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Partial information decompositions (PIDs) aim to categorize how a set of source variables provides information about a target variable redundantly, uniquely, or synergetically. The original proposal for such an analysis used a lattice-based approach and gained significant attention. However, finding a suitable underlying decomposition measure is still an open research question at an arbitrary number of discrete random variables. This work proposes a solution with a non-negative PID that satisfies an inclusion-exclusion relation for any f-information measure. The decomposition is constructed from a pointwise perspective of the target variable to take advantage of the equivalence between the Blackwell and zonogon order in this setting. Zonogons are the Neyman-Pearson region for an indicator variable of each target state, and f-information is the expected value of quantifying its boundary. We prove that the proposed decomposition satisfies the desired axioms and guarantees non-negative partial information results. Moreover, we demonstrate how the obtained decomposition can be transformed between different decomposition lattices and that it directly provides a non-negative decomposition of R & eacute;nyi-information at a transformed inclusion-exclusion relation. Finally, we highlight that the decomposition behaves differently depending on the information measure used and how it can be used for tracing partial information flows through Markov chains.

Analog backscatter enables sensing and communication while consuming significantly lower power than digital backscatter. An analog backscatter tag maps sensor readings directly to backscatter transmissions avoiding power hungry blocks such as ADCs. The sensor value variations are backscattered atop a carrier as changes in frequency and amplitude. Frequency variations are commonly used in backscatter, to avoid the strong self-interference from the carrier. The range of the sensor output linearly maps to the range of base-band frequency variation. Hence a sensor with a wider output range requires a larger base-band frequency range to encode sensor data. This increases the tag oscillator's switching frequency and hence the tag's power consumption. We propose to use higher order harmonic frequencies which allows us to reduce the tag switching frequency and read sensor data even when the carrier masks the fundamental frequency. Our system design lowers the cost and power consumption of the analog backscatter system making it suitable for mobile-based sensing applications. We present experimental results demonstrating the viability of our approach and implement a complete system that includes a lowcost radio receiver. Using a carrier with 0 dBm power, we detect the 15th harmonic up to three meters resulting in 15 times more frequency resolution than the fundamental while reducing the tag oscillator's power consumption by more than 43%. The 7th harmonic is visible up to 18 meters. Increasing the carrier power enables the detection of additional harmonic frequencies.

Smarts-like sampled hardware simulation techniques achieve good accuracy by simulating many small portions of an application in detail. However, while this reduces the simulation time, it results in extensive cache warming times, as each of the many simulation points requires warming the whole memory hierarchy. Adaptive Cache Warming reduces this time by iteratively increasing warming to achieve sufficient accuracy. Unfortunately, each increases requires that the previous warming be redone, nearly doubling the total warming. We address re-warming by developing a technique to merge the cache states from the previous and additional warming iterations. We demonstrate our merging approach on multi-level LRU cache hierarchy and evaluate and address the introduced errors. Our experiments show that Cache Merging delivers an average speedup of 1.44x, 1.84x, and 1.87x for 128kB, 2MB, and 8MB L2 caches, respectively, (vs. a 2x theoretical maximum speedup) with 95-percentile absolute IPC errors of only 0.029, 0.015, and 0.006, respectively. These results demonstrate that Cache Merging yields significantly higher simulation speed with minimal losses.

Temperature sensing and monitoring play a vital role in various applications. Non-invasive, item-level temperature sensing methods that require no direct line of sight with the measuring object are attractive. This paper presents such a temperature sensing method using commodity RFID tags with no infrastructure changes. RFID tags are widely deployed for product identification purposes. We explore the possibility of leveraging the RSSI measurements from commodity RFID tags for temperature sensing. Essentially, we model a relationship between the temperature and the relative permittivity of a material in terms of RSSI. Our method can measure temperature in the range of 22°C to 60°C and achieves a measurement accuracy of 2°C with a mean error of 1.5°C.

The idea of a partial information decomposition (PID) gained significant attention for attributing the components of mutual information from multiple variables about a target to being unique, redundant/shared or synergetic. Since the original measure for this analysis was criticized, several alternatives have been proposed but have failed to satisfy the desired axioms, an inclusion–exclusion principle or have resulted in negative partial information components. For constructing a measure, we interpret the achievable type I/II error pairs for predicting each state of a target variable (reachable decision regions) as notions of pointwise uncertainty. For this representation of uncertainty, we construct a distributive lattice with mutual information as consistent valuation and obtain an algebra for the constructed measure. The resulting definition satisfies the original axioms, an inclusion–exclusion principle and provides a non-negative decomposition for an arbitrary number of variables. We demonstrate practical applications of this approach by tracing the flow of information through Markov chains. This can be used to model and analyze the flow of information in communication networks or data processing systems., Resilient Internet of Things (RIOT)

Obtaining hands-on experience with wireless communication can be challenging due to the limited configurability of most commercial off-the-shelf transceivers. We present a low-cost and open-source educational platform designed to help students experiment and investigate wireless concepts through real-world testing. Our platform provides students with the ability to control the physical-layer configuration, design and implement their backscatter system, and conduct link quality experiments to investigate the resulting system performance. This platform offers students a unique opportunity to gain practical experience and deepen their understanding of wireless communication, while also providing a valuable tool for researchers and educators in the field.

