IMDEA Networks Institute Digital Repository
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1915 research outputs found
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SIGNiPHY: Reconciling Random Access with Directional Reception for Efficient mmWave WLANs
Get PDFMillimeter-Wave (mmWave) WiFi can provide very low latency and multi-Gbps throughput, but real-world deployments usually do not achieve the theoretically feasible performance. One main source of inefficiency is the contention-based random channel access, as it requires omni-directional reception which limits performance. Additionally, carrier sensing at mmWave frequencies is highly unreliable, leading to reduced channel usage. In this paper, we present SIGNalling in the PHY Preamble (SIGNiPHY) for efficient directional communications, a solution that allows to embed user identity in the preamble of data packets. It allows for true early user identification and then immediately steering the beam towards the transmitter while receiving the physical layer preamble. SIGNiPHY enables directional reception in random access mmWave networks, and additionally helps to quickly filter unwanted packets. It does not affect any preamble functions and is backward-compatible with legacy stations. We implement SIGNiPHY on an FPGA-based mmWave testbed and show that it achieves 99.6% decoding accuracy even under very low SINR conditions. We also implement SIGNiPHY in ns-3 to evaluate large networks and show that it achieves throughput gains between 13% and 230% compared to different baseline schemes, due to the lower packet loss rate and improved spatial sharing.Ministerio de Asuntos Económicos y Transformación DigitalEuropean UnionTRUEpu
Bringing millimeter wave technology to any IoT device
Get PDFWith the advancement of the Internet of Things (IoT), many devices will be connected to the Internet, enabling digital twin and smart home applications. However, currently, these IoT devices are operating at lower frequency bands of the wireless spectrum, typically ranging from a few hundred MHz (such as RFID and LoRa) to a few GHz (such as BLE and WiFi). As a result, the current IoT devices not only place a
huge strain on these bands, but also cannot benefit from the large bandwidth available in the higher frequencies of the spectrum such as mmWave bands. In this paper, our goal is to bring mmWave technology to existing IoT devices so they can benefit from the advantages this technology offers, such
as high network capacity, low interference, and Space Division Multiple Access. To this end, we design mmPlug, a novel plug-and-play module which is simple and energy-efficient.
mmPlug can be easily connected to the antenna port of any IoT device, enabling it to operate in the mmWave band. mmPlug is compatible with different wireless technologies (such as WiFi, Lora, etc.) and does not require any modification to the circuit, firmware or communication protocols of the existing IoT devices. mmPlug achieves this by a novel design which can seamlessly be connected to the antenna port
of the IoT device. We have implemented mmPlug on PCB and empirically evaluated its performance. Our results show that mmPlug enables existing IoT devices (such as WiFi and Lora) to operate at mmWave band while achieving accurate localization, uplink and downlink even when they are more than 30 m far from the access point.TRUEpu
Towards sustainable greenhouses using battery-free LiFi-enabled Internet of Things
Get PDFAs the world faces a changing climate, agriculture needs to develop more efficient and sustainable food production systems. Traditional farming methods consume considerable amounts of energy and are largely manually controlled, which leads to sub-optimal production. Greenhouses, which enable year-round crop growth, can play an important role in efficient food production. Leveraging the need for artificial light in greenhouses when the natural sunlight available is not sufficient, we envision that recent progress in Internet of Things (IoT) technology, together with novel Light-Fidelity (LiFi)-based methods have the potential to significantly reduce energy and resources used in food production. In this paper we describe our work towards sustainable and precision greenhouses by using LiFi-enabled IoT. Here we present a battery-free wireless network of IoT sensor nodes that exploit LiFi for both communication and power harvesting, while monitoring environmental conditions for optimal greenhouse operation and plant production. We highlight the research challenges and the way forward to integrate LiFi to monitor and control greenhouses, as well as a proof-of-concept LiFi-enabled IoT system for a real-world greenhouse.European UnionSpanish Ministry of Economic Affairs and Digital TransformationUniversidad Carlos III de MadridTRUEpu
Fast Detection of Cyberattacks on the Metaverse through User-plane Inference
