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Modularity is one of the most widely used measures for evaluating communities in networks. In probabilistic networks, where the existence of edges is uncertain and uncertainty is represented by probabilities, the expected value of modularity can be used instead. However, efficiently computing expected modularity is challenging. To address this challenge, we propose a novel and efficient technique (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textrm{FPWP}$$\end{document}) for computing the probability distribution of modularity and its expected value. In this paper, we implement and compare our method and various general approaches for expected modularity computation in probabilistic networks. These include: (1) translating probabilistic networks into deterministic ones by removing low-probability edges or treating probabilities as weights, (2) using Monte Carlo sampling to approximate expected modularity, and (3) brute-force computation. We evaluate the accuracy and time efficiency of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textrm{FPWP}$$\end{document} through comprehensive experiments on both real-world and synthetic networks with diverse characteristics. Our results demonstrate that removing low-probability edges or treating probabilities as weights produces inaccurate results, while the convergence of the sampling method varies with the parameters of the network. Brute-force computation, though accurate, is prohibitively slow. In contrast, our method is much faster than brute-force computation, but guarantees an accurate result.

Analog backscatter communication is a promising technology for low-power and low-cost communication. The simplicity of the analog backscatter tags make it possible to achieve low-power operation. However, they are limited to perform only simple operations and do not have the capability to perform complex tasks such as estimating the channel parameters. In this work, we propose a novel method to measure the received signal strength at the analog backscatter tag. We achieve this by converting the incident signal strength at the tag, to a frequency to eliminate signal amplitude modifications in the path from the tag to the backscatter receiver. We further enhance the granularity of the signal strength measurement using harmonics that are inherently generated by the backscatter signal. Through experiments, we show that frequency modulation combined with the harmonic spread is a good and a robust indicator of the gain of the carrier-to-tag channel. Our experimental results show a mean error of 1.8% in estimating the received signal power at the tag using backscatter harmonic frequency data.

Intrusion Detection Systems (IDS) play a critical role in safeguarding IoT networks, especially in sectors like healthcare, manufacturing, and smart cities where safety is paramount. Machine learning (ML) holds significant promise for training IDS models, leveraging data from past attacks. However, the effectiveness of these models are dependent on the quality and diversity of training data, which is often limited from the perspective of a single network operator.

This paper delves into the challenges of ML-based IDS model generalization across IoT network scenarios with expected distributional shifts in the data. We examine variations in known attack patterns and changes in IoT network configurations, quantifying their impact on model generalizability. These shifts originates from when multiple network operators seek to share knowledge to enhance their respective IDS capabilities, when a new attack variation is launched, or when an operator reconfigure its network. We explore two approaches to address these challenges: namely data sharing and horizontal federated learning for privacy preservation. While data sharing proves effective across scenarios, it relies on mutual trust among network operators. In contrast, federated learning preserves privacy but is less effective, especially when the network topology is the primary driver of distributional shifts in the train and test data.

To facilitate our study, we implemented Blackhole attack variation strategies within the Cooja network simulator. Our objective was to generate a large dataset enabling comprehensive analysis of attack variations across diverse set of network configurations to study the impact on ML-based IDS for IoT networks.

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.

Inequality measures provide a valuable tool for the analysis, comparison, and optimization based on system models. This work studies the relation between attributes or features of an individual to understand how redundant, unique, and synergetic interactions between attributes construct inequality. For this purpose, we define a family of inequality measures (f-inequality) from f-divergences. Special cases of this family are, among others, the Pietra index and the Generalized Entropy index. We present a decomposition for any f-inequality with intuitive set-theoretic behavior that enables studying the dynamics between attributes. Moreover, we use the Atkinson index as an example to demonstrate how the decomposition can be transformed to measures beyond f-inequality. The presented decomposition provides practical insights for system analyses and complements subgroup decompositions. Additionally, the results present an interesting interpretation of Shapley values and demonstrate the close relation between decomposing measures of inequality and information.

Innovative medical applications based on networked implants foster the development of in-body communication technologies. Among the in-body communication technologies that are being considered, fat intra-body communication (Fat-IBC) is a very recent approach. Its main advantage lies in its higher data rate compared to earlier approaches based on capacitive and galvanic coupling. However, Fat-IBC faces privacy-, security-, as well as safety-related attacks. In this paper, we discuss security and privacy concerns about Fat-IBC, as well as corresponding countermeasures. Furthermore, we present our secure protocol stack for Fat-IBC and suggest directions for future research.

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.

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.

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.