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EmoLeak: Smartphone Motions Reveal Emotions 43

EmoLeak: Smartphone Motions Reveal Emotions

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43rd ieee international conference on distributed computing systems (ICDCS)

作者： Mahdad, Ahmed Tanvir Shi, Cong Ye, Zhengkun Zhao, Tianming Wang, Yan Chen, Yingying Saxena, Nitesh Texas A&M Univ College Stn TX 77843 USA New Jersey Inst Technol Newark NJ 07102 USA Temple Univ Philadelphia PA 19122 USA Univ Dayton Dayton OH 45469 USA Rutgers State Univ New Brunswick NJ 08901 USA

ISBN: (纸本)9798350339864

Emotional state leakage attracts increasing concerns as it reveals rich sensitive information, such as intent, demo graphic, personality, and health information. Existing emotion recognition techniques rely on vision and audio data, which have limited threat due to the requirements of accessing restricted sensors (e.g., cameras and microphones). In this work, we first investigate the feasibility of detecting the emotional state of people in the vibration domain via zero-permission motion sensors. We find that when voice is being played through a smartphone's loudspeaker or ear speaker, it generates vibration signals on the smartphone surface, which encodes rich emotional information. As the smartphone is the go-to device for almost everyone nowadays, our attack based only on motion sensors raises severe concerns about emotion state leakage. We comprehensively study the relationship between vibration data and human emotion based on several publicly available emotion datasets (e.g., SAVEE, TESS). Time-frequency features and machine learning techniques are developed to determine the emotion of the victim based on speech vibrations. We evaluate our attack on both the ear speakers and loudspeakers on a diverse set of smartphones. The results demonstrate our attack can achieve a high accuracy, with around 95.3% (random guess 14.3%) accuracy for the loudspeaker setting and 60.52% (random guess 14.3%) accuracy for the ear speaker setting.

关键词： emotion recognition motion sensor side channel speech privacy

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dcoss-IoT 2023 - Message from the Workshop Chairs

DCOSS-IoT 2023 - Message from the Workshop Chairs

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international conference on distributed computing in sensor systems (dcoss)

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Assessing Wearable Human Activity Recognition systems Against Data Poisoning Attacks in Differentially-Private Federated Learning 9

Assessing Wearable Human Activity Recognition Systems Agains...

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ieee international conference on Smart computing (SMARTCOMP)

作者： Shahid, Abdur R. Imteaj, Ahmed Badsha, Shahriar Hossain, Md Zarif Robert Morris Univ Dept Comp & Informat Syst Moon Twp PA 15108 USA Southern Illinois Univ Sch Comp Carbondale IL 62901 USA Bosch Engn North Amer Farmington Hills MI USA

ISBN: (纸本)9798350322811

Differentially-Private Federated Learning (DPFL) is an emerging privacy-preserving distributed machine learning paradigm that allows for the automatic recognition of human activities using wearable sensors without compromising users' sensitive data. However, this decentralized approach makes the system vulnerable to poisoning attacks, where malicious agents can inject contaminated data during local model training. This paper presents the results of our research on designing, developing, and evaluating a holistic model for data poisoning attacks in DPFL-based human activity recognition (HAR) systems. Specifically, we focus on label-flipping poisoning attacks, where the label of a sensor reading is maliciously changed during data collection. To investigate the impact of such attacks, we develop a simulator that explores key design issues, such as the correlation between the level of differential privacy, the level of poisoning, the number of communication rounds, and the number of agents in the system. Our findings shed light on the effectiveness of label contamination attacks in DPFL-based HAR systems and can inform the development of more robust and secure models.

关键词： Human Activity Recognition Federated Learning distributed Machine Learning Data Poisoning Attack Differential Privacy Wearable Sensing

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Unsupervised Spatio-Temporal Anomalous Thermal Behavior Monitoring of Inside-Built Environments 19

Unsupervised Spatio-Temporal Anomalous Thermal Behavior Moni...

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19th Annual international conference on distributed computing in Smart systems and the Internet of Things, dcoss-IoT 2023

作者： Khan, Naima Al Hafiz Khan, Md Abdullah Roy, Nirmalya Department of Information Systems United States Umbc Center for Real-time Distributed Sensing and Autonomy United States Kennesaw State University Department of Computer Science United States