Get PDFThis paper is accepted for publication as a short paper.The metaverse is envisioned as a digital world where people can experience an immersive three-dimensional Internet, thanks to the profound integration of different technologies like the Internet of Things (IoT), augmented and virtual reality. From a technical point of view, developing a system of such an unprecedented scale and complexity also opens new challenges in security: a prominent one is the capability to detect and respond to cyberattacks in the shortest time possible, so as not to disrupt the live user experience. In this paper, we discuss how recent advances in user-plane inference can be leveraged to identify malicious traffic generated by IoT devices connected to the metaverse at line rate, ensuring a faster reaction than state-of-the-art approaches where the attack detection is performed in the control plane. We demonstrate the viability of the solution in a programmable network testbed composed of off-the-shelf Intel Tofino switches and with real-world traffic hiding a number of different IoT-based cyberattacks. Our experimental results show that Random Forest models implemented in programmable switches can achieve up to 99% accuracy while using less than 5% of the hardware resources on average in the target case study. Moreover, they quantify the existing trade-off between attack detection precision and user plane resource consumption.TRUEPu
On the Limit Performance of Floating Gossip
Get PDFIn this paper we investigate the limit performance of Floating Gossip, a new, fully distributed Gossip Learning scheme which relies on Floating Content to implement location- based probabilistic evolution of machine learning models in an infrastructure-less manner.
We consider dynamic scenarios where continuous learning is necessary, and we adopt a mean field approach to investigate the limit performance of Floating Gossip in terms of amount of data that users can incorporate into their models, as a function of the main system parameters. Different from existing approaches in which either communication or computing aspects of Gossip Learning are analyzed and optimized, our approach accounts for the compound impact of both aspects. We validate our results through detailed simulations, proving good accuracy. Our model shows that Floating Gossip can be very effective in implementing continuous training and update of machine learning models in a cooperative manner, based on opportunistic exchanges among moving users.Ministry of Economic Affairs and Digital Transformation and the European Union NextGeneration-EU in the framework of the Spanish Recovery, Transformation and Resilience PlanTRUEinpres
Offloading Algorithms for Maximizing Inference Accuracy on Edge Device in an Edge Intelligence System
Get PDFWith the emergence of edge computing, the problem of offloading jobs between an Edge Device (ED) and an Edge Server (ES) received significant attention in the past. Motivated by the fact that an increasing number of applications are using Machine Learning (ML) inference from the data samples collected at the EDs, we study the problem of offloading inference jobs by considering the following novel aspects: 1) in contrast to a typical computational job, the processing time of an inference job depends on the size of the ML model, and 2) recently proposed Deep Neural Networks (DNNs) for resource-constrained devices provide the choice of scaling down the model size by trading off the inference accuracy. Considering that multiple ML models are available at the ED, and a powerful ML model is available at the ES, we formulate an Integer Linear Programming (ILP) problem with the objective of maximizing the total inference accuracy of n data samples at the ED subject to a time constraint T on the makespan. Noting that the problem is NP-hard, we propose an approximation algorithm Accuracy Maximization using LP-Relaxation and Rounding (AMR 2 ) and prove that it results in a makespan at most 2T and achieves a total accuracy that is lower by a small constant from the optimal total accuracy implying that AMR 2 is asymptotically optimal. Further, if the data samples are identical we propose Accuracy Maximization using Dynamic Programming (AMDP), an optimal pseudo-polynomial time algorithm. Furthermore, we extend AMR 2 for the case of multiple ESs, where each ES is equipped with a powerful ML model. As proof of concept, we implemented AMR 2 on a Raspberry Pi, equipped with MobileNets, that is connected to a server equipped with ResNet, and studied the total accuracy and makespan performance of AMR 2 for image classification.Jaya Prakash ChampatiTRUEpu
The NetMob23 Dataset: A High-resolution Multi-region Service-level Mobile Data Traffic Cartography
Get PDFDigital sources have been enabling unprecedented data-driven and large-scale investigations across a wide range of domains, including demography, sociology, geography, urbanism, criminology, and engineering. A major barrier to innovation is represented by the limited availability of dependable digital datasets, especially in the context of data gathered by mobile network operators or service providers, due to concerns about user privacy and industrial competition. The resulting lack of reference datasets curbs the production of new research methods and results, and prevents verifiability and reproducibility of research outcomes. The NetMob23 dataset offers a rare opportunity to the multidisciplinary research community to access rich data about the spatio-temporal consumption of mobile applications in a developed country. The generation process of the dataset sets a new quality standard, leading to information about the demands generated by 68 popular mobile services, geo-referenced at a high resolution of 100×100 m2 over 20 metropolitan areas in France, and monitored during 77 consecutive days in 2019Comunidad de MadridFrench National Research Agency (ANR
