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Conventional performance evaluation of IoT device lifetime typically considers only the battery state-of-charge and does not take into account the complex dynamics of the battery itself. Here, we present a IoT device lifetime model that more accurately reflects how batteries respond to IoT-typical loads.

Our key insight is that an IoT device's power-saving sleep/wake cycle imposes intermittent pulse loads on the battery; the load duration and current depend on the operations(s) performed by the device during each awake interval. Our model therefore predicts the minimum output voltage that will be observed at the battery in response to a given pulse load. This value determines whether the battery is able to meet the input voltage requirements of the IoT device.

We have developed an support vector regression (SVR)-based battery model using a dataset consisting of measurements of the voltage response of small lithium titanate (LTO) batteries to over 90K pulse loads. The model predicts the battery's minimum output voltage in response to a given pulse load with an RMAE (Root Mean Absolute Error) of less than 3% over the full operational range of the battery. The accuracy decreases at the more critical lower end of the voltage range, however, falling to ~ 8-10%RMAE.

This pulse-response model allows us to create a "synthetic battery" and track its voltage response to a sequence of pulse loads, supporting a more realistic lifetime model where the IoT device fails as soon as the battery's predicted output voltage fails to meet its input voltage requirements.

Radiometric fingerprinting is a promising passive security measure for low-power IoT devices, that exploits the hardware imperfections of their radio signals. In this paper, we focus on extracting radiometric fingerprints from Bluetooth Low Energy (BLE) devices. We depart from previous work in that we facilitate fingerprinting in embedded network scenarios by extracting features from I/Q samples collected using a widely used BLE System-on-Chip. We introduce a novel approach that leverages instantaneous frequency analysis of signal dynamics to reveal hardware imperfections during symbol transitions in the packet payload. Our objective is to identify the most significant features contributing to device identification. Our experimental evaluation demonstrates that augmenting traditional aggregated FFT-based features with our proposed transition-based features increases identification accuracy from 56% to 74%.

Intrusion Detection Systems (IDS) are crucial for monitoring and managing critical infrastructure; however, their prominence makes them attractive targets for network attacks. Machine Learning (ML) techniques incorporated into IDS have shown promise in detecting and mitigating these threats. Unfortunately, the scarcity of attack samples presents challenges for model training and generalizability, and attackers can easily bypass detection systems by introducing temporal variations in their attacks. This paper develops strategies for detecting network attacks and specifically examines the impact of network configurations on the generalizability of detection models. As an illustrative example, we investigate the performance of Long Short-Term Memory (LSTM) models in capturing temporal changes in network behavior during attacks, and its resilience against distributional changes. Our study considers multiple factors in terms of attack types, variations, and network configurations, including different topologies and numbers of nodes. We provide insights into how these factors impact the generalizability of models trained using knowledge sharing. To support our research, we implemented Blackhole and DIS-flooding attack variations using the Cooja network simulator. Our objective was to generate a large dataset that enables a comprehensive analysis of attack variations across a diverse set of network configurations, focusing on the impact on LSTM-based IDS for IoT networks.

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.

Radiometric fingerprinting systems leverage unique physical-layer signal characteristics originating from individual hardware imperfections to identify transmitter devices. The pure passive nature of such mechanisms entirely relieves the overhead of identification and authentication operations from the end devices, which fits well with resource-constrained applications such as wireless sensor networks. However, existing systems are limited by the need for specialized hardware and non-trivial computations to extract fingerprinting features, hindering their pervasive deployment. For the first time, we ask the question whether it is feasible to implement an entire radiometric fingerprinting system on a low-cost and low-power embedded SoC. We introduce ORF, which demonstrates the feasibility of such a system on an embedded SoC that costs less than 6 dollars. Our experiments show that ORF achieves over 92% average accuracy on the task of identifying one out of 32 different transmitter devices.

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.