ISBN: (纸本)9798350346497

Continual wavering of outside weather degrades the efficiency of inside building envelope over time and leads to additional energy consumption, various structural damages, etc. Frequent monitoring of the indoor built environment with thermal images can assist in identifying the energy-leaking and potentially damage-prone areas. Although in recent years different researches performed deep learning and computer vision based thermal anomaly detection in built environment, several issues related to conducting strategic non-intrusive indoor thermal inspection using temporal thermal images, are still unresolved in uncontrolled environment of residential buildings. In this work, we propose a scalable thermal image-based monitoring approach for building envelopes combining the visual knowledge of structural joint information among different building components and their corresponding temporal thermal status. We collected longitudinal thermal images from indoor scenes of different building components (e.g., door, window, wall) and employed a high-level spatio-temporal graph (st-graph) to represent the structural connection among different building components and their temporal self-changes. Our proposed novel unsupervised spatio-temporal clustering framework assigns the cluster label to nodes in st-graph, combining its structural (the self and neighboring component) and temporal features which achieves better performance in identifying thermal variation compared to other clustering based approaches. We demonstrate thermal variation across the spots which indicates the potential energy leakage areas inside the built environment. The cluster patterns obtained from our proposed model assist in understanding the thermal characteristics of various surfaces at certain conditions, such as sun reflection and airflow in the inside built environment. © 2023 ieee.

关键词： Potential energy

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HeteroSAS: A Heterogeneous Resource Management Framework for "All-in-the-Air" Social Airborne Sensing in Disaster Response 17

HeteroSAS: A Heterogeneous Resource Management Framework for...

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17th Annual ieee international conference on distributed computing in sensor systems (ieee dcoss)

作者： Rashid, Md Tahmid Zhang, Daniel (Yue) Wang, Dong Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

ISBN: (纸本)9781665439299

Social airborne sensing (SAS) is emerging as a new sensing paradigm that leverages the complementary aspects of social sensing and airborne sensing (i.e., UAVs) for reliable information collection. In this paper, we present HeteroSAS, a heterogeneous resource management framework for "all-in-the-air" SAS in disaster response applications. Current SAS approaches use UAVs to only capture data, but carry out computation on ground-based processing nodes that may be unavailable in disaster scenarios and thus consider a single model of UAV along with only one type of task (i.e., data capture). In this paper, we explore the opportunity to exploit the complementary strengths of different UAV models to accomplish all stages of sensing tasks (i.e., data capturing, maneuvering, and computation) exclusively "in-the-air". However, several challenges exist in developing such a resource management framework: i) handling the uncertain social signals in presence of the heterogeneity of UAVs and tasks;and ii) adapting to constantly changing cyber-physical-social environments. The HeteroSAS framework addresses these challenges by building a novel resource management framework that observes the environment and learns the optimal strategy for each UAV using techniques from multi-agent reinforcement learning, game theory, and ensemble learning. The evaluation with a real-world case study shows that HeteroSAS outperforms the state-of-the-art in terms of detection effectiveness, deadline hit rate, and robustness on heterogeneity.

关键词： Social networking (online) Computational modeling Reinforcement learning Robustness Data models sensor systems sensors

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sensor Classifications and Their Applications in IoT systems

Sensor Classifications and Their Applications in IoT Systems

引用

2023 ieee international conference on distributed computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2023

作者： Sajja, Komalsai Bhowmik, Biswajit National Institute of Technology Karnataka Maharshi Vasistha IoT Lab Brics Lab Department of Computer Science and Engineering India

ISBN: (纸本)9798350340129

The Internet of Things (IoT), which connects various systems and devices to create more innovative environments, has completely changed how we interact with technology. sensors are essential to the success of IoT systems because they are vital in gathering and analyzing real-time data from the physical environment. This paper comprehensively analyses sensor classifications and their applications in IoT systems. It explores the different types of sensors commonly used in IoT, their functionality, and their suitability for various IoT-enabled intelligent applications. This paper also provides valuable insights for IoT designers and researchers in selecting appropriate sensors and developing more efficient and reliable intelligent systems for the future. © 2023 ieee.

关键词： Internet of things

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Modular Platform For Collaborative, distributed sensor Fusion 14

Modular Platform For Collaborative, Distributed Sensor Fusio...

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14th ACM/ieee international conference on Cyber-Physical systems (ICCPS) part of CPS-IoT Week

作者： Spencer, R. Zelter, Nate Hunt, David Angell, Kristen Pajic, Miroslav Duke Univ Durham NC 27706 USA

ISBN: (纸本)9798400700361

Leading autonomous vehicle (AV) platforms and testing infrastructures are, unfortunately, proprietary and closed-source. Thus, it is difficult to evaluate how well safety-critical AVs perform and how safe they truly are. Similarly, few platforms exist for much-needed multi-agent analysis. To provide a starting point for analysis of sensor fusion and collaborative & distributed sensing, we design an accessible, modular sensing platform with AVstack [9]. We build collaborative and distributed camera-radar fusion algorithms and demonstrate an evaluation ecosystem of AV datasets, physics-based simulators, and hardware in the physical world. This three-part ecosystem enables testing next-generation configurations that are prohibitively challenging in existing development platforms.