SkillVet: Automated Traceability Analysis of Amazon Alexa Skills
No full textSkills are essential components of Smart Personal Assistants (SPA). Their numbers have grown rapidly, dominated by a changing environment that has no clear business model. Skills can access personal information and this may pose a risk to users. However, there is little information about how this ecosystem works, let alone the tools to facilitate its study. In this paper, we present a systematic measurement of the Alexa skill ecosystem. We study developers practices, including how they collect and justify the need for sensitive information, by designing a methodology to identify over-privileged skills with broken privacy policies. We collect 199,295 skills and uncover that around 43% of the skills (and 50% of developers) that request permissions follow bad privacy practices, including (partially) broken data permissions traceability. To perform this kind of analysis at scale, we present SkillVet that leverages machine learning and natural language processing techniques, and generates high-accuracy prediction sets. We report several concerning practices, including how developers can bypass Alexas permission system through account linking and conversational skills, and offer recommendations on how to improve transparency, privacy and security. Resulting from the responsible disclosure we did, 13% of the reported issues no longer pose a threat at submission time.TRUEpu
A Bioinspired Emergent Control for Smart Grids
No full textSatisfying energy demand has become a global problem that is on the rise due to population growth, infrastructure deterioration, a decline in fossil fuel sources, and high costs for investment, among others. Smart grids, in addition to those challenges that they have at the level of energy generation, have other management challenges derived from the great diversity of components that make them up, such as energy storage systems (batteries, capacitors, etc.), the different types of consumers (controllable, non-controllable loads) and prosumers (electric vehicles, self-sustaining buildings, micro-grid, etc.), among others. Consequently, a distributed control problem is presented, mainly oriented to the coordination of its components. A possible solution is to achieve the participation of each component when conditions are more favorable, such as prioritizing production with renewable energy sources, or taking advantage of prosumers so that they can meet local demand, among other things. Therefore, new strategies with a distributed approach such as bio-inspired emergent controls are necessary. The objective of this work is the specification of an emergent control approach to coordinate a smart grid. This approach allows the coordination of the energy supply in various operating scenarios. The results obtained demonstrate a perfect synchronization between the different smart grid components (agents), prioritizing renewable energy sources, regardless of the operational context (for example, in cases of failures, unsuitable environmental conditions, etc.).TRUEpu
Equalizing Access to Latency-Critical Services Based on In-Network Computing
Get PDFWe consider a portion of a RAN where end-users access services that imply the issue of a request through their associated base station (BS), followed by a computation on one of the available in-network computing facilities, and finally by the return of the result of the computation to the end-user who issued the request. The result must be returned within a specified latency deadline in order to be useful. Since not all BSs are equipped with a computing facility, some end-users may be disadvantaged, because they are associated with a BS from which the delay for a service request to reach a computing facility and for the results of
the computation to come back is longer. Aiming at uniform end-user satisfaction, network operators should strive to on the one hand reduce differences in achieved end-user performance, while on the other obtain an efficient use of network resources. With simple analytical models we investigate the effectiveness of light
network management algorithms, consisting in carefully choosing the routing probabilities of service requests toward one of the available computing facilities. We argue that at least some of such light network management algorithms should be compatible with the very stringent European Network Neutrality rules, and we show that they allow a good trade-off between overall resource utilization and equal performance experienced by end-users.TRUEinpres