Microwave communication through the fat tissue in the human body enables a new channel for wearable devices to communicate with each other. The wearable devices can communicate to the external world through a powerful device in their network called central control unit (CU); for example, a smartphone. Some wearable devices may be out of the range of the CU temporarily due to body movements or permanently due to low signal strength, in a fat channel communication network. Such devices can connect to the CU with the help of their neighbor device in the same network. In this paper, we propose a protocol to ensure secure indirect authentication and key establishment between the out-of-range device and the CU in a fat channel communication network, via an untrusted intermediate device in the network. The proposed protocol is lightweight and resistant to denial-of-sleep attacks on the intermediate device. We analyze the security and the computation overhead of the proposed protocol.

Password security relies heavily on the choice of password by the user but also on the one-way hash functions used to protect stored passwords. To compensate for the increased computing power of attackers, modern password hash functions like Argon2, have been made more complex in terms of computational power and memory requirements. Nowadays, the computation of such hash functions is performed usually by the server (or authenticator) instead of the client. Therefore, constrained Internet of Things devices cannot use such functions when authenticating users. Additionally, the load of computing such functions may expose servers to denial of service attacks. In this work, we discuss client-side hashing as an alternative. We propose Clipaha, a client-side hashing scheme that allows using high-security password hashing even on highly constrained server devices. Clipaha is robust to a broader range of attacks compared to previous work and covers important and complex usage scenarios. Our evaluation discusses critical aspects involved in client-side hashing. We also provide an implementation of Clipaha in the form of a web library1 and benchmark the library on different systems to understand its mixed JavaScript and WebAssembly approach’s limitations. Benchmarks show that our library is 50% faster than similar libraries and can run on some devices where previous work fails. © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0).

Backscatter tags consume extremely low power and are activated by an external carrier for sensing and communication purposes. Analog backscatter tags achieve further power and cost reductions by moving digitization and computation overhead to a remote receiver. Analog transmissions do not have a digital frame structure to convey control information such as addresses. Therefore, when multiple tags are deployed without such ID it is challenging to associate received data with specific tags. We present a novel fully analog tag identification mechanism that is inspired by musical instruments. Each instrument has a unique harmonic content and therefore the human ear can distinguish different musical instruments even when they are playing at the same frequency. By tuning the tags to reflect a unique harmonic content we enable identification of purely analog tags. Our results show that we can create identities for tags by leveraging signal conditioning to modify harmonics, without the need to add dedicated digital blocks for identification purposes. Experiments on our hardware demonstrate that we can successfully identify the tags even in challenging scenarios such as when they are blocked by walls or in motion.

SMARTS-like sampled hardware simulation techniques achieve good accuracy by simulating many small portions of an application in detail. However, while this reduces the detailed simulation time, it results in extensive cache warming times, as each of the many simulation points requires warming the whole memory hierarchy. Adaptive Cache Warming reduces this time by iteratively increasing warming until achieving sufficient accuracy. Unfortunately, each time the warming increases, the previous warming must be redone, nearly doubling the required warming. We address re-warming by developing a technique to merge the cache states from the previous and additional warming iterations. We address re-warming by developing a technique to merge the cache states from the previous and additional warming iterations. We demonstrate our merging approach on multi-level LRU cache hierarchy and evaluate and address the introduced errors. By removing warming redundancy, we expect an ideal 2x warming speedup when using our Cache Merging solution together with Adaptive Cache Warming. Experiments show that Cache Merging delivers an average speedup of 1.44x, 1.84x, and 1.87x for 128kB, 2MB, and 8MB L2 caches, respectively, with 95-percentile absolute IPC errors of only 0.029, 0.015, and 0.006, respectively. These results demonstrate that Cache Merging yields significantly higher simulation speed with minimal losses., Updated 2022-09-07, correcting spelling errors.

This article presents, for the first time, the end-to-end transmission of physiological data from implanted antennas mimicking sensors to a cloud-enabled aggregator device using fat intra-body communication (fat-IBC). The experiment was performed on a live porcine model in full accordance with ethical standards. Measurement data from two different sensors were collected and sent through a fat-IBC network. The fat-IBC network consisted of three nodes, of which two used antennas implanted in the fat tissue of a live porcine model and one used an on-body antenna placed on the skin. The sensor data was forwarded via Bluetooth Low Energy to an Intel Health Application Platform device, which in turn forwarded the encrypted data to a web server. The experimental results demonstrate that the fat channel can be used in an end-to-end communication scheme, which could involve relaying of sensor data from an implanted device to an external web server.

Probabilistic networks are used to model interconnected systems (such as social or biological networks) where we are not certain about the existence of some of the connections. In this paper, we compare different extensions of the existing clique percolation method to identify partial and overlapping clusters in probabilistic networks. We also characterize and experimentally test scenarios where some of these extensions may fail identifying ground-truth clusters.

Radiometric fingerprint schemes have been shown effective in identifying wireless devices based on imperfections in their hardware electronics. The robustness of fingerprint systems under complex channel conditions, however, is a critical challenge that makes their application in real-world scenarios difficult. We systematically evaluate the wireless channel's impact on radiometric fingerprints and find that the channel impacts fingerprint features in a very particular way that depends on the channel's properties. Based on the insights, we present RRF, a system that provides a robust identification/authentication service even under complex channel fading disturbance. Our design deploys a hybrid architecture that combines wireless channel simulation, signal processing and machine learning. In this pipeline, RRF first utilizes a series of structured channel simulations to strategically improve system tolerance towards multipath channel interference. On top of that, in the identification phase, RRF relies on noise compensation and a feature denoising filter to augment the system's stability in noisy conditions with weak signals. Our experimental results show that RRF achieves an average accuracy consistently above 99% in empirical scenarios with complex channels, where the baseline approach from previous work rarely exceeds 50%.