关键词： Ecosystems

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Adaptive ResNet Architecture for distributed Inference in Resource-Constrained IoT systems 19

Adaptive ResNet Architecture for Distributed Inference in Re...

引用

19th ieee international Wireless Communications and Mobile computing (ieee IWCMC)

作者： Khan, Fazeela Mazhar Baccour, Emna Erbad, Aiman Hamdi, Mounir Hamad Bin Khalifa Univ Qatar Fdn Coll Sci & Engn Doha Qatar

ISBN: (纸本)9798350333398

As deep neural networks continue to expand and become more complex, most edge devices are unable to handle their extensive processing requirements. Therefore, the concept of distributed inference is essential to distribute the neural network among a cluster of nodes. However, distribution may lead to additional energy consumption and dependency among devices that suffer from unstable transmission rates. Unstable transmission rates harm real-time performance of IoT devices causing low latency, high energy usage, and potential failures. Hence, for dynamic systems, it is necessary to have a resilient DNN with an adaptive architecture that can downsize as per the available resources. This paper presents an empirical study that identifies the connections in ResNet that can be dropped without significantly impacting the model's performance to enable distribution in case of resource shortage. Based on the results, a multi-objective optimization problem is formulated to minimize latency and maximize accuracy as per available resources. Our experiments demonstrate that an adaptive ResNet architecture can reduce shared data, energy consumption, and latency throughout the distribution while maintaining high accuracy.

关键词： optimization distributed inference neural networks resilience ResNet

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An O(1)-rounds Deterministic distributed Approximation Algorithm for the Traveling Salesman Problem in Congested Clique

An O(1)-rounds Deterministic Distributed Approximation Algor...

引用

international conference on distributed computing in sensor systems (dcoss)

作者： Parikshit Saikia National Institute of Technology Silchar Assam India

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

We study the Traveling Salesman Problem (TSP) in the Congested Clique Model (CCM) of distributed computing. We present a deterministic distributed algorithm that computes a tour for the TSP using O(1) rounds and O(m) messages for a given undirected weighted complete graph of n nodes and m edges with an approximation factor 2 of the optimal. The TSP has wide applications in logistics, planning, manufacturing and testing microchips, DNA sequencing etc., and we claim that our proposed O(1)-rounds approximation algorithm to the TSP, which is fast and efficient, can also be used to minimize the energy consumption in Wireless sensor Networks.

关键词： Wireless sensor networks Sequential analysis Traveling salesman problems Approximation algorithms Planning Manufacturing Internet of Things

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Large Scale Global Optimization Algorithms for IoT Networks: A Comparative Study 17

Large Scale Global Optimization Algorithms for IoT Networks:...

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17th Annual ieee international conference on distributed computing in sensor systems (ieee dcoss)

作者： Goudos, Sotirios K. Boursianis, Achilles D. Mohamed, Ali Wagdy Wan, Shaohua Sarigiannidis, Panagiotis Karagiannidis, George K. Suganthan, Ponnuthurai N. Aristotle Univ Thessaloniki Sch Phys ELEDIA AUTH Thessaloniki Greece Cairo Univ Fac Grad Studies Stat Res Operat Res Dept Giza Egypt Nile Univ Wireless Intelligent Networks Ctr WINC Sch Engn & Appl Sci Giza Egypt Zhongnan Univ Econ & Law Sch Informat & Safety Engn Wuhan Peoples R China Univ Western Macedonia Dept Informat & Telecommun Engn Kozani Greece Aristotle Univ Thessaloniki Sch Elect & Comp Engn Thessaloniki Greece Nanyang Technol Univ Sch Elect Elect Engn Singapore Singapore

ISBN: (纸本)9781665439299

The advent of Internet of Things (IoT) has bring a new era in communication technology by expanding the current inter-networking services and enabling the machine-to-machine communication. IoT massive deployments will create the problem of optimal power allocation. The objective of the optimization problem is to obtain a feasible solution that minimizes the total power consumption of the WSN, when the error probability at the fusion center meets certain criteria. This work studies the optimization of a wireless sensor network (WNS) at higher dimensions by focusing to the power allocation of decentralized detection. More specifically, we apply and compare four algorithms designed to tackle Large scale global optimization (LSGO) problems. These are the memetic linear population size reduction and semi-parameter adaptation (MLSHADE-SPA), the contribution-based cooperative coevolution recursive differential grouping (CBCC-RDG3), the differential grouping with spectral clustering-differential evolution cooperative coevolution (DGSC-DECC), and the enhanced adaptive differential evolution (EADE). To the best of the authors knowledge, this is the first time that LSGO algorithms are applied to the optimal power allocation problem in IoT networks. We evaluate the algorithms performance in several different cases by applying them in cases with 300, 600 and 800 dimensions.

关键词： Internet of Things optimal power allocation wireless sensor network large scale global optimization

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