ResFi: A secure framework for self organized Radio Resource Management in residential WiFi networks

Zehl, Sven; Zubow, Anatolij; Döring, Michael; Wolisz, Adam

doi:10.1109/wowmom.2016.7523511

Cited by 14 publications

(8 citation statements)

References 18 publications

(21 reference statements)

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“…Alternatively, secondary communication interfaces with different radio technologies like ZigBee, LoRa, BLE can also be used as vectors. Once this information is made available to the peers, it can be used to bootstrap direct data exchange [27]. This could also have a lot of potential when integrated with wireless community networks such as Freifunk 1 , NYC Mesh 2 , or guifi.net 3 .…”

Section: Approach and Enabling Technologymentioning

confidence: 99%

Towards Grassroots Peering at the Edge

Bermbach¹,

Lucia²,

Handziski³

et al. 2022

Preprint

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Fog Computing allows applications to address their latency and privacy requirements while coping with bandwidth limitations of Internet service providers (ISPs). Existing research on fog systems has so far mostly taken a very high-level view on the actual fog infrastructure. In this position paper, we identify and discuss the problem of having multiple ISPs in edge-to-edge communication. As a possible solution we propose that edge operators create direct edge-to-edge links in a grassroots fashion and discuss different implementation options. Based on this, we highlight some important open research challenges that result from this.

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Section: Approach and Enabling Technologymentioning

confidence: 99%

Towards Grassroots Peering at the Edge

Bermbach¹,

Lucia²,

Handziski³

et al. 2022

Preprint

Self Cite

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“…Future research efforts in network softwarization should enforce the deployment of management policies that are intelligent enough to automatically adapt to changes in the constantly varying wireless scenario. This aim of automatising network management led to the introduction of Self-Organizing Networks (SONs) [53][54][55] by the Next Generation Mobile Networks (NGMN) alliance. Although these works take into account network conditions and QoS requirements, SONs failed to achieve dynamic data acquisition (which may be spread across various network nodes) and the corresponding processing in order to allow all segments to rapidly react to varying traffic or even infrastructure changes due to connectivity or hardware failures.…”

Section: From Software-defined Networking To Knowledge-defined Networmentioning

confidence: 99%

Adaptive ML-Based Frame Length Optimisation in Enterprise SD-WLANs

2020

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Software-Defined Networking (SDN) is gaining a lot of traction in wireless systems with several practical implementations and numerous proposals being made. Despite instigating a shift from monolithic network architectures towards more modulated operations, automated network management requires the ability to extract, utilise and improve knowledge over time. Beyond simply scrutinizing data, Machine Learning (ML) is evolving from a simple tool applied in networking to an active component in what is known as Knowledge-Defined Networking (KDN). This work discusses the inclusion of ML techniques in the specific case of Software-Defined Wireless Local Area Networks (SD-WLANs), paying particular attention to the frame length optimization problem. With this in mind, we propose an adaptive ML-based approach for frame size selection on a per-user basis by taking into account both specific channel conditions and global performance indicators. By relying on standard frame aggregation mechanisms, the model can be seamlessly embedded into any Enterprise SD-WLAN by obtaining the data needed from the control plane, and then returning the output back to this in order to efficiently adapt the frame size to the needs of each user. Our approach has been gauged by analysing a multitude of scenarios, with the results showing an average improvement of 18.36% in goodput over standard aggregation mechanisms.

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“…The present work is based on our previous work where we proposed an architecture for setting up out-of-band control channel between homogeneous network nodes, namely residential WiFi APs [23]. With LtFi we extended this idea towards cross-technology control between heterogeneous network nodes, here LTE-U and WiFi.…”

Section: Related Workmentioning

confidence: 99%

Enabling Cross-technology Communication between LTE Unlicensed and WiFi

Gawłowicz

Zubow

Wolisz

2018

IEEE INFOCOM 2018 - IEEE Conference on Computer Communications

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Despite exhibiting very high theoretical data rates, in practice, the performance of LTE-U/LAA and WiFi networks is severely limited under cross-technology coexistence scenarios in the unlicensed 5 GHz band. As a remedy, recent research shows the need for collaboration and coordination among colocated networks. However, enabling such collaboration requires an information exchange that is hard to realize due to completely incompatible network protocol stacks. We propose OfdmFi, the first cross-technology communication scheme that enables direct bidirectional over-the-air communication between LTE-U/LAA and WiFi with minimal overhead to their legacy transmissions. Requiring neither hardware nor firmware changes in commodity technologies, OfdmFi leverages the standard-compliant possibility of generating message-bearing power patterns, similar to punched cards from the early days of computers, in the timefrequency resource grid of an OFDM transmitter which can be cross-observed and decoded by a heterogeneous OFDM receiver. As a proof-of-concept, we have designed and implemented a prototype using commodity devices and SDR platforms. Our comprehensive evaluation reveals that OfdmFi achieves robust bidirectional CTC between both systems with a data rate up to 84 kbps, which is more than 125× faster than state-of-the-art.

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ResFi: A secure framework for self organized Radio Resource Management in residential WiFi networks

Cited by 14 publications

References 18 publications

Towards Grassroots Peering at the Edge

Towards Grassroots Peering at the Edge

Adaptive ML-Based Frame Length Optimisation in Enterprise SD-WLANs

Enabling Cross-technology Communication between LTE Unlicensed and WiFi

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