveen Salihundam ◽  
Mohammed Asadullah Khan ◽  
Shailendra Jain ◽  
Yatin Hoskote ◽  
Satish Yada ◽  
...  
Keyword(s):  
Network On Chip ◽  
Core Network ◽  
Traffic Generator ◽  
On Chip

scholarly journals The mining industry in Canada north of the 55th parallel: a maritime traffic generator?

2015 ◽  
Vol 38 (2) ◽  
pp. 107-122 ◽  
Author(s):  
Pierre-Louis Têtu ◽  
Jean-François Pelletier ◽  
Frédéric Lasserre
Keyword(s):  
Mining Industry ◽  
Maritime Traffic ◽  
Traffic Generator

Design of a High Performance Traffic Generator on Network Processor

2008 ◽  
Author(s):  
Gianni Antichi ◽  
Andrea Di Pietro ◽  
Domenico Ficara ◽  
Stefano Giordano ◽  
Gregorio Procissi ◽  
...  
Keyword(s):  
High Performance ◽  
Network Processor ◽  
Traffic Generator

scholarly journals Measure and compare the convergence time of network routing protocols

2018 ◽  
Vol 21 ◽  
pp. 00013
Author(s):  
Marek Bolanowski ◽  
Tomasz Byczek
Keyword(s):  
Network Topology ◽  
Routing Protocols ◽  
Network Routing ◽  
Convergence Time ◽  
Network Nodes ◽  
The Real ◽  
It Systems ◽  
Traffic Generator ◽  
Time Required

Currently, IT systems require more and more reliability. It can be guaranteed only by using redundancy both in the connectivity and network devices. The authors attempted to measure the convergence time for EIGRP and OSPF routing protocols after link or network nodes failure. Research have been conducted with the real devices and hardware traffic generator. In case of both protocols, failures have been simulated in various places in the network topology. On this basis, time in which tested protocol restored full connectivity for a specific topology with backup links have been precisely determined. Knowledge of the time required to restore the network after a failure can be useful during designing services based on networks with implemented routing. This can improve a tolerance of connectivity interruptions.

scholarly journals LoRa Traffic Generator Based on Software Defined Radio Technology for LoRa Modulation Orthogonality Analysis: Empirical and Experimental Evaluation

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4123 ◽  
Author(s):  
Alexandru Lavric ◽  
Adrian I. Petrariu ◽  
Eugen Coca ◽  
Valentin Popa
Keyword(s):  
Software Defined Radio ◽  
High Performance ◽  
Information Technologies ◽  
Empirical Evaluation ◽  
Performance Level ◽  
Communication Performance ◽  
Digital Revolution ◽  
Traffic Generator ◽  
Radio Technology ◽  
Development And Validation

The digital revolution has changed the way we implement and use connected devices and systems by offering Internet communication capabilities to simple objects around us. The growth of information technologies, together with the concept of the Internet of Things (IoT), exponentially amplified the connectivity capabilities of devices. Up to this moment, the Long Range (LoRa) communication technology has been regarded as the perfect candidate, created to solve the issues of the IoT concept, such as scalability and the possibility of integrating a large number of sensors. The goal of this paper is to present an analysis of the communication collisions that occur through the evaluation of performance level in various scenarios for the LoRa technology. The first part addresses an empirical evaluation and the second part presents the development and validation of a LoRa traffic generator. The findings suggest that even if the packet payload increases, the communication resistance to interferences is not drastically affected, as one may expect. These results are analyzed by using a novel Software Defined Radio (SDR) technology LoRa traffic generator, that ensures a high-performance level in terms of generating a large LoRa traffic volume. Despite the use of orthogonal variable spreading factor technique, within the same communication channel, the collisions between LoRa packets may dramatically decrease the communication performance level.

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