https://hdl.handle.net/20.500.12761/1 Mon, 19 Oct 2026 04:52:48 GMT 2026-10-19T04:52:48Z https://hdl.handle.net/20.500.12761/2106 MiniRetro: VLC Backscatter System for Miniaturized and Sustainable Internet of Things Talavante, Javier; Genovés Guzmán, Borja; Giustiniano, Domenico The proliferation of Internet of Things (IoT) devices demands communication technologies that are sustainable, low-power, and compact. While radio-frequency (RF) backscatter has enabled 𝜇��W-level operation, its miniaturization is constrained by the physical limits of RF antennas. Visible light communication (VLC) backscatter offers an alternative by reusing existing lighting infrastructure, yet current designs require a significant area and power consumption at the IoT device. In this paper we present MiniRetro, a miniaturized VLC backscatter module with an active retroreflective area of only 0.25 cm2, combining retroreflective sheetings with liquid crystal (LC) shutters. We model the VLC backscatter channel including the impact of retroreflector type, geometry, and photodiode (PD) placement, determining the configuration that maximizes the reflected signal strength. We also provide the first analytical and experimental framework characterizing the interaction between shutter and retroreflector, capturing polarization and transmittance losses and showing that the relative rotation angle can alter reflected power by up to 66%. We validate these features experimentally with a single-pixel VLC backscatter module, confirming a reliable meter-scale mobile communication while being the first VLC backscatter design to operate with a sub-cm2 active area. Finally, we show its implementation on a small-sized aquatic robot providing uplink connectivity for aquaponics monitoring. Our results demonstrate the feasibility of MiniRetro as a miniaturized and sustainable VLC backscatter system for next-generation IoT connectivity Tue, 01 Dec 2026 00:00:00 GMT https://hdl.handle.net/20.500.12761/2106 2026-12-01T00:00:00Z https://hdl.handle.net/20.500.12761/2105 Actionable Signals in Prediction Markets: Evaluating Public and Private Information through Systematic Trading Saguillo, Oriol; Giacomelli, Fabio; Kiffer, Lucianna Prediction markets aggregate the private beliefs of many participants into publicly observable probability estimates. Beyond prices alone, the trading behavior they generate may reveal public and private signals that are actionable for automated trading agents. We study whether these observable market signals reliably identify profitable trading opportunities. Specifically, we evaluate two complementary classes of trading signals: Risk Arbitrage, which exploits periods where publicly available information has effectively determined an outcome before prices fully converge, and Information Asymmetry, which exploits large positions accumulated while market prices still reflect substantial uncertainty. Using the complete history of matched trades from Polymarket, spanning 26,964 markets between January 2024 and January 2026, we evaluate the reliability of these two classes of signals. We find that they exhibit markedly different levels of reliability. Risk Arbitrage identifies substantial opportunities around effective resolution but suffers significant losses from false positives, yielding a net loss of $23.4M despite $15.3M in gross profit. Information Asymmetry is considerably more concentrated, generating $139.3M in gross profit but only $7.8M net, with activity overwhelmingly driven by the 2024 U.S. presidential election. Across both regimes, profits concentrate among a small number of accounts, while position size reflects capital commitment more strongly than predictive skill. Sun, 01 Nov 2026 00:00:00 GMT https://hdl.handle.net/20.500.12761/2105 2026-11-01T00:00:00Z https://hdl.handle.net/20.500.12761/2104 New QUBO Transformations to Improve Quantum and Simulated Annealing Performance for Quadratic Knapsack Borrajo Carro, Nicolás; Ramirez Rondon, Juan Marcos; Nosrati, Farzam; Aguilar, Jose; Mancuso, Vincenzo; Fernández Anta, Antonio Recent advancements in quantum computing have demonstrated significant potential for solving combinatorial optimization problems, like the quadratic knapsack problem, a constrained binary optimization problem. How- ever, current quantum and quantum-inspired algorithms often require transforming these constrained problems into an unconstrained form, known as Quadratic Unconstrained Binary Optimization (QUBO). Such transfor- mations can significantly impact the algorithms’ speed and efficiency. In this study, we evaluate five existing transformation methods and propose four novel approaches. We assess all nine methods using Simulated An- nealing and find that three of our approaches outperform existing methods in terms of execution time and the quality and quantity of feasible solutions found. Additionally, we tested these transformations on quantum annealers, which were unable to solve even small problem instances, due to limitations in connectivity and error rates. However, our results highlight the advantages of the new approaches, which reduce the total num- ber of variables in the QUBO representation. This is a critical factor for enhanced performance on emerging quantum hardware, since it also reduces the required number of qubits and the embedding chain lengths. Sun, 01 Mar 2026 00:00:00 GMT https://hdl.handle.net/20.500.12761/2104 2026-03-01T00:00:00Z https://hdl.handle.net/20.500.12761/2103 Columnar Packet Traces for Scalable Encrypted-Internet Measurement Rojo, Pablo; Ramirez Rondon, Juan Marcos; Mancuso, Vincenzo; Fernández Anta, Antonio Internet traffic volumes continue to grow while transport and application encryption increasingly limit payload visibility. However, modern transport protocols such as QUIC complicate traditional packet-trace analytics built around pcap (e.g., tshark-to-CSV), and row-oriented extraction increases the cost of repeated analysis. This paper makes repeated offline mea- surement practical by converting packet traces into a column- oriented representation that separates one-time parsing from downstream analytics. Concretely, we introduce an advanced benchmarked software implementation that converts pcap files to the column-oriented Apache parquet format. To the best of our knowledge, this is among the first end-to-end pipelines that (i) converts pcap/pcapng traces into parquet with a schema aimed at repeated offline measurement and (ii) is engineered around a convert-once, query-many workflow with measured end-to-end time-to-insight benefits. The pipeline leverages par- allel parsing to produce a columnar dataset that can be queried efficiently by standard analytics engines. We benchmark end- to-end time-to-insight and storage costs across representative workloads and diverse datasets. Our results show that after a one-time conversion, subsequent analyses can exploit column pruning and vectorized scans to reduce query time compared with re-parsing pcap or re-extracting row-oriented text formats, while maintaining storage costs competitive with compressed pcap and CSV baselines. Mon, 01 Jun 2026 00:00:00 GMT https://hdl.handle.net/20.500.12761/2103 2026-06-01T00:00:00Z