Spatially Distributed Traffic Generation for Stress Testing the Robustness of Mission-Critical Smart Grid Communication

Kaulbars, Dennis; Schweikowski, Florian; Wietfeld, Christian

doi:10.1109/glocomw.2015.7414168

Cited by 9 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We propose the STING framework as a measure to reliably test these upcoming infrastructures for resilience even in high load scenarios. The concept of this work is based on [3]. The authors implemented a distributed traffic generation system for stress-testing networks at locations of smart meters and evaluated the system utilizing LTE.…”

Section: Related Workmentioning

confidence: 99%

Pushing the Limits: Resilience Testing for Mission-Critical Machine-Type Communication

Arendt¹,

Patchou²,

Böcker³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Interdisciplinary application fields, such as automotive, industrial applications or field robotics show an increasing need for reliable and resilient wireless communication even under high load conditions. These mission-critical applications require dependable service quality characteristics in terms of latency and especially stability. Current deployments often use either wired links that lack the flexibility to accommodate them, or wireless technologies that are susceptible to interference. Depending on the application and the surrounding environments, different technologies can meet the associated requirements and have to be tested deliberately to prevent unexpected system failure. To stress test these infrastructures in a reproducible and application-aware manner, we propose STING, a spatially distributed traffic and interference generation framework. STING is evaluated in a remote control test case of an Unmanned Ground Vehicle that serves as a scout in Search and Rescue missions. A significant impact of interference on the remote control quality of experience is shown in tests with different operators, which result in an 80% increase in completion time in our test scenario with high interference on the radio channel. With this case study, we have proven STING to be a reliable and reproducible way to asses resilience against interference of wireless machine-type communication use cases. Our concept can find use for any type of wireless technology, in unlicensed (e.g. Wi-Fi) as well as licensed bands (e.g. 5G).

show abstract

Section: Related Workmentioning

confidence: 99%

Pushing the Limits: Resilience Testing for Mission-Critical Machine-Type Communication

Arendt¹,

Patchou²,

Böcker³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…As shown in Fig. 4, the base station was placed in our office environment, as well as several Smart Traffic Generators (STGs) [14], the DUT, and the sniffer. The sniffer is a software defined radio, which runs the modified software of OPENAIRINTERFACE's UE on a compact generalpurpose computer and writes all captured control-channel data into a log file.…”

Section: A Environment Setupmentioning

confidence: 99%

Discover Your Competition in LTE: Client-Based Passive Data Rate Prediction by Machine Learning

Falkenberg

Heimann

Wietfeld

2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

Self Cite

View full text Add to dashboard Cite

To receive the highest possible data rate or/and the most reliable connection, the User Equipment (UE) may want to choose between different networks. However, current LTE and LTE-Advanced mobile networks do not supply the UE with an explicit indicator about the currently achievable data rate. For this reason, the mobile device will only see what it obtains from the network once it actively sends data. A passive estimation in advance is therefore not doable without further effort. Although the device can identify its current radio conditions based on the received signal strength and quality, it has no information about the cell's traffic load caused by other users. To close this gap we present an Enhanced Client-based Control-Channel Analysis for Connectivity Estimation (E-C 3 ACE), which uncovers the cell load broken down by each single user. Based on this information and in conjunction with existing indicators like Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ), a neural network is trained to perform a data rate prediction for the current LTE link. Compared to an earlier work, our approach reduces the average prediction error below one third. Applied in public networks, the predicted data rate differs by less than 1.5 Mbit/s in 93% of cases.

show abstract

“…Fig. 5 shows a photo of the setup consisting of an eNodeB, several UEs denoted as Smart Traffic Generators (STGs) [12] including a Device Under Test (DUT), and the passive probe which continuously analyses the PDCCH. The STGs, consisting of an embedded computer and an LTE modem, are used to induce selected traffic load into the cell.…”

Section: A Measurement Setupmentioning

confidence: 99%

Client-Based Control Channel Analysis for Connectivity Estimation in LTE Networks

Falkenberg¹,

Ide²,

Wietfeld

2016

2016 IEEE 84th Vehicular Technology Conference (VTC-Fall)

Self Cite

View full text Add to dashboard Cite

Abstract-Advanced Cyber-Physical Systems aim for the balancing of restricted local resources of deeply embedded systems with cloud-based resources depending on the availability of network connectivity: in case of excellent connectivity, the offloading of large amounts of data can be more efficient than the local processing on a resource-constraint platform, while this latter solution is preferred in case of limited connectivity. This paper proposes a Client-Based Control Channel Analysis for Connectivity Estimation (C 3 ACE), a new passive probing mechanism to enable the client-side to estimate the connection quality of 4G networks in range. The results show that by observing and analyzing the control traffic in real-time, the number of active user equipment in a cell can be determined with surprising accuracy (with errors well below 10 −6 ). The specific challenge addressed in this paper lies in a dedicated filtering and validation of the DCI (Downlink Control Information). In a subsequent step the data rates to be expected can be estimated in order to enable decision about the choice of network and the timing of the data offloading to the cloud. The proposed methods have been implemented and validated leveraging the SDR OpenAirInterface, a real-life LTE network and a distributed load generator producing a scalable network traffic by a number of LTE User Equipment.

show abstract

Spatially Distributed Traffic Generation for Stress Testing the Robustness of Mission-Critical Smart Grid Communication

Cited by 9 publications

References 11 publications

Pushing the Limits: Resilience Testing for Mission-Critical Machine-Type Communication

Pushing the Limits: Resilience Testing for Mission-Critical Machine-Type Communication

Discover Your Competition in LTE: Client-Based Passive Data Rate Prediction by Machine Learning

Client-Based Control Channel Analysis for Connectivity Estimation in LTE Networks

Contact Info

Product

Resources

About